基于PSO优化的LSSVM的电力系统电压优化的研究

Study on the Optimization of Voltage Power System Particle Swarm Optimization Based on Least Squares Support Vector Machine

由于传统的方法在处理电压优化与治理问题时存在较大的局限性。针对地区电力系统电压优化和治理问题进行了研究,建立了以有功网损为目标函数的不等式约束的优化问题,并考虑到电压优化问题的控制变量能够进行种群划分,而粒子群算法(PSO)又能够降低搜索空间的运算复杂度,因此提出了基于粒子群优化最小二乘支持向量机(PSO-LSSVM)来对电力系统电压进行优化。通过实例的结果分析,该方法对电压的合格率有所提高,对电压性能有明显的改善,损耗也下降了,且收敛速度加快,有助于解决地区电力系统电压优化和治理问题。

Due to the limitations of traditional mathematical methods in dealing with voltage optimization and control problems, there are some limitations.According to the voltage of the power system optimization and governance issues of research, established the optimization problem with inequality constraints for active power loss of the objective function, and considering the control variable optimization voltage can population division, and the particle swarm algorithm （PSO） can reduce the search space complexity, therefore put forward particle swarm optimization based on least squares support vector machine （PSO-LSSVM） to optimize the power system voltage.Through the example analysis of the results, this method can improve the voltage qualification rate, improve the voltage loss performance also declined, and the convergence speed, helps to solve the regional power system voltage optimization and governance issues.