Alternate Support Vector Machine Decision Trees for Power Systems Rule Extractions

Article Engineering, Electrical & Electronic

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Yifan Zhou et al.

Summary: This paper presents a method that combines quantum computing, data science, and machine learning to address the transient stability assessment issue in power systems. The authors propose a quantum TSA method that uses a high expressibility, low-depth quantum circuit for accurate and noise-resilient stability prediction. They also develop a quantum natural gradient descent algorithm for efficient circuit training. Experimental results demonstrate the accuracy, noise-resilience, scalability, and universality of the proposed method.

IEEE TRANSACTIONS ON POWER SYSTEMS (2023)

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Article Computer Science, Information Systems

Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks

Li Liu et al.

Summary: This article proposes an algorithm for continuous object boundary tracking in industrial wireless sensor networks. The algorithm utilizes the collective intelligence and machine learning capability within the sensor nodes. By determining the upper bound of the event region covered by continuous objects and performing a binary tree-based partition, the algorithm achieves boundary tracking. Simulation results show that the algorithm reduces the number of nodes required for boundary tracking by at least 50%.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Engineering, Electrical & Electronic

Sparse Oblique Decision Tree for Power System Security Rules Extraction and Embedding

Qingchun Hou et al.

Summary: The proposed method utilizes a sparse weighted oblique decision tree to learn security rules for power systems, leading to increased percentage of secure states and reduced average solution time.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Reliability and Vulnerability Assessment of Multi-Energy Systems: An Energy Hub Based Method

Wujing Huang et al.

Summary: This paper proposes a method for assessing the reliability and vulnerability of multi-energy systems using energy hub model. The use of multi-parametric linear programming with a self-adaptive critical region set can significantly reduce computational burden and improve efficiency. Results from a case study demonstrate the effectiveness of the proposed method in evaluating energy supply reliability and identifying critical components in MES planning.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Data-Driven Frequency Dynamic Unit Commitment for Island Systems With High RES Penetration

Dimitris T. Lagos et al.

Summary: In this paper, a data-driven approach based on optimal classification trees is proposed to extract constraints for a frequency dynamic unit commitment formulation from a detailed dynamic model of the system. The method allows for achieving both dynamic security of the system and optimal utilization of renewable and conventional power units.

IEEE TRANSACTIONS ON POWER SYSTEMS (2021)

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Article Engineering, Electrical & Electronic