Adnan Anwar

Vulnerabilities of Smart Grid State Estimation Against False Data Injection Attack

In recent years, Information Security has become a notable issue in the energy sector. After the invention of ‘The Stuxnet worm’ [1] in 2010, data integrity, privacy and confidentiality has received significant importance in the real-time operation of the control centres. New methods and frameworks are being developed to protect the National Critical Infrastructures like- energy sector. In the recent literatures, it has been shown that the key real-time operational tools (e.g., State Estimator) of any Energy Management System (EMS) are vulnerable to Cyber Attacks. In this chapter, one such cyber attack named ‘False Data Injection Attack’ is discussed. A literature review with a case study is considered to explain the characteristics and significance of such data integrity attacks.

Identification of vulnerable node clusters against false data injection attack in an AMI based Smart Grid

In today?s Smart Grid, the power Distribution System Operator (DSO) uses real-time measurement data from the Advanced Metering Infrastructure (AMI) for efficient, accurate and advanced monitoring and control. Smart Grids are vulnerable to sophisticated data integrity attacks like the False Data Injection (FDI) attack on the AMI sensors that produce misleading operational decision of the power system (Liu et al., 2011 1]). Presently, there is a lack of research in the area of power system analysis that relates the FDI attacks with system stability that is important for both analysis of the effect of cyber-attack and for taking preventive measures of protection.In this paper, we study the physical characteristics of the power system, and draw a relationship between the system stability indices and the FDI attacks. We identify the level of vulnerabilities of each AMI node in terms of different degrees of FDI attacks. In order to obtain the interdependent relationship of different nodes, we implement an improved Constriction Factor Particle Swarm Optimization (CF-PSO) based hybrid clustering technique to group the nodes into the most, the moderate and the least vulnerable clusters. With extensive experiments and analysis using two benchmark test systems, we show that the nodes in the most vulnerable cluster exhibit higher likelihood of de-stabilizing system operation compared to other nodes. Complementing research is the construction of FDI attacks and their countermeasures, this paper focuses on the understanding of characteristics and practical effect of FDI attacks on the operation of the Smart Grid by analysing the interdependent nature of its physical properties.

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

Abstract 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.

Optimum allocation and sizing of DG unit for efficiency enhancement of distribution system

In this paper, two generalized algorithms are presented for sizing and allocating distributed generation (DG) units. To determine the size and location of a single DG unit, a heuristic method based on sensitivity analysis and quadratic curve fitting technique has been proposed. Another heuristic technique based on a loss improvement index has been introduced for allocation of multiple DG units. These studies are carried out using two IEEE test distribution systems which are multi-phase and unbalanced in nature. This analysis shows that the use of appropriate size and location of DG reduces total power loss in a distribution system significantly and hence improve the steady-state voltage profile. Methodologies described in this paper gives the distribution system planner an idea about the size and location DG unit which would be the most beneficial in terms of system efficiency and stability.

A new approach for wind and solar type DG placement in power distribution networks to enhance systems stability

This paper proposes a distributed generator (DG) placement methodology based on newly defined term reactive power loadability. The effectiveness of the proposed planning is carried out over a distribution test system representative of the Kumamoto area in Japan. Firstly, this paper provides simulation results showing the sensitivity of the location of renewable energy based DG on voltage profile and stability of the system. Then, a suitable location is identified for two principal types DG, i. e., wind and solar, separately to enhance the stability margin of the system. The analysis shows that the proposed approach can reduce the power loss of the system, which in turn, reduces the size of compensating devices.

A Spatio-temporal Data Summarization Paradigm for Real-time Operation of Smart Grid

