Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Impact Assessment, Detection, and Mitigation of False Data Attacks in Electrical Power Systems

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on PMUs can prompt operators to take wrong actions that can eventually jeopardize the power system reliability. Such threats originating from the cyber-space continue to increase as power grids become more dependent on PMU communication networks. Additionally, these threats are becoming increasingly efficient in remaining undetected for longer periods while gaining deep access into the power networks. An attack on the energy sector immediately impacts national defense, emergency services, and all aspects of human life. Cyber attacks against the electric grid may soon become a tactic of high-intensity warfare between nations in near future and lead to social disorder. Within this context, this dissertation investigates the cyber security of PMUs that affects critical decision-making for a reliable operation of the power grid. In particular, this dissertation focuses on false data attacks, a key vulnerability in the PMU architecture, that inject, alter, block, or delete data in devices or in communication network channels. This dissertation addresses three important cyber security aspects - (1) impact assessment, (2) detection, and (3) mitigation of false data attacks. A comprehensive background of false data attack models targeting various steady-state control blocks is first presented. By investigating inter-dependencies between the cyber and the physical layers, this dissertation then identifies possible points of ingress and categorizes risk at different levels of threats. In particular, the likelihood of cyber attacks against the steady-state power system control block causing the worst-case impacts such as cascading failures is investigated. The case study results indicate that false data attacks do not often lead to widespread blackouts, but do result in subsequent line overloads and load shedding. The impacts are magnified when attacks are coordinated with physical failures of generators, transformers, or heavily loaded lines. Further, this dissertation develops a data-driven false data attack detection method that is independent of existing in-built security mechanisms in the state estimator. It is observed that a convolutional neural network classifier can quickly detect and isolate false measurements compared to other deep learning and traditional classifiers. Finally, this dissertation develops a recovery plan that minimizes the consequence of threats when sophisticated attacks remain undetected and have already caused multiple failures. Two new controlled islanding methods are developed that minimize the impact of attacks under the lack of, or partial information on the threats. The results indicate that the system operators can successfully contain the negative impacts of cyber attacks while creating stable and observable islands. Overall, this dissertation presents a comprehensive plan for fast and effective detection and mitigation of false data attacks, improving cyber security preparedness, and enabling continuity of operations

Portuguese transmission grid incidents risk assessment

Documento confidencial. Não pode ser disponibilizado para consultaTese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Data-driven methods for real-time dynamic stability assessment and control

Electric power systems are becoming increasingly complex to operate; a trend driven by an increased demand for electricity, large-scale integration of renewable energy resources, and new system components with power electronic interfaces. In this thesis, a new real-time monitoring and control tool that can support system operators to allow more efficient utilization of the transmission grid has been developed. The developed tool is comprised of four methods aimed to handle the following complementary tasks in power system operation: 1) preventive monitoring, 2) preventive control, 3) emergency monitoring, and 4) emergency control. The methods are based on recent advances in machine learning and deep reinforcement learning to allow real-time assessment and optimized control, while taking into account the dynamic stability of a power system. The developed method for preventive monitoring is proposed to be used to ensure a secure operation by providing real-time estimates of a power system’s dynamic security margins. The method is based on a two-step approach, where neural networks are first used to estimate the security margin, which then is followed by a validation of the estimates using a search algorithm and actual time-domain simulations. The two-step approach is proposed to mitigate any inconsistency issues associated with neural networks under new or unseen operating conditions. The method is shown to reduce the total computation time of the security margin by approximately 70 % for the given test system. Whenever the security margins are below a certain threshold, another developed method, aimed at preventive control, is used to determine the optimal control actions that can restore the security margins to a level above a pre-defined threshold. This method is based on deep reinforcement learning and uses a hybrid control scheme that is capable of simultaneously adjusting both discrete and continuous action variables. The results show that the developed method quickly learns an effective control policy to ensure a sufficient security margin for a range of different system scenarios. In case of severe disturbances and when the preventive methods have not been sufficient to guarantee a stable operation, system operators are required to rely on emergency monitoring and control methods. In the thesis, a method for emergency monitoring is developed that can quickly detect the onset of instability and predict whether the present system state is stable or if it will evolve into an alert or an emergency state in the near future. As time progresses and if new events occur in the system, the network can update the assessment continuously. The results from case studies show good performance and the network can accurately, within only a few seconds after a disturbance, predict voltage instability in almost all test cases. Finally, a method for emergency control is developed, which is based on deep reinforcement learning and is aimed to mitigate long-term voltage instability in real-time. Once trained, the method can continuously assess the system stability and suggest fast and efficient control actions to system operators in case of voltage instability. The control is trained to use load curtailment supplied from demand response and energy storage systems as an efficient and flexible alternative to stabilize the system. The results show that the developed method learns an effective control policy that can stabilize the system quickly while also minimizing the amount of required load curtailment

Towards optimal operation of power systems with high IBR penetration: a stability-constrained optimization approach

Renewable Energy Sources (RES) have been massively integrated into the modern electric power system in the past few decades due to the environmental and sustainability concerns throughout the world. As a result, the power electronic converters are anticipated to acquire a steadily increasing role as they are the key element for the interface between RES and the grid. However, owing to the intermittency of the RES and the distinguished features of the Inverter-Based Resources (IBRs). The main focus of this thesis is to develop optimal system operation strategies to maintain the security and stability of the grid while considering the fast and accurate control of the IBR units. To achieve this, we investigate challenges in different areas. Regarding system frequency and low inertia issues, the main challenges are the incorporation of differential equation-based frequency dynamics into algebraic equation-based optimization problem as well as the optimal utilization of the frequency support from different sources. We first target on the optimal system scheduling on a transmission system level to achieve system operation cost minimization while maintaining the frequency security. In addition, the frequency stability problem in microgrids after unintentional islanding events is also studied. We consider the frequency support from WTs, PV and storage systems as well as noncritical load shedding to ensure the microgrid frequency security after unintentional islanding events. Furthermore, a SCC-constrained Unit Commitment (UC) model is developed, maintaining a minimum SCC level at different locations in the system such that enough reactive current could be supplied during the fault to trigger the protection devices and maintain the post-fault voltages. Moreover, the static voltage stability in systems with high IBR penetration is also investigated considering the interactions among the IBR units and their reactive power support capability within rating limits.Open Acces

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

AI Applications to Power Systems

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered