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发明公开
CN110674984A 一种基于Tri-Training-Lasso-BP网络的静态电压稳定裕度预测方法 审中-实审

一种基于Tri-Training-Lasso-BP网络的静态电压稳定裕度预测方法
摘要：
本发明公开了一种基于Tri-Training-Lasso-BP网络的静态电压稳定裕度预测方法。该方法将神经网络、半监督训练、集成学习等技术应用于电力系统静态电压稳定裕度的预测中，提出了基于Tri-Training-Lasso-BP网络的在线预测方法，由三体训练法(Tri-Training)、最小绝对值收缩选择(least absolute shrinkageand select operator，Lasso)方法和BP(back propagation)神经网络组成。本发明方法能够降低对训练集数据数量和质量的要求，发挥电力系统日常运行过程中采集的海量数据的优势，提高网络的泛化能力和预测精度，减少人工干预，弥补了传统方法难以实现在线实时预测电压稳定裕度、需要大量训练样本、容易出现过拟合的问题。
摘要（英）：

The invention discloses a quiescent voltage stability margin prediction method based on a Tri-Training-Lasso-BP network. The quiescent voltage stability margin prediction method applies technologies such as a neural network, semi-supervised training and ensemble learning to prediction of the quiescent voltage stability margin of the power system, proposes an online prediction method based on the Tri-Training-Lasso-BP network, and is formed by a three-body training method (Tri-Training), a least absolute shrinkage and select operator Lasso method, and a BP (back propagation) neural network. Themethod is characterized in that the method is composed of a Lasso method and a BP (back propagation) neural network. The quiescent voltage stability margin prediction method can reduce the requirements on the data quantity and quality of the training set, gives play to the advantages of mass data collected in the daily operation process of a power system, improves the generalization capability and prediction precision of a network, reduces the manual intervention, and irons out the problems that a conventional method is difficult to achieve the online real-time prediction of the voltage stability margin, needs a large number of training samples, and is liable to cause the over-fitting.

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