scholarly journals A New Controller for Voltage and Stability Improvement of Multi Machine Power System Tuned by Wind Turbine

2021 ◽  
Vol 8 (1) ◽  
pp. 81-88
Author(s):  
Issam Griche ◽  
Sabir Messalti ◽  
Kamel Saoudi ◽  
Mohamed Yaakoub Touafek ◽  
Fares Zitouni
Keyword(s):  
Power Systems ◽  
Power System ◽  
Wind Turbine ◽  
Transient Stability ◽  
Fuzzy Inference ◽  
Dynamical Response ◽  
Power Networks ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Wide Range

This paper proposes a new controller for stability and voltage improvement of power networks equipped by wind turbine which optimize the dynamical response of power systems performances (voltage and transient stability) after fault. The proposed control algorithm based on new Adaptive Neuro-Fuzzy Inference System (ANFIS) controller to enhance the mechanical power of the synchronous machine into power system. The efficiency of developed control strategy has been tested using IEEE 9 Bus. Simulation results have showed that the proposed method perform better performances over wide range of disturbances for three considered scenarios studied.

scholarly journals Comparison of Damping Performance of Conventional And Neuro–Fuzzy Based Power System Stabilizers Applied in Multi–Machine Power Systems

2013 ◽  
Vol 64 (6) ◽  
pp. 366-370 ◽  
Author(s):  
Duraiswamy Murali ◽  
Marimuthu Rajaram
Keyword(s):  
Power Systems ◽  
Power System ◽  
Fuzzy Inference ◽  
Low Frequency ◽  
Operating Conditions ◽  
Power System Stabilizers ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Time Domain ◽  
Low Frequency Power

Abstract The objective of this paper is to investigate the power system damping enhancement via power system stabilizers (PSSs). However, the conventional power system stabilizers (CPSSs) have certain drawbacks. There are many techniques proposed in the literature for damping improvement of low frequency power system oscillations. In this paper, adaptive neuro-fuzzy inference system (ANFIS) technology has been proposed to coordinate the CPSSs in a multi-machine power system. The time-domain simulations are carried out in Matlab/Simulink environment to validate the effectiveness of the proposed control scheme under different operating conditions.

scholarly journals Instantaneous Power Control Strategy for Voltage Improvement in Power Network Equipped by Wind Generator

2021 ◽  
Vol 54 (1) ◽  
pp. 147-154
Author(s):  
Issam Griche ◽  
Sabir Messalti ◽  
Kamel Saoudi
Keyword(s):  
Power Systems ◽  
Power Control ◽  
Wind Power ◽  
Control Strategy ◽  
Large Scale ◽  
Transient Stability ◽  
Short Circuit ◽  
Dynamical Response ◽  
Instantaneous Power ◽  
Wide Range

The uncertainty of wind power brings great challenges to large-scale wind power integration. The conventional integration of wind power is difficult to adapt the demand of power grid planning and operation. This paper proposes an instantaneous power control strategy for voltage improvement in power networks using wind turbine improving the dynamical response of power systems performances (voltage and transient stability) after fault. In which the proposed control algorithm based on a new advanced control strategy to control the injected wind power into power system. The efficiency of developed control strategy has been tested using IEEE 9 Bus. Simulation results have showed that the proposed method perform better to preserve optimal performances over wide range of disturbances for both considered scenarios studied short circuit and variable loads.

Speaker-Independent Malay Syllable Recognition Using Singular and Modular Neural Networks

2012 ◽  
Author(s):  
Hua Nong Ting ◽  
Jasmy Yunus ◽  
Sheikh Hussain Shaikh Salleh
Keyword(s):  
Power System ◽  
Fuzzy Inference ◽  
Design Procedure ◽  
Test System ◽  
Power System Stabilizer ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Input Signals ◽  
System Stabilizer

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro–fuzzy inference system (ANFIS) and investigates their robustness for a multi–machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four–machine and a two–area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead–lag based power system stabilizer controller.

Designing Power System Stabilizer for Multimachine Power System Using Neuro-Fuzzy Algorithm

2012 ◽  
Author(s):  
M.F. Othman ◽  
M. Mahfouf ◽  
D.A. Linkens
Keyword(s):  
Power System ◽  
Fuzzy Inference ◽  
Design Procedure ◽  
Test System ◽  
Power System Stabilizer ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Three Phase ◽  
Input Signals ◽  
System Stabilizer

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro–fuzzy inference system (ANFIS) and investigates their robustness for a multi–machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four–machine and a two–area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead–lag based power system stabilizer controller.

scholarly journals ANFIS for Tamil Phoneme Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 2907-2914
Keyword(s):  
Neural Networks ◽  
Markov Models ◽  
Fuzzy Inference ◽  
Hybrid Architecture ◽  
Phoneme Recognition ◽  
Inference System ◽  
Phoneme Classification ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Non Linear Systems

Phoneme recognition is an intricate problem lying under non-linear systems. Most research in this area revolve around try to model the pattern of features observed in the speech spectra with the use of Hidden Markov Models (HMM), various types of neural networks like deep recurrent neural networks, time delay neural networks, etc. for efficient phoneme recognition. In this paper, we study the effectiveness of the hybrid architecture, the Adaptive Neuro-Fuzzy Inference System (ANFIS) for capturing the spectral features of the speech signal to handle the problem of Phoneme Recognition. In spite of a wide range of research in this field, here we examine the power of ANFIS for least explored Tamil phoneme recognition problem. The experimental results have shown the ability of the model to learn the patterns associated with various phonetic classes, indicated with recognition improvement in terms of accuracy to its counterparts.

scholarly journals A Neuro-fuzzy Approach for Predicting Load Peak Profile

2015 ◽  
Vol 5 (6) ◽  
pp. 1304 ◽  
Author(s):  
Abdellah Draidi ◽  
Djamel Labed
Keyword(s):  
Power Systems ◽  
Fuzzy Inference ◽  
Load Forecasting ◽  
Infrastructure Development ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables ◽  
Load Peak ◽  
Inference Systems ◽  
Mathematical Process

<p>Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development.</p> <p>Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain.</p> <p>Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously.</p> In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.

scholarly journals Predictive Modeling of Henry’s Law Constant in Chemical Structures Using LSSVM and ANFIS Algorithms

2020 ◽  
Author(s):  
Qikai Wang ◽  
Aiqin Yao ◽  
Manouchehr Shokri ◽  
Adrienn A. Dineva
Keyword(s):  
Chemical Structure ◽  
Fuzzy Inference ◽  
Low Cost ◽  
Support Vector ◽  
Inference System ◽  
Chemical Structures ◽  
Neuro Fuzzy ◽  
Wide Range ◽  
Accuracy Of Measurement

Henry&rsquo;s constants for different existing compounds in water have great importance in transfer calculations. Measurement of these constants face different difficulties including high costs of experiment and low accuracy of measurement apparatus. Due to these facts, proposing a low cost and accurate approach becomes highlighted. To this end, adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) have been used as Henry&rsquo;s constant predictor tools. The molecular structure of compounds has been used as inputs of models. After training the models, the visual and mathematical studies of outputs have been done. The coefficients of determination of LSSVM and ANFIS algorithms are 0.999 and 0.990 respectively. According to the comprehensiveness of databank and accurate prediction of algorithms, it can be concluded that LSSVM and ANFIS algorithms are accurate methods for prediction of Henry&rsquo;s constant in wide range of chemical structure of compounds in water.

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