Dou An

On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures

It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent studies show that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard bus systems, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data-injection attacks, we propose a protection-based defense and a detection-based defense, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.

EMD and Wavelet Transform Based Fault Diagnosis for Wind Turbine Gear Box

Wind turbines are mainly located in harsh environment, and the maintenance is therefore very difficult. The wind turbine faults are mostly from the gear box, and the fault signal is generally nonlinear and nonstationary. The traditional fault diagnosis methods such as Fast Fourier transform (FFT) and the inverted frequency spectrum identification method based on FFT are not satisfactory in processing this kind of signal. This paper proposes a hybrid fault diagnosis method which combines the empirical mode decomposition (EMD) and wavelet transform. The vibration signal is analyzed through wavelet transform, and the aliasing in high-frequency signals is then addressed by conducting EMD to the original signals. The experimental results based on a specific wind turbine gear box demonstrate that this method can diagnose the faults and locate their positions accurately.

Towards statistical modeling and machine learning based energy usage forecasting in smart grid

Developing effective energy resource management strategies in the smart grid is challenging due to the entities on both the demand and supply sides experiencing numerous fluctuations. In this paper, we address the issue of quantifying uncertainties on the energy demand side. Specifically, we first develop approaches using statistical modeling analysis to derive a statistical distribution of energy usage. We then utilize several machine learning based approaches such as the Support Vector Machines (SVM) and neural networks to carry out accurate forecasting on energy usage. We perform extensive experiments of our proposed approaches using a real-world meter reading data set. Our experimental data shows that the statistical distribution of meter reading data can be largely approximated with a Gaussian distribution and the two SVM-based machine learning approaches to achieve a high accuracy of forecasting energy usage. Extensions to other smart grid applications (e.g., forecasting energy generation, determining optimal demand response, and anomaly detection of malicious energy usage) are discussed as well.

On statistical modeling and forecasting of energy usage in smart grid

Developing effective energy resource management strategies in the smart grid is challenging because the entities in both demand and supply sides experience numerous fluctuations. In this paper, we address the issue of quantifying uncertainties on the energy demand side. Specifically, we first develop approaches using statistical modeling analysis to derive a statistical distribution of energy usage. We then utilize machine learning based approaches such as the Support Vector Machines (SVM) and neural networks to conduct accurate forecasting on energy usage. We perform extensive experiments of our proposed approaches using a real-world meter reading data set. Our experimental data shows that the statistical distribution of meter reading data can be largely approximated with a Gaussian distribution and the two SVM-based machine learning approaches achieve a high accuracy of forecasting energy usage.

On Optimal PMU Placement-Based Defense Against Data Integrity Attacks in Smart Grid

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. Nonetheless, leveraging optimal phasor measurement unit (PMU) placement to defend against these attacks, while simultaneously ensuring the system observability, has yet to be addressed without incurring significant overhead. In this paper, we enhance the least-effort attack model, which computes the minimum number of sensors that must be compromised to manipulate a given number of states, and develop an effective greedy algorithm for optimal PMU placement to defend against data integrity attacks. Regarding the least-effort attack model, we prove the existence of smallest set of sensors to compromise and propose a feasible reduced row echelon form (RRE)-based method to efficiently compute the optimal attack vector. Based on the IEEE standard systems, we validate the efficiency of the RRE algorithm, in terms of a low computation complexity. Regarding the defense strategy, we propose an effective PMU-based greedy algorithm, which cannot only defend against data integrity attacks, but also ensure the system observability with low overhead. The experimental results obtained based on various IEEE standard systems show the effectiveness of the proposed defense scheme against data integrity attacks.

On data integrity attacks against optimal power flow in power grid systems

In this paper, we investigate the data integrity attack against Optimal Power Flow (OPF) with the least effort from the adversarys perspective. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector (with a goal to minimize the amount of information to manipulate) as an optimal attack strategy. To defend against such an attack, we develop the defensive scheme by protecting the critical nodes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes.

