☆ 4.8 Review

Power quality monitoring in electric grid integrating offshore wind energy: A review

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2024)

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Article Engineering, Electrical & Electronic

A Powerful Tool for Power System Monitoring: Distributed Dynamic State Estimation Based on a Full-View Synchronized Measurement System

Tianshu Bi et al.

Summary: Modern power systems are undergoing a rapid transition due to the increase in intermittent nonsynchronous renewable generation and distributed energy resources. China's wind and solar energy capacity accounted for 26.7% of the total power generation capacity at the end of 2021. In terms of electricity generation, wind and solar energy contributed 11.8% to the total electricity use in that year. The transition poses challenges for system operators in accurately and comprehensively assessing the actual status of power systems.

IEEE POWER & ENERGY MAGAZINE (2023)

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Article Engineering, Multidisciplinary

PQ Event Detection After Noise Removal Using Fuzzy Transform and Hilbert Spectral Analysis on EMD

Jesmin Khan

Summary: In this paper, a noise removal technique for Power Quality (PQ) signals is proposed, which utilizes a soft thresholding method based on Fuzzy Membership Function (MF) and Empirical Mode Decomposition (EMD). The technique is applied to detect PQ disturbances from denoised power system signals. The proposed method is compared with a wavelet-transform-based technique and applied to real and simulated power system disturbance data for detection of PQ events.

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2023)

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Article Engineering, Electrical & Electronic

A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems

Kursat Rasim Mestav et al.

Summary: A deep learning approach is proposed for detecting data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. The proposed approach assumes unknown temporal dependencies and probability distributions for both the anomaly and anomaly-free measurement models. By applying a generative adversarial network, the approach transforms anomaly-free observations into uniform independent and identically distributed sequences for uniformity test-based anomaly detection at the sensor level. A distributed detection scheme is also proposed to combine sensor-level detections for more reliable results. Numerical results demonstrate significant improvement compared to state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.

IEEE TRANSACTIONS ON POWER SYSTEMS (2023)

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Article Computer Science, Information Systems

Classification of Power Quality Disturbance Using Segmented and Modified S-Transform and DCNN-MSVM Hybrid Model

Mingping Liu et al.

Summary: In this paper, a novel approach using SMST, DCNN, and MSVM is proposed for classifying PQ disturbance signals. The Gaussian window function with adjustable parameters is used for frequency segmentation. This approach achieves accurate time-frequency localization and efficient feature extraction. SMST analyzes the signals and generates 2D contour maps, which are then processed by DCNN for feature extraction. Finally, MSVM is used for the classification of PQ disturbance signals. Extensive simulations show that the proposed method outperforms several state-of-the-art algorithms in classifying PQ disturbances under different noise levels.

IEEE ACCESS (2023)

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Article Computer Science, Information Systems

An Overview on Power Quality Issues and Control Strategies for Distribution Networks With the Presence of Distributed Generation Resources

Darioush Razmi et al.

Summary: Power systems based on centralized production face limitations of fossil fuel scarcity and pollution reduction requirements. Therefore, the significance of distributed generation resources (DGs) has increased by integrating renewable energy systems into the grid. This paper emphasizes the importance of researching power quality issues in distribution networks due to the increasing penetration of renewable energies.

IEEE ACCESS (2023)

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Review Energy & Fuels

A systematic review of real-time detection and classification of power quality disturbances

Joaquin E. Caicedo et al.

Summary: This paper provides a comprehensive literature review on the real-time detection and classification of Power Quality Disturbances (PQDs), with a specific focus on voltage sags and notches. A systematic method based on scientometrics, text similarity, and the analytic hierarchy process is proposed to structure the review and select relevant literature. Bibliometric analysis is performed to reveal patterns such as publication trends, top publishing countries, and distribution of topics. A critical review is conducted on selected articles, covering various aspects of PQD detection and classification.

PROTECTION AND CONTROL OF MODERN POWER SYSTEMS (2023)

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Article Engineering, Electrical & Electronic

LSTM power quality disturbance classification with wavelets and attention mechanism

Dar Hung Chiam et al.

Summary: This paper analyzes the classification performance of power quality disturbances using a hybrid model of multi-resolution analysis and long short-term memory network. The proposed model increases the resolution of input signals through four-level decomposition wavelet transform and highlights spatial and temporal features using attention mechanism. The sequence features are then extracted and classified using multiple dense layers. The results show that the model is effective in classifying different disturbance signals under the condition of added noise.

ELECTRICAL ENGINEERING (2023)

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Article Automation & Control Systems

A Fast Adaptive S-Transform for Complex Quality Disturbance Feature Extraction

Pan Li et al.

Summary: This article proposes a fast adaptive S-transform (FAST) to improve the time-frequency resolution and computational efficiency of power quality disturbances (PQDs) feature extraction. FAST directly controls the standard deviation to reduce the difficulty of optimizing time-frequency resolution. It only needs to calculate characteristic frequency points determined by the maximum envelope curve, eliminating redundant calculation without losing effective feature information.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023)

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Article Automation & Control Systems

Complex Disturbances Identification: A Novel PQDs Decomposition and Modeling Method

Kunzhi Zhu et al.

Summary: In this article, an automatic approach for power quality disturbances (PQDs) classification is proposed, which is suitable for complex phenomena. The proposed method includes ensemble intrinsic timescale decomposition (EITD) for decomposing PQDs and a global depthwise shuffle CNN (GSCNN) for improving performance and reducing parameters. Based on EITD and GSCNN, an automatic framework is created to identify and classify complex PQDs.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023)

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Review Green & Sustainable Science & Technology

Wideband oscillation monitoring in power systems with high-penetration of renewable energy sources and power electronics: A review

Lei Chen et al.

