Artificial Optimization Techniques Research Articles

Optimum design of spacer material of gas-insulated switchgear by utilizing the technology of functionally graded material using different artificial optimization techniques

Considering the reliable operation of Gas – Insulated Switchgear (GIS) system within higher voltages, the optimal performance of solid spacer is one of the most effective topologies in improving the quality of this system. The quality of GIS system is related to the minimization of maximum electric field strengths at hot triple junctions along the tracing paths defining the geometrical zone of field intensification considering spacer material, insulation material and electrodes. In this research, the operational features of the proposed Basin Cone – Shaped Spacer (BCSS) is enhanced considering the optimum design of spacer material utilizing the technology of Functionally Graded Material (FGM). For first methodology step, the Gaussian Distribution Function (GDF) technology is innovatively provided to normally distribute the FGM permittivity along the proposed BCSS model. To overcome the randomness of GDF in normalizing efficiently the weighed data, the proposed optimizers of Water Cycle Algorithm (WCA), Moth – Flame Optimization (MFO) and Fire Hawk Optimizer (FHO) are provided to optimize GDF coefficients for optimal distribution of FGM permittivity. Then, the stability of the proposed optimizers is studied to efficiently validate the effectiveness of their estimated results. In next step, this paper provides an innovative methodology to calculate the optimum number of layers of the proposed BCSS model with FGM technology regarding the industrial constraints and the proposed optimizers. Then, the optimal permittivity distribution scenarios with the optimum GDF coefficients and number of BCSS layers are defined for the field strength calculation along the proposed tracing paths containing the intensified triple junctions using COMSOL Multiphysics 6.0 program with the computation of Finite Element Method (FEM). Comparatively, the computational efficiency of field strengths of the optimal FGM permittivity distribution scenarios is validated considering the common distributions without FGM and the standard distributions of this technology.

Https://dsr.mutah.edu.jo/index.php/jje/article/view/3

This paper presents a new islanding detection technique based on an artificial neural network (ANN) for a doubly fed induction wind turbine (DFIG). This technique takes advantage of ANN as pattern classifiers. Five different ANN systems are presented in this paper based on various inputs: three phase power, phase voltage, phase current, neutral voltage, and neutral current. An ANN structure is trained for each input, and the comparison between the different structures is presented. Feedforward ANN structures are used for the five systems. Three different learning algorithms are used: backpropagation and two artificial optimization techniques: Genetic Algorithm (GA) and Cuckoo optimization algorithm. For each method in each training technique, the results and the cost function are presented. The comparison of different inputs different algorithms is conducted. MATLAB 2020a is used to simulate the ANN structure and code the training algorithms. A detailed discussion of the input sample rate has also been manipulated to make the computational burden a factor in assessing the performance.

An interactive dynamic approach based on hybrid swarm optimization for solving multiobjective programming problem with fuzzy parameters

Real engineering design problems are generally characterized by the presence of many often conflicting and incommensurable objectives. Naturally, these objectives involve many parameters whose possible values may be assigned by the experts. The aim of this paper is to introduce a hybrid approach combining three optimization techniques, dynamic programming (DP), genetic algorithms and particle swarm optimization (PSO). Our approach integrates the merits of both DP and artificial optimization techniques and it has two characteristic features. Firstly, the proposed algorithm converts fuzzy multiobjective optimization problem to a sequence of a crisp nonlinear programming problems. Secondly, the proposed algorithm uses H-SOA for solving nonlinear programming problem. In which, any complex problem under certain structure can be solved and there is no need for the existence of some properties rather than traditional methods that need some features of the problem such as differentiability and continuity. Finally, with different degree of α we get different α-Pareto optimal solution of the problem. A numerical example is given to illustrate the results developed in this paper.