The rapidly expanding power system grid infrastructure and the need to reduce the occurrence of major blackouts and prevention or hardening of systems against cyber-attacks, have led to increased interest in the improved resilience of the electrical grid. Distributed and decentralized control have been widely applied to computer science research. However, for power system applications, the real-time application of decentralized and distributed control algorithms introduce several challenges. In this dissertation, new algorithms and methods for decentralized control, protection and energy management of Wide Area Monitoring, Protection and Control (WAMPAC) and the Active Distribution Network (ADN) are developed to improve the resiliency of the power system. To evaluate the findings of this dissertation, a laboratory-scale integrated Wide WAMPAC and ADN control platform was designed and implemented. The developed platform consists of phasor measurement units (PMU), intelligent electronic devices (IED) and programmable logic controllers (PLC). On top of the designed hardware control platform, a multi-agent cyber-physical interoperability viii framework was developed for real-time verification of the developed decentralized and distributed algorithms using local wireless and Internet-based cloud communication. A novel real-time multiagent system interoperability testbed was developed to enable utility independent private microgrids standardized interoperability framework and define behavioral models for expandability and plug-and-play operation. The state-of-theart power system multiagent framework is improved by providing specific attributes and a deliberative behavior modeling capability. The proposed multi-agent framework is validated in a laboratory based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results are demonstrated for both decentralized and distributed control approaches. A new adaptive real-time protection and remedial action scheme (RAS) method using agent-based distributed communication was developed for autonomous hybrid AC/DC microgrids to increase resiliency and continuous operability after fault conditions. Unlike the conventional consecutive time delay-based overcurrent protection schemes, the developed technique defines a selectivity mechanism considering the RAS of the microgrid after fault instant based on feeder characteristics and the location of the IEDs. The experimental results showed a significant improvement in terms of resiliency of microgrids through protection using agent-based distributed communication.

In this research, a focus is on the development of simplified models to represent the behavior of electric machinery within the time-domain models of power systems. Toward this goal, a generator model is considered in which the states include the machine’s active and reactive power. In the case of the induction machine, rotor slip is utilized as a state and the steady-state equivalent circuit of the machine is used to calculate active and reactive power. The power network model is then configured to accept the generator and induction machine active and reactive power as inputs and provide machine terminal voltage amplitude and angle as outputs. The potential offered by these models is that the number of dynamic states is greatly reduced compared to traditional machine models. This can lead to increased simulation speed, which has potential benefits in model-based control. A potential disadvantage is that the relationship between the reactive power and terminal voltage requires the solution of nonlinear equations, which can lead to challenges when attempting to predict system dynamics in real-time optimal control. In addition, the accuracy of the generator model is greatly reduced with variations in rotor speed. Evaluation of the models is performed by comparing their predictions to those of traditional machine models in which stator dynamics are included and neglected.

Biomass is one of the renewable energy sources being used widely for power generation. This research work includes developing a comprehensive model for a biomass based power generation system as well as analyzing the technical, economical, and environmental impacts. The research objectives include modeling of the system, stability studies, and sensitivity analysis using MATLAB/Simulink. A mathematical model for the gas turbine has been developed and successfully interconnected with the distribution network. Transient stability of the power system has been carried out for four bus and six bus test case systems. Maximum rotor speed deviation, oscillation duration, rotor angle, and mechanical power have been taken as the stability indicators to analyze the system characteristics. Additionally, the sensitivity of the system to the changes of gas turbine parameters has been investigated under balanced and unbalanced fault scenarios. The economical and environmental impacts of the biomass have been analyzed using HOMER software developed by the National Renewable Energy Laboratory (NREL). The net present cost of the four biomass resources namely agricultural resources, forest residues, animal waste, and energy crops were obtained and the comparison of the costs of the biomass fuels as well as the diesel have been carried out. To investigate the environmental impact, carbon emissions of the different biomass fuels have been explored using HOMER.

