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 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 Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
İsmail Kıyak ◽  
Gökhan Gökmen ◽  
Gökhan Koçyiğit
Keyword(s):  
Fuzzy Inference ◽  
Prediction Method ◽  
Operating Conditions ◽  
Lifetime Prediction ◽  
Statistical Characteristics ◽  
Ambient Conditions ◽  
Inference System ◽  
Initial Task ◽  
Neuro Fuzzy ◽  
A Cell

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

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.

Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices

2016 ◽  
Vol 26 (02) ◽  
pp. 1750034 ◽  
Author(s):  
J. Sangeetha ◽  
P. Renuga
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Controller Design ◽  
Test System ◽  
Operating Conditions ◽  
Superior Performance ◽  
Control Technique ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Oscillation Damping

This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated controller design is to tune the parameters of SVC and TCSC fuzzy lead lag compensator simultaneously to minimize the deviation of rotor angle and rotor speed of the generators. The performance of the system is enhanced by optimally tuning the membership functions of fuzzy lead lag controller parameter of the flexible AC transmission system (FACTS) by R-ANFIS controller. The training data for F-ANFIS and R-ANFIS are generated by conventional linear control technique under various operating conditions. The offline trained controller tunes the parameter of lead lag controller in online. The oscillation damping ability of the system is analyzed for three-machine test system by calculating the standard deviation and cost function. The superior performance of R-ANFIS controller is compared with various particle swarm optimization-based feedforward ANFIS controllers available in literature.

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 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.

Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Genetic Algorithms ◽  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Vibration Data ◽  
Neuro Fuzzy ◽  
Machine Fault ◽  
The Time Domain ◽  
Selection Of

A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.

Robust design of power system stabilizer using bacterial foraging algorithm

2013 ◽  
Vol 62 (1) ◽  
pp. 141-152 ◽  
Author(s):  
K. Abdul Hameed ◽  
S. Palani
Keyword(s):  
Power System ◽  
Low Frequency ◽  
Operating Conditions ◽  
Power System Stabilizer ◽  
Bacterial Foraging ◽  
Wide Range ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results ◽  
The Time Domain ◽  
System Stabilizer

Abstract In this paper, a novel bacterial foraging algorithm (BFA) based approach for robust and optimal design of PID controller connected to power system stabilizer (PSS) is proposed for damping low frequency power oscillations of a single machine infinite bus bar (SMIB) power system. This paper attempts to optimize three parameters (Kp, Ki, Kd) of PID-PSS based on foraging behaviour of Escherichia coli bacteria in human intestine. The problem of robustly selecting the parameters of the power system stabilizer is converted to an optimization problem which is solved by a bacterial foraging algorithm with a carefully selected objective function. The eigenvalue analysis and the simulation results obtained for internal and external disturbances for a wide range of operating conditions show the effectiveness and robustness of the proposed BFAPSS. Further, the time domain simulation results when compared with those obtained using conventional PSS and Genetic Algorithm (GA) based PSS show the superiority of the proposed design.

scholarly journals Tuning of power system stabilizer for small signal stability improvement of interconnected power system

2017 ◽  
Vol 16 (1/2) ◽  
pp. 3-28 ◽  
Author(s):  
Prasenjit Dey ◽  
Aniruddha Bhattacharya ◽  
Priyanath Das
Keyword(s):  
Power System ◽  
Low Frequency ◽  
Operating Conditions ◽  
System Stability ◽  
Interconnected Systems ◽  
Small Signal ◽  
Small Signal Stability ◽  
Power System Stabilizers ◽  
Signal Stability ◽  
Low Frequency Oscillations

This paper reports a new technique for achieving optimized design for power system stabilizers. In any large scale interconnected systems, disturbances of small magnitudes are very common and low frequency oscillations pose a major problem. Hence small signal stability analysis is very important for analyzing system stability and performance. Power System Stabilizers (PSS) are used in these large interconnected systems for damping out low-frequency oscillations by providing auxiliary control signals to the generator excitation input. In this paper, collective decision optimization (CDO) algorithm, a meta-heuristic approach based on the decision making approach of human beings, has been applied for the optimal design of PSS. PSS parameters are tuned for the objective function, involving eigenvalues and damping ratios of the lightly damped electromechanical modes over a wide range of operating conditions. Also, optimal locations for PSS placement have been derived. Comparative study of the results obtained using CDO with those of grey wolf optimizer (GWO), differential Evolution (DE), Whale Optimization Algorithm (WOA) and crow search algorithm (CSA) methods, established the robustness of the algorithm in designing PSS under different operating conditions.

scholarly journals Design of Neuro-Fuzzy Controller for Stabilization of Single Phase Stand-Alone PV Power System

2018 ◽  
Vol 7 (3) ◽  
pp. 176
Author(s):  
Hitendra Singh Thakur ◽  
Ram Narayan Patel
Keyword(s):  
Power System ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Phase System ◽  
Inference System ◽  
Current Harmonics ◽  
Synchronous Reference Frame ◽  
Neuro Fuzzy ◽  
Three Phase

For the three phase power electronic and drive applications, vector control or the synchronous reference frame (SRF) based control concept is well accepted and settled amongst the research communities. Although the SRF concept has gained popularity and appreciation in developing the three phase controllers, still the concept has not reached the same level in case of a single phase system. The work presented in this paper is mainly concerned to the design of a hybrid Artificial Neural Network and Fuzzy Logic based controller for a single phase stand-alone photo-voltaic (PV) power system. The adaptive neuro fuzzy inference system (ANFIS) controller proposed in this paper is chiefly meant for improving the transient and steady state responses; for minimizing the distorting effect of the low order load current harmonics encountered particularly in case of switching the drive based inductive loads and to help maintain the inverter output voltage constant under different loading circumstances. The result obtained through simulation work, shows the effectiveness of the proposed controller as compared with the previously established research works.

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