Smart Grid State Estimation Research Articles

Toward Data Integrity Attacks Against Distributed Dynamic State Estimation in Smart Grid

With the continuous expansion of the power grid nodes scale, traditional centralized state estimation method shows certain limitations in estimation efficiency and accuracy. Recently, some power grids adopt a distributed state estimation method, in which each partition independently estimates the partial state information by partitioning the entire power system. However, the deviation of the state estimation in certain partition will result in the deviation of the estimation results in the entire power grid system. In this paper, we propose the attack strategy against the distributed state estimation in smart grids from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Moreover, the theoretical analysis of the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations are formalized. The effectiveness of the proposed attack strategy is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of a distributed system can indirectly affect the state estimation results of other partitions and attacking against the measurement value of coordination center can directly threaten the state estimation results of the entire power grid. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes two attack strategies against the distributed state estimation of power grid from two perspectives, i.e. the attack against local physical measurement value of the power system partition and the attack against the measurement value of coordination center. Most of the previous works fail to formalize the state estimation deviation of both partial and entire state estimation results of power grid after the attacker launches the attack against the distributed state estimation. We formalize the state estimation deviation caused by the proposed data integrity attack and the propagation processes of proposed attack vectors in measurement calculations. The effectiveness of the proposed attack strategy against the state estimation of power grid is verified in the IEEE-30 bus and IEEE-118 bus systems. Simulation results show that attacking against a certain partition of power grid can indirectly cause the deviation in the state estimation results of entire power grid and attacking against the measurement value of the coordination center can directly threaten the state estimation results of the entire power grid. In conclusion, the proposed attack strategies are helpful for the research community to design detection strategies in a targeted manner and can be conveniently applied to the real-world security management system of smart grid.

EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

A Privacy-Preserving State Estimation Scheme for Smart Grids

With the appearance of electric energy market deregulation, there exists a growing concern over the potential privacy leakage of commercial data among competing power companies where data sharing is essential in the applications such as smart grid state estimation. Most of the existing solutions are either perturbation-based or conventional cryptography-based where a trusted central 3rd party would often be required. This paper proposes privacy-preserving state estimation protocols for DC and AC models. The proposed idea is to distribute the overall task of the system state estimation into sub-tasks which can be performed by local sub-grid operators with their private data. A masking method is designed inside a homomorphic encryption scheme which is then used to ensure both the input and output data privacy during the collaboration process among individual sub-task players. Security is achieved via the computationally indistinguishable post-quantum security guaranteed by a levelled homomorphic encryption scheme over real numbers and the differential privacy of the output estimated states provided by the Laplace mechanism perturbation integrated into the masking linear transformation. Simulation results are presented to demonstrate the validity of our proposed privacy-preserving system state estimation protocols.

Extended Moving Target Defense for AC State Estimation in Smart Grids

The moving target defense (MTD) that proactively changes series reactance of transmission lines has recently been proposed as an effective defense approach to resist false data injection attacks in smart grids. However, the defense effectiveness analyses of MTD in existing research are mainly focused on linear DC state estimation. To bring the state-of-the-art research to practice, MTD for AC state estimation is investigated in this paper. Specifically, based on a thorough analysis, an extended MTD (EMTD) approach that coordinately changes series reactance and parallel susceptance of lines in smart grids is proposed to improve the traditional MTD. Moreover, the impact of EMTD on electricity market is analyzed. On this basis, the variation of locational marginal price, the variation of active power loss and the cost of devices for executing EMTD are treated as the cost of system defense. Furthermore, to find the trade-off between the defense effectiveness and the cost of EMTD, optimal construction of cost-minimization EMTD topology parameter scheme and defense time interval are also proposed. Finally, extensive simulations are conducted on the standard IEEE test system to demonstrate the effectiveness of the proposed approach.

Localization of False Data Injection Attack in Smart Grids Based on SSA-CNN

In recent years, smart grids have integrated information and communication technologies into power networks, which brings new network security issues. Among the existing cyberattacks, the false data injection attack (FDIA) compromises state estimation in smart grids by injecting false data into the meter measurements, which adversely affects the smart grids. Current studies on FDIAs mainly focus on the detection of its existence, but there are few studies on its localization. Most attack localization methods have difficulty locating the specific bus or line that is under attack quickly and accurately, have high computational complexity and are difficult to apply to large power networks. Therefore, this paper proposes a localization method for FDIAs that is based on a convolutional neural network and optimized with a sparrow search algorithm (SSA–CNN). Based on the physical meaning of measurement vectors, the proposed method can precisely locate a specific bus or line with relatively low computational complexity. To address the difficulty of selecting hyperparameters in the CNN, which leads to the degradation of localization accuracy, a SSA is used to optimize the hyperparameters of the CNN so that the hyperparameters are optimal when using the model for localization. Finally, simulation experiments are conducted on IEEE14-bus and IEEE118-bus test systems, and the simulation results show that the method proposed in this paper has a high localization accuracy and can largely reduce the false-alarm rate.

