State Estimation and Bad Data Detection in Hybrid AC/DC Systems with LCC/MMC
赵化时 1黄耀辉 2宋智强 2许建中 2郑可欣 2梁康康2
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作者信息
1. 中国南方电网电力调度控制中心,广东广州 510670
2. 新能源电力系统国家重点实验室(华北电力大学),北京 102206
折叠
摘要
基于调度系统导出的通用信息模型(common information model,CIM)中的XML和E文档,从数据生成的角度出发,首先将导出文档转化为状态估计原始输入数据,考虑交流系统与电网换相换流器(line commutated converter,LCC)、模块化多电平换流器(modular multilevel converter,MMC)以及LCC与MMC间的相互影响,采用统一迭代法对500 kV子网络进行交直流状态估计建模;其次,在原始量测数据的基础上施加高斯噪声,借助最大化残差检验方法以进行不良数据的检测与辨识;最后,通过仿真数据验证了交直流状态估计模型及不良数据检测与辨识的有效性.
Abstract
Based on the CIM/XML and CIM/E documents exported from the regional dispatching system,this paper focuses on data generation and starts by converting the exported documents into raw input data for state estimation.Considering the interactions between the AC system and LCC,MMC,and between LCC and MMC,the unified iterative method is used to model the AC/DC state estimation of the 500kV subnetwork.Subsequently,the Gaussian noise is added to the original measurement data,and the maximum residual test method is employed for detecting and identifying bad data.Finally,the effectiveness of the proposed models for AC/DC state estimation and the detection and identification of bad data are validated through simulation data.
关键词
CIM/XML/交直流状态估计/LCC/MMC/不良数据的检测与辨识/最大化残差检验
Key words
CIM/XML/AC/DC state estimation/LCC/MMC/detection and identification of bad data/maximum likelihood residual test
维普
万方数据