基于遗传算法的低压配电网无功补偿优化方法研究

Research on Reactive Power Compensation Optimization Method of Low Voltage Distribution Network Based on Genetic Algorithm

当前的低压配电网无功补偿优化约束条件一般为单层级的,优化范围受限制,导致无功补偿极限损耗值增加,为此提出对基于遗传算法的低压配电网无功补偿优化方法研究。根据实际的优化需求及标准,先进行无功补偿点选择,采用多阶形式,打破优化范围的限制,设定低压配电网多阶约束条件,构建遗传算法无功补偿优化模型,采用无功谐波潮流限制实现优化处理。最终的测试结果表明:对比于传统多层级协同无功补偿优化测试组、传统粒子群算法无功补偿优化测试组,此次所设计的遗传算法无功补偿优化测试组最终得出的无功补偿极限损耗值均被较好地控制在了150kW以下,说明该种优化形式的针对性与稳定性较高,在进行无功补偿优化的过程中控制效果更佳,具有实际的应用价值。

The current reactive power compensation optimization constraint conditions of low-voltage distribution network are generally single-layer level,and the optimization range is limited,which leads to the increase of reactive power compensation limit loss value.Therefore,this paper proposes the research on the reactive power compensation optimization method of low-voltage distribution network based on genetic algorithm.According to the actual optimization requirements and standards,the reactive power compensation points were selected first,and the multi-stage form was adopted to break the limit of optimization range.The multi-stage constraint conditions of low-voltage distribution network were set up.The reactive power compensation optimization model of genetic algorithm was constructed,and the reactive harmonic power flow limitation was adopted to realize the optimization processing.The final test results show that:Compared with the traditional multi-level collaborative reactive compensation optimization test group and the traditional particle swarm optimization reactive compensation optimization test group,the ultimate reactive compensation loss value obtained by the designed genetic algorithm reactive compensation optimization test group is well controlled below 150kW,indicating that this optimization form is more targeted and stable.In the process of reactive power compensation optimization,the control effect is better and it has practical application value.