Summary: Wideband oscillation events frequently occur in renewable power systems, with frequencies ranging from several Hz to several kHz, due to interactions between generators/power electronic devices and the grids. In recent years, wideband oscillation monitoring techniques have emerged to identify and analyze these events. This paper reviews the synchronized wideband phasor measurement techniques and waveform measurement techniques. It also discusses the applications of these measurements in wideband oscillation identification, early warning, mitigation, and other power system operations. Challenges in wideband phasor estimation and waveform data compression are addressed, along with potential solutions. Future research opportunities in wideband oscillation and power quality applications are highlighted.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2023)

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Article Engineering, Electrical & Electronic

The use of deep learning and 2-D wavelet scalograms for power quality disturbances classification

Rafael S. Salles et al.

Summary: This work explores the application of advanced signal processing and deep learning techniques for recognizing and classifying power quality disturbances. The study uses continuous wavelet transform to generate 2-D images representing time-frequency information from voltage disturbance signals. Convolutional neural networks are employed to classify the data based on the images' distortion. The research demonstrates the feasibility of using CNN for voltage disturbance classification and contributes to the development of a methodology combining DL and transfer learning.

ELECTRIC POWER SYSTEMS RESEARCH (2023)

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Article Engineering, Electrical & Electronic

Deep learning for power quality

Roger Alves de Oliveira et al.

Summary: This paper introduces deep learning to the power quality community by reviewing applications and challenges. It shows that most applications are based on synthetic data and lack proper labelling. Implementing deep learning in power quality faces barriers such as lack of novelty, transparency, and benchmark databases. The paper identifies research gaps in semi-supervised learning, explainable deep learning, and hybrid approaches. Suggestions for improvement include collaboration, labelling and enlarging datasets, explaining decision making, and providing open-access databases.

ELECTRIC POWER SYSTEMS RESEARCH (2023)

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Article Engineering, Electrical & Electronic

Red deer optimized recurrent neural network for the classification of power quality disturbance

Zamrooth Dawood et al.

Summary: Power Quality Disturbance (PQD) in power grid distribution can degrade the power quality for users. Therefore, timely detection of disturbances in the power grid is crucial for diagnosing network failures. In this study, a deep recurrent neural network (DRNN) is used to classify PQD, and the Red Deer Optimization (RDO) algorithm is employed to optimize the weights of DRNN. By considering the behavior of deer roaring rate, RDO optimizes the weights of DRNN. Signal processing is conducted using S-transform (ST) due to its superior performance in detecting signals with high noise levels. The proposed method is implemented in Simulink tool and compared with existing methods, demonstrating higher accuracy (99.95%) and precision (99.98%) in classifying power disturbances.

ELECTRICAL ENGINEERING (2023)

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Article Engineering, Electrical & Electronic

Multiple power quality disturbances detection and classification with fluctuations of amplitude and decision tree algorithm

Amin Akbarpour et al.

Summary: In this study, a new classification method for power quality (PQ) events is proposed and three classifiers are used to recognize different types of PQ events. The results show that the decision tree algorithm is more accurate and capable in classifying PQ events.

ELECTRICAL ENGINEERING (2022)

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Article Automation & Control Systems

A Kaiser Window-Based S-Transform for Time-Frequency Analysis of Power Quality Signals

Chengbin Liang et al.

Summary: The proposed Kaiser window-based S-transform (KST) accurately detects power quality disturbances in power systems and achieves significant advantages in time-frequency analysis.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Automation & Control Systems

A Power System Disturbance Classification Method Robust to PMU Data Quality Issues

Zikang Li et al.

Summary: This article proposes a fast disturbance classification method that is robust to PMU data quality issues. The impacts of bad PMU measurements on disturbance classification are investigated by analyzing the feature distributions of deep learning methods. A new feature extraction scheme using UTCN-DAE is proposed to capture the temporal feature representation and is robust to bad data. Based on the features encoded by UTCN-DAE, a two-stream enhanced network is proposed for optimal feature extraction of multivariate time series. Classification is performed using a multilayered deep neural network and Softmax classifier. Experimental results show that the proposed method achieves the highest classification accuracy and computational efficiency compared to other deep learning algorithms.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Automation & Control Systems

Spectral Variation-Based Signal Compression Technique for Gapless Power Quality Waveform Recording in Smart Grids

Eder B. Kapisch et al.

Summary: This article proposes a real-time data compression method based on the power signal spectral content variation to address the bottleneck issue in data transferring for expanding electric power systems. The method provides high-fidelity reconstruction for long-term gapless oscillographic analysis and achieves a compression rate more than ten times greater than other existing techniques, with compression ratio values of up to 10780:1 for synthetic signals and 9470:1 for real signals.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Engineering, Electrical & Electronic

Synchronized Waveforms - A Frontier of Data-Based Power System and Apparatus Monitoring, Protection, and Control

Wilsun Xu et al.

Summary: This paper provides an in-depth review and analysis of the advancements in synchronized waveform data in power systems. It explores the potential applications of synchronized waveform data and proposes five strategies for discovering and developing such applications. The paper also introduces complementary measurement platforms and data screening algorithms for application implementation.

IEEE TRANSACTIONS ON POWER DELIVERY (2022)

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Article Engineering, Electrical & Electronic

Type identification and time location of multiple power quality disturbances based on KF-ML-aided DBN

Yanhui Xi et al.

