| Electrical Engineering |
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| Prediction of voltage stability margin in power system based on extreme gradient boosting algorithm |
Hui-fang WANG*( ),Chen-yu ZHANG |
| Department of Electrical Engineering, Zhejiang University, Hangzhou 310027, China |
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Abstract The extreme gradient boosting (XGBoost) algorithm was applied in power system voltage stability assessment problem. According to the characteristics of the voltage stability problem, a feature set which could reflect the state of a power system was defined. Taking the absolute value of voltage stability margin as the mapping target, the method to generate the sample set was studied. Based on the introduction of the basic principle of the XGBoost algorithm, the technical details of the algorithm were discussed. The algorithm was evaluated in the IEEE-39 power system. Results show that the XGBoost algorithm has better performance than other machine learning models according to two evaluation metrics: R squared value and mean absolute percentage error value, and has the fastest computation speed, which can meet the demand of online application. Meanwhile, the XGBoost algorithm is proved to be robust when the data errors and data missing happen. And data supplement can be taken for the samples with large prediction deviation to update the model, thus making the performance of the model more stable.
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Received: 03 March 2019
Published: 05 March 2020
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Corresponding Authors:
Hui-fang WANG
E-mail: huifangwang@zju.edu.cn
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采用极限梯度提升算法的电力系统电压稳定裕度预测
将极限梯度提升树（XGBoost）算法应用于电力系统电压稳定评估问题. 根据电压稳定问题特点，提出能够反映电力系统运行状态的特征集；把电压稳定裕度绝对值作为映射目标，并介绍生成样本集的方法. 在介绍XGBoost算法基本原理的基础上，研究该算法的技术细节. 在IEEE-39节点系统上进行验证，结果表明，XGBoost算法在R方值和平均绝对百分误差2项回归指标上均优于其他几类机器学习算法，且模型的计算速度最快，可以满足在线应用要求. 同时，XGBoost算法具有良好的数值错误和数值缺失容错性，并可以针对预测偏差较大的样本进行数据补充，实现模型的更新，使得模型表现趋于稳定.
关键词：
电力系统,
电压稳定性,
机器学习,
人工智能,
极限梯度提升树（XGBoost）算法
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