State estimation within ied based smart grid using kalman estimates
- Rashed, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder, Islam, Syed
- Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 15 (2021), p.
- Full Text:
- Reviewed:
- Description: 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 15 (2021), p.
- Full Text:
- Reviewed:
- Description: 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
False data detection in a clustered smart grid using unscented Kalman filter
- Rashed, Muhammad, Kamruzzaman, Joarder, Gondal, Iqbal, Islam, Syed
- Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
- Full Text:
- Reviewed:
- Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.
- Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 78548-78556
- Full Text:
- Reviewed:
- Description: The smart grid accessibility over the Internet of Things (IoT) is becoming attractive to electrical grid operators as it brings considerable operational and cost efficiencies. However, this in return creates significant cyber security challenges, such as fortification of state estimation data such as state variables against false data injection attacks (FDIAs). In this paper, a clustered partitioning state estimation (CPSE) technique is proposed to detect FDIA by using static state estimation, namely, weighted least square (WLS) method in conjunction with dynamic state estimation using minimum variance unscented Kalman filter (MV-UKF) which improves the accuracy of state estimation. The estimates acquired from the MV-UKF do not deviate like WLS as these are purely based on the previous iteration saved in the transition matrix. The deviation between the corresponding estimations of WLS and MV-UKF are utilised to partition the smart grid into smaller sub-systems to detect FDIA and then identify its location. To validate the proposed detection technique, FIDAs are injected into IEEE 14-bus, IEEE 30-bus, IEEE 118-bus, and IEEE 300-bus distribution feeder using MATPOWER simulation platform. Our results clearly demonstrate that the proposed technique can locate the attack area efficiently compared to other techniques such as chi square. © 2013 IEEE.
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