DEEP LEARNING BASED POWER SYSTEM STABILITY ASSESSMENT FOR REDUCED WECC SYSTEM

Power system stability is the ability of power system, for a giving initial operating condition, to reach a new operation condition with most of the system variables bounded in normal range after subjecting to a short or long disturbance. Traditional power system stability mainly uses time-domain simulation which is very time consuming and only appropriate for offline assessment. Nowadays, with increasing penetration of inverter based renewable, large-scale distributed energy storage integration and operation uncertainty brought by weather and electricity market, system dynamic and operating condition is more dramatic, and traditional power system stability assessment based on scheduling may not be able to cover all the real-time dispatch scenarios, also online assessment and self-awareness for modern power system becomes more and more important and urgent for power system dynamic security. With the development of fast computation resources and more available online dataset, machine learning techniques have been developed and applied to many areas recently and could potentially applied to power system application. In this dissertation, a deep learning-based power system stability assessment is proposed. Its accurate and fast assessment for power system dynamic security is useful in many places, including day-ahead scheduling, real-time operation, and long-term planning. The simplified Western Electricity Coordinating Council (WECC) 240-bus system with renewable penetration up to 49.2% is used as the study system. The dataset generation, model training and error analysis are demonstrated, and the results show that the proposed deep learning-based method can accurately and fast predict the power system stability. Compared with traditional time simulation method, its near millisecond prediction makes the online assessment and self-awareness possible in future power system application

MACHINE LEARNING-BASED FRAMEWORK FOR REMEDIAL CONTROL ACTION PREDICTION USING WIDE-AREA MEASUREMENTS IN INTERCONNECTED POWER SYSTEMS

Growing demand for power systems, economic, and environmental issues, lead to power systems operating close to their stability margin. Power systems are always exposed to disturbances, leading to either instability or cascading outages and blackouts in the worst cases. Although numerous methods have been proposed since 1920 to prevent disturbances, instability and blackout still exist. Among all the instabilities, the fastest occurring one is rotor angle instability or transient instability. Since this instability happens in a fraction of a second, time must be considered in designing remedial control actions (RCAs). Different types of remedial control actions have been proposed in the past, but due to the lack of time consideration in their design, they are not practical for those cases quickly lead to transient instability. Additionally, pre-planned remedial control actions have been employed to overcome time limitations, but they are not able to cover most of the possible scenarios that may occur in the power system. Based on the literature done for this research, predicting remedial control actions has not been implemented yet. This study presents an innovative idea to predict remedial control action schemes that are able to include time limitations and cover possible scenarios properly. There are numerous challenges to consider in performing such a method, such as remedial control actions selection, implementation, practical aspects, and wide-area measurement systems (WAMS). In this study, the different parts of the framework are discussed in detail and implemented. Based on the above discussion, first, an optimized artificial neural network (ANN) is implemented to make a comprehensive framework that can predict a proper remedial control action to prevent cascading outages and blackouts. The different steps of the framework are predicted using this comprehensive algorithm. A micro model strategy has been employed, which builds a model for each line separately. This micro model decreases prediction complexity and increases the prediction accuracies of the modules. The common RCAs, including controlled islanding, load shedding, and generator rejection, are implemented in this research project. To address controlled islanding prediction, in the first step, using voltage data, the stability status was predicted. In the second step, a new method to identify coherent groups of generators was developed, and based on that method; the coherency patterns have been predicted. In the third step, a combination of islanding and load shedding is selected as a control action, and a mixed-integer linear programming (MILP) method is designed to compute islands, the amount of load shedding, and load buses. Since the load shedding prediction has two aspects and it is a very challenging problem, a new concept called the specific set of loads (SSLs) had been proposed to simplify this issue. Finally, the islanding and load shedding patterns are predicted. The framework was tested via the IEEE 39 bus system and 74-bus Nordic power system, and the results show the effectiveness of the framework. To implement generator rejection prediction, the bus voltage data are used to predict the stability status. Next, the critical generators are predicted. Then, using the equal area criterion, the amount of generator rejection for each critical generator is calculated, and the patterns are extracted. Finally, the number of generator rejections is predicted using the dataset and designed ANN. The performance of the generator rejection prediction framework is tested via the IEEE 9-bus system and 74-Bus Nordic power network

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

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

System Simulation and Implementation of SIPS in Taiwan

This study describes the system simulation and implementation of the system integrity protection schemes (SIPS) in an independent, intensive, and island-type power system. It also elucidates a smart grid plan to provide grid security in this power grid. The proposed SIPS can prevent blackouts that could otherwise result from the transient instability of N-3 contingencies and has been fully implemented and operated. The entire SIPS installation comprises two stages. The first-stage SIPS takes generator tripping system simulation and the second-stage SIPS involves generator tripping, load rejection, and bus-tie switching countermeasures. The proposed SIPS can prevent isolated power system blackout from extreme contingencies system and provide a valuable system simulation experience for similar independent power grids

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