摘要
为提高电能质量综合量化评估的全面性和准确性,首先将暂态电能质量指标量化并将其考虑到综合评估指标中,然后将基于相似理论的智能评估方法引入电能质量综合评估,提出了2种新的评估方法,即基于粒子群优化(PSO)算法的Shepard相似插值(PSO-SSI)算法以及基于PSO算法的理想区间法(PSO-IIM)。实践证明,2种方法均能精确识别出一个电能质量等级之间的差别。同时,PSO-SSI算法较为直观、简便,但对样本具有依赖性;而PSO-IIM降低了对样本的依赖性,但评估过程稍显复杂。
To provide comprehensive and accurate quantitative evaluation of power quality, this paper first quantifies the transient indices of power quality and takes them into account in the synthetic evaluation, and then introduces two similarity theory based intelligent assessment methods into the evaluation, namely, the particle swarm optimization-Shepard similarity interpolation （PSO-SSI） algorithm and particle swarm optimization-ideal interval method （PSO-IIM） ： Test results indicate that both methods can accurately identify the differences at the same power quality level. Moreover, the PSO-SSI algorithm is relatively intuitive and convenient, but its performance is dependent on the sample characteristic, while PSO-IIM decreases this dependency, but is relatively complicated.
出处
《电力系统自动化》
EI
CSCD
北大核心
2008年第21期37-40,101,共5页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(50677044)~~
关键词
电能质量
相似理论
智能评估方法
Shepard相似插值算法
理想区间法
粒子群算法
power quality
similarity theory
intelligent assessment method
Shepard similarity interpolation algorithm
ideal interval method
particle swarm optimization
