Author: Hussain Kassim Ahmad
System for Detecting and Preventing Energy Theft
https://ieeexplore.ieee.org/document/10516340
Optimum Identification of Induction Generator Parameters in Wind Energy System based on Particle Swarm Optimization
Abstract— Electric power generation using non-traditional sources of energy such as wind energy became one of the techniques that attracted much attention worldwide. The induction generator is used in the exploitation of this energy and converts it into electrical energy because of the advantages that distinguish it from other types of generators. In this paper, an optimal identification of induction generator parameters is proposed. Particle Swarm Optimization technique (PSO) is used to identify the main parameters of the induction generator in cases of wind speed change, load change and fault cases. The simulation results obtained indicate that the particle swarm optimization is suitable for controlling and optimization the voltage, frequency and generated power. The simulation programming is implemented using MATLAB. Index Terms— Wind Energy (WE), Induction Generator (IG), Doubly Fed Induction Generator (DFIG), Particle Swarm Optimization (PSO). DOI link:https://www.ijser.org/onlineResearchPaperViewer.aspx?Optimum-Identification-of-Induction-Generator-Parameters-in-Wind-Energy.pdf
Optimal Identification of Doubly Fed Induction Generator Parameters in Wind Power System using Particle Swarm Optimization and Artificial Neural Network
Abstract Wind energy became one of the techniques that attracted much attention worldwide. The induction generator is used in the exploitation of this energy and converts it into electrical energy because of the advantages that distinguish it from other types of generators. In this paper, an optimal identification of induction generator parameters is proposed. Particle Swarm Optimization technique (PSO) trained using Artificial Neural Network (ANN) is used to identify the main parameters of the induction generator in cases of wind speed change, load change and fault cases. The simulation results obtained indicate that the particle swarm optimization is suitable for neural networks training for controlling of the voltage, frequency and generated power. The simulation programming is implemented using MATLAB. Keywords: Wind Energy, WE, Induction Generator, IG, Particle Swarm Optimization, PSO. DNI link: https://www.iasj.net/iasj/article/99873
Ac Power Flow Analysis Using Fast Decoupled Newton-Raphson Algorithm Compared With Gaussian- Seidel Approach
ABSTRACT Optimal power flow is an optimizing equipment dedicated to power system processing analysis, task arrangement and energy dealing. Utilization of the optimal power flow has been arising to be much interesting due to its abilities for managing among different states and cases. Here in case catches the optimization for such equitable functions which could manage numerous schemes whereas gratifying a set of implicational as well substantial restraints. The OPF management is introduced with numerous aims as well conditions which are well examined. In this research, an essential concentration has been applied on the analyzing of the random optimization approaches those have been utilized by literature in order to determine such optimal power flow issue. Several real employments are introduced. Actually, this enquire introduces a near investigation of the Gauss Seidel compared with Newton-Raphson polar directions strategies using fast decoupled algorithm for power stream investigation. The adequacy of these strategies has been assessed as well tried by an alternate IEEE transport test framework based upon number of emphasis, computational time, resilience esteem and union. The fast-decoupled Newton-Raphson algorithm shows a tremendous enhancement in computational time compared to standard ones well as to the Gauss Seidel technique. Keywords Convergence time, power flow, optimization, Gauss-Seidel, number of emphasis, Newton-Raphson, power stream investigation.
Design of Intelligent Controller for Wind Power System
Abstract— Electric power generation using non-traditional sources of energy such as Wind Energy (WE) became one of the most energy sources that attracted much attention worldwide. In this paper, fault analysis in wind power system such as voltage drop is presented using intelligent techniques. Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN)techniques are used in this analysis. A comparison between the proposed and traditional PI technique is presented. Thesimulation results obtained showed that the proposed method is effective and suitable for dealing with such fault according to the time response and then for controlling the generated power. Thes imulation programming is implemented using MATLAB. Key words: Wind Energy (WE), Particle Swarm Optimization (PSO) Artificial Neural Network (ANN). DOI link: https://www.researchgate.net/publication/345135192_Design_of_Intelligent_Controller_for_Wind_Power_System#fullTextFileContent