基于短期负荷预测的电力系统无功电压控制优化策略研究  

Research on Optimal Strategy of Power System Reactive Power and Voltage Control Based on Short-term Load Forecast

作　　者：廖雨 LIAO Yu(State Grid Sanmenxia Power Supply Company,Sanmenxia 472000,China)

机构地区：[1]国网三门峡供电公司,河南三门峡472000

出　　处：《电工技术》2024年第24期53-58,64,共7页Electric Engineering

摘　　要：随着电力系统和用电设备的发展,对电力系统的运行稳定性和电能质量的要求越来越高。为了提高电力系统中的电能质量,根据负荷曲线的周期性,在传统的无功电压控制的基础上,提出了基于短期负荷预测的电力系统无功电压控制优化策略。构建了人工神经网络负荷预测模型实现对短期负荷的预测,设计了人工神经网络算法和原对偶内点算法结合的复合算法进行无功电压的优化控制。最后对研究的人工神经网络负荷预测模型和复合无功电压优化控制算法进行了仿真对比测试,实验结果证明了人工神经网络负荷预测模型预测精度更高、误差较小,和灰色预测模型相比,相对误差降低了15.2%。利用复合算法无功优化控制后各个节点的电压幅值有了明显提高,证明了复合算法在无功优化控制上的优越性能,该策略对电力系统的发展具有一定的研究和实用意义。With the development of power systems and electrical equipment,the requirements for the operational stability and power quality of the power system are getting higher and higher.In order to improve the power quality in the power system,according to the periodicity of the load curve,this article focuses on the traditional reactive power On the basis of voltage control,a power system reactive voltage control optimization strategy based on short-term load forecasting is proposed.The artificial neural network load forecasting model is constructed to realize the short-term load forecasting,and the composite algorithm combining the artificial neural network algorithm and the original dual interior point algorithm is designed to optimize the reactive voltage control.Finally,the artificial neural network load forecasting model and the compound reactive voltage optimization control algorithm studied in this paper are simulated and compared.The experimental results prove that the artificial neural network load forecasting model has higher forecasting accuracy and smaller errors,compared with the gray forecasting model.The relative error is reduced by 15.2%,and the voltage amplitude of each node has been significantly improved after the reactive power optimization control of the composite algorithm.This proves the superior performance of the composite algorithm in the reactive power optimization control.Certain research and practical significance.

关 键 词：电能质量 负荷预测 人工神经网络 无功电压控制 

分 类 号：TM933[电气工程—电力电子与电力传动]