Frontiers in Energy Research (Jul 2022)
A Novel Bi-LSTM-Based Method for Thevenin Equivalent Parameter Identification
Abstract
Insufficient power system operation data sets hinder the extensive application of various artificial intelligence algorithms. The solution of the Thevenin equivalent parameters especially depends on the power flow data of the grid. This article proposes a Kirchhoff circuit law-based power flow sample generation method, which can overcome the operation state observation difficulty and power flow calculation complexity of the power system. To a large extent, the quality of the sample determines the effect of the machine learning algorithm. This method is different in mechanism from traditional power flow calculations, which is applied to generate the state-based power flow sample data sets by using Kirchhoff circuit laws instead of the iterative calculation of power flow starting from the initial value. In this way, the efficiency of power system sample generation required by machine learning algorithms is enhanced significantly. Besides, this article finds the power characteristic parameter suitable for Thevenin’s equivalent parameter machine learning, that is, the load power differential ratio. A clustering method suitable for the Bi-LSTM (bidirectional long short-term memory) model for processing power state samples, which can improve learning performance, was studied. The case studies demonstrate the sample generation efficiency of this method and verify the learning effect of the Bi-LSTM algorithm.
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