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Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

Author

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  • Mingxiao Zhu

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Jiacai Li

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Dingge Chang

    (State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Guanjun Zhang

    (State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jiming Chen

    (College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

A radio frequency antenna array was adopted to localize partial discharge (PD) sources in an entire substation. The deployment of an antenna array is a significant factor affecting the localization accuracy, and the array needs to be carefully selected. In this work, a hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) is proposed to optimize the array deployment. A direction-of-arrival (DOA) estimation algorithm applicable to arbitrary array configurations is firstly presented. The Cramér-Rao lower bound (CRLB) was employed to evaluate the localization accuracy of different arrays, and two objective functions characterizing the estimation errors of coordinates and the DOA are proposed. With the goal of minimizing the objective functions, the array deployments for the coordinate and DOA localizations were optimized by using the hybrid PSO/GA algorithm. Using the developed method, optimal antenna configurations for different constraint areas, aspect ratios, and numbers of sensors were investigated. The results indicate that the optimal deployments for coordinate and DOA estimations are different; specifically speaking, superior DOA performance is achieved when all antennas are placed on the outer boundary of the constraint area while part of the antennas in the optimal coordinate array are placed in the middle position.

Suggested Citation

  • Mingxiao Zhu & Jiacai Li & Dingge Chang & Guanjun Zhang & Jiming Chen, 2018. "Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1813-:d:157354
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    Cited by:

    1. Junjun Zhang & Yaojie Sun & Meiyin Liu & Wei Dong & Pingping Han, 2018. "Research on Modeling of Microgrid Based on Data Testing and Parameter Identification," Energies, MDPI, vol. 11(10), pages 1-15, September.
    2. Kuihua Wu & Kun Li & Rong Liang & Runze Ma & Yuxuan Zhao & Jian Wang & Lujie Qi & Shengyuan Liu & Chang Han & Li Yang & Minxiang Huang, 2018. "A Joint Planning Method for Substations and Lines in Distribution Systems Based on the Parallel Bird Swarm Algorithm," Energies, MDPI, vol. 11(10), pages 1-14, October.
    3. Mehdi Tavakkoli & Jafar Adabi & Sasan Zabihi & Radu Godina & Edris Pouresmaeil, 2018. "Reserve Allocation of Photovoltaic Systems to Improve Frequency Stability in Hybrid Power Systems," Energies, MDPI, vol. 11(10), pages 1-19, September.

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