Design of PSS for damping low frequency oscillations using bacteria foraging tuned non-linear neuro-fuzzy controller

2011 ◽  
Author(s):  
M. Marsaline Beno ◽  
N. Albert Singh ◽  
M. Ciba Therase ◽  
M. Mohamed Syed Ibrahim
Keyword(s):  
Fuzzy Controller ◽  
Low Frequency ◽  
Low Frequency Oscillations ◽  
Neuro Fuzzy ◽  
Non Linear

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

2021 ◽  
pp. 014233122110427
Author(s):  
Aliyu Sabo ◽  
Noor Izzri Abdul Wahab ◽  
Mohammad Lutfi Othman ◽  
Mai Zurwatul Ahlam Mohd Jaffar ◽  
Hamzeh Beiranvand ◽  
...  
Keyword(s):  
Power System ◽  
Parameter Optimization ◽  
Single Machine ◽  
Fuzzy Controller ◽  
Low Frequency ◽  
Test System ◽  
Low Frequency Oscillations ◽  
Neuro Fuzzy ◽  
Single Machine Infinite Bus ◽  
Damping Controller

Generally, power systems experience a variety of disturbances that can result in low frequency electromechanical oscillations. These low frequency oscillations (LFOs) take place among the rotors of synchronous generators connected to the power system. These oscillations may sustain and grow to cause system separation if no adequate damping is provided. Power system stabilizers (PSSs) are one of the alternative devices used to solve this rotor oscillation problem. The major limitation of using PSSs at the excitation system of synchronous machine is that the conventional PSS is a permanent parameter type operating under a particular system operating condition, and its parameters are acquired through trial and error. An efficient way of operating the PSS has been by designing the PSS parameters using a powerful optimization procedure. However, designing PSS damping controller is a cumbersome task as it needs a comprehensive test system modeling and a heavy computational burden on the system. In this research, a novel, model-free neuro-fuzzy controller (NFC) is designed as the LFOs’ damping controller to substitute the traditional PSS system making the power system simple without complications in PSS design and parameter optimization. The proposed controller application implements the LFOs’ control without a linearized mathematical model of the system, as such it makes the system less complex and bulky. Single machine infinite bus (SMIB) test system was simulated in SIMULINK domain with the PSSs and with the proposed controller to compare the NFC effectiveness. The simulation outcome for the eigenvalue study with NFC stabilizer yields steady eigenvalues that enhanced the damping status of the system greater than 0.1 with decreased overshoots and time to rise via the proposed NFC process than with the conventional FFA-PSS. Similarly, the generator transient reaction also presents the ω and δ based on the time to settle was improved by 64.66% and 28.78%, respectively, via the proposed NFC process than with the conventional FFA-PSS. Finally, the conventional PSS was found to be complicated in its design, parameter optimization and less effective than the proposed controller for the LFOs’ control.

Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization

2012 ◽  
pp. 81-98
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Control Approach ◽  
Search Spaces ◽  
Neuro Fuzzy ◽  
Non Linear

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold to control the velocity of the particles, avoids clustering of the particles, and maintains the diversity of the population in the search space. The mechanism of MPSO has better potential to explore good solutions in new search spaces. The proposed MPSO algorithm is also used to tune and optimize the controller parameters like the scaling factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to control the behavior of both non-linear single machine power systems and non-linear inverted pendulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can effectively and robustly enhance the damping of oscillations.

Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization

2010 ◽  
Vol 1 (4) ◽  
pp. 1-16
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Rule Base ◽  
Swarm Optimization ◽  
Linear Dynamical Systems ◽  
Neuro Fuzzy ◽  
Non Linear

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold to control the velocity of the particles, avoids clustering of the particles, and maintains the diversity of the population in the search space. The mechanism of MPSO has better potential to explore good solutions in new search spaces. The proposed MPSO algorithm is also used to tune and optimize the controller parameters like the scaling factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to control the behavior of both non-linear single machine power systems and non-linear inverted pendulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can effectively and robustly enhance the damping of oscillations.

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