Evolutionary Large-Scale Multiobjective Optimization for Ratio Error Estimation of Voltage Transformers

多目标优化 数学优化 测试套件 计算机科学 水准点(测量) 进化算法 最优化问题 树(集合论) 进化计算 电力系统 测试用例 数学 功率(物理) 机器学习 数学分析 物理 回归分析 大地测量学 量子力学 地理
作者
Cheng He,Ran Cheng,Chuanji Zhang,Ye Tian,Qin Chen,Xin Yao
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:24 (5): 868-881 被引量:76
标识
DOI:10.1109/tevc.2020.2967501
摘要

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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