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|dc.contributor||Department of Electrical Engineering||en_US|
|dc.publisher||Hong Kong Polytechnic University||-|
|dc.rights||All rights reserved||en_US|
|dc.title||Reactive power optimization||en_US|
|dcterms.abstract||The reactive power optimization problem is one of the most important aspects in economic operation of power system. It involves the efficient use of all available reactive power sources and control of voltages at voltage-controlled (PV) buses so as to minimize the total network active power loss with the satisfaction of all operation constraints. Such operation constraints may include the capacity of reactive power sources, limit of bus voltages and transformer tap position. On account of reducing system losses, appreciable MW savings, or in other words, cost savings can be achieved. Because of this goal, the reactive power dispatch problem has been formulated as a complicated constrained optimization problem with partially discrete, partially continuous and non-differentiable nonlinear objective function. More recently, there has been a growing interest in evolution-based algorithm aiming to solve different kind of real world problems. In view of this, Genetic Algorithm (GA) is chosen to solve the optimal reactive power dispatch problem in this project in which no any complicated differentiation of the objective function and relating numerical methods are required. Only evaluating the performance of a scalar fitness function is necessary to evolve the solution. A computer program has been developed that applies both Simple Genetic Algorithm (SGA) and Adaptive Genetic Algorithm (AGA) to solve the reactive power optimization problem of utility systems, preferably with 30 buses size or larger. In SGA, both crossover and mutation rate are fixed throughout the whole evolution search. Whereas the AGA adopts adaptive crossover and mutation rate in which they are varied depending on the fitness values of the solutions in the evolution process such that the convergence performance is improved. In order to evaluate the effectiveness of the program being developed, a 6-bus power system and IEEE 30-bus power system are chosen as the test system. At this juncture, all GA parameters' setting remains the same as in ref. [4, 18]. While examining the simulation results, it is found to be consistent with the result in the papers. Furthermore, simulation of a practical simplified Hong Kong power system is also presented. To have a visual information on the convergence of GA, it is worthwhile to keep a record of the best, average, and worst fitness value for each generation. Both graphs of "Fitness" and "Power injected at slack bus" against "Generation" are also displayed for comparing the performance between SGA and AGA. These comprehensive simulation results not only demonstrate the usefulness of the program for determining the near-global-optimal solution, but also show that GA is a suitable method to solve reactive power optimization problem. In comparison, AGA gives a better solution than SGA. It can really select the best regulation of generator bus voltages, transformer taps and reactive power pattern of available reactive power sources so that transmission power loss of the network is minimized while keeping the bus voltages and reactive power generations in their secure operating limits.||en_US|
|dcterms.extent||vi, 86,  leaves : ill. ; 30 cm. + 1 computer optical disc||en_US|
|dcterms.LCSH||Reactive power (Electrical engineering)||en_US|
|dcterms.LCSH||Hong Kong Polytechnic University -- Dissertations||en_US|
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