一种可加速预测误差最小化的系统惯量在线评估方法

Online Evaluation Method for System Inertia with Accelerated Prediction Error Minimization

摘要

针对现有电力系统惯量在线评估方法存在鲁棒性差、准确度低和计算复杂等问题,提出了一种可加速预测误差最小化(PEM)的系统节点等效惯量连续在线评估方法．该方法以电力系统节点为对象,基于多变量输出误差状态空间(MOESP)模型辨识和 PEM 的协同优化算法,可同时实现电力系统节点等效惯性常数和阻尼系数的解耦评估．相较于现有在线惯量评估方法,通过 PEM 算法的迭代特性弥补 MOESP 算法可能产生随机误差的缺陷,不仅保留了 PEM 算法的鲁棒性和准确性,还利用 MOESP 算法提升了 PEM 算法的速度,两者相辅相成,稳定且高效地实现电力系统惯量的在线快速评估,利于推动电力系统更加稳定、高效地运行．最后通过改进型 IEEE 9 总线电力系统对所提方法的有效性和准确性进行了验证,结果表明,采用所提方法得到的节点惯性常数评估相对误差≤2.12%,阻尼系数评估相对误差≤4.00%,较传统方法如状态子空间(N4SID)模型辨识方法精度提升约 64.96%．

Abstract

To address the issues of existing power system inertia online evaluation methods, including insufficient robustness, low prediction accuracy, and computational complexity, this paper proposed a continuous online evaluation method for equivalent nodal inertia featuring accelerated prediction error minimization(PEM). For power system nodes, a collaborative optimization algorithm is proposed to synergistically integrate multivariable output error state space(MOESP) model identification and PEM principles, thereby enabling simultaneous real-time evaluation of equivalent inertia constants and damping coefficient at power system nodes. Compared with existing online inertia evaluation techniques, the iterative characteristics of the PEM algorithm compensate for the potential stochastic errors inherent in the MOESP algorithm. This approach preserves the robustness and accuracy of PEM while enhancing computational efficiency by using the MOESP algorithm. The complementary integration of these methods enables stable and high-speed online evaluation of power system inertia, thereby promoting stable and efficient grid operation. Finally, the effectiveness and accuracy of the proposed method were verified using an improved IEEE 9-bus power system. Results show that the proposed method achieves a relative error of less than or equal to 2.12% in evaluating the node inertia constant and less than or equal to 4.00% in evaluating the dampling coefficient. Compared with traditional methods such as the state subspace(N4SID) model identification method, the accuracy is improved by approximately 64.96%.

关键词

电力系统节点惯量 / 惯性常数 / 阻尼系数 / 在线评估 / 协同优化算法

Key words

power system nodal inertia / inertia constant / damping coefficient / online evaluation / collaborative optimization algorithm

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基金资助

直流输电技术全国重点实验室开放基金资助项目(SKLHVDC-2023-KF-09)