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.

Assessing reliability of smart grid against cyberattacks using stability index

- Rashed, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder, Islam, Syed

**Authors:**Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed**Date:**2021**Type:**Text , Conference paper**Relation:**31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021**Full Text:**false**Reviewed:****Description:**The degradation of stability index within smart grid leads to incorrect power generation and poor load balancing. The remote data dependency of the central energy management system (CEMS) causes communication delay that further leads to poor synchronization within the system. This becomes worse in the presence of cyber-attacks such as stealth or false data injection attack (FDIA). We used dynamic estimation to obtain state data after the inception of false data attack and analyzed its impact on the stability index of the smart grid. A lookup table was constructed based on the fluctuations within the voltage estimates of IEEE-Bus system. An index number was assigned to output estimates at the bus that highlights the level of severity within the grid. We used IEEE-57 Bus using MATLAB to capture and plot the results related to voltage estimates, latency, and inception time delay. The results demonstrate a clear relationship between stability index and state estimates especially when the system is under the influence of a cyber-attack. © 2021 IEEE.

State estimation in the presence of cyber attacks using distributed partition technique

- Rashed, Muhammad, Gondal, Iqbal, Kamruzzuman, Joarder, Islam, Syed

**Authors:**Rashed, Muhammad , Gondal, Iqbal , Kamruzzuman, Joarder , Islam, Syed**Date:**2020**Type:**Text , Conference paper**Relation:**2020 Australasian Universities Power Engineering Conference, AUPEC 2020, Hobart, 29 November 2020 to 2 December 2020, 2020 Australasian Universities Power Engineering Conference, AUPEC 2020 - Proceedings**Full Text:**false**Reviewed:****Description:**The security of smart grid (SG) is an open problem. False data injection attacks (FDIAs) could pose serious risks to automated smart grid and can cause power system outages which eventually could lead to huge economical losses. Cyber-attacks on critical infrastructure are big concerns to the nation's energy reliability; and attackers come up with new attack strategies that couldn't be detected by the traditional bad data detection methods. Although bad data detection (BDD) schemes based on traditional state estimation and chi-square tests within power systems have been used and considered very reliable in detecting false measurements, these BDD schemes and state estimators have been found vulnerable and failed to combat engineered cyber-attacks. In this paper, a novel chi-square detector has been used with a combination of two state estimators in Distributed Partitioning State Estimation (DPSE), results show it is very effective to combat FDIAs when compared with traditional state estimation techniques. The main idea of DPSE is to increase the sensitivity of the chi-square tests by partitioning the large grids into small blocks and applying the tests on each partition individually. State estimator modelled on a novel chi-square detector which is based on particle swarm optimization (PSO) algorithm significantly improved the results. Numerical simulations conducted in MATPOWER confirm the feasibility and effectiveness of the proposed method. © 2020 University of Tasmania.

False data injection attack detection in smart grid

**Authors:**Rashed, Muhammad**Date:**2022**Type:**Text , Thesis , PhD**Full Text:****Description:**Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.**Description:**Doctor of Philosophy

**Authors:**Rashed, Muhammad**Date:**2022**Type:**Text , Thesis , PhD**Full Text:****Description:**Smart grid is a distributed and autonomous energy delivery infrastructure that constantly monitors the operational state of its overall network using smart techniques and state estimation. State estimation is a powerful technique that is used to determine the overall operational state of the system based on a limited set of measurements collected through metering systems. Cyber-attacks pose serious risks to a smart grid state estimation that can cause disruptions and power outages resulting in huge economical losses and are therefore a big concern to a reliable national grid operation. False data injection attacks (FDIAs), engineered on the basis of the knowledge of the network configuration, are difficult to detect using the traditional data detection mechanisms. These detection schemes have been found vulnerable and failed to detect these FDIAs. FDIAs specifically target the state data and can manipulate the state measurements in such a way that these false measurements appear real to the main control systems. This research work explores the possibility of FDIA detection using state estimation in a distributed and partitioned smart grid. In order to detect FDIAs we use measurements for residual-based testing which creates an objective function; and the probability of erroneous data is determined from this residual test. In this test, a preset threshold is determined based on the prior history of the state data. FDIA cases are simulated within a smart grid considering that the Chi-square detection state estimator fails in identifying such attacks. We compute the objective function using the standard weighted least problem and then test the objective function against the value in the Chi-square table. The gain matrix and the Jacobian matrix are computed. The state variables are computed in the form of a voltage magnitude. The state variables are computed after the inception of an attack to assess these state magnitude results. Different sizes of partitioning are used to improve the overall sensitivity of the Chi-square results. Our additional estimator is based on a Kalman estimation that consists of the state prediction and state correction steps. In the first step, it obtains the state and matrix covariance prediction, and in the second step, it calculates the Kalman gain and the state and matrix covariance update steps. The set of points is created for the state vector x at a time instant t. The initial vector and covariance matrix are based on a priori knowledge of the historical estimates. A set of sigma points is estimated by the state update function. Sigma points refer to the minimal set of sampling points that are selected and transformed using nonlinear function, and the new mean and the covariance are formed out of these transformed points. The idea behind this is that it is easier to compute a Gaussian distribution than an arbitrary nonlinear function. The filter gain, the mean and the covariance are used to estimate the next state. Our simulation results show that the combination of Kalman estimation and distributed state estimation improves the overall stability index and vulnerability assessment score of the smart grid. We built a stability index table for a smart grid based on the state estimates value after the inception of an FDIA. The vulnerability assessment score of the smart grid is based on common vulnerability scoring system (CVSS) and state estimates under the influence of an FDIA. The simulations are conducted in the MATPOWER program and different electrical bus systems such as IEEE 14, 30, 39, 118 and 300 are tested. All the contributions have been published in reputable journals and conferences.**Description:**Doctor of Philosophy

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