基于测量阻抗动态轨迹的大型调相机失磁保护

大型调相机失磁故障严重影响设备本体安全以及电网稳定,现有基于静态阈值的低电压与无功反向判据可靠性与选择性不足。文中提出一种可反映调相机运行状态的机端测量阻抗全局动态轨迹智能识别的失磁保护原理,从运动学角度建立能够准确反映失磁与其它工况下测量阻抗轨迹的特征量时间序列,基于统计学提取解释性强的特征量。利用自适应权重的全局与局部核函数组合训练多核支持向量机(MKL-SVM),在保证模型学习能力的同时增强其泛化能力；提出基于分类核空间距离的两阶段识别策略,可在保证可靠性的前提下提高保护速动性。基于PSCAD仿真平台搭建调相机接入电网模型进行验证,结果表明所提失磁保护方案无需采集转子侧电气量,识别准确,面对新能源接入和未知扰动时仍具有优良的适用性。

Loss of excitation protection for large condenser based on measured impedance dynamic trajectory

The loss of excitation fault of large condenser will seriously affect the safety and stability of equipment and system. The reliability and selectivity of existing low-voltage and reactive power reverse criteria based on local static threshold are insufficient. In this paper, a loss of excitation protection principle based on intelligent identification of the global dynamic trajectory of the measured impedance, which can reflect the operating state of the condenser, is proposed. From the point of view of kinematics, characteristic quantity time series that can accurately restore the measured impedance trajectory under loss of excitation and other conditions is formed, and statistics is further introduced to extract the highly explanatory features. Multiple kernel support vector machine (MKL-SVM) is trained by using the combination of global and local kernel functions of adaptive weights to ensure the learning ability of the classification model while enhancing its generalization ability. A two-stage recognition strategy based on the space distance of the classification core is proposed, which can improve the protection reliability while ensuring the system security. The AC/DC system is built based on PSCAD simulation platform for simulation verification, and simulation results show that the proposed method does not need to collect the electrical quantities at the rotor side with high identification accuracy, and it still has excellent applicability in the face of new energy access and unknown disturbances.Keywords: condenser; loss of excitation protection; measured impedance trajectory; MKL-SVM; two-stage recognition; generalization ability