Summary: The paper proposes a hybrid approach combining KF-ML and DBN to identify power quality disturbances effectively, with experimental results showing close detection time to set time, minimal absolute error in time location, and high classification accuracy under different noise levels.

IET GENERATION TRANSMISSION & DISTRIBUTION (2022)

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Article Engineering, Multidisciplinary

Classification of power quality disturbances using visual attention mechanism and feed-forward neural network

Yuwei Zhang et al.

Summary: Power quality disturbances caused by nonlinear loads and distributed generations have a significant impact on the safe operation of precision computers and microprocessors. A novel method using visual attention mechanism and feed-forward neural network has been proposed to accurately classify these disturbances, demonstrating improved classification accuracy compared to existing methods.

MEASUREMENT (2022)

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Article Engineering, Electrical & Electronic

A Synchronized Lissajous-Based Method to Detect and Classify Events in Synchro-Waveform Measurements in Power Distribution Networks

Milad Izadi et al.

Summary: This paper proposes a method for detecting and classifying power quality events using synchro-waveform measurements. The shape of synchronized Lissajous curves is analyzed to identify disturbances and events, and a Convolutional Neural Network (CNN) is used for event classification. The effectiveness of the proposed methods is demonstrated through computer simulations and real-world field data, showing accurate event detection and classification of power quality events.

IEEE TRANSACTIONS ON SMART GRID (2022)

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Article Computer Science, Hardware & Architecture

Power quality events recognition using enhanced empirical mode decomposition and optimized extreme learning machine

Indu Sekhar Samanta et al.

Summary: This paper proposes a novel approach based on EMD and ELM for the detection and classification of PQEs. The EMD technique is used to compute the prominent features of PQE signals, and a down-sampled KI-EMD approach is suggested to enhance the performance. The ELM is applied for the classification of PQDs, considering all the derived features through the KI-EMD approach. The experimental results demonstrate a 2-5% improvement in accuracy, speed, and robustness compared to other conventional methods.

COMPUTERS & ELECTRICAL ENGINEERING (2022)

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Article Engineering, Electrical & Electronic

Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model

Yunpeng Gao et al.

Summary: A new method combining adaptive wavelet threshold denoising and deep belief network fusion extreme learning machine (DBN-ELM) is proposed to solve the problems of noise interference and artificial feature extraction in power quality disturbance (PQD) classification. The simulation result and experimental verification show that the proposed method can effectively suppress PQD noise and performs well on DBN-ELM classification.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

Newly Implemented Real-Time PQ Monitoring for Transmission 4.0 Substations

Matheus Alberto et al.

Summary: This paper presents a recently implemented real-time power quality monitoring system based on the concept of Digitally Enabled Substation Architecture, and provides field results from a transmission system in the north of Brazil.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

An efficient algorithm for atomic decomposition of power quality disturbance signals using convolutional neural network

Yang Han et al.

Summary: This paper optimizes the CAD algorithm for PQD signals by introducing a CNN-based sub-dictionary predictor, aiming to improve the computational efficiency and accuracy of atomic decomposition for power quality disturbance signals.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

A new deep learning method for the classification of power quality disturbances in hybrid power system

Belkis Eristi et al.

Summary: This paper presents a new deep learning-based system for detecting power quality disturbances in hybrid power systems. The proposed recognition system, named ST and Bayesian optimization-based CNN (STBOACNN), utilizes Stockwell Transform and a convolutional neural network with optimum hyperparameters determined by Bayesian optimization algorithm. Experimental results show that STBOACNN is an effective approach for accurately classifying power quality disturbances in hybrid power systems.

ELECTRICAL ENGINEERING (2022)

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Review Thermodynamics

Review of integrated installation technologies for offshore wind turbines: Current progress and future development trends

Yaohua Guo et al.

Summary: Wind power is driving the global energy structure towards cleanliness and low-carbon as one of the fastest-growing renewable energy sources. This paper reviews over 280 works on offshore wind turbine installation processes and summarizes the latest progress and development trend in this field. It focuses on the integrated installation technology based on a wide and shallow suction bucket foundation. The paper describes different installation methods, summarizes existing floating wind turbine infrastructures and their installation methods, and comprehensively analyzes the integrated floating and installation technology based on the wide and shallow suction bucket foundation. The paper also discusses future technical challenges and introduces the latest research progress in possible solutions.

ENERGY CONVERSION AND MANAGEMENT (2022)

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Article Automation & Control Systems

PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment

Lei Fu et al.

Summary: This article proposes a practical method called PowerCog for accurately recognizing PQDs in noisy environments, utilizing techniques such as wavelet transform, tritraining, neural network structures, and a support vector machine classifier. The method improves accuracy and automation in real-world scenarios through feature extraction and selection. Multiple comparative experiments verify the effectiveness of PowerCog in complex environments.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Engineering, Electrical & Electronic

Multi-Objective Optimization Aiming to Minimize the Number of Power Quality Monitors and Multiple Fault Estimations in Unbalanced Power Distribution Systems

Paulo Estevao Teixeira Martins et al.

Summary: This paper proposes a multi-objective binary integer linear programming model for power distribution systems, addressing the issues of power quality monitor allocation, the impact of distributed generation on allocation methods, and fault location.

IEEE TRANSACTIONS ON POWER DELIVERY (2022)

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Article Engineering, Electrical & Electronic

An improved automated PQD classification method for distributed generators with hybrid SVM-based approach using un-decimated wavelet transform

Alper Yilmaz et al.

