scholarly journals Torsional Oscillations Mitigation via Interval Type-2 Fuzzy Logic Brake Control

2020 ◽  
Vol 19 (2) ◽  
pp. 16-21
Author(s):  
Mohamed Fayez ◽  
Fahmy Metwally Bendary ◽  
Mohamed El-Hadidy ◽  
Mohamed Adel Mandor
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Logic Controller ◽  
Power Grids ◽  
Turbine Generator ◽  
Torsional Oscillations ◽  
Single Machine Infinite Bus ◽  
Interval Type ◽  
Dynamic Braking

Turbine-generator shaft torsional oscillations is an interdisciplinary power system dynamic problem because it encompasses mechanical and electrical sectors of power grids. They give rise to a premature expenditure of fatigue life of the turbine-generator shaft metal which could lead to shaft cracks. This paper introduces an interval type-2 fuzzy logic controller to regularize the dynamic braking interventions of a novel braking resistor model for mitigation of torsional oscillations resulting from unsuccessful autoreclosure procedures near generation stations. The effectiveness of proposed scheme is elucidated by considering the unsuccessful autoreclosure of three-phase-to-ground fault in a single machine infinite bus power system via MATLAB/Simulink-based modeling and simulation environment with the help of interval type-2 fuzzy logic controller toolbox. The comparative simulation results with and without the suggested mitigation regime show that the proposed scheme is effective in the mitigation of torsional torque oscillations

scholarly journals Type-2 Fuzzy Logic Controller Based PSS for Large Scale Power Systems Stability

2018 ◽  
Vol 8 (5) ◽  
pp. 3380-3386 ◽  
Author(s):  
O. Kahouli ◽  
B. Ashammari ◽  
K. Sebaa ◽  
M. Djebali ◽  
H. H. Abdallah
Keyword(s):  
New York ◽  
Fuzzy Logic ◽  
Power Systems ◽  
Power System ◽  
New England ◽  
Large Scale ◽  
Fuzzy Logic Controller ◽  
Interconnected Power System ◽  
Interval Type

In this paper, the application of the fuzzy logic based power systems stabilizer (FLPSS) to damp power system oscillation is presented. Various types of fuzzy logic controller are used to replace the conventional power system stabilizer (CPSS). The classic fuzzy logic controller based PSS (FLCPSS), the polar FLC (PFLCPSS) and the interval type-2 fuzzy logic controller based PSS (IT2FLCPSS) are applied to the New England - New York interconnected power system and the obtained results are compared. For coordination purposes, genetic algorithm (GA) is used to tune the FLCPSS’s gains. The non-linear simulation in the presence of noise confirms the robustness and the superiority of the IT2FLCPSS.

scholarly journals Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

2021 ◽  
Vol 54 (3-4) ◽  
pp. 303-323
Author(s):  
Amjad J Humaidi ◽  
Huda T Najem ◽  
Ayad Q Al-Dujaili ◽  
Daniel A Pereira ◽  
Ibraheem Kasim Ibraheem ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Parallel Robot ◽  
Design Parameters ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Interval Type

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

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