Power system generators are interconnected in a distributed network to allow sharing of power. If one of the generators cannot meet the power demand, spare power is diverted from neighbouring generators. However, this approach also allows for propagation of electric disturbances. An oscillation arising from a disturbance at a given generator site will affect the normal operation of neighbouring generators and might cause them to fail. Hours of production time will be lost in the time it takes to restart the power plant. If the disturbance is detected early, appropriate control measures can be applied to ensure system stability. The aim of this study is to improve existing algorithms that estimate the oscillation parameters from acquired generator data to detect potentially dangerous power system disturbances. When disturbances occur in power systems (due to load changes or faults), damped oscillations (or &quotmodes") are created. Modes which are heavily damped die out quickly and pose no threat to system stability. Lightly damped modes, by contrast, die out slowly and are more problematic. Of more concern still are &quotnegatively damped" modes which grow exponentially with time and can ultimately cause the power system to fail. Widespread blackouts are then possible. To avert power system failures it is necessary to monitor the damping of the oscillating modes. This thesis proposes a number of damping estimation algorithms for this task. If the damping is found to be very small or even negative, then additional damping needs to be introduced via appropriate control strategies. This thesis presents a number of new algorithms for estimating the damping of modal oscillations in power systems. The first of these algorithms uses multiple orthogonal sliding windows along with least-squares techniques to estimate the modal damping. This algorithm produces results which are superior to those of earlier sliding window algorithms (that use only one pair of sliding windows to estimate the damping). The second algorithm uses a different modification of the standard sliding window damping estimation algorithm - the algorithm exploits the fact that the Signal to Noise Ratio (SNR) within the Fourier transform of practical power system signals is typically constant across a wide frequency range. Accordingly, damping estimates are obtained at a range of frequencies and then averaged. The third algorithm applied to power system analysis is based on optimal estimation theory. It is computationally efficient and gives optimal accuracy, at least for modes which are well separated in frequency.

The role of electric power systems has grown steadily in both scope and importance over time making electricity increasingly recognized as a key to social and economic progress in many developing countries. In a sense, reliable power systems constitute the foundation of all prospering societies. The constant expansion in electric power systems, along with increased energy demand, requires that power systems become more and more complex. Such complexity results in much uncertainty which demands comprehensive reliability and security assessment to ensure reliable energy supply. Power industries in many countries are facing these challenges and are trying to increase the computational capability to handle the ever-increasing data and analytical needs of operations and planning. Moreover, the deregulated electricity markets have been in operation in a number of countries since the 1990s. During the deregulation process, vertically integrated power utilities have been reformed into competitive markets, with initial goals to improve market efficiency, minimize production costs and reduce the electricity price. Given the benefits that have been achieved by deregulation, several new challenges are also observed in the market. Due to fundamental changes to the electric power industry, traditional management and analysis methods cannot deal with these new challenges. Deterministic reliability assessment criteria still exists but it doesn’t satisfy the probabilistic nature of power systems. In the deterministic approach the worst case analysis results in excess operating costs. On the other hand, probabilistic methods are now widely accepted. The analytical method uses a mathematical formula for reliability evaluation and generates results more quickly but it needs accurate and a lot of assumptions and is not suitable for large and complex systems. Simulation based techniques take care of much uncertainty and simulates the random behavior of the system. However, it requires much computing power, memory and other computing resources. Power engineers have to run thousands of times domain simulations to determine the stability for a set of credible disturbances before dispatching. For example, security analysis is associated with the steady state and dynamic response of the power system to various disturbances. It is highly desirable to have real time security assessment, especially in the market environment. Therefore, novel analysis methods are required for power systems reliability and security in the deregulated environment, which can provide comprehensive results, and high performance computing (HPC) power in order to carry out such analysis within a limited time. Further, with the deregulation in power industry, operation control has been distributed among many organizations. The power grid is a complex network involving a range of energy resources including nuclear, fossil and renewable energy resources with many operational levels and layers including control centers, power plants and transmission and distribution systems. The energy resources are managed by different organizations in the electricity market and all these participants (including producers, consumers and operators) can affect the operational state of the power grid at any time. Moreover, adequacy analysis is an important task in power system planning and can be regarded as collaborative tasks, which demands the collaboration among the electricity market participants for reliable energy supply. Grid computing is gaining attention from power engineering experts as an ideal solution to the computational difficulties being faced by the power industry. Grid computing infrastructure involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid computing technology offers potentially feasible support to the design and development of grid computing based infrastructure for power system reliability and security analysis. It can help in building infrastructure, which can provide a high performance computing and collaborative environment, and offer an optimal solution between cast and efficiency. While power system analysis is a vast topic, only a limited amount of research has been initiated in several places to investigate the applications of grid computing in power systems. This thesis will investigate probabilistic based reliability and security analysis of complex power systems in order to develop new techniques for providing comprehensive result with enormous efficiency. A review of existing techniques was conducted to determine the computational needs in the area of power systems. The main objective of this research is to propose and develop a general framework of computing grid and special grid services for probabilistic power system reliability and security assessment in the electricity market. As a result of this research, grid computing based techniques are proposed for power systems probabilistic load flow analysis, probabilistic small signal analysis, probabilistic transient stability analysis, and probabilistic contingencies analysis. Moreover, a grid computing based system is designed and developed for the monitoring and control of distributed generation systems. As a part of this research, a detailed review is presented about the possible applications of this technology in other aspects of power systems. It is proposed that these grid based techniques will provide comprehensive results that will lead to great efficiency, and ultimately enhance the existing computing capabilities of power companies in a cost-effective manner. At a part of this research, a small scale computing grid is developed which will consist of grid services for probabilistic reliability and security assessment techniques. A significant outcome of this research will be the improved performance, accuracy, and security of data sharing and collaboration. More importantly grid based computing will improve the capability of power system analysis in a deregulated environment where complex and large amounts of data would otherwise be impossible to analyze without huge investments in computing facilities.

