高比例新能源并网下电力系统多源融合惯量估计方法OA

To address the pronounced time-varying characteristics of power system inertia under high penetration of renewable energy and the difficulty of achieving both accuracy and robustness using a single estimation method,a hierarchical multi-source fusion architecture for inertia estimation is proposed.At the front layer,weighted least squares and a Conv-Transformer hybrid neural network are integrated to achieve preliminary inertia estimations from mechanism-driven and data-driven perspectives,respectively.At the intermediate layer,a parameter tracking strategy based on adaptive Huber residual-weighted recursive least squares is employed.Through the coordinated use of a dynamic forgetting factor and Huber influence function,the damping coefficient(D)and synchronizing power coefficient(K)are robustly tracked.At the back layer,a dynamic weight extended Kalman filter adaptively adjusts the observation noise covariance according to the discrepancies among multi-source estimates,enabling adaptive fusion and rapid tracking of system inertia.Simulations based on the New England 10-machine system demonstrate that the proposed method exhibits excellent performance under extreme conditions such as vertical steps and parameter drift,with strong capabilities for tracking time-varying inertia and rejecting disturbances.The proposed method offers an effective solution for online inertia estimation in power systems with high renewable energy penetration.