Summary: A new hybrid, un-decimated wavelet-transform (UWT)-based feature extraction method using a support vector machine (SVM) with a ' a trous algorithm is proposed to classify power quality disturbances (PQDs) in distributed generators (DGs). Experimental and simulation results showed that the proposed UWT-based method provides more successful results in classification than existing wavelet methods in the literature, with better noise sensitivity performance especially in real-time applications.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2022)

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Article Engineering, Electrical & Electronic

Wavelet packet transform and improved complete ensemble empirical mode decomposition with adaptive noise based power quality disturbance detection

Yu Mei et al.

Summary: This paper proposes a power quality disturbance detection method based on wavelet packet transform and improved complete ensemble empirical mode decomposition with adaptive noise. The method improves detection accuracy and speed, and has a strong anti-noise capability.

JOURNAL OF POWER ELECTRONICS (2022)

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Article Green & Sustainable Science & Technology

Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables

Robert Smolenski et al.

Summary: This research identifies the location of power quality disturbances in the low voltage utility grid and proposes a novel approach using low capacity energy storage units to mitigate these disturbances. The study also evaluates various parameters of different energy storage technologies and finds that deploying small energy storage units in the low voltage distribution grid is cost-effective and has great potential for widespread implementation.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2022)

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Article Computer Science, Artificial Intelligence

A Survey on Multi-Task Learning

Yu Zhang et al.

Summary: This paper provides a survey of Multi-Task Learning (MTL) from the perspective of algorithmic modeling, applications, and theoretical analyses. It discusses different MTL algorithms and their characteristics, as well as the combination of MTL with other learning paradigms. The paper also reviews MTL models for large-scale tasks or high-dimensional data, as well as dimensionality reduction and feature hashing. Real-world applications of MTL are examined, and theoretical analyses and future directions are discussed.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2022)

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Article Engineering, Electrical & Electronic

FFNet: An automated identification framework for complex power quality disturbances

Jie Liu et al.

Summary: This study proposes a novel feature fusion network (FFNet) for the automated detection and classification of complex power quality (PQ) disturbances. By using an adaptive double-resolution Stransform (ADRST) algorithm for time-frequency analysis and an improved convolutional neural network (CNN) for feature extraction and disturbances classification, the accuracy of PQ disturbances identification is improved.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Automation & Control Systems

A Novel Label-Guided Attention Method for Multilabel Classification of Multiple Power Quality Disturbances

Dexi Gu et al.

Summary: In this article, a novel multilabel method named LGAN is proposed, which incorporates deep learning and explores the correlations between PQD labels to improve performance. Comparative experiments show that LGAN outperforms existing multilabel methods in terms of PQD.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Automation & Control Systems

Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI-Explainable Artificial Intelligence

Ram Machlev et al.

Summary: This article proposes a method that uses explainable artificial intelligence to explain the outputs of PQD classifiers, making the results more transparent for experts to make informed decisions.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Engineering, Electrical & Electronic

Improved S-Transform for Time-Frequency Analysis for Power Quality Disturbances

Chengbin Liang et al.

Summary: With the global trend of carbon emission reduction, renewable energy sources will be deployed more vigorously. However, the connection of numerous renewable energy sources poses an increasingly critical challenge to power quality (PQ) issues. This paper proposes an improved S-transform (IST) method that can accurately detect various disturbances. The method can be implemented quickly using fast Fourier transform (FFT), and it has excellent energy concentration performance at different detection frequencies.

IEEE TRANSACTIONS ON POWER DELIVERY (2022)

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Article Chemistry, Physical

Deep learning-based multi-model ensemble method for classification of PQDs in a hydrogen energy-based microgrid using modified weighted majority algorithm

Gökay Bayrak et al.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2022)

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Article Engineering, Electrical & Electronic

A New Classification Scheme Based on Extended Kalman Filter and Support Vector Machine

Yamina Simhamed et al.

Summary: This paper proposes an efficient classification scheme based on EKF and SVM for automatic detection, identification, and classification of power quality disturbances. The scheme extracts features and applies SVM classifier to classify disturbances into specific categories. Simulation results demonstrate the effectiveness of the proposed scheme, and performance comparisons with other methods in the literature are conducted.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

Highly accurate detection of power quality disturbance using segmented and modified S-transform

Mingping Liu et al.

Summary: This paper proposes an algorithm based on the segmented and modified S-transform (SMST) to accurately detect PQ disturbances. The algorithm utilizes a modified Gaussian window with adaptive parameters and the idea of segmentation to satisfy different detection requirements in different frequency bands, resulting in superior accuracy of detection.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

Measurement and simulation of power quality issues in grid connected wind farms

T. Magesh et al.

Summary: This paper addresses the power quality issues of a wind farm synchronous generator in Tamilnadu, India. Simulation models and control strategies are used to analyze the impact of different circumstances on the performance of the wind farm and validate the accuracy of the models. The findings will be valuable for turbine operators and manufacturers in assessing and resolving power quality issues.

ELECTRIC POWER SYSTEMS RESEARCH (2022)

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Article Engineering, Electrical & Electronic

A Full-View Synchronized Measurement System for the Renewables, Controls, Loads, and Waveforms of Power-Electronics-Enabled Power Distribution Grids

Hao Liu et al.

