Application of fitness distance balance based heuristic optimization algorithms to power system problems

In this thesis study, it is aimed to develop hybrid optimization algorithms for the solution of constrained power system problems that are critical in the planning and operation of modern power systems. In this context, research was carried out on two important issues in the field of optimization and significant achievements were obtained. The first of these issues was to improve the search performance of single-objective meta-heuristic algorithms by using Fitness-Distance Balance (FDB) and Dynamic Fitness-Distance Balance (dFDB) selection methods. FDB selection method was used to eliminate the handicaps of Adaptive Gaining-Sharing Knowledge (AGSK) and Levy Flight Distribution (LFD) algorithms, which suffer from premature convergence and poor diversity, and thus improve the search performance. The selection strategies of the original algorithms were redesigned using the FDB method, and thus hybrid FDBAGSK and FDBLFD algorithms were developed, which have the ability to effectively solve global optimization problems. The dFDB selection method, which was developed as one of the original contributions of this study, was applied to the Manta Ray Foraging Optimization (MRFO) algorithm and a new hybrid algorithm called dFDB-MRFO was proposed. The second subject on which research was conducted was the optimization of constrained power system problems. In this regard, firstly, the optimization model of the Multi-Objective Optimal Power Flow (MO-OPF) problem in IEEE 30-bus power system incorporating stochastic wind, solar, hydro, tidal energy sources and Multi-Terminal High Voltage Direct Current (MTHVDC) transmission links were presented. In the optimization of the proposed problem, the Multi-objective Grasshopper Optimization Algorithm (MOGOA) and the powerful optimization methods in the literature are used. The simulation results showed that the MOGOA method obtained very competitive results in terms of the accuracy of the obtained Pareto optimal solutions and their distribution. Then, the FDBAGSK algorithm is applied to the solution of the Alternating Current/Direct Current Optimal Reactive Power Flow (AC/DC ORPF) problem in IEEE 30 and IEEE 57-bus power systems incorporating distributed generation and two-terminal HVDC transmission links. Findings from the simulation results showed that the proposed FDBAGSK algorithm can efficiently solve different configurations of the ORPF problem in large-scale power systems. After that, the hybrid FDBLFD algorithm was applied to the optimization of the PID, PIDF, FOPID and PIDD2 controller parameters used in the optimal design of the Automatic Voltage Regulator (AVR). The best controller performance for optimal AVR design was achieved by FDBLFD based PIDD2. Finally, the Directional Overcurrent Relays (DOCRs) coordination problem was optimized using the hybrid dFDB-MRFO algorithm. To verify the effectiveness of the proposed optimization method, a comprehensive simulation was performed on five test systems with different complexities. The simulation results confirmed that the proposed hybrid algorithm is an efficient and reliable method to solve the DOCRs coordination problem. When all the results are considered together, it has been observed that the fitness distance balance-based hybrid optimization algorithms proposed in the thesis study can produce optimum solutions with lower error and high accuracy compared to other algorithms for power system problems.