Transient stability assessment method based on adaptive capture of instability patterns
DOI：10.19783/j.cnki.pspc.240132
Key Words:deep learning  transient stability assessment  instability pattern  ensemble learning  adaptive evaluation
Author NameAffiliation
ZHANG Shiyi1 1. Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University), Fuzhou 350108, China
2. State Grid Qinghai Electric Power Company, Xining 810001, China 
WANG Huaiyuan1 1. Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University), Fuzhou 350108, China
2. State Grid Qinghai Electric Power Company, Xining 810001, China 
LI Jian2 1. Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University), Fuzhou 350108, China
2. State Grid Qinghai Electric Power Company, Xining 810001, China 
LU Guoqiang2 1. Key Laboratory of New Energy Generation and Power Conversion (Fuzhou University), Fuzhou 350108, China
2. State Grid Qinghai Electric Power Company, Xining 810001, China 
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Abstract:In the transient stability assessment (TSA) problem of a power system, the limited data mining ability of common machine learning algorithms prevents further improvement of TSA model evaluation accuracy. To solve this problem, taking different instability patterns of power system data as the entry point, a TSA method based on adaptive capture of instability patterns is proposed. An adaptive stability judgment combination model is constructed, one which is composed of multiple sub-evaluation models and an instability pattern discrimination model. First, the original data set is classified according to the different instability patterns, and several sub-evaluation models are trained for different instability patterns. Then, the weight value of the instability pattern discrimination model output is used to integrate the sub-evaluation model, and the instability pattern of the input data is captured adaptively. Finally, a case study is performed on the IEEE39-bus system and the East China power grid system. Simulation results show that the proposed method can reduce the negative rate of unstable samples and further improve model evaluation accuracy, which verifies the effectiveness of the proposed method.
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