Summary: This paper presents a full-view synchronized measurement system (SYMS) for power-electronics-enabled power distribution grids, covering renewables, controls, loads, and waveforms. The system is composed of different types of synchronized measurement devices (SMDs) and a data center. The proposed synchrophasor estimation method, combined with hardware design and a communication protocol, allows for accurate measurement of various power electronics devices. The SYMS serves as a comprehensive and open data platform for future researchers.

IEEE TRANSACTIONS ON SMART GRID (2022)

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Article Engineering, Electrical & Electronic

Dual-Channel Convolutional Network-Based Fault Cause Identification for Active Distribution System Using Realistic Waveform Measurements

Hao Liu et al.

Summary: This paper proposes a method based on dual-channel convolutional neural network for identifying distribution system fault cause. By analyzing a large amount of field waveform data, frequency-domain and time-domain features are extracted and improved through multimodal information fusion. The superiority of the proposed method is demonstrated through extensive tests.

IEEE TRANSACTIONS ON SMART GRID (2022)

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Article Chemistry, Multidisciplinary

A Machine-Learning Pipeline for Large-Scale Power-Quality Forecasting in the Mexican Distribution Grid

Juan J. Flores et al.

Summary: This article presents a case study of a region-sized state-owned Mexican distribution grid, where the company needs to maintain PQ standards in numerous distribution circuits and quality-control nodes. A machine-learning pipeline with nearly 4000 univariate forecasting models was implemented to address this challenge. The implemented system, MIRD, is an unprecedented effort in forecasting models for PQ indices monitoring.

APPLIED SCIENCES-BASEL (2022)

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Article Engineering, Electrical & Electronic

An Innovative Single Shot Power Quality Disturbance Detector Algorithm

Carlos Iturrino-Garcia et al.

Summary: Power quality disturbances have become a concern for many due to the increasing number of nonlinear loads and renewable sources connected to the grid. This work presents a novel algorithm, SSPQDD, which outperforms other algorithms in terms of computational resources, accuracy, and layers. Experimental results using simulation and real measurement data validate the effectiveness of SSPQDD in detecting PQDs with an overall accuracy of 96.55%.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2022)

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Article Computer Science, Information Systems

A Comprehensive Overview on Modified Versions of Stockwell Transform For Power Quality Monitoring

Rajat Kumar et al.

Summary: This paper presents a comprehensive literature review on several modified versions of Standard ST for the first time, aiming to reduce the computational complexity of the algorithm and maximize the energy concentration of the time-frequency plane. A comparative analysis of all the modified STs has been presented in tabular form to provide the key characteristics of each technique. Additionally, a case study has been presented to substantiate the highest accuracy of the proposed algorithm over the other ST variants, indicating miscellaneous applications of Standard ST and its modified variants.

IEEE ACCESS (2022)

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Proceedings Paper Materials Science, Multidisciplinary

Review on detection and classification of underlying causes of power quality disturbances using signal processing and soft computing technique

G. N. Bonde et al.

Summary: This article provides a comprehensive review of signal processing and soft computing techniques used for the detection and recognition of the underlying cause of power quality disturbances, which is of great importance for researching and addressing PQ disturbances.

MATERIALS TODAY-PROCEEDINGS (2022)

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Article Engineering, Electrical & Electronic

Susceptibility of Large Wind Power Plants to Voltage Disturbances - Recommendations to Stakeholders

Roger Alves de Oliveira et al.

Summary: This paper provides a guide on fault ride-through (FRT) for large wind power plants, discussing the characteristics of disturbances in the transmission system and their impact on wind turbines and power plants. It emphasizes the importance of considering voltage disturbances in both transmission system and wind turbine terminals, and recommends the inclusion of additional features in FRT analysis.

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY (2022)

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Article Engineering, Electrical & Electronic

Power quality event classification using optimized Bayesian convolutional neural networks

Sami Ekici et al.

Summary: Management of the electrical grid is crucial for the sustainability and reliability of the electrical energy supply. This article introduces a novel method for classifying power quality disturbances using deep learning and image processing, achieving a high accuracy of 99.8%.

ELECTRICAL ENGINEERING (2021)

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Review Automation & Control Systems

Review on Oscillatory Stability in Power Grids With Renewable Energy Sources: Monitoring, Analysis, and Control Using Synchrophasor Technology

Lasantha Gunaruwan Meegahapola et al.

Summary: The article reviews the state-of-the-art oscillatory stability monitoring, analysis, and control techniques based on synchrophasor technology, emphasizing oscillations induced from PEC-interfaced renewable energy generation. While oscillatory stability analysis techniques using synchrophasor technology are well established in power system engineering, further research is needed to effectively utilize this technology to address PEC-induced oscillations. New big data analytics techniques could potentially be used on synchrophasor data streams to develop oscillatory stability monitoring, analysis, and damping techniques.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)

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Review Energy & Fuels

Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review

Ana Rita Nunes et al.

Summary: The paper reviews how to utilize machine learning techniques for wind energy turbines to improve availability. By framing the issue as a machine learning problem and using specific evaluation metrics, early prediction of future faults can be achieved, leading to increased turbine availability and energy production.

ENERGIES (2021)

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Article Engineering, Multidisciplinary

Fast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sources

O. Cortes-Robles et al.

Summary: This paper presents a deep learning approach for power quality monitoring in systems with distributed generation sources, focusing on multi-scale analysis of multi-component signals for disturbance classification. The method combines VMD signal processing stage and FFNN deep learning stage, allowing minimum training time for disturbance classification. The proposed method is validated in a real-world environment through lab measurements.

MEASUREMENT (2021)

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Article Engineering, Electrical & Electronic

Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data

Xinyi Wang et al.

