山东电力技术2026，Vol.53Issue(8)：84-99,16.DOI:10.20097/j.cnki.issn1007-9904.250404
类噪声扰动下基于物理信息神经网络的电网惯量在线评估
Online Evaluation of Grid Inertia Based on Physics-informed Neural Networks Under Noise-like Disturbance
摘要
Abstract
To address the problem of online evaluation of grid inertia in power systems with high-proportion renewable energy,this paper proposes an online evaluation method for grid inertia and damping coefficients based on physics-informed neural networks(PINN).By combining physical equation constraints with data-driven approaches,the inertia evaluation is carried out using persistent noise-like disturbance data in the power system.A PINN model is constructed with time series and power disturbances as inputs and frequency deviations as outputs.The neural network embeds residual constraints of the rotor motion equation to establish a composite loss function including data fitting,physical regularization,and initial conditions.Through the automatic differentiation calculation of the differential terms of physical equations,the network output is forced to satisfy the dynamic laws of the system,realizing the synchronous identification of inertia and damping coefficients.Simulation results verify the high accuracy and rapidity of the method,with an average error of 1.84%for test data,a maximum error of 4.846%,and a frequency prediction curve fitting degree of 95.6%.The method can effectively avoid dependence on specific disturbances and precise models,significantly reducing data dependency.Integrating physical equation constraints into neural network training effectively avoids overfitting and enhances the model′s generalization ability and physical credibility.It is of great significance for real-time tracking of the equivalent inertia of the power grid under fluctuating renewable energy output and promoting the consumption of high-proportion renewable energy.关键词
新型电力系统/惯量评估/物理信息神经网络/类噪声扰动/在线评估/频率动态响应Key words
new power systems/inertia evaluation/physics-informed neural networks/noise-like disturbances/online assessments/frequency dynamic response分类
信息技术与安全科学引用本文复制引用
李雅然,张宁宇,邹小明,乔勇,陈中..类噪声扰动下基于物理信息神经网络的电网惯量在线评估[J].山东电力技术,2026,53(8):84-99,16.基金项目
江苏省电力公司科技项目(SGJSDKSYNYJS2400586). Science and Technology Project of Jiangsu Electric Power Company(SGJSDKSYNYJS2400586). （SGJSDKSYNYJS2400586）
