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

Abstract

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.

An improved and simplified approach for the steady-state analysis of self-excited induction generators using binary search algorithm

“…This optimizer calculates the SEIG performance without requiring the development of complex equations. Elkholy (2018) introduced a technique based on the teaching-learning-based optimization algorithm and Benhacine et al (2019) presented an approach based on an iterative two-step technique. These methods have inherent disadvantages, such as the need for the appropriate selection of the range for each of the unknown variables, large number of iterations and the computation time taken for the convergence to the final solution for the unknowns.…”

“…This optimizer calculates the SEIG performance without requiring the development of complex equations. Elkholy (2018) introduced a technique based on the teaching–learning-based optimization algorithm and Benhacine et al. (2019) presented an approach based on an iterative two-step technique.…”

Optimal maximum power point tracking of wind turbine doubly fed induction generator based on driving training algorithm

“…The most popular current MPPT methods are optimal torque method, power signal feedback method, optimal tip speed ratio method, hill climb search method, and perturb and observer method (Hemanth Kumar et al, 2018; Kumar and Chatterjee, 2016; Mousa et al, 2021). A large variety of optimization algorithms with different variants and modifications are proposed to solve many complex engineering problems, which are often non-continuous and non-linear problems (Bakir et al, 2020, 2021; Elkholy, 2018; Elkholy and Abd-Elkader, 2019; Han et al, 2021; Manohar et al, 2021; Memon et al, 2021; Shutari et al, 2021). The major benefits of these algorithms are robustness, flexibility, easy implementation, efficient, and computational efficiency compared to alternative mathematical methods (Behera et al, 2015; Sompracha et al, 2019).…”

Evaluation of the Influencing Factors of English Teachers' Online Small-class Blended Teaching from the Perspective of Psychology

“…Elkholy M M aimed to introduce an application of TLBO algorithm and Static Var Compensator (SVC) to improve the steady-state and dynamic performance of Self-excited Induction Generator (SEIG). He proposed an application of meta-heuristics and SVC to analyze the steady-state and dynamic performance of SEIG with optimal performance [5]. Zhai Z proposed a new version of TLBO which adds error correction strategy and Cauchy distribution.…”