Summary: The ASBC technique achieves real-time streaming of high-resolution continuous waveforms and PMU measurements through adaptive subband compression. The technology conforms to industry standards and effectively reduces the sampling rate of CPOW.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

CNN/Bi-LSTM-based deep learning algorithm for classification of power quality disturbances by using spectrogram images

Ilyas Ozer et al.

Summary: This paper introduces a novel deep learning algorithm based on CNN and Bi-LSTM for classifying power quality disturbances using an inverse signal approach, achieving a high classification accuracy of 99.33% through spectrograms and RGB images.

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

A Voltage Sag Detection Method Based on Modified S Transform With Digital Prolate Spheroidal Window

Jianmin Li et al.

Summary: A novel algorithm based on modified S transform with digital prolate spheroidal window (DPSW) is proposed for accurate detection of voltage sag, obtaining characteristics of sag signals by analyzing the two-dimensional complex matrix. Simulation and experimental results validate the accuracy and validity of the proposed algorithm.

IEEE TRANSACTIONS ON POWER DELIVERY (2021)

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Article Engineering, Electrical & Electronic

Automated Distribution Network Fault Cause Identification With Advanced Similarity Metrics

Xu Jiang et al.

Summary: The paper proposes a method to infer fault cause from minimal amounts of historical fault data by applying a novel structural similarity metric to substation current data, and demonstrates an improvement in classification accuracy over comparable techniques on an industrially relevant benchmark data set.

IEEE TRANSACTIONS ON POWER DELIVERY (2021)

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Article Engineering, Electrical & Electronic

FPGA-Based Deep Convolutional Neural Network of Process Adaptive VMD Data With Online Sequential RVFLN for Power Quality Events Recognition

Mrutyunjaya Sahani et al.

Summary: This article introduces a method that combines SAVMD, DCNN, and OSRVFLN for real-time categorization of power quality events. The method optimizes the number of decompositions and data-fidelity factor to extract efficient features, automatically extracts discriminative features using DCNN, and trains a feature vector with OSRVFLN classifier to achieve maximum classification accuracy.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2021)

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Article Computer Science, Artificial Intelligence

A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification

Hatem Sindi et al.

Summary: This paper presents a novel hybrid convolutional neural network method for processing power signals and classifying power quality disturbances. The method combines 1D and 2D convolutional neural network structures, resulting in relatively high classification performance while maintaining almost the same computational complexity.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Article Engineering, Electrical & Electronic

Classification and Visualization of Power Quality Disturbance-Events Using Space Vector Ellipse in Complex Plane

Mollah Rezaul Alam et al.

Summary: This article introduces a novel algorithm employing SVE in a complex plane to classify and visualize power quality disturbance-events. By mapping signals in a complex 2D coordinates and utilizing ellipse parameters to classify nine types of PQDEs, the proposed method's effectiveness is demonstrated through extensive real-time simulation and classification of noisy and practical signals.

IEEE TRANSACTIONS ON POWER DELIVERY (2021)

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Article Green & Sustainable Science & Technology

Power system stability issues, classifications and research prospects in the context of high-penetration of renewables and power electronics

Jan Shair et al.

Summary: This paper discusses the emerging power system stability issues, classification, and research prospects under a high penetration of renewables and power electronics. It highlights the challenges introduced by the high penetration of renewables and power electronic devices in modern power systems, and proposes a new classification framework for power system stability to adapt to the evolving stability issues. The paper also emphasizes the impacts of emerging stability challenges on classical stability problems and classifications, providing insights for future research directions in power system stability.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

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Article Engineering, Electrical & Electronic

Synchronous Waveform Measurements to Locate Transient Events and Incipient Faults in Power Distribution Networks

Milad Izadi et al.

Summary: The proposed method utilizes synchronized measurements from WMUs to identify the location of transient events in power distribution systems. It involves multi-signal modal analysis, circuit model construction, and forward and backward analyses. The method only requires installing 2 WMUs and demonstrates accuracy and robustness in different scenarios.

IEEE TRANSACTIONS ON SMART GRID (2021)

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Article Engineering, Electrical & Electronic

Dual-Band Microwave Sensor Based on Planar Rectangular Cavity Loaded With Pairs of Improved Resonator for Differential Sensing Applications

Weina Liu et al.

Summary: A dual-band sensor based on a planar rectangular cavity loaded with pairs of improved planar resonators is used to measure the permittivity difference of liquids with a small volume. The sensor can detect changes in the dielectric characteristics of liquids and classify liquids, with a miniaturized design.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2021)

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Review Computer Science, Information Systems

Offshore Wind Farm-Grid Integration: A Review on Infrastructure, Challenges, and Grid Solutions

Syed Wajahat Ali et al.

Summary: This study compares and relates the power quality (PQ) and stability challenges faced by offshore wind power plants in electrical power systems, emphasizing low voltage ride through (LVRT) schemes and associated grid codes, while summarizing and comparing various PQ issues and mitigation options.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic

Hyperbolic Window S-Transform Aided Deep Neural Network Model-Based Power Quality Monitoring Framework in Electrical Power System

Kiron Nandi et al.

Summary: This study developed a deep neural network for feature extraction and classification of power quality disturbances in electrical power system network, demonstrating high accuracy in classifying various PQ events. The framework proposed is practical for power quality monitoring in electrical power systems.

IEEE SENSORS JOURNAL (2021)

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Review Computer Science, Theory & Methods

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi et al.

Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.

