PROJECT TITLE :

Multi-Objective Predictability Based Optimal Placement and Parameters

ABSTRACT:

Operators of the electrical systems face a difficult problem when it comes to managing uncertainty in their decisions. Systems with stochastic behaviour, such as those with large amounts of renewable energy, necessitate a high degree of system state prediction. Operators, of course, are more interested in power systems that can be reliably predicted. For optimal placement and parameter setup of a unified power flow controller (UPFC) considering system predictability, this research provides a multi-objective framework. The well-known multiobjective nondominated sorting genetic algorithm is used to manage numerous objective functions, such as active power losses and system predictability in the presence of operational constraints and uncertainties. Wind power's stochastic nature is modelled using the point estimate method. There are numerous advantages to employing the proposed method, such as obtaining statistical information on voltage magnitude and perceived power of UPFC converters. The IEEE 57-bus test system is used to conduct extensive simulations. A multi-objective particle swarm optimization technique is also implemented and the results of two algorithms are compared to each other to confirm the achieved results.