State estimation within ied based smart grid using kalman estimates

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

출력제어 최소화를 위한 가상 발전소 사업자의 허위정보주입공격

학위논문(석사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2023. 2. 윤용태.Studies on false data injection attacks (FDIA) against state estimation were mainly conducted on the transmission system. However, recently, as entities such as distributed energy resources (DERs), virtual power plants (VPPs), energy storage systems (ESSs), and EV charging stations, that are vulnerable to cyber-attacks, began to appear in the distribution system, research on FDIA in the distribution system is being actively conducted. Among them, this paper deals with the FDIA that VPPs attempt in the distribution system. As the number of DERs in the distribution system increases, the curtailment for DERs owned by VPP increases. This paper proposes FDIA model by VPPs to avoid curtailment under the realistic conditions. In the model, VPPs can implement an FDIA that deceives the distribution system operator (DSO)s state estimation with only information obtained from the DERs they own. To verify this, IEEE 33 test feeder was used and the result shows that the attack was successful without being caught in the DSO's bad data detection (BDD). This paper provides the basic concept of VPPs FDIA and shows that future DSOs need algorithms to defend against VPPs FDIA.상태추정에 대한 허위정보주입공격 연구는 주로 송전계통을 대상으로 연구되어 왔다. 하지만 소규모 분산 자원, 가상 발전소, 에너지 저장장치, 전기차 충전소 등 가상공격에 취약한 자원들이 배전계통에 등장하면서 배전계통에 대한 허위정보주입공격 관련 연구가 최근 활발히 연구되고 있다. 그 중, 이 연구는 가상 발전소 사업자가 배전계통 내에서 시도할 수 있는 허위정보주입공격을 다룬다. 배전계통 내 태양광 발전소와 같은 소규모 분산자원들이 증가하면서 가상 발전소 사업자가 소유한 태양광 발전소에 내려지는 출력제어 조치가 함께 증가하고 있다. 이 연구는 현실적인 조건하에 가상 발전소 사업자가 출력제어 조치를 피하기 위해 시도할 수 있는 허위정보주입공격 모델을 제시한다. 이 공격모델은 가상 발전소 사업자가 자신들이 소유한 태양광 발전소에서 얻는 정보만으로 배전계통 운영자의 상태추정을 속이는 공격이 가능함을 보인다. 이를 증명하기 위해, IEEE 33 테스트 계통을 사용해 본 모델이 배전계통 운영자의 거짓정보감지를 우회할 수 있음을 보였다. 본 연구는 미래에 발생할 수 있는 가상 발전소 사업자의 허위정보주입공격에 대한 기본 개념을 제시하고 미래 배전계통 운영자가 본 연구에 제시한 허위정보주입공격을 방어할 수 있는 알고리즘이 필요함을 보인다.1 Introduction 1 1.1 Research background and motivation 1 1.2 Research objective and contents 4 1.3 Research procedure 5 2 Literature review and contribution 6 2.1 Attempting false data injection attack in various condition 6 2.2 Impact of false data injection attack 7 2.3 Cyber-attack related to distributed energy resources 8 2.4 Contribution of this study 9 3 Theoretical background 10 3.1 State estimation 10 3.2 Distribution System State Estimation (DSSE) 12 3.3 Bad data detection (BDD) 15 3.4 False data injection attack (FDIA) 16 4 VPP's local false data injection attack 18 4.1 DSO assumptions 18 4.2 VPP assumptions 20 5 Simulation Setting and Results 24 5.1 Simulation Environment 24 5.2 Non-intelligent attack 28 5.3 Intelligent attack 30 6 Conclusion 33 Bibliography 35 초록 38석

Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. {The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation.} We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of {both observable and unobservable false data attacks} demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network Security (CNS) 201

False Analog Data Injection Attack Towards Topology Errors: Formulation and Feasibility Analysis

In this paper, we propose a class of false analog data injection attack that can misguide the system as if topology errors had occurred. By utilizing the measurement redundancy with respect to the state variables, the adversary who knows the system configuration is shown to be capable of computing the corresponding measurement value with the intentionally misguided topology. The attack is designed such that the state as well as residue distribution after state estimation will converge to those in the system with a topology error. It is shown that the attack can be launched even if the attacker is constrained to some specific meters. The attack is detrimental to the system since manipulation of analog data will lead to a forged digital topology status, and the state after the error is identified and modified will be significantly biased with the intended wrong topology. The feasibility of the proposed attack is demonstrated with an IEEE 14-bus system.Comment: 5 pages, 7 figures, Proc. of 2018 IEEE Power and Energy Society General Meetin

Modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements

The false data injection (FDI) attack cannot be detected by the traditional anomaly detection techniques used in the energy system state estimators. In this paper, we demonstrate how FDI attacks can be constructed blindly, i.e., without system knowledge, including topological connectivity and line reactance information. Our analysis reveals that existing FDI attacks become detectable (consequently unsuccessful) by the state estimator if the data contains grossly corrupted measurements such as device malfunction and communication errors. The proposed sparse optimization based stealthy attacks construction strategy overcomes this limitation by separating the gross errors from the measurement matrix. Extensive theoretical modeling and experimental evaluation show that the proposed technique performs more stealthily (has less relative error) and efficiently (fast enough to maintain time requirement) compared to other methods on IEEE benchmark test systems.Comment: Keywords: Smart grid, False data injection, Blind attack, Principal component analysis (PCA), Journal of Computer and System Sciences, Elsevier, 201