JOURNAL OF BIG DATA (2021)

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Article Engineering, Electrical & Electronic

A simple gated recurrent network for detection of power quality disturbances

Xiangrong Zu et al.

Summary: A new concise deep learning-based sequence model is proposed in this paper for detecting power quality disturbances, achieving higher prediction accuracy and more stable training process compared to traditional models. The developed simple gated recurrent network structure outperforms common neural network structures in terms of memory cost and detection speed.

IET GENERATION TRANSMISSION & DISTRIBUTION (2021)

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Article Computer Science, Information Systems

A Critical Analysis of Methodologies for Detection and Classification of Power Quality Events in Smart Grid

Rajender Kumar Beniwal et al.

Summary: This paper discusses the research interest in power quality issues, as well as the application of smart grid technology and IoT technology in power quality analysis. It provides an in-depth study from the perspective of signal processing techniques and artificial intelligence tools, while also giving an outlook on future development prospects.

IEEE ACCESS (2021)

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Review Energy & Fuels

Grid integrated renewable DG systems: A review of power quality challenges and state-of-the-art mitigation techniques

Mohit Bajaj et al.

INTERNATIONAL JOURNAL OF ENERGY RESEARCH (2020)

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Article Engineering, Electrical & Electronic

An analytic hierarchy process-based novel approach for benchmarking the power quality performance of grid-integrated renewable energy systems

Mohit Bajaj et al.

ELECTRICAL ENGINEERING (2020)

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Article Automation & Control Systems

Compressive Informative Sparse Representation-Based Power Quality Events Classification

Mohammad Babakmehr et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Engineering, Electrical & Electronic

Identifying and Ranking Sources of SSR Based on the Concept of Subsynchronous Power

Bo Gao et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2020)

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Article Engineering, Electrical & Electronic

Assessment of power quality in the utility grid integrated with wind energy generation

Om Prakash Mahela et al.

IET POWER ELECTRONICS (2020)

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Article Engineering, Multidisciplinary

A qualitative-quantitative hybrid approach for power quality disturbance monitoring on microgrid systems

O. Cortes-Robles et al.

MEASUREMENT (2020)

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Article Computer Science, Artificial Intelligence

Red deer algorithm (RDA): a new nature-inspired meta-heuristic

Amir Mohammad Fathollahi-Fard et al.

SOFT COMPUTING (2020)

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Article Engineering, Electrical & Electronic

Fault Location on Radial Distribution Networks via Distributed Synchronized Traveling Wave Detectors

Ali Tashakkori et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2020)

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Review Green & Sustainable Science & Technology

Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review

R. Igual et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)

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Article Engineering, Electrical & Electronic

A Resampling Method Based on Filter Designed by Window Function Considering Frequency Aliasing

Hao Liu et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS (2020)

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Article Energy & Fuels

Energy Quality: A Definition

Xiao-Ping Zhang et al.

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY (2020)

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Article Automation & Control Systems

Power Quality Assessment and Event Detection in Distribution Network With Wind Energy Penetration Using Stockwell Transform and Fuzzy Clustering

Om Prakash Mahela et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

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Article Computer Science, Information Systems

Recognition of Complex Power Quality Disturbances Using S-Transform Based Ruled Decision Tree

Om Prakash Mahela et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural Network

Huixin Li et al.

IEEE ACCESS (2020)

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Article Computer Science, Information Systems

A New Convolutional Network Structure for Power Quality Disturbance Identification and Classification in Micro-Grids

Renxi Gong et al.

IEEE ACCESS (2020)

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Article Automation & Control Systems

Classification of Complex Power Quality Disturbances Using Optimized S-Transform and Kernel SVM

Qiu Tang et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)

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Article Engineering, Multidisciplinary

Real-time voltage sag detection and classification for power quality diagnostics

Erick A. Nagata et al.

MEASUREMENT (2020)

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Review Computer Science, Information Systems

Grid Integration Challenges of Wind Energy: A Review

Shakir D. Ahmed et al.

IEEE ACCESS (2020)

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Review Computer Science, Information Systems

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Gajendra Singh Chawda et al.

IEEE ACCESS (2020)

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Article Engineering, Electrical & Electronic

Data-Driven Multi-Hidden Markov Model-Based Power Quality Disturbance Prediction That Incorporates Weather Conditions

Fei Xiao et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2019)

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Article Engineering, Electrical & Electronic

A New Method With Hilbert Transform and Slip-SVD-Based Noise-Suppression Algorithm for Noisy Power Quality Monitoring

Yan Wang et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2019)

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Article Green & Sustainable Science & Technology

Fault Tree Analysis of floating offshore wind turbines

Jichuan Kang et al.

RENEWABLE ENERGY (2019)

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Article Energy & Fuels

A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network

Shouxiang Wang et al.

APPLIED ENERGY (2019)

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Article Energy & Fuels

Power Quality in DC Distribution Networks

Julio Barros et al.

ENERGIES (2019)

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Article Green & Sustainable Science & Technology

Comparative analysis of European grid codes relevant to offshore renewable energy installations

Eider Robles et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)

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Article Energy & Fuels

Power Quality Disturbance Monitoring and Classification Based on Improved PCA and Convolution Neural Network for Wind-Grid Distribution Systems

Yue Shen et al.

ENERGIES (2019)

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Article Engineering, Electrical & Electronic

Power Quality Disturbances Recognition Using Modified S Transform and Parallel Stack Sparse Auto-encoder

Wei Qiu et al.

