电力系统无功优化智能算法研究

Research on Intelligent Algorithms for Reactive Power Optimization in Power System

文章分析了无功优化的意义,能够降低网损,有助于提高用户的体验感和电力公司的经济效益。再对电力系统无功优化模型进行了分析,阐述了4种不同情境下无功优化模型,其中包含着多个约束条件和多个变量,可知求解电力系统无功优化比较复杂。最后对遗传算法、粒子群算法和模拟退火算法进行了详细分析,分析了其原理和优缺点。每一种智能算法各有其优点和缺点,所以智能算法选择问题上要通过综合的考虑,如果算法选择不巧当,将会严重影响电力系统无功优化模型结果的准确度。

The paper analyzes the significance of reactive power optimization,which can reduce network loss,and help improve user experience and economic benefits of power companies.Then,the reactive power optimization model of power system is analyzed,and the reactive power optimization model in four different scenarios is de⁃scribed,which contains multiple constraints and multiple variables.It can be seen that solving the reactive power optimization of power system is more complicated.Finally,the genetic algorithm,particle swarm algorithm and sim⁃ulated annealing algorithm are analyzed in detail,and their principles,advantages and disadvantages are analyzed.Each intelligent algorithm has its own advantages and disadvantages.Therefore,comprehensive consideration must be given to the selection of intelligent algorithms.If the algorithm is not chosen properly,it will seriously affect the accuracy of the power system reactive power optimization model results.