Abstract
State Estimation is a traditional and reliable technique within power distribution and control systems. It is used for building a topology of the power grid network based on state measurements and current operational state of different nodes & buses. The protection of sensors and measurement units such as Intelligent Electronic Devices (IED) in Central Energy Management System (CEMS) against False Data Injection Attacks (FDIAs) is a big concern to grid operators. These are special kind of cyber-attacks that are directed towards the state & measurement data in such a way that mislead the CEMS into making incorrect decisions and create generation load imbalance. These are known to bypass the traditional bad data detection systems within central estimators. This paper presents the use of an additional novel state estimator based on Kalman filter along with traditional Distributed State Estimation (DSE) which is based on Weighted Least Square (WLS). Kalman filter is a feedback control mechanism that constantly updates itself based on state prediction and state correction technique and shows improvement in the estimates. The additional estimator output is compared with the results of DSE in order to identify anomalies and injection of false data. We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus. The results clearly demonstrate the superiority of the proposed method over traditional state estimation.
Highlights
We evaluated our methodology by simulating proposed technique using MATPOWER over IEEE-14, IEEE-30, IEEE-118, IEEE-300 bus
The mean xt and the covariance Pt are estimated as: In order to evaluate the performance of the proposed algorithm, the simulations were conducted on IEEE-14, IEEE-30, IEEE-118 and IEEE-300 bus systems using MATLAB &
It is concluded that the case of False Data Injection Attacks (FDIAs), the deviation the results of and Kalman filter was larger in the case of Weighted Least Square (WLS) and Kalman filter was larger in the case of FDIA
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Set of measurements, it fails estimate the for distributed statestate estimation in that improves thepurpose, result bywe performing state estimation a quasi-steady load profile [14,15]. For this proposed the use of distriband false data detectionininthat a distributed fashion. This reduces the computational uted state estimation improves the result[16]. Dynamic state estimation is installation of PMUs at multiple locations there is a trade-off between number of useful and can help with identifying presence of bad data and topology errors.
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