基于机器学习的可再生能源电力系统检修计划优化

Optimization of Renewable Energy Power System Maintenance Plan Based on Machine Learning

随着可再生能源的不断发展,可再生能源在电网的占比越来越大,为了保证考虑可再生能源接入的电力系统的供电安全,检修工作的最佳协调变得越来越重要。目前检修计划制定工具受运行安全标准约束和电网复杂性的影响,具有可操作性低、模拟意外事故计算量大等问题,为了减轻人工计算负担,提出利用机器学习模型以快速可靠的方式预测应急情况的结果。该方法在兰州某地区设备中进行了测试,涵盖了10 kV和220 kV的电压等级。通过测试和比较朴素贝叶斯分类器、支持向量机和基于决策树的模型,可知基于决策树的随机森林算法在识别可安全检修时间段的准确率高于90%,始终优于其他算法。另外,通过实验表明,可再生能源发电的预期增长将影响未来电力系统的可检修性,部分地区非安全检修时间段将增加20%。

With the continuous development of renewable energy sources and the increasing share of renewable energy in the grid,optimal coordination of maintenance work becomes increasingly important in order to ensure the safety of power supply in power systems considering renewable energy access.Current tools for maintenance planning are constrained by operational safety standards and the complexity of the grid,and have problems such as low operability and high computational effort to simulate accidents.To reduce the burden of manual computation,the use of machine learning models was proposed to predict the outcome of emergency situations in a fast and reliable manner.The method was tested in a regional facility in Lanzhou,covering voltage levels of 10 kV and 220 kV.By testing and comparing a plain Bayesian classifier,a support vector machine(SVM)and a decision tree-based model,it was shown that the decision tree-based random forest algorithm is consistently better than other algorithms in identifying safe serviceable time periods with an accuracy rate higher than 90%.In addition,it was shown experimentally that the expected growth in renewable energy generation will affect the future serviceability of the power system,with a 20%increase in non-safe serviceable time periods in some areas.