Abstract
Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.
Highlights
The growing demand in power system network due to increasing load has caused voltage decay, leading to current increase and system loss
To curb the voltage problems, several power compensation schemes can be implemented. This requires the use of optimisation processes; among the important optimisation techniques are evolutionary programming (EP), genetic algorithm (GA) and differential evolution (DE)
The loss profile is lower when embedded differential evolutionary programming (EDEP) was implemented to the system through the transformer tap changer optimisation exercise
Summary
The growing demand in power system network due to increasing load has caused voltage decay, leading to current increase and system loss. To curb the voltage problems, several power compensation schemes can be implemented This requires the use of optimisation processes; among the important optimisation techniques are evolutionary programming (EP), genetic algorithm (GA) and differential evolution (DE). OPF problems can be assessed by various techniques, such as non-linear programming, quadratic programming, mixed-integer programming and interior-point method. This types of method are categories as traditional methods. Genetic algorithm (GA) is an optimisation method for solving both constrained and unconstrained problems that are based on the natural selection of the population [16, 17]. Implementation on a reliability test system produced promising results; highlighting its superiority over the traditional EP
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