摘要
电能质量在线监测网络的拓扑结构需要动态调整,以减少设备投资,更好的追踪动态变化的电能质量扰动,但这个问题目前少有研究涉及。依据电能质量在线监测的分析数据及监测网络拓扑结构数据,结合遗传算法及粒子群算法,提出一种混合电能质量在线监测网络拓扑结构调整算法。算法利用遗传算法对结构调整进行全局优化,而利用粒子群算法进行局部优化,并通过在全局及局部优化之间进行平衡以更快收敛。给出了混合算法的描述,算法执行流程以及收敛性证明。在IEEE 30总线系统上的实验证明了该算法的有效性,而其后的实际应用进一步证明应用效果。
The topology of online power quality monitoring network should be dynamically adjusted for cost consideration and tracing of dynamically changing power quality disturbance, which is not deeply researched for the time being. Analyzing online monitoring data and network topology, combining Genetic Algorithm （GA） and Particle Swarm Optimization （PSO）, a hybrid algorithm for online power quality event monitoring network topology adjustment is proposed. GA is used for global optimization and PSO is used for local optimization, and these two kinds of optimizations should be balanced for regression performance. The description, flow and regression testimony is given. On an IEEE 30 bus system and in our implementation case, the algorithm proves its feasibility.
出处
《东北电力大学学报》
2008年第6期95-100,共6页
Journal of Northeast Electric Power University
关键词
电能质量
拓扑结构
动态调整
遗传算法
粒子群算法
Power quality
Topology framework
Dynamical adjustment
Genetic Algorithm
Particle Swarm Algorithm
