摘要
为进一步提高接地网腐蚀速率的预测精度,解决传统模型易陷入局部最优且随机初始化模型参数影响预测准确性和稳定性的问题。文中首先将混沌映射、动态搜索半径策略和优化气味浓度判定公式引入果蝇优化算法(Fruit Fly Optimization Algorithm,FOA)得到改进后的果蝇优化算法(Update Fruit Fly Optimization Algorithm,UFOA);然后利用UFOA优化BP神经网络的初始权值和阈值,建立基于UFOA优化的BP神经网络接地网腐蚀速率预测模型(UFOA-BP);最后以重庆24座变电站的接地网数据进行实验验证。结果表明相对FOA优化的BP神经网络模型、BP神经网络模型、人工蜂群算法优化的支持向量机模型和广义回归神经网络模型,文中提出的UFOA-BP模型在预测精度和模型稳定性方面均优于其他四种模型,验证了该模型的有效性和可行性,为运维人员提前发现接地网安全隐患,并安排检修,进而保障电网安全稳定运行提供帮助。
In order to solve the problem that the traditional prediction model is prone to fall into the local optimum and the parameters of the model are randomly initialized which affect the accuracy and stability of the prediction,so that the prediction accuracy of corrosion rate of grounding grid is further improved.Firstly,the chaos mapping,dynamic search radius strategy and optimized odor concentration determination formula are introduced into fruit fly optimization algorithm(FOA)to obtain the update fruit fly optimization algorithm(UFOA).Then,the initial weights values and thresholds of BP neural network are adjusted adaptively by UFOA,and a corrosion rate prediction model of grounding grid is established based on UFOA.Finally,in the measurement data of Chongqing 24 substations experimental verification,the results show that,compared with BP neural network model which is optimized by FOA,BP neural network model,support vector machine(SVM)model which is optimized by artificial bee colony(ABC)and generalized regression neural network(GRNN)model,UFOA-BP model proposed in this paper is superior to the other four models in terms of prediction accuracy and stability.It illustrates that the proposed UFOA-BP model is effective and feasible,which provides help for operation and maintenance personnel to find potential safety hazards of grounding grid in advance and arrange maintenance to ensure safe and stable operation of power grid.
作者
程宏伟
高莲
于虹
李鹏
Cheng Hongwei;Gao Lian;Yu Hong;Li Peng(School of Information,Yunnan University,Kunming 650500,China;Electric Power Research Institute of Yunnan Power Grid Co.,Ltd.,Kunming 650500,China)
出处
《电测与仪表》
北大核心
2022年第11期71-78,共8页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(61763049)
云南省应用基础研究计划重点项目(2018FA032)。