Blind false data injection attacks in smart grids subject to measurement outliers

ABSTRACT False data injection attacks (FDIAs) can manipulate measurement data from Supervisory Control and Data Acquisition (SCADA) system and threat state estimation in smart grids. Blind FDIAs (BFDIAs) enhance traditional FDIAs, which eliminate the limitation of grasping measurement Jacobian matrix H in advance, but when there are outliers in measurement data, attack performance is degraded. In this paper, improved BFDIAs are proposed. In off-line phase, low-dimensional measurement matrix without outliers calculated by Linear Local Tangent Space Alignment algorithm (LLTSA) is sent into Continuous Deep Belief Network (CDBN) as training data to learn their probability distribution. In on-line phase, real-time low-dimensional measurement matrix with outliers are sent into the trained model as inputs, and outputs are reconstructed by the probability distribution in off-line phase, which eliminates the influence of outliers indirectly. Simulations are implemented on PJM 5-bus and IEEE 14-bus systems to verify the performance of proposed strategy compared with PCA-based BFDIAs.

Adversarial attack and defense methods for neural network based state estimation in smart grid

Deep learning has been recently used in safety-critical cyber-physical systems (CPS) such as the smart grid. The security assessment of such learning-based methods within CPS algorithms, however, is still an open problem. Despite existing research on adversarial attacks against deep learning models, only few works are concerned about safety-critical energy CPS, especially the state estimation routine. This paper investigates security issues of neural network based state estimation in the smart grid. Specifically, the problem of adversarial attacks against neural network based state estimation is analysed and an efficient adversarial attack method is proposed. To thwart this attack, two defense methods based on protection and adversarial training, respectively, are proposed further. The experiments demonstrate that the proposed attack method poses a major threat to neural network based state estimation models. In addition, our results present that defense methods can improve the ability of neural network models to defend against such adversarial attacks.

On the security of ANN-based AC state estimation in smart grid

With the deployment of new elements in the smart grid, traditional state estimation methods are challenged by growing dynamics and system size. Artificial neural network (ANN) based AC state estimation has been shown to provide faster results than traditional methods. However, researchers have discovered that ANNs could be easily fooled by adversarial examples. In this paper, we initiate a new study of adversarial false data injection attacks against ANN-based state estimation. By injecting a deliberate attack vector into measurements, the attacker can degrade the accuracy of ANN state estimation while remaining undetected. We propose two algorithms to generate the attack vectors, a population-based algorithm (differential evolution or DE) and a gradient-based algorithm (sequential least square quadratic programming or SLSQP). The performance of these algorithms is evaluated through simulations on IEEE 9-bus, 14-bus, and 30-bus systems under various attack scenarios. Simulation results show that DE is more effective than SLSQP on all simulation cases. The attack examples generated by the DE algorithm successfully degrade the ANN state estimation accuracy with high probability (more than 80% in all simulation cases), despite having a small number of compromised meters and low injection strength. We further discuss the potential defense strategy to mitigate such attacks, which provides insights for robustness improvement in future research.

Zero-Parameter-Information Data Integrity Attacks and Countermeasures in IoT-Based Smart Grid

Data integrity attack (DIA) is one class of threatening cyber attacks against the Internet-of-Things (IoT)-based smart grid. With the assumption that the attacker is capable of obtaining complete or incomplete information of the system topology and branch parameters, it has been widely recognized that the highly synthesized DIA can evade being detected and undermine the smart grid state estimation. However, the branch parameters cannot be easily obtained or inferred by the attacker in practice. They can be changed or disturbed with time. In this article, we complete the class of DIA by designing the zero-parameter-information DIA (ZDIA), which makes it possible for the attacker to execute stealthy data tampering attacks without any information of the branch parameters. Only the topology information about the cut line is required to construct such attack. We prove that, the attacker can arbitrarily modify the state estimate of a one-degree bus, which is connected to the outside only by a single cut line; and modify the state estimates of all buses, with the same arbitrary bias, in a one-degree super-bus, which is a group of buses that is connected to the outside only by a single cut line. Besides, we extend ZDIA to the cases where a bus and super-bus are connected to the outside only by several cut lines. Moreover, we propose two countermeasures to address the topology vulnerability exploited by ZDIA, and present a branch perturbation strategy to defend against general DIAs. Finally, we conduct extensive simulations with the IEEE standard power systems to validate the theoretical results.

