Graduate Theses, Dissertations, and Problem Reports 2014 Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber-Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System Vivek Joshi Follow this and additional works at: https://researchrepository.edu/etd Recommended Citation Joshi, Vivek, "Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber- Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System" (2014). Graduate Theses, Dissertations, and Problem Reports.edu/etd/7099 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use.
For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact researchrepository@mail. Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber-Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System By Vivek Joshi Thesis submitted to the Benjamin M.
Statler College of Engineering and Mineral Resources at West Virginia University in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Dr. Jignesh Solanki, Ph. Sarika Khushalani Solanki, Ph. Radhey Sharma, Ph.
Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2014 Keywords: Distribution System, Capacitor Control, DG, MLR, System modelling parameter inconsistencies, Deception Attack, Load Redistribution Attack, OpenDSS. Copyright 2014 Vivek Joshi ABSTRACT Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber- Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System Vivek Joshi Master of Science in Electrical Engineering West Virginia University Advisor: Dr. Jignesh Solanki, Ph. In the present grid real time control systems are the energy management systems and distribution management systems that utilize measurements from real-time units (RTUs) and Supervisory Control and Data Acquisition (SCADA).
The SCADA systems are designed to operate on isolated, private networks without even basic security features which are now being migrated to modern IP-based communications providing near real time information from measuring and controlling units. To function “brain” (SCADA) properly “heart” (RTUs) should provide necessary response thereby creating a coupling which makes SCADA systems as targets for cyber-attacks to cripple either part of the electric transmission grid or fully shut down (create blackout) the grid. Cyber-security research for a distribution grid is a topic yet to be addressed. To date firewalls and classic signature-based intrusion detection systems have provided access control and awareness of suspicious network traffic but typically have not offered any real-time detection and defense solutions for electric distribution grids.
This thesis work not only addresses the cyber security modeling, detection and prevention but also addresses model inconsistencies for effectively utilizing and controlling distribution management systems. Inconsistencies in the electrical design parameters of the distribution network or cyber-attack conditions may result in failing of the automated operations or distribution state estimation process which might lead the system to a catastrophic condition or give erroneous solutions for the probable problems. This research work also develops a robust and reliable voltage controller based on Multiple Linear Regression (MLR) to maintain the voltage profile in a smart distribution system under cyber-attacks and model inconsistencies. The developed cyber-attack detection and mitigation algorithms have been tested on IEEE 13 node and 600+ node real American electric distribution systems modeled in Electric Power Research Institute’s (EPRI) OpenDSS software.
ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my advisor, Dr. Jignesh Solanki, for his invaluable guidance, support, and encouragement. Jignesh for believing in me that I can accomplish this goal with hard work and sincere effort. I thank him for providing me that confidence which lead to completion of this research work.
Next, I would like to thank my second committee member Dr. Sarika Khushalani Solanki. It was from her that I learned the difference between mere hard work and hard work with focus and dedication. Sarika Khushalani Solanki and Dr.
Jignesh Solanki have been with me, guiding me throughout my two years of research. I would also like to thank my other committee member Dr. Radhey Sharma, whose feedback and reviews helped me improve the quality of this thesis. I would like to thank my parents for their moral support throughout my graduate studies.
Their love and affection have helped me overcome the toughest of challenges during these two hard years. Lastly, but in no sense the least, I am thankful to all my friends who made my stay at the West Virginia University a memorable and valuable experience. iii Contents ABSTRACT. i List of Figures.
vi List of Tables. vii Chapter 1: INTRODUCTION .5 Cyber Attack in Power System .1 Voltage Controller Strategy .2 Cyber Attack Detection Algorithm .3 Electrical Design Parameters Inconsistency effect on Losses Calculation. 9 Chapter 2: LITERATURE REVIEW .1 Voltage Control in distribution system .2 Cyber Attacks in Power Systems. 14 Chapter 3: MATHEMATICAL MODEL AND FORMULATION .1 Theory of Multiple Linear Regression (MLR) [48] .2 Modeling attacks and anomalies .1 Inconsistencies in electrical design parameters.2 Data integrity attack [29] .3 Load redistribution attack .3 Distributed Cyber Attack Detection Method .4 Proposed Voltage Controller Methodology.
24 Chapter 4: SIMULATION TOOLS AND SOFTWARE .1 Open Distribution System Simulator (OpenDSS).1 Extensive Range of Solution Modes. 29 Chapter 5: SIMULATION AND RESULTS .1 13-Bus Distribution System .2 AEP Test Circuit .1 MLR based Statistical Voltage Controller .2 MLR based Controller Performance .3 Cyber Attack Distributed Detection Method. 40 Chapter 6: CONCLUSION AND FUTURE WORK .1 Voltage Controller Strategy .2 Distributed cyber-attack detection technique. 47 v List of Figures Figure 1 Voltage Control in Distribution System.
4 Figure 2 Power Grid Cyber-Physical Infrastructure [13]. 6 Figure 3 Divided Areas based on Reactive Power Domains [24]. 13 Figure 4 Cyber-Attack on Control System [29]. 15 Figure 5 Deception Attack on State Estimator in a Power Grid [34].
