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.

scholarly journals Neuro-Fuzzy Dynamic Position Prediction for Autonomous Work-Class ROV Docking

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 693
Author(s):  
Petar Trslić ◽  
Edin Omerdic ◽  
Gerard Dooly ◽  
Daniel Toal
Keyword(s):  
Fuzzy Inference ◽  
North Atlantic Ocean ◽  
Prediction Method ◽  
Hydrodynamic Drag ◽  
Remotely Operated Vehicle ◽  
Inference System ◽  
Drag Forces ◽  
Neuro Fuzzy ◽  
Heave Motion ◽  
Work Class

This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system.

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.

scholarly journals Combined ANFIS–Wavelet Technique to Improve the Estimation Accuracy of the Power Output of Neighboring PV Systems during Cloud Events

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1613 ◽  
Author(s):  
Hasanain A. H. Al-Hilfi ◽  
Ahmed Abu-Siada ◽  
Farhad Shahnia
Keyword(s):  
Power Output ◽  
Fuzzy Inference ◽  
Estimation Accuracy ◽  
Ambient Conditions ◽  
Pv System ◽  
Inference System ◽  
Pv Systems ◽  
Neuro Fuzzy ◽  
Irradiance Data ◽  
Reduction Index

The short-term variability of photovoltaic (PV) system-generated power due to ambient conditions, such as passing clouds, represents a key challenge for network planners and operators. Such variability can be reduced using a geographical smoothing technique based on installing multiple PV systems over certain locations at distances of meters to kilometers. To accurately estimate the PV system’s generated power during cloud events, a variability reduction index (VRI), which is a function of several parameters, should be calculated precisely. In this paper, the Wavelet Transform Technique (WTT) along with Adaptive Neuro Fuzzy Inference System (ANFIS) are used to develop new models to estimate the PV system’s power output during cloud events. In this context, irradiance data collected from one PV system along with other parameters, including ambient conditions, were used to develop the proposed models. Ultimately, the models were validated through their application on a 0.7 km2 PV plant with 16 rooftop PV systems in Brisbane, Australia.

scholarly journals Artificial Intelligence-Based Controller for DC-DC Flyback Converter

2019 ◽  
Vol 9 (23) ◽  
pp. 5108
Author(s):  
Muhammad Arslan Shahid ◽  
Ghulam Abbas ◽  
Mohammad Rashid Hussain ◽  
Muhammad Usman Asad ◽  
Umar Farooq ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Input Voltage ◽  
Operating Conditions ◽  
Model Parameters ◽  
Learning Capability ◽  
Inference System ◽  
Flyback Converter ◽  
Response Characteristics ◽  
Voltage Controller ◽  
Neuro Fuzzy

This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

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.

Application of Adaptive Neuro-Fuzzy Inference System Techniques to Predict Water Activity in Proton Exchange Membrane Fuel Cell

2018 ◽  
Vol 15 (4) ◽  
Author(s):  
Khaled Mammar ◽  
Slimane Laribi
Keyword(s):  
Fuel Cell ◽  
Water Activity ◽  
Fuzzy Inference System ◽  
Proton Exchange Membrane ◽  
Fuzzy Inference ◽  
Proton Exchange ◽  
Operating Conditions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Exchange Membrane

This work defines and implements a technique to predict water activity in proton exchange membrane fuel cell. This technique is based on the electrochemical impedance spectroscopy (EIS) as sensor and adaptive neuro-fuzzy inference system (ANFIS) as estimator. For this purpose, a proton exchange membrane fuel cell (PEMFC) model has been proposed to study the performances of the fuel cell for different operating conditions where the simulation model for water activity behavior is in the proposed structure. The technique based on ANFIS predicts the PEM fuel cell relative humidity (RH) from the EIS. For creation of ANFIS training and checking database, a new method based on factorial design of experimental is used. To check the proposed technique, the ANFIS estimator will be compared with the output humidity relative observation.

scholarly journals Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS)

2020 ◽  
Vol 12 (12) ◽  
pp. 4952 ◽  
Author(s):  
Tabbi Wilberforce ◽  
Abdul Ghani Olabi
Keyword(s):  
Experimental Data ◽  
Proton Exchange Membrane ◽  
Fuzzy Inference ◽  
Proton Exchange ◽  
Ambient Conditions ◽  
Inference System ◽  
Fuel Cell Performance ◽  
Neuro Fuzzy ◽  
Independent Variable ◽  
Exchange Membrane

This investigation explored the performance of PEMFC for varying ambient conditions with the aid of an adaptive neuro-fuzzy inference system. The experimental data obtained from the laboratory were initially trained using both the input and output parameters. The model that was trained was then evaluated using an independent variable. The training and testing of the model were then utilized in the prediction of the cell-characteristic performance. The model exhibited a perfect correlation between the predicted and experimental data, and this stipulates that ANFIS can predict characteristic behavior of fuel cell performance with very high accuracy.

Adaptive Neuro Fuzzy Control Design of a Solar Power Plant’s Complete Oil Cycle

2009 ◽  
Author(s):  
P. Shahmaleki ◽  
M. Amiri ◽  
M. Mahzoon
Keyword(s):  
Power Plant ◽  
Solar Power ◽  
Fuzzy Inference ◽  
Operating Conditions ◽  
Solar Power Plant ◽  
Inference System ◽  
System A ◽  
Neuro Fuzzy ◽  
Plant Simulation ◽  
Safe Operating

To enhance the performance and achieve a controlled condition with an optimized system a more precise modeling for power plant dynamics is needed. In this paper, a complete oil cycle of Shiraz solar power plant is modeled and controlled. Also, adaptive network-based fuzzy inference system (ANFIS) was employed to control collectors’ field. Furthermore, fuzzy switching control is utilized In order to prevent chattering phenomena of this multi-loop plant. Simulation results of the oil cycle solar power plant and the controller system show that the applied controller system can manage the oil cycle in different situations within safe operating conditions and with better performances.

scholarly journals A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1071 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Xiaohui Cui
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Oil Consumption ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Prediction Problems

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

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