Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations

doi:10.11930/j.issn.1004-9649.202208009

Abstract

Abstract: In the context of high penetration of renewables, it is very important for new power system dynamic analysis to establish a dynamic model that can accurately describe the portal dynamics of renewable-integrated regional power networks under the influence of complex environmental factors. Therefore a neural differential-algebraic equations-based portal dynamics learning method is proposed for renewable-integrated regional power networks. In this method, the differential-algebraic neural network is used to learn the portal dynamics model expressed in the form of neural network based on the time series measurements of the access point of the regional power networks and the environmental measurement data such as the radiation intensity and temperature. The learned model is composed of an initial state extracting block, a neural differential equation block and an algebraic equation block, and can be directly integrated into power system transient simulations to analyze the overall dynamics of power systems. The proposed method is tested through simulation in the IEEE-39 system, and the test results show that the obtained model can adapt to different environmental scenarios with acceptable accuracy, which verifies the effectiveness of the proposed method. The modelling method only needs portal time series measurements and has great application potential in the dynamic analysis of new power systems.

Key words: renewable energy, portal dynamics modeling, differential-algebraic equation, neural network, dynamic simulation

CAO Bin, SU Ke, YUAN Shuai, XIAO Tannan, CHEN Ying. Portal Dynamics Learning Method for Renewable-integrated Regional Power Networks Based on Neural Differential-Algebraic Equations[J]. Electric Power, 2023, 56(2): 23-31.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202208009

https://www.electricpower.com.cn/EN/Y2023/V56/I2/23

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