16 Figure 6 SCADA Controlled Voltage Loop in a Transmission System [44]. 17 Figure 7 Detection of Cyber-Attack with Local Agents for each Area [40]. 18 Figure 8 A Statistical Reactive Power Model Algorithm. 25 Figure 9 OpenDSS Configuration [46].
28 Figure 10 IEEE 13 Bus Distribution Test Feeder. 30 Figure 11 Normal Probability Plots for the Regression Models for Buses 652 and 684. 31 Figure 12 AEP Feeder 1 Network Diagram. 33 Figure 13 Normal Probability Plots for the Regression Models for Buses 164_west, 146_west, 143_west and 132_west.
35 Figure 14 Controller Validations for the Kvar Calculation Models for the Buses 164_west, 146_west, 143_west and 132_west. 36 Figure 15 Losses in Four Maximum Losses giving Lines with Each Type of Line Geometry for 2- Conductor Type Line. 38 Figure 16 Losses in Four Maximum Losses giving Lines with Each Type of Line Geometry for 4- Conductor Type Line. 38 Figure 17 Controller Testing for the Voltage Controllers against Inconsistencies in Electrical Design Parameters, Deception Attack and LR Attack.
39 Figure 18 Cluster Division for AEP Feeder 1 Network. 41 Figure 19 Normal Probability Plots for the Regression Models of the Local Agents. 42 Figure 20 Decision Table for Deception Attack. 44 Figure 21 Decision Table for LR Attack.
44 vi List of Tables Table 1. Regression Models for Voltage in p. for Buses 652 and 684. Controller Validation for the kVar Calculation Models.
Regression Models for Voltage in p. for Buses 164_west, 146_west, 143_west and 132_west 34 Table 4. Statistical Models for Attack Detection. Total number of buses and the centroid value.
42 vii Chapter 1: INTRODUCTION 1.1 Background The electric power system consists of three fields- generation, transmission and distribution; which are constantly evolving to supply the ever increasing demand in a more cost effective, efficient and reliable manner, both for the utilities and the customers. For this purpose the electric power grid has become the most complex and highly invested industry undergoing constant technological renovations. These technological advancements led to the concepts of SCADA, Energy Management systems (EMS), Distribution System Management (DMS), Smart Grid and Distribution Automation (DA) in the power grid.2 Smart Grid The concept of a smart grid started with the formation of Independent System Operator (ISO) and Regional Transmission Organization (RTO) under the recommendation of Federal Energy Regulatory Commission (FERC). The ISOs and RTOs are formed to make a smarter electrical grid keeping in mind the demands of the 21st century.
The US Department of Energy (DOE) defines the overall vision of Smart Grid as the following [1]. Intelligent Automation– having sensors to sense overload conditions and rerouting power and avoiding outage conditions; automatic isolation of faulted areas with minimum disruption of power. Smooth Integration of Distributed Generation (DG) – integration of any fuel source including solar and wind as easily and transparently as coal and natural gas; also other technologies like energy storage. Sophisticated Demand Response Capabilities – supporting real-time communication between the consumer and utility so consumers can alter their energy consumption based on individual inclinations, like price and/or environmental concerns.
Quality-centric – capable of delivering the power which is free of sags, spikes, disturbances and interruptions. Robust – highly resistant to cyber-attack and natural disasters as it becomes more decentralized. There are vast benefits to the country with the commencement of Smart Grid [2]. The chances of cascading outages and dependency on foreign fuel are reduced.
One of the important objectives of smart grid concept is to allow high penetration of DG and new storage technologies into the present grid smoothly. DGs are small scale power generation technologies located close to the load having capabilities of lowering costs, improving reliability and reducing emissions.3 Distributed generation With the advent of smart grid and advancement of new technologies, the utilities are focused towards adding DG into their existing infrastructure. The addition of DG does bring along different technological and environmental benefits to the power grid like locally fulfilling the consumer demands, reducing power losses and avoiding transmission and distribution system expansion [3]. Earlier conventional power sources were used for these purposes but in the last few years, renewable energy has taken their place as a feasible future source of electric energy as they can eradicate the problems of increasing consumer demand, fluctuating fossil fuel prices and also solve problems related to environmental issues.
The prevalent forms of DG are wind power, solar photovoltaic, fuel cells and micro-turbines. The DGs that are of electromechanical type could be directly interfaced whereas other DGs require inverter based systems to connect to the power grid. Although there are many advantages of integrating DGs into the grid there are some negative impacts too. The integration of DGs changes the unidirectional power flow of a traditional radial distribution network to a two-way power flow because of the addition of generators in the distribution side [4].
This also affects the traditional relays and protection devices as they generally do not have directional capabilities. The power quality can also be affected as DG devices are connected to the power grid by power electronic devices which might cause distortion of the current and voltage waveforms and induce harmonics [5].1 Photovoltaic Systems Solar energy is the world’s most copiously available form of renewable energy source and so photovoltaic generators are one of the fastest emerging DG technologies, with an estimate annual growth rate of 25-35% in the power market [1]. The reason for this remarkable growth in spite of their high installation cost can be given to the advancements in power electronics field, storage devices, etc.