Super Resolution Perception for Improving Data Completeness in Smart Grid State Estimation

Abstract The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.

PMU Placement Optimization for Efficient State Estimation in Smart Grid

This paper investigates phasor measurement unit (PMU) placement for informative state estimation in smart grid by incorporating various constraints for observability. Observability constitutes an important property for PMU placement to characterize the depth of the buses’ reachability by the placed PMUs, but addressing it solely by binary linear programming as in many works still does not guarantee a good estimate for the grid state. Some existing works have considered optimization of some estimation indices by ignoring the observability requirements for computational ease and thus potentially lead to trivial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state subject to observability constraints, which incorporate operating conditions such as presence of zero injection buses, contingency of measurement loss, and limitation of communication channels per PMU. The proposed design is thus free from the fundamental shortcomings in the existing PMU placement designs. The problems are posed as large scale binary nonlinear optimization problems involving thousands of binary variables, for which this paper develops efficient algorithms for computational solutions. Their performance is analyzed in detail through numerical examples on large scale IEEE power networks. The solution method is also shown to be extendable to AC power flow models, which are formulated by nonlinear equations.

Partition-based multi-agent optimization in the presence of lossy and asynchronous communication

We address the problem of multi-agent partition-based convex optimization which arises, for example, in robot localization problems and in regional state estimation in smart grids. More specifically, the global cost function is the sum of locally coupled cost functions that depend only on each agent variables and their neighbors’ variables. Inspired by a generalized gradient descent strategy, namely the Block Jacobi iteration, we propose an algorithm amenable to a scalable distributed implementation, i.e., each agent eventually computes only the optimal values for its own variables via local communication with its neighbors. In particular, we provide sufficient conditions for global and semi-global exponential stability for the proposed algorithms even in the presence of lossy communications and asynchronous updates. The theoretical analysis relies on novel tools on Lyapunov theory based on separation of time scales and averaging theory for discrete-time systems. Finally, the proposed algorithm is numerically tested on the IEEE 123 nodes distribution feeder in the context of multi-area robust state estimation of smart grids in the presence of measurement outliers.

False data injection attacks against smart gird state estimation: Construction, detection and defense

As a typical representative of the so-called cyber-physical system, smart grid reveals its high efficiency, robustness and reliability compared with conventional power grid. However, due to the deep integration of electrical components and computinginformation in cyber space, smart grid is vulnerable to malicious attacks, especially for a type of attacks named false data injection attacks (FDIAs). FDIAs are capable of tampering meter measurements and affecting the results of state estimation stealthily, which severely threat the security of smart grid. Due to the significantinfluence of FDIAs on smart grid, the research related to FDIAs has received considerable attention over the past decade. This paper aims to summarize recent advances in FDIAs against smart grid state estimation, especially from the aspects of background materials, construction methods, detection and defense strategies. Moreover, future research directions are discussed and outlined by analyzing existing results. It is expected that through the review of FDIAs, the vulnerabilities of smart grid to malicious attacks can be further revealed and more attention can be devoted to the detection and defense of cyber-physical attacks against smart grid.

Protecting the grid topology and user consumption patterns during state estimation in smart grids based on data obfuscation

Smart grids promise a more reliable, efficient, economically viable, and environment-friendly electricity infrastructure for the future. State estimation in smart grids plays a pivotal role in system monitoring, reliable operation, automation, and grid stabilization. However, the power consumption data collected from the users during state estimation can be privacy-sensitive. Furthermore, the topology of the grid can be exploited by malicious entities during state estimation to launch attacks without getting detected. Motivated by the essence of a secure state estimation process, we consider a weighted-least-squares estimation carried out batch-wise at repeated intervals, where the resource-constrained clients utilize a malicious cloud for computation services. We propose a secure masking protocol based on data obfuscation that is computationally efficient and successfully verifiable in the presence of a malicious adversary. Simulation results show that the state estimates calculated from the original and obfuscated dataset are exactly the same while demonstrating a high level of obscurity between the original and the obfuscated dataset both in time and frequency domain.

A novel online detection method of data injection attack against dynamic state estimation in smart grid

Dynamic state estimation is usually employed to provide real-time and effective supervision for the smart grid (SG) operation. However, dynamic state estimators have been recently found vulnerable to data injection attack, which are misled without posing any anomalies to bad data detection (BDD). To improve the robustness of the SG, it is firstly necessary to find the system vulnerability by developing an imperfect data injection attack strategy with minimum attack residual increment. In this attack strategy, these targeted state variables are chosen by a designed search approach, and their values are then determined by solving an optimal problem based on particle swarm optimization (PSO) algorithm. Considering the characters of traditional chi-square detection method and history statistical information of state variables without being attacked, a new online chi-square detection method associated with two kinds of state estimates is proposed to make up for the system vulnerability. Numerical simulations confirm the feasibility and effectiveness of the proposed method.