Towards optimal PMU placement against data integrity attacks in smart grid

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. In this paper, we consider the existing least-effort attack model that computes the minimum number of sensors that must be compromised in order to manipulate a given number of states, and develop an effective greedy-based algorithm for optimal PMU placement to defend against data integrity attacks. We develop a greedy-based algorithm for optimal PMU placement, which can not only combat data integrity attacks, but also ensure the system observability with low overhead. The experimental data obtained based on IEEE standard systems demonstrates the effectiveness of the proposed defense scheme against data integrity attacks.

Towards Stochastic Optimization-Based Electric Vehicle Penetration in a Novel Archipelago Microgrid.

Due to the advantage of avoiding upstream disturbance and voltage fluctuation from a power transmission system, Islanded Micro-Grids (IMG) have attracted much attention. In this paper, we first propose a novel self-sufficient Cyber-Physical System (CPS) supported by Internet of Things (IoT) techniques, namely “archipelago micro-grid (MG)”, which integrates the power grid and sensor networks to make the grid operation effective and is comprised of multiple MGs while disconnected with the utility grid. The Electric Vehicles (EVs) are used to replace a portion of Conventional Vehicles (CVs) to reduce CO2 emission and operation cost. Nonetheless, the intermittent nature and uncertainty of Renewable Energy Sources (RESs) remain a challenging issue in managing energy resources in the system. To address these issues, we formalize the optimal EV penetration problem as a two-stage Stochastic Optimal Penetration (SOP) model, which aims to minimize the emission and operation cost in the system. Uncertainties coming from RESs (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by the Monte Carlo-based method. To enable the reasonable deployment of EVs in each MGs, we develop two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. An extensive simulation study based on a modified 9 bus system with three MGs has been carried out to show the effectiveness of our proposed schemes. The evaluation data indicates that our proposed strategy can reduce both the environmental pollution created by CO2 emissions and operation costs in UCS and LCS.

Toward Data Integrity Attacks Against Optimal Power Flow in Smart Grid

In this paper, we address the security issue of optimal power flow (OPF) (as a key component in the smart grid). To be specific, we investigate the data integrity attack against OPF with the least effort from the adversary’s perspective, and propose effectively defense schemes to combat the data integrity attack, with respect to the number of nodes to compromise and the amount of information to manipulate. The investigated attack can first select the minimum number of target nodes to compromise by analyzing the difference between the capacity of transmission line and the real transmission power, and then search for a critical attack vector as an optimal attack strategy. To defend against such an attack, we develop the defensive schemes by not only protecting the critical nodes but also detecting the existence of attacks based on false measurement detection schemes. Based on various IEEE standard systems, we show the effectiveness of our investigated attack scheme and the corresponding defense schemes. The experimental results show that the discovered compromised nodes and critical attack vector could lead to the increase of the fuel cost from the power generation by compromising the least number of nodes and injecting the least amount of false information, in comparison with the random attack as the baseline attack strategy. In addition, our two developed defensive schemes are capable of making OPF resilient to the data integrity attack via protecting critical nodes and identifying the falsified measurements accurately in the system.

On stochastic optimal bidding strategy for microgrids

In this paper, we addressed the issue of a stochastic optimal bidding problem for a system with microgrids (MGs). The optimal bidding problem is formulated as a two-stage stochastic programming process, which aims to minimize the system operation cost and to expand energy interactions among local MGs that are geographically close. Uncertainties come from both energy supply and demand sides (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by using the Monte Carlo method. To enable an optimal electricity trading between local MGs, we presented two bidding schemes: (i) Cournot equilibrium based Dynamic Backtrack Energy Trading (DBET), and (ii) double auction based Dual Decomposition Auction (DDA). Experimental results on an IEEE-33 bus based system with MGs were presented to show the effectiveness of our proposed schemes. Experimental results show that our proposed bidding schemes can reduce the operation cost of the system, while the DDA scheme achieves better performance in terms of system social welfare than the DBET scheme.