基于神经网络的智能电网稳定性预测模型

Kishore Bingi, B. Prusty
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引用次数: 4

摘要

提出了基于神经网络的智能电网稳定性预测模型。使用阻尼最小二乘技术对模型进行训练,以反应时间、消耗/产生的功率和弹性系数作为输入变量来预测作为输出变量的稳定性。利用R2和均方误差对模型的精度和有效性进行了数值比较。对于所有模型,在隐藏层选择的激活函数为tansig,在输出层选择的激活函数为purelin。结果表明,基于神经网络的预测模型在训练和测试阶段具有良好的性能。对于所考虑的系统,前馈神经网络在误差最小和R2值最高方面的最佳性能是正确的。
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Neural Network-Based Models for Prediction of Smart Grid Stability
This paper focuses on proposing neural network-based prediction models for smart grid stability. The models are trained using the damped least-squares technique with reaction time, consumed/generated power, and elasticity coefficient as input variables to predict the stability as an output variable. The models' accuracy and effectiveness are compared numerically using R2 and mean square errors. For all the models, the selected activation functions at the hidden layer is tansig, and purelin at the output layer. The results demonstrated the neural network-based prediction models' adequate performance for training and testing phases. For the system under consideration, the feed-forward neural network's best performance is true in terms of least error and the highest R2 values.
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