Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

A New Approach To The Solution Of Economic Dispatch Using Genetic Algorithm

Economic dispatch is the process of finding the optimal generation scheduling of a number of electricity generation facilities to meet the load of the system at lowest possible cost, subject to transmission and operational constraints on the system. The main idea of this paper focuses on the application of genetic algorithm in order to identify the best solution to an economic dispatch problem by using a new approach depending on Bmn coefficients and arithmetic crossover of the genetic algorithm. In this study, the proposed method solves the economic dispatch problem of three generator units whilst taking into consideration the coefficient losses to find the most important factors in electrical generation, which are the output power and total cost. The results of this study are compared with the classical optimization calculation techniques, and it is found that the results were almost equal. The MATLAB simulation is run to demonstrate clearly the effectiveness of the new genetic algorithm approach as a very important method in the solution of economic dispatch problems

Pressure vessel design simulation using hybrid harmony search algorithm

Recently the development of optimization algorithm is rapidly increased. Among several optimization algorithms, Harmony Search (HS) has been recently proposed for solving engineering optimization problems. The HS has some weaknesses such as parameters selection and falling in local optima. Many variants proposed to solve these problems. This paper presents successful hybrid algorithms with high performance to solve the pressure vessel design simulation. The hybrid algorithms consist of well-known variants of HS and an opposition-based learning technique. The hybrid algorithm improved the HS exploration and avoiding falling in local optima, which lead the algorithm to provide significant results