The role of electric power systems has grown steadily in both scope and importance over time making electricity increasingly recognized as a key to social and economic progress in many developing countries. In a sense, reliable power systems constitute the foundation of all prospering societies. The constant expansion in electric power systems, along with increased energy demand, requires that power systems become more and more complex. Such complexity results in much uncertainty which demands comprehensive reliability and security assessment to ensure reliable energy supply. Power industries in many countries are facing these challenges and are trying to increase the computational capability to handle the ever-increasing data and analytical needs of operations and planning. Moreover, the deregulated electricity markets have been in operation in a number of countries since the 1990s. During the deregulation process, vertically integrated power utilities have been reformed into competitive markets, with initial goals to improve market efficiency, minimize production costs and reduce the electricity price. Given the benefits that have been achieved by deregulation, several new challenges are also observed in the market. Due to fundamental changes to the electric power industry, traditional management and analysis methods cannot deal with these new challenges. Deterministic reliability assessment criteria still exists but it doesn’t satisfy the probabilistic nature of power systems. In the deterministic approach the worst case analysis results in excess operating costs. On the other hand, probabilistic methods are now widely accepted. The analytical method uses a mathematical formula for reliability evaluation and generates results more quickly but it needs accurate and a lot of assumptions and is not suitable for large and complex systems. Simulation based techniques take care of much uncertainty and simulates the random behavior of the system. However, it requires much computing power, memory and other computing resources. Power engineers have to run thousands of times domain simulations to determine the stability for a set of credible disturbances before dispatching. For example, security analysis is associated with the steady state and dynamic response of the power system to various disturbances. It is highly desirable to have real time security assessment, especially in the market environment. Therefore, novel analysis methods are required for power systems reliability and security in the deregulated environment, which can provide comprehensive results, and high performance computing (HPC) power in order to carry out such analysis within a limited time. Further, with the deregulation in power industry, operation control has been distributed among many organizations. The power grid is a complex network involving a range of energy resources including nuclear, fossil and renewable energy resources with many operational levels and layers including control centers, power plants and transmission and distribution systems. The energy resources are managed by different organizations in the electricity market and all these participants (including producers, consumers and operators) can affect the operational state of the power grid at any time. Moreover, adequacy analysis is an important task in power system planning and can be regarded as collaborative tasks, which demands the collaboration among the electricity market participants for reliable energy supply. Grid computing is gaining attention from power engineering experts as an ideal solution to the computational difficulties being faced by the power industry. Grid computing infrastructure involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid computing technology offers potentially feasible support to the design and development of grid computing based infrastructure for power system reliability and security analysis. It can help in building infrastructure, which can provide a high performance computing and collaborative environment, and offer an optimal solution between cast and efficiency. While power system analysis is a vast topic, only a limited amount of research has been initiated in several places to investigate the applications of grid computing in power systems. This thesis will investigate probabilistic based reliability and security analysis of complex power systems in order to develop new techniques for providing comprehensive result with enormous efficiency. A review of existing techniques was conducted to determine the computational needs in the area of power systems. The main objective of this research is to propose and develop a general framework of computing grid and special grid services for probabilistic power system reliability and security assessment in the electricity market. As a result of this research, grid computing based techniques are proposed for power systems probabilistic load flow analysis, probabilistic small signal analysis, probabilistic transient stability analysis, and probabilistic contingencies analysis. Moreover, a grid computing based system is designed and developed for the monitoring and control of distributed generation systems. As a part of this research, a detailed review is presented about the possible applications of this technology in other aspects of power systems. It is proposed that these grid based techniques will provide comprehensive results that will lead to great efficiency, and ultimately enhance the existing computing capabilities of power companies in a cost-effective manner. At a part of this research, a small scale computing grid is developed which will consist of grid services for probabilistic reliability and security assessment techniques. A significant outcome of this research will be the improved performance, accuracy, and security of data sharing and collaboration. More importantly grid based computing will improve the capability of power system analysis in a deregulated environment where complex and large amounts of data would otherwise be impossible to analyze without huge investments in computing facilities.

