This thesis presents a new way of combining non-linear optimization algorithms and electromagnetic transient (EMT) simulation. In this new combination, a non-linear optimization algorithm utilizes a real-time EMT simulation environment as objective function evaluator. However, as more applications of the off-line EMT simulation software implementation were made, the combination between non-linear optimization algorithms and off-line EMT simulation software revealed new need, which this research work attempts to address. The first need arose from the speed of simulation of the off-line EMT simulation software. With a certain breed of non-linear optimization algorithms, heuristics bases algorithms in particular, a large number of objective function evaluations are required before the termination or convergence criterion in the selected algorithms is satisfied. Sometimes, the number of evaluations as well as the complexity of the simulation case where the objective function is based upon translates into a very long simulation time, which goes beyond the boundary of given resources. This research work attempts to address this simulation timing issue by capitalizing on the real timeness of the simulation environment as well as utilizing the multiple instances of the simulation environment in parallel. The second need arose from the modeling requirement of the off-line EMT simulation software. In order to properly design the necessary objective function evaluator, which is largely a simulation case, a large amount of information needs to be embedded into the case. Under certain circumstances, the necessary information would not be available. Therefore, the simulation case needs to include approximations which may cause compromise in the end result. This limitation becomes more obvious when a real world device such as a commercial controller becomes involved. On the contrary, this limitation can be addressed by the real-time simulation environment because this environment can be directly interfaced with the real world device. In this way, the need for detailed information regarding the device is eliminated. This elimination would enlarge the application of the combination, between the non-linear optimization algorithm and EMT type simulation environment. The effectiveness of the proposed approach is demonstrated by various examples throughout this thesis.

This thesis presents a new way of combining non-linear optimization algorithms and electromagnetic transient (EMT) simulation. In this new combination, a non-linear optimization algorithm utilizes a real-time EMT simulation environment as objective function evaluator. However, as more applications of the off-line EMT simulation software implementation were made, the combination between non-linear optimization algorithms and off-line EMT simulation software revealed new need, which this research work attempts to address. The first need arose from the speed of simulation of the off-line EMT simulation software. With a certain breed of non-linear optimization algorithms, heuristics bases algorithms in particular, a large number of objective function evaluations are required before the termination or convergence criterion in the selected algorithms is satisfied. Sometimes, the number of evaluations as well as the complexity of the simulation case where the objective function is based upon translates into a very long simulation time, which goes beyond the boundary of given resources. This research work attempts to address this simulation timing issue by capitalizing on the real timeness of the simulation environment as well as utilizing the multiple instances of the simulation environment in parallel. The second need arose from the modeling requirement of the off-line EMT simulation software. In order to properly design the necessary objective function evaluator, which is largely a simulation case, a large amount of information needs to be embedded into the case. Under certain circumstances, the necessary information would not be available. Therefore, the simulation case needs to include approximations which may cause compromise in the end result. This limitation becomes more obvious when a real world device such as a commercial controller becomes involved. On the contrary, this limitation can be addressed by the real-time simulation environment because this environment can be directly interfaced with the real world device. In this way, the need for detailed information regarding the device is eliminated. This elimination would enlarge the application of the combination, between the non-linear optimization algorithm and EMT type simulation environment. The effectiveness of the proposed approach is demonstrated by various examples throughout this thesis.

The thesis introduces an approach for obtaining higher level decision support information using electromagnetic transient (EMT) simulation programs. In this approach, a suite of higher level driver programs (decision support tools) control the simulator to gain important information about the system being simulated. These tools conduct a sequence of simulation runs, in each of which the study parameters are carefully selected based on the observations of the earlier runs in the sequence. In this research two such tools have been developed in conjunction with the PSCAD/EMTDC electromagnetic transient simulation program. The first tool is an improved optimization algorithm, which is used for automatic optimization of the system parameters to achieve a desired performance. This algorithm improves the capabilities of the previously reported method of optimization-enabled electromagnetic transient simulation by using an enhanced gradient-based optimization algorithm with constraint handling techniques. In addition to allow handling of design problems with more than one objective the thesis proposes to augment the optimization tool with the technique of Pareto optimality. A sequence of optimization runs are conducted to obtain the Pareto frontier, which quantifies the tradeoffs between the design objectives. The frontier can be used by the designer for decision making process. The second tool developed in this research helps the designer to study the effects of uncertainties in a design. By using a similar multiple-run approach this sensitivity analysis tool provides surrogate models of the system, which are simple mathematical functions that represent different aspects of the system performance. These models allow the designer to analyze the effects of uncertainties on system performance without having to conduct any further time-consuming EMT simulations. In this research it has been also proposed to add probabilistic analysis capabilities to the developed sensitivity analysis tool. Since probabilistic analysis of a system using conventional techniques (e.g. Monte-Carlo simulations) normally requires a large number of EMT simulation runs, using surrogate models instead of the actual simulation runs yields significant savings in terms of shortened simulation time. A number of examples have been used throughout the thesis to demonstrate the application and usefulness of the proposed tools.

Natural lightning strikes induce impulsive overvoltages on transmission lines and its terminal equipment. These overvoltages may cause failures in insulation mechanisms of electrical devices in the power system. It is important to test the insulation strength of a device against these impulsive overvoltages. Usually, Marx generators are used to generate impulse waveforms for testing purposes. A novel approach is proposed to obtain resistor settings of a Marx generator for impulse testing of power transformers. This approach enables us to overcome most of the major challenges in the commonly used trial-and-error method, including excessive time consumption and potential damage to the transformer. The proposed approach uses the frequency response of the transformer to synthesize a circuit model. Then, a genetic algorithm based optimization-enabled electromagnetic transient simulation approach is used to obtain the resistor settings. The proposed approach is validated by a real impulse test conducted on a three phase power transformer.

The thesis introduces an approach for obtaining higher level decision support information using electromagnetic transient (EMT) simulation programs. In this approach, a suite of higher level driver programs (decision support tools) control the simulator to gain important information about the system being simulated. These tools conduct a sequence of simulation runs, in each of which the study parameters are carefully selected based on the observations of the earlier runs in the sequence. In this research two such tools have been developed in conjunction with the PSCAD/EMTDC electromagnetic transient simulation program. The first tool is an improved optimization algorithm, which is used for automatic optimization of the system parameters to achieve a desired performance. This algorithm improves the capabilities of the previously reported method of optimization-enabled electromagnetic transient simulation by using an enhanced gradient-based optimization algorithm with constraint handling techniques. In addition to allow handling of design problems with more than one objective the thesis proposes to augment the optimization tool with the technique of Pareto optimality. A sequence of optimization runs are conducted to obtain the Pareto frontier, which quantifies the tradeoffs between the design objectives. The frontier can be used by the designer for decision making process. The second tool developed in this research helps the designer to study the effects of uncertainties in a design. By using a similar multiple-run approach this sensitivity analysis tool provides surrogate models of the system, which are simple mathematical functions that represent different aspects of the system performance. These models allow the designer to analyze the effects of uncertainties on system performance without having to conduct any further time-consuming EMT simulations. In this research it has been also proposed to add probabilistic analysis capabilities to the developed sensitivity analysis tool. Since probabilistic analysis of a system using conventional techniques (e.g. Monte-Carlo simulations) normally requires a large number of EMT simulation runs, using surrogate models instead of the actual simulation runs yields significant savings in terms of shortened simulation time. A number of examples have been used throughout the thesis to demonstrate the application and usefulness of the proposed tools.