Parallel domain decomposition based distributed state estimation for large-scale power systems

Karimipour, Hadis; Dinavahi, Venkata

doi:10.1109/icps.2015.7266420

Cited by 23 publications

(15 citation statements)

References 15 publications

(5 reference statements)

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“…Other alternative filtering algorithms to EKF for PSSE have also been developed, including the iterative Kalman filter [34], the unscented Kalman filter [35], [36], the particle filter [37], the robust filter [38], the disturbance filter [33], [39], the adaptive filter [40], [41] and the filter for joint estimation of state and parameter [42], [43]. Moreover, to speed up the estimation algorithm applied in large-scale power grids, parallel EKF-based dynamic state estimator has been proposed in [44]- [46]. In addition, computational intelligence tools (e.g.…”

Section: B Estimation Methodsmentioning

confidence: 99%

Recent advances on state estimation for power grids with unconventional measurements

Wang

Liu

et al. 2017

IET Control Theory & Applications

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State estimation problem for power systems has long been a fundamental issue that demands a variety of methodologies dependent on the system settings. With recent introduction of advanced devices of phasor measurement units (PMUs) and dedicated communication networks, the infrastructure of power grids has been greatly improved. Coupled with the infrastructure improvements are three emerging issues for the state estimation problems, namely, the coexistence of both traditional and PMU measurements, the incomplete information resulting from delayed, asynchronous and missing measurements due to communication constraints, and the cyber-attacks on the communication channels. In this paper, we aim to survey some recent advances on the state estimation methods which tackle the above three issues in power grids. Traditional state estimation methods applied in power grids are first introduced. Latest results on state estimation with mixed measurements and incomplete measurements are then discussed in great detail. In addition, the techniques developed to ensure the cyber-security of the state estimation schemes for power grids are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out.

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Section: B Estimation Methodsmentioning

confidence: 99%

Recent advances on state estimation for power grids with unconventional measurements

Wang

Liu

et al. 2017

IET Control Theory & Applications

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“…There is a wealth of research related to the power system state estimation. To begin with, a distributed weighted least square state estimation method using additive Schwarz domain decomposition technique is proposed in [11]. This decomposition divides the data set into several subsets to reduce the execution time.…”

Section: A Related Literaturementioning

confidence: 99%

Distributed State Estimation Over Unreliable Communication Networks With an Application to Smart Grids

Rana

2017

IEEE Trans. on Green Commun. Netw.

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In contrast to the traditional centralised power system state estimation methods, this paper investigates the interconnected optimal filtering problem for distributed dynamic state estimation considering packet losses. Specifically, the power system incorporating microgrids is modelled as a state-space linear equation where sensors are deployed to obtain measurements. Basically, the sensing information is transmitted to the energy management system (EMS) through a lossy communication network where measurements are lost. This can seriously deteriorate the system monitoring performance and even lose network stability. Secondly, as the system states are unavailable, so the estimation is essential to know the overall operating conditions of the electricity network. Availability of the system states provides designers an accurate picture of the power network, so a suitable control strategy can be applied to avoid massive blackouts due to losing network stability. Particularly, the proposed estimator is based on the mean squared error between the actual state and its estimate. To obtain the distributed estimation, the optimal local and neighbouring gains are computed to reach a consensus estimation after exchanging their information with the neighbouring estimators. Then the convergence of the developed algorithm is theoretically proved. Afterwards, a distributed controller is designed based on the semidefinite programming approach. Simulation results demonstrate the accuracy of the developed approaches under the condition of missing measurements.

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“…Critical infrastructures are highly complex systems that utilize cyber and physical components in their daily operations. The backbone of these facilities consists of an Industrial Control System..(ICS), which plays an important role in the monitoring and control of critical infrastructures such as smart power grids, oil and gas, aerospace, and transportation [1], [2]. Therefore, the safety and security of ICSs are paramount for national security.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

2020

Self Cite

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The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. Traditional Intrusion Detection Systems (IDSs), which are mainly developed to support information technology systems, count vastly on predefined models and are trained mostly on specific cyber-attacks. Besides, most IDSs do not consider the imbalanced nature of ICS datasets, thereby suffering from low accuracy and high false-positive when being put to use. In this paper, we propose a deep learning model to construct new balanced representations of the imbalanced datasets. The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. The proposed attack detection model leverages Deep Neural Network (DNN) and Decision Tree (DT) classifiers to detect cyber-attacks from the new representations. The performance of the proposed model is evaluated based on 10-fold crossvalidation on two real ICS datasets. The results show that the proposed method outperforms conventional classifiers, including Random Forest (RF), DNN, and AdaBoost, as well as recent existing models in the literature. The proposed approach is a generalized technique, which can be implemented in existing ICS infrastructures with minimum effort. INDEX TERMS Cyber-attacks, critical infrastructure, industrial control system, integrity attack, operation technology, information technology, deep learning, neural network.

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Parallel domain decomposition based distributed state estimation for large-scale power systems

Cited by 23 publications

References 15 publications

Recent advances on state estimation for power grids with unconventional measurements

Recent advances on state estimation for power grids with unconventional measurements

Distributed State Estimation Over Unreliable Communication Networks With an Application to Smart Grids

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

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