摘要
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他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(multiple kernel learning support vector machine,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须采集转子侧电气量,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。
The loss of excitation fault of large condenser seriously affects 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 is proposed,which can reflect the operating state of the condenser.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 learning 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 model of condenser connected to power grid is built based on PSCAD simulation platform for 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.
作者
陈晓强
康纪良
刘超
曹明宣
肖仕武
CHEN Xiaoqiang;KANG Jiliang;LIU Chao;CAO Mingxuan;XIAO Shiwu(Guangdong Yuedian Huizhou LNG Power Plant,Huizhou 516003,China;State Grid Jining Power Supply Company of Shandong Electric Power Company,Jining 272000,China;School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China)
出处
《电力工程技术》
北大核心
2024年第2期218-228,共11页
Electric Power Engineering Technology
基金
国家重点研发计划资助项目(2021YFB1600200)。
关键词
调相机
失磁保护
测量阻抗轨迹
多核支持向量机(MKL-SVM)
两阶段识别
泛化能力
condenser
loss of excitation protection
measured impedance trajectory
multiple kernel learning support vector machine(MKL-SVM)
two-stage recognition
generalization ability
