Optimization of Capacitor Placement Using Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) on the Kenduruhan Feeder to Reduce Power Loss and Improve Voltage Profile

In practice, one of the main issues in electrical power distribution systems is high power losses. This leads to a decline in the voltage profile, particularly in networks with unbalanced loads and exceptionally long lines. One technical solution to mitigate this problem is the installation of capacitors, which function to compensate for reactive power within the system. This installation improves the power factor, reduces power losses, and enhances voltage quality. However, a study on the optimal placement of capacitors is highly necessary to achieve the best results in terms of both power losses and voltage. This research aims to optimize capacitor placement in the distribution network by integrating electrical simulation modeling using DIgSILENT PowerFactory with a metaheuristic algorithm-based optimization process in Python. The Grey Wolf Optimizer (GWO) method was selected due to its capability to find global solutions for non-linear problems, such as equipment placement in power systems. For comparison, the Particle Swarm Optimization (PSO) method, which shares similar characteristics, is also utilized in this study. The integration of Python and DIgSILENT enables automated interaction between the optimization process in Python and the load flow simulation in DIgSILENT, thereby accelerating both the simulation process and the validation of results. This research was conducted on a 20 kV distribution network at the Kenduruhan Feeder under the PLN ULP Jatirogo operational area. This study is expected to provide an optimal and targeted solution for implementing capacitor installations in distribution networks.