An Integrated Fuzzy Inference-based Monitoring, Diagnostic, and Prognostic System for Intelligent Control and Maintenance

2008 ◽  
pp. 203-222 ◽  
Author(s):  
Dustin R. Garvey ◽  
J. Wesley Hines
Keyword(s):  
Intelligent Control ◽  
Fuzzy Inference ◽  
Prognostic System

scholarly journals Intelligent control of islanded AC microgrids based on adaptive neuro-fuzzy inference system

2021 ◽  
Vol 133 ◽  
pp. 107161
Author(s):  
Hossein Karimi ◽  
Mohammad T.H. Beheshti ◽  
Amin Ramezani ◽  
Hamidreza Zareipour
Keyword(s):  
Intelligent Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Path Planning and Control for a Flexible Transfer System

2000 ◽  
Vol 12 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Naoyuki Kubota ◽  
◽  
Yusuke Nojima ◽  
Fumio Kojima ◽  
Toshio Fukuda ◽  
...  
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Intelligent Control ◽  
Manufacturing System ◽  
Fuzzy Inference ◽  
Transfer System ◽  
Learning Automaton ◽  
Planning And Control ◽  
Manufacturing Line ◽  
And Control

We studied intelligent control of self-organizing manufacturing system (SOMS) composed of modules that self-organize based on time-series information from other modules and environment. Modules create output through interaction with other modules. We discuss intelligent control and path planning in a manufacturing line of conveyer units and machining centers. Genetic algorithm are applied to conveyor pallet path planning in global decision making and learning automaton is applied to local conveyer decision making. We use simplified fuzzy inference to control pallets providing interval, verifying its feasibility by simulation.

scholarly journals Intelligent Control of a Photovoltaic Generator for Charging and Discharging Battery Using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
El Hadji Mbaye Ndiaye ◽  
Alphousseyni Ndiaye ◽  
Mactar Faye ◽  
Samba Gueye
Keyword(s):  
Intelligent Control ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Pv System ◽  
Inference System ◽  
Point Tracking ◽  
Neuro Fuzzy ◽  
Bus Voltage ◽  
Power Point

This paper presents a method of intelligent control of a photovoltaic generator (PVG) connected to a load and a battery. The system consists of charging and discharging a battery. An intelligent algorithm based on adaptive neuro-fuzzy inference system (ANFIS) is presented in this work. It performs two separate tasks simultaneously. First, it is used as a PVG Maximum Power Point Tracking (MPPT) command. This same algorithm is used secondly for protecting the battery against deep charges and discharges. A regulation of the DC bus voltage is also carried out by means of a PI corrector for a good supply of the load. The simulation results under MATLAB/Simulink show that the method proposed in this work allows the PV system to function normally by charging and discharging the battery whatever the weather conditions.

Wide area adaptive hybrid fuzzy STATCOM controller for dynamic stability enhancement

2016 ◽  
Vol 35 (5) ◽  
pp. 1830-1849 ◽  
Author(s):  
Aman Ganesh ◽  
Ratna Dahiya ◽  
Girish Kumar Singh
Keyword(s):  
Power System ◽  
Intelligent Control ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Wide Area ◽  
Neural Controller ◽  
Content Type ◽  
Adaptive Fuzzy Controller ◽  
Control Scheme ◽  
Wide Operating

Purpose The purpose of this paper is to develop an adaptive fuzzy controller for STATCOM to damp low-frequency inter-area oscillation over wide operating range using wide area signals in multimachine power system. Design/methodology/approach In this paper tuneable fuzzy model is proposed where the parameters of the fuzzy inference system are tuned by using the adaptive characteristic of the artificial neural network. Based on back propagation algorithm and method of least square estimation, the fuzzy inference rule base is tweaked according to the data from which they are modelled. The wide area control signals, for the proposed controller, available in the power system are selected on the basis of eigenvalue sensitivity defined in terms of participation factor. Findings The effectiveness of the proposed controller with wide area signals is tested on two test cases, namely, two area network and IEEE 12 bus benchmark system. The comparative analysis of the proposed adaptive fuzzy controller is carried out with conventional STATCOM controller along with fuzzy-and neural-based supplementary controller all using selected wide area signals. The results show that neural network tuned fuzzy controller leads to better system identification and have enhanced damping characteristics over wide operating range. Originality/value In the available literature, numerous researchers have indicated the use of fuzzy logic controller and neural controller along with their hybrid schemes as STATCOM controller for improving the dynamics of the multimachine power system using local signals. The main contribution of the paper is in using the hybrid intelligent control scheme for STATCOM using wide area signals. The advantage of proposed scheme is that the performance of well-designed fuzzy system can be enhanced with the same training data that are used for designing a neural controller thus giving enhanced performance in comparison to individual intelligent control scheme.

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