Robust Ensemble Data Analytics for Incomplete PMU Measurements-Based Power System Stability Assessment

Zhang, Yuchen; Xu, Yan; Zhang, Dong

doi:10.1109/tpwrs.2017.2698239

Cited by 81 publications

(47 citation statements)

References 4 publications

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“…The ability of power systems to maintain stability and to ensure continuous supply of electrical power to customers in the event of a disturbance is of critical importance [1][2][3]. As the power system is spread over large geographic regions, the probability of facing different types of faults and failures is high [4].…”

Section: An Overview and Motivationsmentioning

confidence: 99%

A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges

et al. 2019

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Power systems are the most complex systems and have great importance in modern life. They have direct impacts on the modernization, economic, political and social aspects. To operate such systems in a stable mode, several control and protection techniques are required. However, modern systems are equipped with several protection schemes with the aim of avoiding the unpredicted events and power outages, power systems are still encountering emergency and mal-operation situations. The most severe emergencies put the whole or at least a part of the system in danger. If the emergency is not well managed, the power system is likely to have cascading failures that might lead to a blackout. Due to the consequences, many countries around the world have research and expert teams who work to avoid blackouts on their systems. In this paper, a comprehensive review on the major blackouts and cascading events that have occurred in the last decade are introduced. A particular focus is given on the US power system outages and their causes since it is one of the leading power producers in the world and it is also due to the ready availability of data for the past events. The paper also highlights the root causes of different blackouts around the globe. Furthermore, blackout and cascading analysis methods and the consequences of blackouts are surveyed. Moreover, the challenges in the existing protective schemes and research gaps in the topic of power system blackout and cascading events are marked out. Research directions and issues to be considered in future power system blackout studies are also proposed.

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Section: An Overview and Motivationsmentioning

confidence: 99%

A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges

et al. 2019

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“…Sometimes only some important generators and hub substations are equipped with PMUs, which brings great challenges to online application. In this paper, according to the two PMU configuration schemes in reference [35], the power angle information of the nodes that the PMU cannot detect is deleted, and the features are re-extracted. The training process is consistent with the above.…”

Section: ) Model Evaluation Performance When Pmu Is Incomplete Measumentioning

confidence: 99%

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

et al. 2020

IEEE Access

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In order to maintain the stable operation of power systems, quick and accurate transient stability assessment (TSA) after the fault clearance is important. Machine learning methods have been widely used in the transient stability analysis of power systems. However, how to make good use of time series data of PMUs and effectively balance the contradiction between rapidity and accuracy brings new challenges to TSA. To address this problem, we propose an anti-jitter dynamic evaluation method based on long-short term memory (LSTM) network. In this model, the trajectory cluster characteristics of generators power angles after fault clearance are taken as inputs, and an improved LSTM is used to learn the nonlinear mapping relationship between the input characteristics and the transient stability. Meanwhile, by the use of sliding time windows and anti-jitter mechanism, a hierarchical real-time prediction framework is constructed to effectively utilize the time series data of PMUs. The case studies on two systems indicate that the proposed method has superior evaluation accuracy and general performance. In addition, the proposed method can effectively evaluate the stability margin or instability degree of samples, which provides reliable reference information for emergency control. INDEX TERMS Deep learning, long-short term memory, recurrent neural network, transient stability assessment, artificial intelligence.

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“…In practice, data may be subject to unavoidable interference in the process of measurement and transmission [32]. Thus, it is required that the classification model has strong robustness to noise.…”

Section: Performance Analysis Considering Noisementioning

confidence: 99%

Transient stability evaluation model based on SSDAE with imbalanced correction

Wang

2020

IET Generation, Transmission & Distribution

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Robust Ensemble Data Analytics for Incomplete PMU Measurements-Based Power System Stability Assessment

Cited by 81 publications

References 4 publications

A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges

A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges

Anti-Jitter and Refined Power System Transient Stability Assessment Based on Long-Short Term Memory Network

Transient stability evaluation model based on SSDAE with imbalanced correction

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