基于SDAE-ELM伪量测建模的高容错快速状态估计方法  

High Fault-Tolerant Fast State-Estimation Method Based on SDAE-ELM Pseudo-Measurement Modeling

作　　者：陈防渐 王玉彬 陈奇芳 夏明超[1] 杨晓楠[2] 韩锋[2] CHEN Fangjian;WANG Yubin;CHEN Qifang;XIA Mingchao;YANG Xiaonan;HAN Feng(School of Electrical Engineering,Beijing Jiaotong University,Beijing 100044,China;State Key Laboratory of Power Grid Safety and Energy Conservation(China Electric Power Research Institute),Beijing 100192,China)

机构地区：[1]北京交通大学电气工程学院,北京市100044 [2]电网安全与节能国家重点实验室(中国电力科学研究院),北京市100192

出　　处：《电力建设》2019年第11期65-72,共8页Electric Power Construction

基　　金：2018电网安全与节能国家重点实验室开放基金(FX83-18-002)~~

摘　　要：目前在电力系统中无法保证相量量测单元完全覆盖的情况下,状态估计需要采用相量量测单元(phasor measurement unit, PMU)与数据采集与监控(supervisory control and data acquisition, SCADA)混合量测进行传统非线性状态估计,但是SCADA数据精度低,含有较多不良数据,同时混合数据需要迭代求解,会导致计算效率低且存在截断误差。针对该问题,文章提出了一种基于堆叠去噪自编码器(stack denoising autoencoder, SDAE)与极限学习机(extreme learning machine, ELM)伪量测建模的电力系统高容错快速状态估计方法。其将含有不良量测的SCADA量测数据作为SDAE-ELM伪量测模型的输入,节点电压实部与虚部作为输出,根据历史数据进行训练得到伪量测值与伪量测误差模型,训练完成后得到精度较高的伪量测;将伪量测与PMU量测一起进行快速的线性状态估计。仿真结果表明,所提方法在保证估计精度的基础上,提高了计算效率,验证了所提方法的有效性。In the cases that the phasor measurement units(PMUs) cannot be deployed with all buses in the power system, the traditional nonlinear state estimation needs to be carried out by using the hybrid measurement of PMU and supervisory control and data acquisition(SCADA). SCADA data are low accurate and have bad data, and the iterative solution process of mixed data will lead to low computational efficiency and truncation error. In order to solve this problem, a high fault-tolerant fast state-estimation method for power systems on the basis of stack denoising auto-encoder(SDAE) and extreme learning machine(ELM) pseudo-measurement modeling is proposed in this paper. The SCADA measurement data with bad measurement are used as the input of the SDAE-ELM pseudo-measurement model, and the real and imaginary parts of the bus voltage phasors are used as outputs, the pseudo-measurement value and the pseudo-measurement error model are obtained by training the historical data. The pseudo measurement with high precision is obtained after the training and then the pseudo-measurement and PMU measurement are used for fast linear state estimation. The simulation results show that the proposed method improves the computational efficiency and verify the effectiveness of the proposed method on the basis of ensuring the estimation accuracy.

关 键 词：高容错 快速 状态估计 堆叠去噪自编码器 极限学习机 

分 类 号：TM73[电气工程—电力系统及自动化]