基于二元粒子群算法的电力系统不良数据处理

Amit Kumar, Sachin Gaur
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摘要

由于从rtu接收到控制中心的测量数据是通过电话、光纤、无线介质等传输介质传输的,因此传输的数据不可能总是100%无误差。在控制中心收到错误测量结果的其他原因可能是由于仪表读数错误。测量值被记录为错误的原因可能有很多,例如仪表故障、仪表漂移和仪表偏差。因此,接收到的测量值有时会出现误差,从而使状态估计结果具有误导性,从而给电力系统的监测和控制带来问题。因此,有必要从测量集中去除不良数据或建立一些鲁棒状态估计技术,以消除不良数据对估计状态的影响。本文将多个不良测量的检测与识别问题定义为一个二元变量优化问题,并利用二元粒子群算法求解该问题。结果表明,该方法可用于识别多个相互作用的误差测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bad Data Processing in Electrical Power System using Binary Particle Swarm Optimization
As the measurements received from RTUs to the Control Center are transmitted via a transmission medium e.g. telephone, fibre optics, wireless medium, it is not possible that the data transmitted is 100% error free always. Other reasons for receiving bad measurements at control centers may be due to wrong reading of the meter. There can be a number of reasons for measurement’s value to be recorded as wrong, e.g. outage of meter, drift in meter and bias in the meter.Therefore the measurements received may be erroneous sometimes, due to which the state estimation results may be misleading, and consequently can cause problem in monitoring and control of power system. Thus it is necessary to remove bad data from the measurement set or establish some robust state estimation techniques which can remove the effects of bad data on the estimated states.In this paper the problem of multiple bad measurements detection and identification is defined as a binary variables optimization problem and it’s solutions are obtained by using Binary Particle Swarm Optimization (BPSO). It is observed that this method can be used to identify multiple interacting erroneous measurements.
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