基于改进自适应粒子群优化的高压设备检修计划自适应优化

Adaptive Optimization of High-voltage Equipment Maintenance PlanBased on Improved Adaptive Particle Swarm Optimization

为了保证电网在高压设备检修过程中的稳定运行,提出基于改进自适应粒子群优化算法的高压设备检修计划自适应优化方法。采用模糊推理规则算法评估高压设备运行状态,依据评估结果确定电网运行风险最小化为高压设备检修优化的目标函数,并确定同时约束、检修资源约束等5个约束条件,采用改进自适应粒子群优化求解目标函数,输出优化后的高压设备检修计划结果。测试结果显示,该方法检修时电压最大和最小结果之间的差距为0.105 pu,较为平稳。

To ensure the stable operation of the power grid during the maintenance process of high-voltage equipment,an adaptive optimization method for high-voltage equipment maintenance plan based on improved adaptive particle swarm optimization algorithm is proposed.The fuzzy inference rule algorithm is used to evaluate the operating status of high-voltage equipment,based on the evaluation results,the objective function of minimizing the risk of power grid operation is determined as the optimization of high-voltage equipment maintenance,and five constraint conditions such as simultaneous constraint and maintenance resource constraint are determined.Improved adaptive particle swarm optimization is used to solve the objective function,and the optimized high-voltage equipment maintenance plan result is outputted.The test results show that the difference between the maximum voltage and minimum voltage during maintenance by using this method is 0.105 pu,which is relatively stable.