Rezaei-Zare, AfshinLamichhane, Anusha2022-12-142022-12-142022-09-232022-12-14http://hdl.handle.net/10315/40772An efficient energy transfer from the solar wind into the earth’s space environment causes temporary disturbance to the earth’s magnetosphere. Solar flares and coronal mass ejections (CME) of charged and magnetized particles can disturb the earth’s magnetic field and cause geomagnetic disturbance (GMD). GMDs are of particular concern as they give rise to geomagnetically induced currents (GIC) which have adverse effects on the national power grid and potentially damage transformers on the grid. GIC flowing along transmission lines and through the transformers in power systems can be attributed to problems ranging from overheating of power transformers, harmonic generation, and voltage collapse due to the half-cycle saturation of power transformers. To prevent the power system and its equipment from the adverse effects of GMD, blocking device (BD) can be placed to block the GIC flow in the transformers. However, BD placement is a complex problem, and the cost of BD is very high, so optimization techniques should be employed for BD placement to minimize the number and costs of BDs. Although there has been research on placing blocking devices and their optimal placement, none of them considers the hotspot temperature rise in transformers during GIC. Therefore, Voltage violation and rise in hotspot temperature of transformers are the main concerns in this thesis. This work presents two approaches for the optimal placement of blocking devices on the neutral of high voltage transformers to prevent the power system from the impacts of GIC caused by geomagnetic disturbance. The thesis focuses on the optimization problem based on overheating of power transformers due to GIC and maintaining the hotspot temperature of transformers within the limit, as well as maintaining the voltage profile of the power system. The problem is formulated by first calculating the GIC and increased reactive power demand of each transformer during the GIC flow, performing power flow analysis, checking if system voltage has been violated, calculating the transformers’ windings and metallic hotspot temperatures, checking if the limits are reached, and optimally placing BDs on selective transformers such that the hotspot temperature of transformers is within maximum limits, and the system voltage is recovered above minimum permissible voltage. The optimization is done using the Surrogate optimization and Genetic algorithm of the MATLAB optimization toolbox and made sure that the number of BDs is minimized. A comparative analysis is done from the results obtained from both of the methods. The findings of the thesis highlight the optimization approach for the placement of blocking devices that takes into account the hotspot temperature rise of transformer tie-plates and windings and a realistic criterion that includes the cost of the repair or replacement of transformers based on the hotspot temperature rise of transformers into the optimization approach. The thesis presents the selection criteria for the two optimization solvers, surrogate optimization and genetic algorithm, after researching and reviewing different solvers from the MATLAB optimization toolbox. The total cost of BD placement is reduced where the total load is reduced to some extent based on different levels of geoelectric field ($E$) to maintain the bus voltages above minimum permissible voltage, and the cost can be calculated based on the loss of load, and extra number of BDs can be avoided. The results obtained from surrogate optimization are proved to be effective and efficient as the total number of BDs resulting from surrogate optimization is less than the total number of BDs resulting from genetic algorithm. The nature of genetic algorithm is stochastic in nature, the result not converging to the global minimum, and the time taken by genetic algorithm for the program execution were major drawbacks. In contrast, the characteristics of surrogate algorithm, such as a result, proved to be converging, non-stochastic in nature, unlike genetic algorithm, and comparatively less time consuming than genetic algorithm proving surrogate optimization to be more reliable and efficient.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Electrical engineeringEngineeringOptimal Mitigation of Geomagnetically Induced Current Effects in Power Systems Considering Transformer Thermal LimitsElectronic Thesis or Dissertation2022-12-14Geomagnetic disturbanceGeomagnetically induced currentBlocking deviceTransformer saturationVoltage stabilitySurrogate optimizationGenetic algorithm