Electric Power ›› 2020, Vol. 53 ›› Issue (11): 40-49.DOI: 10.11930/j.issn.1004-9649.202007181

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Optimal Allocation Strategy for Power Quality Control Devices Based on Harmonic and Three-Phase Unbalance Comprehensive Evaluation Indices for Distribution Network

ZHUO Fang1, YANG Zebin1, YI Hao1, YANG Guangyu1, WANG Meng1, YIN Xiaoqing1, ZHU Chengzhi2   

  1. 1. State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China;
    2. State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310007, China
  • Received:2020-07-31 Revised:2020-09-22 Online:2020-11-05 Published:2020-11-05

Abstract: Power quality control devices represented by active power filter are mostly used to compensate harmonic and unbalanced current generated by local loads. With the massive access of non-linear loads in the distribution network, the local compensation configuration strategy becomes expensive and inefficient. Therefore, the device configuration strategy for global improvement of network power quality is necessary. Firstly, a global evaluation strategy for power quality indices based on analytic hierarchy process is proposed to evaluate the configuration effects of control devices. Secondly, taking the global configuration effects, the total number and capacity of control devices as the optimization goals, and regarding the harmonic distortions and unbalance degrees of the nodes satisfying the standard as the constraint condition, the optimal configuration node and capacity of each device is determined by multi-objective particle swarm algorithm. Finally, an IEEE-18 node simulation model with non-ideal loads is built to verify the effectiveness and superiority of the proposed global evaluation and power quality control device configuration strategy for the comprehensive optimization of network harmonic and unbalance voltage.

Key words: global evaluation of power quality, analytic hierarchy process, fuzzy membership function, harmonic comprehensive control, three-phase unbalanced comprehensive control, control device configuration, multi-objective particle swarm optimization