In a smart grid distribution management system, operation, planning, forecasting and decision making relies on demand-side management functions, which require real-time smart grid data. This data has significant dollar value because it is extremely useful for efficient control and intelligent prediction of the energy consumption, and expert management of residential and commercial load. However, the huge amount of (smart grid) data generated at a very high velocity poses a number of challenges. Utility companies have a huge demand for efficient summarization techniques to mine interesting patterns and extracting useful and actionable intelligence. Research from various domains has shown that data summarization can significantly improve the scalability and efficiency of various data analytic tasks (e.g., transactional database mining, data streams mining, network monitoring). This paper proposes a summarization approach (i.e., a set of algorithms, data structures, and query mechanisms) that enables the utility company to accurately infer various energy consumption patterns in real-time by automatic monitoring of smart grid data using significantly less computational resources. The proposed summarization approach is suitable for processing spatiotemporal streams, and it can also provide answers in real-time to various smart grid applications (e.g., demand-side management, direct load control, smart pricing and Volt-VAr control). Both theoretical bound and experimental evaluation are presented in this paper, which shows that the memory required for the proposed data structure grows linearly for the first 52 weeks; but interestingly, after the first year, the memory growth is negligible. The experimental results show that the proposed approach can process around 4 million smart meter readings every second or 120 million readings every minute. The proposed approach outperforms widely commercially used Database Management Systems (DBMSs) in terms of update and query costs: it is about 200 times faster than DBMSs in terms of update time, and about 340 times faster than DBMSs in terms of query time.

Swarm intelligence based multi-phase OPF for peak power loss reduction in a smart grid

Recently there has been increasing interest in improving smart grids efficiency using computational intelligence. A key challenge in future smart grid is designing Optimal Power Flow tool to solve important planning problems including optimal DG capacities. Although, a number of OPF tools exists for balanced networks there is a lack of research for unbalanced multi-phase distribution networks. In this paper, a new OPF technique has been proposed for the DG capacity planning of a smart grid. During the formulation of the proposed algorithm, multi-phase power distribution system is considered which has unbalanced loadings, voltage control and reactive power compensation devices. The proposed algorithm is built upon a co-simulation framework that optimizes the objective by adapting a constriction factor Particle Swarm optimization. The proposed multi-phase OPF technique is validated using IEEE 8500-node benchmark distribution system.

False Data Injection Attack Targeting the LTC Transformers to Disrupt Smart Grid Operation

Load Tap Changing (LTC) Transformers are widely used in a Power Distribution System to regulate the voltage level within standard operational limit. In a SCADA connected network, the performance of LTC transformers can be improved by utilizing a closed loop monitoring and control mechanism. The widely used SCADA communication protocols, including Modbus and DNP3, have been proven vulnerable under cyber attack. In this paper, we conduct a vulnerability analysis of LTC transformers under malicious modification of measurement data. Here, we define two different attack strategies, (i)attack targeting energy system efficiency, and (ii) attack targeting energy system stability. With theoretical background and simulation results, we demonstrate that the attack strategies can significantly affect the power distribution system operations in terms of energy efficiency and stability. The experiments are performed considering IEEE benchmark 123 node test distribution system.

CF-PSO based loss sensitivity clustering technique to identify optimal DG allocation nodes for energy efficient smart grid operation

Recently there has been increasing interest in improving smart grid energy efficiency using computational intelligence. In a smart grid, Distributed Generation (DG) has gained much attention due to numerous advantages. However, inappropriate selection of DG allocation nodes may increase the total power loss of the distribution system. Therefore, it is important to identify similar type of nodes where energy efficient DG allocation is possible. In this paper, Constriction Factor Particle Swarm Optimization (CF-PSO), which is a major variant of Swarm Intelligence (SI), has been used with traditional well studied k-means algorithm to enhance the clustering performance. Experiments are performed considering test data from UCI repository of machine learning databases which shows that the CF-PSO based hybrid clustering outperforms the traditional k-means algorithm. This improved clustering algorithm is then employed to identify the potential nodes for DG allocation using loss sensitivity indices. Extensive experiments have been carried out considering IEEE benchmark 123 node test distribution system to justify the clustering output. Results show that the clustering algorithm provides an insight to select the appropriate DG integration nodes for power loss reduction.

Design a Unified Power Quality Conditioner using V2G technology

Unified Power Quality Conditioner (UPQC) can fulfil multiple power quality control objectives such as needs of reactive power compensation, voltage flicker and harmonics current compensation. However UPQCs are quite expensive and therefore are not widely used. In this paper the potential of Plug in Hybrid Electric Vehicles (PHEV) in a V2G mode of operation is explained, which gives a low-cost solution for designing a virtual UPQC using PHEV charging station. A third order dynamic battery model is used here to represent the PHEV. Simulations have been carried out and demonstrated that PHEVs have the potential to work as a virtual UPQC to improve power quality.