期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 24, 期 5, 页码 868-881出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2020.2967501
关键词
Voltage measurement; Optimization; Calibration; Substations; Estimation; Error analysis; Benchmark testing; Benchmark test suite; inequality constraint; large-scale multiobjective optimization; time-varying ratio error estimation (TREE); voltage transformer (VT)
资金
- National Natural Science Foundation of China [61903178, 61906081]
- Program for Guangdong Introducing Innovative and Entrepreneurial Teams [2017ZT07X386]
- Shenzhen Peacock Plan [KQTD2016112514355531]
- Program for University Key Laboratory of Guangdong Province [2017KSYS008]
Ratio error (RE) estimation of the voltage transformers (VTs) plays an important role in modern power delivery systems. Existing RE estimation methods mainly focus on periodical calibration but ignore the time-varying property. Consequently, it is difficult to efficiently estimate the state of the VTs in real time. To address this issue, we formulate a time-varying RE estimation (TREE) problem into a large-scale multiobjective optimization problem, where the multiple objectives and inequality constraints are formulated by statistical and physical rules extracted from the power delivery systems. Furthermore, a set of TREE problems from different substations is systematically formulated into a benchmark test suite for characterizing their different properties. The formulation of these TREE problems not only transfers an expensive RE estimation task to a relatively cheaper optimization problem but also promotes the research in large-scale multiobjective optimization by providing a real-world benchmark test suite with complex variable interactions and correlations to different objectives. To the best of our knowledge, this is the first time to formulate a real-world problem into a benchmark test suite for large-scale multiobjective optimization, and it is also the first work proposing to solve TREE problems via evolutionary multiobjective optimization.
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