The role of electric power systems has grown steadily in both scope and importance over time making electricity increasingly recognized as a key to social and economic progress in many developing countries. In a sense, reliable power systems constitute the foundation of all prospering societies. The constant expansion in electric power systems, along with increased energy demand, requires that power systems become more and more complex. Such complexity results in much uncertainty which demands comprehensive reliability and security assessment to ensure reliable energy supply. Power industries in many countries are facing these challenges and are trying to increase the computational capability to handle the ever-increasing data and analytical needs of operations and planning. Moreover, the deregulated electricity markets have been in operation in a number of countries since the 1990s. During the deregulation process, vertically integrated power utilities have been reformed into competitive markets, with initial goals to improve market efficiency, minimize production costs and reduce the electricity price. Given the benefits that have been achieved by deregulation, several new challenges are also observed in the market. Due to fundamental changes to the electric power industry, traditional management and analysis methods cannot deal with these new challenges. Deterministic reliability assessment criteria still exists but it doesn’t satisfy the probabilistic nature of power systems. In the deterministic approach the worst case analysis results in excess operating costs. On the other hand, probabilistic methods are now widely accepted. The analytical method uses a mathematical formula for reliability evaluation and generates results more quickly but it needs accurate and a lot of assumptions and is not suitable for large and complex systems. Simulation based techniques take care of much uncertainty and simulates the random behavior of the system. However, it requires much computing power, memory and other computing resources. Power engineers have to run thousands of times domain simulations to determine the stability for a set of credible disturbances before dispatching. For example, security analysis is associated with the steady state and dynamic response of the power system to various disturbances. It is highly desirable to have real time security assessment, especially in the market environment. Therefore, novel analysis methods are required for power systems reliability and security in the deregulated environment, which can provide comprehensive results, and high performance computing (HPC) power in order to carry out such analysis within a limited time. Further, with the deregulation in power industry, operation control has been distributed among many organizations. The power grid is a complex network involving a range of energy resources including nuclear, fossil and renewable energy resources with many operational levels and layers including control centers, power plants and transmission and distribution systems. The energy resources are managed by different organizations in the electricity market and all these participants (including producers, consumers and operators) can affect the operational state of the power grid at any time. Moreover, adequacy analysis is an important task in power system planning and can be regarded as collaborative tasks, which demands the collaboration among the electricity market participants for reliable energy supply. Grid computing is gaining attention from power engineering experts as an ideal solution to the computational difficulties being faced by the power industry. Grid computing infrastructure involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid computing technology offers potentially feasible support to the design and development of grid computing based infrastructure for power system reliability and security analysis. It can help in building infrastructure, which can provide a high performance computing and collaborative environment, and offer an optimal solution between cast and efficiency. While power system analysis is a vast topic, only a limited amount of research has been initiated in several places to investigate the applications of grid computing in power systems. This thesis will investigate probabilistic based reliability and security analysis of complex power systems in order to develop new techniques for providing comprehensive result with enormous efficiency. A review of existing techniques was conducted to determine the computational needs in the area of power systems. The main objective of this research is to propose and develop a general framework of computing grid and special grid services for probabilistic power system reliability and security assessment in the electricity market. As a result of this research, grid computing based techniques are proposed for power systems probabilistic load flow analysis, probabilistic small signal analysis, probabilistic transient stability analysis, and probabilistic contingencies analysis. Moreover, a grid computing based system is designed and developed for the monitoring and control of distributed generation systems. As a part of this research, a detailed review is presented about the possible applications of this technology in other aspects of power systems. It is proposed that these grid based techniques will provide comprehensive results that will lead to great efficiency, and ultimately enhance the existing computing capabilities of power companies in a cost-effective manner. At a part of this research, a small scale computing grid is developed which will consist of grid services for probabilistic reliability and security assessment techniques. A significant outcome of this research will be the improved performance, accuracy, and security of data sharing and collaboration. More importantly grid based computing will improve the capability of power system analysis in a deregulated environment where complex and large amounts of data would otherwise be impossible to analyze without huge investments in computing facilities.