Noncircular Attacks on Phasor Measurement Units for State Estimation in Smart Grid

With the evolution of phasor measurement units (PMUs) and the proposition to incorporate a large number of PMUs in future smart grids, it is critical to identify and prevent potential (zero-day) cyber attacks on phasor signals. The PMUs are the forefront of sensor technologies used in the smart grid and produce phasor voltage and current readings, which are complex-valued in nature. In this regard, the paper investigates potential attacks on complex-valued PMU signals and proposes the new paradigm of data-injection attacks, referred to as noncircular attacks. Existing state estimation algorithms and attack monitoring solutions assume that the PMU observations have statistical characteristics similar to that of real-valued signals. This assumption makes PMUs extremely defenseless against the proposed noncircular attacks. In this paper, we introduce the noncircular attack model, evaluate (both analytically and via experiments) the potential destructive nature of such attacks, propose a Bhattacharyya distance detector for monitoring the system against cyber attacks by transforming the detection problem to an equivalent problem of comparing innovation sequences in distribution via statistical distance measures, and propose a circularization approach, which enables the conventional detection algorithms to identify noncircular attacks.

Smart grid state estimation and stabilisation

This paper proposes a smart grid state estimation and stabilisation algorithm. It relies on the principle of Bayesian filter structure where the smart grid state information is estimated in an iterative way. This approach assumes that the system state is a set of stochastic variables with mean and covariance values, which are shifted between the factor and variable nodes to obtain an accurate estimation. Afterwards, a semidefinite programming based optimal feedback controller is designed to stabilise the system states. Using the standard Schur complement, the system state matrix is written into the linear matrix inequality form. After solving the proposed convex optimisation problem, the designed feedback gain can stabilise the system states. Numerical results illustrate that the proposed scheme is able to estimate and stabilise the system states within a very short time.

A Novel Smart Grid State Estimation Method Based on Neural Networks

The rapid development in smart grids needs efficient state estimation methods. This paper presents a novel method for smart grid state estimation (e.g., voltages, active and reactive power loss) using artificial neural networks (ANNs). The proposed method which is called SE-NN (state estimation using neural network) can evaluate the state at any point of smart grid systems considering fluctuated loads. To demonstrate the effectiveness of the proposed method, it has been applied on IEEE 33-bus distribution system with different data resolutions. The accuracy of the proposed method is validated by comparing the results with an exact power flow method. The proposed SE-NN method is a very fast tool to estimate voltages and re/active power loss with a high accuracy compared to the traditional methods.

Cloud-based IoT solution for state estimation in smart grids: Exploiting virtualization and edge-intelligence technologies

Smart Grids (SGs) are expected to be equipped with a number of smart devices able to generate vast amounts of data about the network status, becoming the key components for an efficient State Estimation (SE) of complex grids. To exploit their potentials, the ICT infrastructure needs to be scalable to follow the increasing amount of data flows and flexible to give the possibility to assign and re-assign grid functions and data flow control policies at runtime, possibly in a context-aware manner. In this scenario, this paper proposes and validates a Cloud-IoT-based architectural solution for SE in SG that combines cloud-capabilities and edge-computing advantages and uses virtualization technologies to decouple the handling of measurement data from the underlying physical devices. Case studies in the field of distribution networks monitoring are also analyzed, demonstrating that the proposed architecture is capable to accomplish the assigned operational tasks, while satisfying the needed quality level from both the communication and the grid perspectives with a significant degree of flexibility and adaptability with respect to state of the art solutions.

Detection of false data injection attacks against state estimation in smart grids based on a mixture Gaussian distribution learning method

One of the most addressed attacks in power networks is false data injection (FDI) which affects monitoring, fault detection, and state estimation integrity by tampering measurement data. To detect such devastating attack, the authors propose a statistical anomaly detection approach based on Gaussian mixture model, while some appropriate machine learning approaches are evaluated for detecting FDI. It should be noted that a finite mixture model is a convex combination of some probability density functions and combining the properties of several probability functions, making the mixture models capable of approximating any arbitrary distribution. Simulations results confirm superior performance of the proposed method over conventional bad data detection (BDD) tests and other learning approaches that studied in this article. It should be noted that using data which change significantly over a day can be highly clustered, and therefore, detected much easier compared with small changes in the loads. So without loss of generality, in the simulations it is assumed that the power demand follows a uniform distribution in a small range. However, the detector can be trained regularly based on the updated load profile.