ELECTRIC POWER SYSTEMS RESEARCH (2019)

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Article Automation & Control Systems

A Sequence-to-Sequence Deep Learning Architecture Based on Bidirectional GRU for Type Recognition and Time Location of Combined Power Quality Disturbance

Yaping Deng et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

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Article Engineering, Electrical & Electronic

A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data

Chao Ren et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2019)

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Article Computer Science, Information Systems

Power Quality Disturbance Classification Based on Compressed Sensing and Deep Convolution Neural Networks

Jidong Wang et al.

IEEE ACCESS (2019)

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Review Computer Science, Information Systems

Harmonic Source Detection Methods: A Systematic Literature Review

Rosalia Sinvula et al.

IEEE ACCESS (2019)

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Article Computer Science, Information Systems

Power Quality Disturbance Feature Selection and Pattern Recognition Based on Image Enhancement Techniques

Lin Lin et al.

IEEE ACCESS (2019)

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Article Automation & Control Systems

Short-Time Fourier Transform Based Transient Analysis of VSC Interfaced Point-to-Point DC System

Kuntal Satpathi et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

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Article Automation & Control Systems

Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

Mrutyunjaya Sahani et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)

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Article Engineering, Electrical & Electronic

A Modified S-Transform and Random Forests-Based Power Quality Assessment Framework

Motakatla Venkateswara Reddy et al.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2018)

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Article Engineering, Electrical & Electronic

A System-Wide Protection Against Unstable SSCI in Series-Compensated Wind Power Systems

Xiaorong Xie et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2018)

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Article Engineering, Electrical & Electronic

A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification

Azam Bagheri et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2018)

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Article Computer Science, Artificial Intelligence

Adaptive Learning-Based k-Nearest Neighbor Classifiers With Resilience to Class Imbalance

Sankha Subhra Mullick et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

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Article Engineering, Electrical & Electronic

Tunable-Q Wavelet Transform and Dual Multiclass SVM for Online Automatic Detection of Power Quality Disturbances

Karthik Thirumala et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

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Article Engineering, Electrical & Electronic

Variational Mode Decomposition and Decision Tree Based Detection and Classification of Power Quality Disturbances in Grid-Connected Distributed Generation System

Pankaj D. Achlerkar et al.

IEEE TRANSACTIONS ON SMART GRID (2018)

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Article Engineering, Electrical & Electronic

Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

Manish Kumar Saini et al.

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS (2018)

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Article Computer Science, Information Systems

Analysis and Mitigation of Power Quality Issues in Distributed Generation Systems Using Custom Power Devices

Eklas Hossain et al.

IEEE ACCESS (2018)

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Article Computer Science, Artificial Intelligence

Micro-genetic algorithms for detecting and classifying electric power disturbances

Arturo Yosimar Jaen-Cuellar et al.

NEURAL COMPUTING & APPLICATIONS (2017)

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Article Engineering, Multidisciplinary

Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources

Xiaodong Liang

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS (2017)

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Article Engineering, Electrical & Electronic

Wavelet-Based Event Detection Method Using PMU Data

Do-In Kim et al.

IEEE TRANSACTIONS ON SMART GRID (2017)

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Article Engineering, Electrical & Electronic

Power Quality Concerns in Implementing Smart Distribution-Grid Applications

Math H. J. Bollen et al.

IEEE TRANSACTIONS ON SMART GRID (2017)

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Article Computer Science, Information Systems

DC Microgrid Technology: System Architectures, AC Grid Interfaces, Grounding Schemes, Power Quality, Communication Networks, Applications, and Standardizations Aspects

Dinesh Kumar et al.

IEEE ACCESS (2017)

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Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

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Article Automation & Control Systems

Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals

Fabbio A. S. Borges et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2016)

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Article Green & Sustainable Science & Technology

Do onshore and offshore wind farm development patterns differ?

Peter Enevoldsen et al.

ENERGY FOR SUSTAINABLE DEVELOPMENT (2016)

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Review Energy & Fuels

Power Quality in DC Power Distribution Systems and Microgrids

Stephen Whaite et al.

ENERGIES (2015)

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Review Green & Sustainable Science & Technology

A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances

Suhail Khokhar et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)

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Review Green & Sustainable Science & Technology

A critical review of detection and classification of power quality events

Om Prakash Mahela et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)

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Article Radiology, Nuclear Medicine & Medical Imaging

Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

Abdel Aziz Taha et al.

BMC MEDICAL IMAGING (2015)

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Article Engineering, Electrical & Electronic

A Classification Method for Complex Power Quality Disturbances Using EEMD and Rank Wavelet SVM

Zhigang Liu et al.

IEEE TRANSACTIONS ON SMART GRID (2015)

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Article Green & Sustainable Science & Technology

Comparative Study of Advanced Signal Processing Techniques for Islanding Detection in a Hybrid Distributed Generation System

Soumya R. Mohanty et al.

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY (2015)

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Article Engineering, Biomedical

Improved complete ensemble EMD: A suitable tool for biomedical signal processing

Marcelo A. Colominas et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2014)

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Article Engineering, Electrical & Electronic

Transmission-Line Fault Analysis Using Synchronized Sampling

Papiya Dutta et al.

IEEE TRANSACTIONS ON POWER DELIVERY (2014)

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Article Engineering, Electrical & Electronic

Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier

Milan Biswal et al.

DIGITAL SIGNAL PROCESSING (2013)

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Review Green & Sustainable Science & Technology

A review of wind energy technologies

G. M. Joselin Herbert et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2007)

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