The use of a genetic algorithm and fuzzy logic to estimate load allocation for adaptive voltage control in an electrical power distribution network

Voltage regulation is a critical function in the supply and distribution of electricity. End use equipment operates efficiently within a narrow band of voltage limits. This thesis examines one approach for voltage control which can adapt to changing load conditions in a medium voltage distribution network. The voltage control seeks to obtain as narrow a regulation range as possible centered at the rated nominal voltage.; Knowledge of the voltage at each customer location is necessary for the voltage control optimization. An estimate of these voltages is possible if a load distribution estimate can be made for the network on a real time basis. This thesis creates a genetic algorithm with fuzzy logic to estimate the load distribution. An unbalanced load flow solution is derived to provide the fast convergence needed by the genetic algorithm as part of its fitness evaluation. Results of a test case with the proposed algorithm as part of its fitness evaluation. Results of a test case with the proposed approach are compared to a benchmark practice. The benchmark practice maintains a constant voltage at the substation bus which is designed to meet industry standard voltage limits at the customer premises.