Surge detection for smart grid power distribution using a regression-based signal processing model

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

Benefits of energy recovery from the undesirable components of electric signals in electric power systems

Moises Ribeiro et al.

Summary: This paper introduces an analytical framework for analyzing the benefits of using the waste-to-energy concept to improve power quality and energy efficiency in electric power systems. The framework quantifies and recovers the wasted energy associated with undesirable components in electric signals through closed-form expressions for evaluating the analytic cross-power spectral density.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2022)

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Article Engineering, Electrical & Electronic

Forecasting voltage harmonic distortion in residential distribution networks using smart meter data

Pablo Rodriguez-Pajaron et al.

Summary: This paper introduces a methodology for forecasting voltage total harmonic distortion (THD) at low voltage busbars of residential distribution feeders based on data from smart meters. The methodology provides relevant power quality indices using existing monitoring infrastructure for demand response operation. Different algorithms for voltage THD forecasting are implemented and their performance is tested and compared.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2022)

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Article Energy & Fuels

Harmonic analysis in distributed power system based on IoT and dynamic compressed sensing

Yuqing Niu et al.

Summary: This paper proposes a real-time harmonic monitoring and analysis framework based on Internet of Things (IoT) and dynamic compressed sensing for monitoring the power quality in massive distributed power systems. The framework utilizes continuous sampling and compression of power quality signals at sensing nodes, and employs a homotopy optimization algorithm at edge nodes to achieve data recovery with reduced computational complexity. The proposed framework offers better harmonic detection accuracy compared to existing methods, as demonstrated by tests on a Siemens benchmark microgrid model.

ENERGY REPORTS (2022)

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Article Engineering, Electrical & Electronic

Real-time transient stability prediction of power systems based on the energy of signals obtained from PMUs

Sevda Jafarzadeh et al.

Summary: A novel methodology based on signal processing and machine learning approaches is proposed for real-time transient stability prediction in power systems. The method utilizes signals from PMUs and employs different classifiers, with two alternative approaches for choosing the measurement window. The utilization of the energy concept in TSP allows for robust predictions and makes feature selection methods directly applicable to achieve TSP with less PMUs.

ELECTRIC POWER SYSTEMS RESEARCH (2021)

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Article Engineering, Electrical & Electronic

On the solution of small signal stability of power systems by a block-Krylov subspace algorithm

Lucas B. Rezende et al.

Summary: The paper proposes a block-Krylov algorithm based on the augmented block Householder Arnoldi method for computing eigenvalues in small-signal stability problems with CME matrices. Through numerical experiments, it is found that the algorithm performs better in terms of efficiency and robustness for CME matrices compared to conventional methods.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Harmonic Linearization and Investigation of Three-Phase Parallel-Structured Signal Decomposition Algorithms in Grid-Connected Applications

Saeed Golestan et al.

Summary: This article discusses the importance of monitoring and compensating for imbalances and harmonic pollution in supply voltage, as well as the role of modern power converter-based renewable energy sources in mitigating power quality issues. It also explores the signal processing tools used for online detection of fundamental sequence components and harmonics, comparing the use of discrete Fourier transform and time-domain signal decomposition algorithms.

IEEE TRANSACTIONS ON POWER ELECTRONICS (2021)

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Article Engineering, Electrical & Electronic

Wireless Power Transfer for Future Networks: Signal Processing, Machine Learning, Computing, and Sensing

Bruno Clerckx et al.

Summary: Wireless power transfer is an emerging technology that aims to provide sufficient energy supply to devices in future networks. It involves signal processing, machine learning, sensing, and computing, with the goal of providing cost-effective and real-time power supplies to wireless devices.

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2021)

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Article Computer Science, Information Systems

A New Advanced Method for an Accurate Assessment of Harmonic and Supraharmonic Distortion in Power System Waveforms

Guido Carpinelli et al.

Summary: This paper discusses a joint method for improving the performance of existing methods in evaluating waveform distortion in Smart Grids. The method utilizes Discrete Wavelet Transform to divide the waveform, and then combines the sliding-window modified ESPRIT method and sliding-window Discrete Fourier Transform to analyze low and high-frequency bands, respectively, in order to enhance the accuracy of estimating each frequency spectral component.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic

Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid

Raksha Ramakrishna et al.

Summary: This paper explores power systems data through graph signal processing (GSP), presenting the Grid-GSP framework which interprets voltage phasor measurements and ties their spatio-temporal structure to generator voltage dynamics. The Grid-GSP generative model is successfully applied to investigate problems related to grid data sampling, interpolation, network inference, anomaly detection, and data compression. Numerical results verify the efficacy of Grid-GSP methods in analyzing electric grid data.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2021)

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Review Engineering, Electrical & Electronic