基于极限学习机的电力系统暂态稳定评估方法

doi:10.16183/j.cnki.jsjtu.2019.06.017

摘要/Abstract

摘要： 随着清洁能源替代和电力系统电力电子化的趋势增强，传统的基于理论模型的电力系统数值仿真方法将面临新的挑战，不依赖于元件模型的数据驱动型电网稳定评估方法逐渐受到重视.基于极限学习机(ELM)理论，提出适于在线应用的电力系统暂态稳定评估方法.首先，通过调节稳定和失稳仿真样本的比例进行样本筛选，减轻样本集中失稳样本较少而引起的样本不均衡现象，并引入递归特征消除法进一步处理样本集；然后利用交叉验证法优化ELM的网络结构，并用处理后的样本集进行ELM的训练；最后，根据神经网络的输出结果预测系统的稳定性，并改进泛化能力评价标准对结果的可靠性进行评估.算例分析表明，递归特征消除法可明显降低特征冗余度，改善模型性能，所提出算法的训练时间短且具有较高的预测准确度.

关键词: 电力系统；暂态稳定；极限学习机；递归特征消除；交叉验证

Abstract: With the enhanced trend of alternative clean energy and power electronics in power system, the traditional numerical simulation methods based on theoretical model will face new challenges, while the self-adaptive data-driven power system stability assessment method is gaining more and more attention. Based on the theory of extreme learning machine (ELM), a transient stability assessment method suitable for online application is proposed. Firstly, the samples are screened by adjusting the ratio of stable and unstable simulation samples, to reduce the sample imbalance in which unstable samples are far less than stable ones in the sample set, and the recursive feature elimination is used to further process the sample set. Then, the cross-validation is used to optimize the ELM network structure, and the processed sample set is used to train the ELM. Finally, the system stability based on the output of the neural network is predicted, and the reliability of the results with the improved evaluation criteria is evaluated. Test results show that the recursive feature elimination can significantly reduce the feature redundancy and improve the performance of the model, and the proposed algorithm has a shorter training time while can provide more accurate results.

Key words: power system; transient stability; extreme learning machine (ELM); recursive feature elimination; cross-validation

中图分类号:

TM 712

张林林，胡熊伟，李鹏，石访，于之虹. 基于极限学习机的电力系统暂态稳定评估方法[J]. 上海交通大学学报, 2019, 53(6): 749-756.

ZHANG Linlin,HU Xiongwei,LI Peng,SHI Fang,YU Zhihong. Power System Transient Stability Assessment Based on Extreme Learning Machine[J]. Journal of Shanghai Jiaotong University, 2019, 53(6): 749-756.

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