The role of electric power systems has grown steadily in both scope and importance over time making electricity increasingly recognized as a key to social and economic progress in many developing countries. In a sense, reliable power systems constitute the foundation of all prospering societies. The constant expansion in electric power systems, along with increased energy demand, requires that power systems become more and more complex. Such complexity results in much uncertainty which demands comprehensive reliability and security assessment to ensure reliable energy supply. Power industries in many countries are facing these challenges and are trying to increase the computational capability to handle the ever-increasing data and analytical needs of operations and planning. Moreover, the deregulated electricity markets have been in operation in a number of countries since the 1990s. During the deregulation process, vertically integrated power utilities have been reformed into competitive markets, with initial goals to improve market efficiency, minimize production costs and reduce the electricity price. Given the benefits that have been achieved by deregulation, several new challenges are also observed in the market. Due to fundamental changes to the electric power industry, traditional management and analysis methods cannot deal with these new challenges. Deterministic reliability assessment criteria still exists but it doesn’t satisfy the probabilistic nature of power systems. In the deterministic approach the worst case analysis results in excess operating costs. On the other hand, probabilistic methods are now widely accepted. The analytical method uses a mathematical formula for reliability evaluation and generates results more quickly but it needs accurate and a lot of assumptions and is not suitable for large and complex systems. Simulation based techniques take care of much uncertainty and simulates the random behavior of the system. However, it requires much computing power, memory and other computing resources. Power engineers have to run thousands of times domain simulations to determine the stability for a set of credible disturbances before dispatching. For example, security analysis is associated with the steady state and dynamic response of the power system to various disturbances. It is highly desirable to have real time security assessment, especially in the market environment. Therefore, novel analysis methods are required for power systems reliability and security in the deregulated environment, which can provide comprehensive results, and high performance computing (HPC) power in order to carry out such analysis within a limited time. Further, with the deregulation in power industry, operation control has been distributed among many organizations. The power grid is a complex network involving a range of energy resources including nuclear, fossil and renewable energy resources with many operational levels and layers including control centers, power plants and transmission and distribution systems. The energy resources are managed by different organizations in the electricity market and all these participants (including producers, consumers and operators) can affect the operational state of the power grid at any time. Moreover, adequacy analysis is an important task in power system planning and can be regarded as collaborative tasks, which demands the collaboration among the electricity market participants for reliable energy supply. Grid computing is gaining attention from power engineering experts as an ideal solution to the computational difficulties being faced by the power industry. Grid computing infrastructure involves the integrated and collaborative use of computers, networks, databases and scientific instruments owned and managed by multiple organizations. Grid computing technology offers potentially feasible support to the design and development of grid computing based infrastructure for power system reliability and security analysis. It can help in building infrastructure, which can provide a high performance computing and collaborative environment, and offer an optimal solution between cast and efficiency. While power system analysis is a vast topic, only a limited amount of research has been initiated in several places to investigate the applications of grid computing in power systems. This thesis will investigate probabilistic based reliability and security analysis of complex power systems in order to develop new techniques for providing comprehensive result with enormous efficiency. A review of existing techniques was conducted to determine the computational needs in the area of power systems. The main objective of this research is to propose and develop a general framework of computing grid and special grid services for probabilistic power system reliability and security assessment in the electricity market. As a result of this research, grid computing based techniques are proposed for power systems probabilistic load flow analysis, probabilistic small signal analysis, probabilistic transient stability analysis, and probabilistic contingencies analysis. Moreover, a grid computing based system is designed and developed for the monitoring and control of distributed generation systems. As a part of this research, a detailed review is presented about the possible applications of this technology in other aspects of power systems. It is proposed that these grid based techniques will provide comprehensive results that will lead to great efficiency, and ultimately enhance the existing computing capabilities of power companies in a cost-effective manner. At a part of this research, a small scale computing grid is developed which will consist of grid services for probabilistic reliability and security assessment techniques. A significant outcome of this research will be the improved performance, accuracy, and security of data sharing and collaboration. More importantly grid based computing will improve the capability of power system analysis in a deregulated environment where complex and large amounts of data would otherwise be impossible to analyze without huge investments in computing facilities.

