Optimal performance evaluation of thermal AGC units based on multi-dimensional feature analysis

Bin Li, Shuai Wang, Botong Li, Hongbo Li and Jianzhong Wu

Applied Energy, 2023, vol. 339, issue C, No S0306261923003586

Abstract: In modern energy system, automatic generation control (AGC) is the core technology of real-time output regulation for thermal power generator. The performance of thermal AGC units must be accurately evaluated to measure their actual contribution to the energy system. However, based on current conventional evaluation methods, the difficulty of the tasks undertaken by AGC units has not been distinguished and quantified. An optimal performance evaluation method based on multi-dimensional feature analysis is proposed. Firstly, a performance index describing the difference between the expected regulating energy and the actual regulated energy of AGC units is designed, which improves the universality of the evaluation to the actual engineering scenarios. Additionally, after data preprocessing and data cleaning, a sample space is constructed to significantly distinguish the difficulty of tasks performed by AGC units. Finally, a multi-dimensional feature analysis in the sample space is proposed to find the optimal performance points of AGC units. Based on historical data, the proposed methods were verified on real AGC units. The experimental results show that the proposed method obtains detailed evaluation results of thermal AGC units with different control requirements and solves the problem of evaluation failure in traditional method.

Keywords: Automatic generation control; Performance evaluation; Sample space; Feature analysis; Optimal dynamic performance (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2023.120994

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