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Optimal allocation of distributed generations (DGs) is vital to the proper operation of the distribution systems, which leads to power loss minimization and acceptable voltage regulation. In this paper, an Enhanced Artificial Ecosystem-based Optimization (EAEO) algorithm is proposed and used to solve the optimization problem of DG allocations to minimize the power loss in distribution systems. In the suggested algorithm, the search space is reduced using operator G and sine-cosine function. The G-operator affects the balance between explorative and exploitative phases. At the same time, it gradually decreases during the iterative process in order to converge to the optimal global solutions. On the other hand, the sine-cosine function creates different and random solutions. The EAEO algorithm is applied for solving the standard 33-bus 69-bus, and 119-bus distribution systems with the aim of minimizing the total power losses. Multiple DG units operating at various power factors, including unity-, fixed-, and optimal-power factors, are considered. Both single and multiple objectives are considered to minimize the total voltage deviation (TVD), maximize the system stability, and reduce the total power losses. The obtained results are compared with those obtained by the AEO and other algorithms. The results demonstrate a significant reduction of total power losses and improvement of the voltage profile of the network, especially for the DGs operating at their optimal power factors. Comparisons show the dominance of the proposed EAEO algorithm against other analytical, metaheuristic, or hybrid algorithms. Moreover, the EAEO outperforms the original AEO algorithm with a faster convergence speed and better system performance.

Recently, extracting the precise values of unknown parameters of the polymer electrolyte membrane fuel cell (PEMFC) is considered one of the most widely nonlinear and semi-empirical optimization problems. This paper proposes and applies a Modified Artificial Ecosystem Optimization (MAEO) algorithm to solve the problem of PEMFC parameters extraction. The conventional AEO is a novel optimization technique that is inspired by the energy flow in a natural ecosystem which is defined as abiotic, which includes non-living bodies and elements such as light, water and air. The proposed optimization algorithm, MAEO, is used to enhance the performance of conventional AEO and provide faster convergence rate as well as to be far away from falling into the local optima. In the proposed MAEO, an operator is suggested to improve the balance between exploitation and Exploration phases. The accurate estimation of PEMFC unknown parameters leads to develop a precise mathematical model which simulates the electrochemical and electrical characteristics of PEMFC. The objective function of the studied optimization problem is formulated as the sum of squared errors (SSE) between the measured and simulated stack voltages. To prove the reliability and capability of the proposed MAEO algorithm in solving this problem compared with other recent algorithms, it is tested on four different PEMFC stack models, namely, BCS-500W, SR-12 500W, 250W and Temasek 1 kW stacks. Moreover, statistical measures are performed to assess the superiority and robustness of the proposed algorithm. In addition, the accuracy of optimized parameters is assessed through the dynamic characteristics of PEMFCs under varying the reactants' pressures and temperature of the cell. However, the simulation results confirm that the proposed MAEO algorithm has high accuracy and reliability in extracting the PEMFC optimal parameters compared with the conventional AEO and other effective algorithms.

This paper presents an application of Harris Hawks Algorithm (HHA) as an intelligent method for optimal tuning of integral classical controllers to design the appropriate load frequency controllers. A mathematically model is presented for investigating the optimized controller parameters and enhancing the system performance. It consists of two-area interconnected power system with different participation of DFIG based wind turbine at the first area only. This article solves the load frequency control (LFC) problem under step load perturbation (SLP) by 2%. Simulation results confirm that, oscillation of frequency at both areas and tie-line power oscillation are effectively damped using HHA. Also, when DFIG participation level is high, the optimum performance of power system is achieved. All time domain simulation results are carried out using MATLAB program for investigating the effectiveness of this algorithm.

This paper describes an important topic regarding with stability of the electrical grid, it is the load frequency control (LFC) problem. At any interconnected-areas of power systems, LFC problem must be solved to ensure system stability without any disturbance. When the amount of generating power is insufficient to meet the electrical load needs, frequency deviation is appeared with instability in power system. Doubly fed induction generator (DFIG) has the ability to support the power system with an additional inertia by supplying it with an extra active power. The studied system in this paper is a two-area thermal-thermal power plants with different participation levels of DFIG at the first area. The exchanged power is flowing between neighboring areas in the studied system through a tie-line. The two power plants in each model are subjected to 2% load perturbations in each area. These load perturbations are applied for examining the ability of Grey Wolf Optimizer (GWO) to restore the stability of studied systems. The optimum performance of power system is achieved when DFIG participation level is high. All time domain simulation results are carried out using MATLAB (R2015a) program for investigating the effectiveness of this method.

