Steady state and dynamic performance of self-excited induction generator using FACTS controller and teaching learning-based optimization algorithm

2018 ◽  
Vol 37 (1) ◽  
pp. 77-97
Author(s):  
Mahmoud M. Elkholy
Keyword(s):  
Steady State ◽  
Dynamic Performance ◽  
Induction Generator ◽  
Prime Mover ◽  
Content Type ◽  
Load Voltage ◽  
Tlbo Algorithm ◽  
Three Phase ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Purpose The paper aims to present an application of teaching learning-based optimization (TLBO) algorithm and static Var compensator (SVC) to improve the steady state and dynamic performance of self-excited induction generators (SEIG). Design/methodology/approach The TLBO algorithm is applied to generate the optimal capacitance to maintain rated voltage with different types of prime mover. For a constant speed prime mover, the TLBO algorithm attains the optimal capacitance to have rated load voltage at different loading conditions. In the case of variable speed prime mover, the TLBO methodology is used to obtain the optimal capacitance and prime mover speed to have rated load voltage and frequency. The SVC of fixed capacitor and controlled reactor is used to have a fine tune in capacitance value and control the reactive power. The parameters of SVC are obtained using the TLBO algorithm. Findings The whole system of three-phase induction generator and SVC are established under MatLab/Simulink environment. The performance of the SEIG is demonstrated on two different ratings (i.e. 7.5 kW and 1.5 kW) using the TLBO algorithm and SVC. An experimental setup is built-up using a 1.5 kW three-phase induction machine to confirm the theoretical analysis. The TLBO results are matched with other meta heuristic optimization techniques. Originality/value The paper presents an application of the meta-heuristic algorithms and SVC to analysis the steady state and dynamic performance of SEIG with optimal performance.

scholarly journals Analysis of a proposed connection for the two-winding single-phase self-excited induction generator operating at constant voltage and frequency

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
H. H. Hanafy ◽  
Heba. M. Soufi ◽  
Amr A. Saleh ◽  
Magdy B. Eteiba
Keyword(s):  
Steady State ◽  
Single Phase ◽  
Constant Load ◽  
Impedance Method ◽  
Induction Generator ◽  
Variable Load ◽  
Prime Mover ◽  
Load Impedance ◽  
Load Voltage ◽  
Load Conditions

AbstractThis paper introduces the steady-state and dynamic behaviors of a proposed connection for the two-winding single-phase self-excited induction generator (TWSPSEIG) equipped with an excitation capacitor and a compensation capacitor for operation at constant load voltage and frequency irrespective of the no-load or different load conditions. The performance equations at steady-state conditions are attained by applying loop impedance method via the exact equivalent circuit models of the TWSPSEIG based on the double revolving field theory. Keeping the values of the excitation capacitor and the compensation capacitor as unknowns, two second-order equations, for given values of generator parameters, prime mover speed, frequency and load impedance, are derived. These equations are solved using simple iterative method to calculate the optimum values of the two capacitors under the constraints that the load voltage and frequency are constant. The range of capacitor variations for variable load at variable prime mover speed is also calculated. The steady-state results are verified by developing the dynamic model of the proposed connection incorporating its nonlinearity behavior and various no-load and load conditions. The dynamic behavior of the proposed connection proves the capabilities of the proposed configuration and calculation method to maintain both the load voltage and frequency constants. A comparative study between the performances of the proposed connection and the traditional connection of the TWSPSEIG is presented to illustrate the merits of the proposed connection.

Improved version of teaching learning-based optimization algorithm using random local search

2019 ◽  
Vol 38 (3) ◽  
pp. 1048-1060 ◽  
Author(s):  
Bourahla Kheireddine ◽  
Belli Zoubida ◽  
Hacib Tarik
Keyword(s):  
Local Search ◽  
Design Methodology ◽  
Solution Space ◽  
Bat Algorithm ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Content Type ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Purpose This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS). Findings At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work. Originality value New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

scholarly journals Three-Phase Steady-State Model of Doubly Fed Induction Generator Considering Various Rotor Speeds

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 9479-9488 ◽  
Author(s):  
Yuntao Ju ◽  
Fuchao Ge ◽  
Wenchuan Wu ◽  
Yi Lin ◽  
Jing Wang
Keyword(s):  
Steady State ◽  
State Model ◽  
Induction Generator ◽  
Doubly Fed Induction Generator ◽  
Three Phase ◽  
Steady State Model ◽  
Doubly Fed

scholarly journals Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zong-Sheng Wu ◽  
Wei-Ping Fu ◽  
Ru Xue
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Random Number ◽  
Nonlinear Problems ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Linear And Nonlinear ◽  
Weighted Factor ◽  
Teaching Learning ◽  
Dynamic Inertia

Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.

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