基于改进PSO-LSSVM和蒙特卡洛法的电力系统可靠性评估

针对电力系统可靠性评估方法耗时长、误差大等问题,提出一种用改进粒子群优化算法(PSO)优化最小二乘支持向量机(LSSVM)参数,并将构建好的PSO-LSSVM模型与蒙特卡洛法(MCS)相结合用于发输电系统可靠性评估的方法。该方法通过对PSO算法进行合理的改进,得到更为精确的LSSVM模型参数,建立用于分类系统状态样本的PSO-LSSVM模型。对MCS方法抽取的系统状态样本分类得到故障状态和正常状态,仅对故障状态样本进行可靠性指标计算,统计输出可靠性评估结果。采用该方法对IEEE-RTS 79系统不同运行情况下的可靠性指标进行计算,结果表明该方法保证计算时间不变的同时提高了LSSVM-MCS方法的评估精度。

Reliability evaluation of power system based on improved PSO-LSSVM and Monte Carlo simulation

The power system reliability evaluation methods are time-consuming and generate large errors. In order to solve these problems, a method for reliability evaluation of power systems is proposed based on the improved particle swarm optimization(PSO), the least squares support vector machine(LSSVM), and Monte Carlo simulation(MCS), where the improved PSO is used to optimize the parameters of SVM. The method can obtain more accurate LSSVM parameters through reasonable improvement of PSO, and a PSO-LSSVM model for classification of system state samples has been established. Using the PSO-LSSVM model to classify the system samples extracted by the MCS method, failure state samples and normal state samples were obtained with the PSO-LSSVM model. Reliability indices only for failure state samples were calculated and reliability evaluation results were output. This method was used to calculate the reliability index of the IEEE-RTS 79 system under different operation conditions, and the results show that the method improves the evaluation precision of the LSSVM-MCS method with a consistent amount of computation time.