Electrical Power System is one of the most complex real time operating systems. It is probably one of the best examples of a large interconnected nonlinear system of varying nature. The system needs to be operated and controlled with component or system problems, often with combinatorial complexity. In addition, time scales of operation and control can vary from milliseconds to minutes to hours. It is difficult to maintain such a system at constant operating condition due to both small and large disturbances such as sudden change in loads, change in network configuration, fluctuations in turbine output, and various types of faults etc. The system is therefore affected by a variety of instability problems. Among all these instability problems one of the important modes of instability is related to dynamic instability or more precisely the small perturbation oscillatory instability. Oscillations of small magnitude and low frequency (in the range of 0.1Hz to 2.5Hz) could persist for long periods, limiting the power transfer capability of the transmission lines. Power System Stabilizers (PSS) were developed as auxiliary controllers on the excitation system to improve the system damping performance by modulating the generator excitation voltage. However, the synthesis of an effective PSS for all operating conditions still remains a difficult and challenging task. The design and tuning of PSS for robust operation is a laborious process. The existing PSS design techniques require considerable expertise, the complete system information and extensive eigenvalue calculations which increases the computational burden as the system size increases. Conventional automatic voltage regulator (AVR) and PSS designs are based on linearized models of power systems which fail to stabilize the system over a wide range of operating conditions. In the last decade or so, a variety of nonlinear control techniques have become available. In this thesis, an attempt is made to explore the suitability of some of these design techniques for designing excitation controllers to enhance small perturbation stability of power systems over a wide range of operating and system conditions. This thesis first proposes a method of designing power system stabilizers based on local measurements alone, in multi-machine systems. Next, a method has been developed to analyze and quantify the small signal performance benefits of replacing the existing AVR+PSS structure with nonlinear voltage regulators. A number of new nonlinear controller designs have been proposed subsequently. These include, (a) a new decentralized nonlinear voltage regulator for multi machine power systems with a single tunable parameter that can achieve effective trade of between both the voltage regulation and small signal objectives, (b) a decentralized Interconnection and Damping Assignment Passivity Based Controller in addition to a proportional controller that can achieve all the requirements of an excitation system and (c) a Nonlinear Quadratic Regulator PSS using Single Network Adaptive Critic architecture in the frame work of approximate dynamic programming. Performance of all the proposed controllers has been analyzed using a number of multi machine test systems over a range of operating conditions.

Indiana University-Purdue University Indianapolis (IUPUI) / In a wide-area power system, detecting dynamic events is critical to maintaining system stability. Large events, such as the loss of a generator or fault on a transmission line, can compromise the stability of the system by causing the generator rotor angles to diverge and lose synchronism with the rest of the system. If these events can be detected as they happen, controls can be applied to the system to prevent it from losing synchronous stability. In order to detect these events, pattern recognition tools can be applied to system measurements. In this thesis, the pattern recognition tool decision trees (DTs) were used for event detection. A single DT produced rules distinguishing between and the event and no event cases by learning on a training set of simulations of a power system model. The rules were then applied to test cases to determine the accuracy of the event detection. To use a DT to detect events, the variables used to produce the rules must be chosen. These variables can be direct system measurements, such as the phase angle of bus voltages, or indices created by a combination of system measurements. One index used in this thesis was the integral square bus angle (ISBA) index, which provided a measure of the overall activity of the bus angles in the system. Other indices used were the variance and rate of change of the ISBA. Fitting a polynomial curve to a sliding window of these indices and then taking the difference between the polynomial and the actual index was found to produce a new index that was non-zero during the event and zero all other times for most simulations. After the index to detect events was chosen to be the error between the curve and the ISBA indices, a set of power system cases were created to be used as the training data set for the DT. All of these cases contained one event, either a small or large power injection at a load bus in the system model. The DT was then trained to detect the large power injection but not the small one. This was done so that the rules produced would detect large events on the system that could potentially cause the system to lose synchronous stability but ignore small events that have no effect on the overall system. This DT was then combined with a second DT that predicted instability such that the second DT made the decision whether or not to apply controls only for a short time after the end of every event, when controls would be most effective in stabilizing the system.