基于机器学习的变电站故障预测方法研究

The Fault Prediction of Converting Station Based on Big Data Technology and Machine Learning

针对当前变电站故障数据处理存在的不足,提出了基于机器学习的变电站故障预测方法。该方法首先收集变电站正常运行和故障发生期间的各种数据,将数据集按照一定比例分为训练集、验证集和测试集。之后选择合适的特征进行提取,并采用分布式机器学习算法进行模型训练和预测。最后通过对两个实际变电站产生的数据进行分析,验证了所提出方法的可行性。实验结果表明该方法能够有效提高故障预测的准确度和效率,并且可以帮助电力系统实现智能化管理和优化运行。

In response to the shortcomings of current substation fault data processing,a machine learning based substation fault prediction method is proposed.Firstly,various data are collected during the normal operation and fault occurrence of the substation,and the dataset is divided into a training set,a validation set,and a testing set according to a certain proportion.Afterwards,appropriate features are selected for extraction,and distributed machine learning algorithms are used for model training and prediction.Finally,the feasibility of the proposed method is verified by analyzing the data generated from two actual substations.The experimental results show that the proposed method can effectively improve the accuracy and efficiency of fault prediction,and can help the power system achieve intelligent management and optimized operation.