This paper describes an important topic regarding with stability of the electrical grid, it is the load frequency control (LFC) problem. At any interconnected-areas of power systems, LFC problem must be solved to ensure system stability without any disturbance. When the amount of generating power is insufficient to meet the electrical load needs, frequency deviation is appeared with instability in power system. Doubly fed induction generator (DFIG) has the ability to support the power system with an additional inertia by supplying it with an extra active power. The studied system is a two-area thermal-thermal power plants with different participation level of DFIG at the first area. An exchanging power is flowing between neighboring areas in the studied system through a tie-line. The two power plants in each model are subjected to 2% load perturbations in each area. These load perturbations are applied for examining the ability of Sine Cosine Algorithm (SCA) technique to restore the stability of studied systems. when DFIG participation level is high, the optimum performance of power system is achieved. All time domain simulation results are carried out by MATLAB (R2015a) program for investigating the effectiveness of this method.

Protective relays and circuit breakers are major components of the large interconnected power system. Protection system is used to isolate only faulted parts from the power system network in shortest possible time leaving healthy parts in service causing a minimum disturbance to the power system. The protective system should be able to discriminate between normal or abnormal or fault conditions which called the discrimination. Protection coordination is the important task in protection system. Without good protection coordination, the protection system will lose its features i.e. selectivity, dependability, and sensitivity. The protection mis-coordination is the main problem in the power system. This paper presents short circuit analysis and relay coordination between overcurrent relays of a 132/33/11kV substation which located in Upper Egypt. The short circuit analysis, and relays coordination are performed by ETAP 12.6 simulation program.

This paper proposes the application of Most Valuable Player Algorithm (MVPA) to find the optimal solution for directional overcurrent relays (DOCRs) coordination problem. The coordination problem of DOCRs is considered as a non-linear optimization problem. The decision variables for this problem are relay settings. Minimize the summation of the operating times of all primary relays with maintaining the coordination margin between relays pairs is the main goal of this study. The feasibility and performance of the proposed algorithm for solving the coordination problem of DOCRs are assessed in two test system (8-bus and IEEE-30 bus). The results show the ability of the proposed algorithm for solving DOCRs coordination problem without any miscoordination. The results of the proposed optimization algorithm are compared with other previously optimization techniques.

A novel application of the evaporation rate water cycle algorithm (ER-WCA) to solve the coordination problem of directional overcurrent releays (DOCRs) in power system networks is presented in this paper. Coordination problem of DOCRs has been considered as a non-linear optimization problem with many constraints. The relay settings are considered as the decision variables for this optimization problem. Minimize the summation of the operating times of all primary relays with maintaining the coordination margin between relays pairs is the main goal of this study. The performance of the ER-WCA is evaluated in a large distribution system. The results show the ability and reliability of the ER-WCA to solve DOCRs coordination problem without any miscoordination. The effectiveness of ER-WCA is compared with other previously optimization techniques. The results proved that ER-WCA is quite competent in dealing with such problems.

The optimal coordination of DOCRs becomes a very hard task as the power system becomes complex and larger. In this paper, the recent GWO technique is proposed to find the optimal settings for directional overcurrent relays (DOCR), minimize the total operation time for all primary relays, and maintain the relay coordination margin between relay pairs using GWO. The relay settings, pick up current (Ip) and time dial setting (TDS), are considered as decision variables. The operating time of a relay depends on these variables. The availability of the suggested technique is tested using fifteen bus network system using MATLAB. The results using GWO algorithm are compared with electromagnetic field optimization (EMF), Cuckoo Search Algorithm (CSA), and modified electromagnetic field optimization (MEFO). The results using GWO prove the viability of the proposed algorithms for solving DOCRs coordination problem compare with other optimization algorithms.

Several optimization techniques are applied to distribution system reconfiguration such as particle swarm optimization technique (PSO), genetic algorithm optimization (GAO) and grey wolf optimization technique (GWO). In this paper, grasshopper optimization algorithm is used to simulate distribution network reconfiguration to determine the optimal switches combination for maximizing power losses reduction taking into account the constraints of the system structure. The proposed technique mathematically simulates feeder reconfiguration inspired from behavior of grasshopper in nature, and then it is used to find the optimal configuration of network. The proposed method is tested on IEEE 33-bus radial distribution system and gave the optimal solution in a reasonable time of computation. The proposed simulation results proved reasonable computing time and high performance of the proposed method when compared with other optimization techniques.