Prediction of power network stability based on an adaptive neural network
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摘要: 电网安全稳定是电力企业乃至整个社会改革、发展、稳定的基础.随着电网结构复杂度的增加,更需要电网安全和稳定地运行,这是保证国民经济快速良好发展的重要要求.基于机器学习方法,提出了一种优化神经网络的电网稳定性预测模型,并和经典机器学习方法进行了横向对比.通过UCI2018年电网稳定性仿真数据集的实验分析,结果表明,所提出的方法可以达到更高的预测准确率,同时也为电力大数据的研究提供了新思路.Abstract: The safety and stability of the power grid serves as the basis for reform, development, and stability of power enterprises as well as for broader society. With the increasing complexity of power grid structures, safety and stability of the power grid is important for ensuring the rapid and effective development of the national economy. In this paper, we propose an optimal neural network stability prediction model and compare performance with classical machine learning methods. By analyzing the UCI2018 grid stability simulation dataset, the experimental results show that the proposed method can achieve higher prediction accuracy and provide a new approach for research of power big data.
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Key words:
- grid stability /
- support vector machine /
- decision tree /
- neural network
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表 1 数据集描述
Tab. 1 Dataset description
变量名 类型 单位 描述 tau$[x]$ 数值 s 参与者的反应时间(范围为[0.5, 10]) $p[x]$ 数值 $s^{-2}$ 能耗和价格弹性系数的单位, 名义功率消耗(负)/产生(正), 范围为[-0.5, -2];在模拟网络中, $p$1=abs($p$2+$p$3+$p$4), 其中, $p$1表示发电机的功率, 为正值(表示生成电能), $p$2、$p$3、$p$4表示用户节点, 为负值(表示消耗电能), abs表示绝对值 $g[x]$ 数值 $s^{-1}$ 能耗和价格弹性系数的单位, 系数($\gamma $)与价格弹性成正比 Stab 数值 根据系统状态方程矩阵计算出的特征根的实部数值, 表示系统的稳定状态, 正数表示不稳定, 负数表示稳定(稳定性判据), 即特征方程根的最大实部(大于0表示系统是线性不稳定的) Stab-Label 类别 根据Stab数值做的标签信息, 稳定/不稳定 表 2 模型参数
Tab. 2 Model parameters
模型 参数 SVM 惩罚因子$c = 0.09$, 核函数采用线性核函数, 使用概率估计模式 DT 分类准则为Gini系数, 分类策略为最佳分类策略, 决策树的深度不做限制, 采用交叉验证和网格搜索的方式优化网络, 最大叶节点设置为200 NN 网络采用3层结构18-10-2, 其中前3层采用Relu作为激活函数, 输出层采用Sigmoid作为激活函数, 损失函数采用交叉熵代价函数, 优化器采用自适应学习率优化器, 模型评估采用Accuracy的性能指标 表 3 准确率和AUC值比较
Tab. 3 Accuracy and AUC value comparison
模型 准确率 AUC值 SVM 0.836 2 0.914 9 DT 0.829 9 0.883 4 NN 0.959 9 0.992 9 表 4 混淆矩阵对比
Tab. 4 Confusion matrix contrast
模型 准确率 精确率 召回率 F1 SVM 0.836 2 0.767 3 0.872 8 0.816 7 DT 0.829 9 0.767 3 0.860 0 0.811 0 NN 0.959 9 0.941 4 0.974 1 0.957 5 -
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