摘要
为进一步提升配电网的系统稳定性并降低运行损耗,提出一种基于改进粒子群优化算法的电网无功电压控制方法,在降低网损的同时稳定偏差电压。其中,构建配电网的多目标无功优化模型,并以粒子群算法为基础的模型求解优化方法,以获取最优的配电网参数,为进一步提升模型的优化效果,引入鲸鱼群算法对粒子群算法进行改进。实验结果表明,与优化前的系统相比,经过优化后的系统的电压偏差更加稳定,均在安全范围内;与其他优化算法相比,基于鲸鱼群算法改进的粒子群优化算法具有更好的优化性能,收敛速度更快,能够使得配电网系统的网损保持在更低数值。综上,构建的电网无功电压控制方法性能良好,能够使得配电网系统达到更加稳定的状态,同时保持网损较低,能够应用于实际的配电网降损任务中,具有一定的参考价值。
To further improve the system stability of the distribution network and reduce operational losses,a reactive power and voltage control method based on improved particle swarm optimization algorithm is proposed,which stabilizes the bias voltage while reducing network losses.Among them,a multi-objective reactive power optimization model for the distribution network is constructed,and a model based on particle swarm optimization algorithm is used to solve the optimization method to obtain the optimal distribution network parameters.To further improve the optimization effect of the model,whale swarm algorithm is introduced to improve the particle swarm algorithm.The experimental results show that compared with the pre optimized system,the voltage deviation of the optimized system is more stable and within the safe range;Compared with other optimization algorithms,the particle swarm optimization algorithm based on Whale Swarm Optimization has better optimization performance,faster convergence speed,and can maintain the network loss of the distribution network system at a lower value.In summary,the constructed reactive power and voltage control method for the power grid has good performance,which can achieve a more stable state of the distribution network system while maintaining low network losses.It can be applied to actual distribution network loss reduction tasks and has certain reference value.
作者
陈晶腾
陈芳
CHEN Jingteng;CHEN Fang(State Grid Putian Electric Power Company,Putian,Fujian 351100,China)
出处
《自动化与仪器仪表》
2024年第6期291-295,共5页
Automation & Instrumentation
基金
国家电网有限公司科技项目资助《面向高比例分布式新能源接入的配电网全监测分层协调控制降损技术研究》(521320230004)。
关键词
配电网
系统降损
电压偏差
粒子群优化算法
鲸鱼群优化算法
distribution network
system loss reduction
voltage deviation
particle swarm optimization algorithm
whale swarm optimization algorithm