配电网络电能质量综合治理设备优化配置策略OA北大核心
Optimal Allocation Strategy for Comprehensive Control Equipment of Power Quality in Distribution Area
针对配电网络电能质量治理设备缺乏全局统筹配置的现状,提出了一种基于多目标粒子群优化算法对谐波、无功及三相不平衡治理设备的综合优化配置策略.分别采用有源电力滤波器抑制谐波、智能电容器补偿无功、换相开关治理三相不平衡,并以每类电能质量问题的治理效果和投入成本为优化对象,以满足相关电能质量标准为约束条件,通过多 目标粒子群算法确定治理设备配置节点和相应投入容量的优化配置方案.建立了电能质量评估模型,搭建了基于IEEE-18节点的配电系统仿真模型,并分散接入谐波、无功和三相不平衡负载,模拟电能质量问题,通过仿真验证了所提出的治理设备综合优化配置策略的可行性和相较于传统电能质量治理方案在电能质量治理上的优越性.
Aimed at the problem that the power quality control equipment in distribution network is lack of collab-orative allocation,an optimal allocation strategy for the control equipment of harmonics,reactive power and three-phase imbalance is proposed,which is based on the multi-objective particle swarm optimization(MOPSO)algorithm.The active power filter(APF)is used to suppress harmonics,the intelligent capacitor is used to compensate reactive power,and the phase-change switch is used to reduce three-phase imbalance.The control effect and operating cost about each power quality issue are taken as optimization objects,and the relevant power quality standards are considered as constraints.Through the MOPSO algorithm,an optimal allocation scheme for the allocation nodes and relevant access capacity of control equipment can be obtained.Furthermore,a power quality assessment model is built,and a simulation model based on an improved IEEE 18-node distribution system is also constructed.The harmonics,reactive power and three-phase imbalance loads are separately connected to simulate power quality issues,and simulation results verify the feasibility of the proposed strategy and its advantages compared with the tradi-tional scheme for power quality control equipment.
付勉;刘志涵;宋振浩;周娟;杜少通
中国矿业大学电气与动力工程学院,徐州市 221008国网上海能源互联网研究院有限公司,上海市 201213河南理工大学电气学院,焦作市 450001
动力与电气工程
电能质量多目标粒子群优化配置策略配电网络
Power qualitymulti-objective particle swarmoptimal allocation and sizing strategydistribution network
《电源学报》 2024 (002)
336-344 / 9
国家电网有限公司总部科技项目"配电台区电能质量智能化感知与提升关键技术研究"(520600200039)This work is supported by Science and Technology Project of State Grid"Research on Key Technology of Intelligent Power Quality Perception and Improvement in Distribution Area"under the grant 520600200039
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