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- W4385489844 abstract "Identifying correct load model parameters is crucial for power system simulation analysis and control strategy design. To obtain real load characteristics matching up with actual operation, measurement-based methods for load model parameter identification are usually preferred. However, these methods mainly rely on iterative parameter searching process, which encounters challenges in time consumption when model complexity is high. In this paper, a parameter identification method for generalized synthetic load model (SLM) based on extreme learning machine (ELM) is proposed, aiming to improve computation efficiency. Moreover, a quantum particle swarm optimization (QPSO) algorithm is selected to train ELM model for better fitting to load response curves. Finally, the proposed QPSO-ELM based SLM parameter identification method is verified with standard test system together with parameter sensitivity analysis and simulation results prove effectiveness." @default.
- W4385489844 created "2023-08-03" @default.
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- W4385489844 date "2023-01-01" @default.
- W4385489844 modified "2023-09-28" @default.
- W4385489844 title "A QPSO-ELM Based Method for Load Model Parameters Identification" @default.
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- W4385489844 doi "https://doi.org/10.1007/978-981-99-4334-0_109" @default.
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