基于SO优化神经网络的新能源汽车充电桩故障预测系统研究

Research of Fault Prediction System for New Energy Vehicle Charging Pile Based on SO Optimized Neural Network

随着新能源汽车的广泛应用,充电桩作为电网终端设备,其故障会对电网稳定性产生影响。为提高充电桩故障预测的准确性,本文提出一种基于蛇群算法(SO)优化神经网络的新能源汽车充电桩故障预测方法。该方法将SO算法与神经网络模型相结合,利用SO算法的全局优化搜索能力对神经网络权重进行训练优化,以提升模型的预测性能。在充电桩故障分类数据集上,本文构建三层全连接神经网络,并采用SO算法优化网络参数。优化后模型的各项指标如AUC、准确率、召回率等明显提高,较单一神经网络和其他优化算法效果更好。研究表明,SO算法可以有效提升神经网络在充电桩故障预测任务上的性能,为充电桩的状态监测和故障预警提供了有效解决方案。本研究的发现为未来充电桩故障预测方法的研究提供了有益的参考,同时也为实际的充电桩状态监测和故障预警系统的设计提供了支持。

With the widespread adoption of new energy vehicles,charging piles as terminal equipment of the power grid,their malfunctions can impact the stability of the electrical grid.To enhance the accuracy of fault prediction in charging piles,this paper proposes a method for predicting faults in new energy vehicle charging piles by optimizing neural networks based on Snake Optimization Algorithm(SO).This method integrates the SO algorithm with the neural network model,utilizing the global optimization search capability of the SO algorithm to train and optimize the weights of the neural network,thereby improving the model's predictive performance.On the fault classification dataset of charging piles,this paper constructs a three-layer fully connected neural network and optimizes the network parameters using the SO algorithm.The optimized model shows significant improvements in various metrics such as AUC,accuracy,and recall rate,outperforming the single neural network and other optimization algorithms.The study demonstrates that the SO algorithm can effectively enhance the performance of neural networks in the task of fault prediction for charging piles,providing an effective solution for the status monitoring and fault alarming of charging piles.The findings of this research offer valuable references for future studies on fault prediction methods of charging piles and also provide support for the design of practical status monitoring and fault alarming systems for charging piles.