Bacterial foraging optimization algorithm used to adjust the parameters of Power System Stabilizers and Thyristor Controlled Series Capacitor-Power Oscillation Damping controller

2014 ◽  
Author(s):  
Maxwell Martins de Menezes ◽  
Percival Bueno de Araujo ◽  
Elenilson de Vargas Fortes
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Bacterial Foraging Optimization ◽  
Power System Stabilizers ◽  
Power Oscillation ◽  
Power Oscillation Damping ◽  
Thyristor Controlled Series Capacitor ◽  
Oscillation Damping ◽  
Bacterial Foraging Optimization Algorithm ◽  
Series Capacitor

Projeto Coordenado de Estabilizadores e Controladores TCSC-POD usando o Coyote Optimization Algorithm

2020 ◽  
Author(s):  
Matheus A. G. Calsavara ◽  
Wesley Peres
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Power System Stabilizers ◽  
Power Oscillation ◽  
Thyristor Controlled Series Capacitor ◽  
Series Capacitor

O amortecimento de oscilações eletromecânicas de baixa frequência é crucial para uma operação confiável dos sistemas elétricos. Estas são resultado do desbalanço entre os torques elétrico e mecânico nas máquinas síncronas após pequenas variações de carga e de geração. Caso não sejam amortecidas, estas podem deteriorar os geradores, reduzir os limites de transferência de potência entre áreas e causar blecautes. Desde a década de setenta, estabilizadores de sistemas de potência (power system stabilizers – PSS) têm sido usados para o amortecimento de oscilações através do controle da excitação dos geradores. Com o advento dos dispositivos FACTS, surgiram novas oportunidades para um controle mais eficaz em regime permanente dos sistemas bem como para o amortecimento de oscilações (através dos controladores power oscillation damper – POD). Um dos dispositivos FACTS mais usados é o Compensador Série Controlado a Tiristor (Thyristor Controlled Series Capacitor - TCSC) usualmente empregado para controle de potência. Uma importante tarefa é o ajuste de vários controladores PSS e POD para o amortecimento de oscilações considerando vários pontos de operação (que traduzem a incerteza na operação do sistema). Nesse trabalho, é investigada a aplicação do algoritmo de otimização bioinspirado no comportamento dos coiotes (Coyote Optimization Algorithm - COA) no ajuste coordenado de controladores PSS e TCSC-POD para o amortecimento de oscilações considerando vários pontos de operação. Resultados promissores são obtidos pelo COA para o sistema Sul-Brasileiro.

Effective Hybrid Optimization Algorithm for Power Oscillation Damping

2012 ◽  
Vol 622-623 ◽  
pp. 1168-1172
Author(s):  
Mahdiyeh Eslami ◽  
Hussain Shareef ◽  
Azah Mohamed ◽  
Mohammad Khajehzadeh
Keyword(s):  
Power System ◽  
New England ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Power System Stabilizers ◽  
Operation Conditions ◽  
Parameters Tuning ◽  
Power Oscillation Damping ◽  
Oscillation Damping

In this paper, a hybrid optimization method, GA-SQP, is presented in which the genetic algorithm (GA) is a stochastic method is combined with the sequential quadratic programming (SQP) method, which is a deterministic method. The power system stabilizers parameters tuning problem is converted to an optimization problem which is solved by hybrid GA-SQP optimization algorithm. The New England 10-unit 39-bus standard power system, under various operation conditions, is employed to illustrate the performance of the proposed method. The results are very encouraging and suggest that the hybrid GA-SQP algorithm is very efficient in damping improvement of the power system.

scholarly journals Saddle Node Bifurcation Point Analysis of Voltage Stability of IEEE 30-BUS Power System for Removal of Generation through Bacteria Foraging Optimization Algorithm

2018 ◽  
Vol 7 (3.31) ◽  
pp. 36
Author(s):  
Srikanth B. Venkata ◽  
Lakshmi Devi Ai
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Reactive Power Compensation ◽  
Bacterial Foraging Optimization ◽  
Saddle Node Bifurcation ◽  
Saddle Node ◽  
Point Analysis ◽  
Bacteria Foraging Optimization Algorithm ◽  
Bacterial Foraging Optimization Algorithm

This paper deals with the identification of instability nodes of IEEE 30 BUS power system to generation removal. Optimal sizing and locations of reactive power compensations are obtained. Firstly one of the generators is assumed to be removed from service and the saddle node bifurcation (SNB) point voltages are evaluated without reactive power compensation. Secondly two generators are assumed to be removed from service and the saddle node point voltage magnitudes are obtained without reactive power compensation. For both cases the study is conducted by placing optimal reactive power compensations at optimal locations using Bacterial Foraging Optimization Algorithm (BFOA).  

scholarly journals HARMONIC COMPONENTS ESTIMATION IN POWER SYSTEM USING BACTERIAL FORAGING OPTIMIZATION ALGORITHM AND STOCHASTIC GRADIENT ALGORITHM WITH VARIABLE FORGETTING FACTOR

2016 ◽  
Vol 17 (1) ◽  
pp. 127-146
Author(s):  
Ahmad Mohammadzadeh ◽  
Jalil Sadati ◽  
Behrooz Rezaie
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Convergence Speed ◽  
Stochastic Gradient ◽  
Recursive Least Squares ◽  
Forgetting Factor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Variable Forgetting Factor ◽  
Bacterial Foraging Optimization Algorithm

In this paper, a hybrid configuration algorithm called stochastic gradient method with variable forgetting factor (SGVFF) is proposed to better estimate unknown parameters in a power system such as amplitude and phase of harmonics using variable forgetting factor following the bacterial foraging optimization algorithm (BFO). It must be mentioned that harmonic estimation is a nonlinear problem and using linear optimization algorithms for solving this problem reduces the convergence speed. Thus, BFO algorithm is used for initial estimation. In this paper, first, using little information and by applying BFO algorithm in an off-line procedure initial value for SGVFF algorithm is achieved and then SGVFF algorithm is gained in an on-line procedure. In the hybrid algorithm applied in this paper, amplitudes and phases are estimated simultaneously. Simulation results indicate that the proposed method has faster convergence speed, better performance and higher accuracy in a noisy system in comparison with recursive least squares variable forgetting factors algorithm (RLSVFF). This proves the superiority of the proposed method.KEYWORDS:  Power system harmonic; BFO algorithm; SGVFF method; RLSVFF method

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