scholarly journals Forest Fire Forecasting Using Fuzzy Logic Models

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1005
Author(s):  
Àngela Nebot ◽  
Francisco Mugica
Keyword(s):  
Fuzzy Logic ◽  
Forest Fires ◽  
Fuzzy Inference ◽  
Inductive Reasoning ◽  
Burned Area ◽  
Prediction Ability ◽  
Inference System ◽  
Burned Areas ◽  
Neuro Fuzzy ◽  
Fuzzy Techniques

In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature.

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

2021 ◽  
pp. 014459872110417
Author(s):  
Ya-Jun Fan ◽  
Hai-tong Xu ◽  
Zhao-Yu He
Keyword(s):  
Fuzzy Logic ◽  
Wind Energy ◽  
Output Power ◽  
Wind Turbines ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Hybrid Control ◽  
Inference System ◽  
Neuro Fuzzy

Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

scholarly journals An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

2016 ◽  
Vol 6 (3) ◽  
pp. 1294
Author(s):  
Dragan Mlakić ◽  
Srete N Nikolovski ◽  
Goran Knežević
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Voltage ◽  
Distribution Networks ◽  
Investment Planning ◽  
Smart Meters ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

2016 ◽  
Vol 6 (3) ◽  
pp. 1294
Author(s):  
Dragan Mlakić ◽  
Srete N Nikolovski ◽  
Goran Knežević
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Voltage ◽  
Distribution Networks ◽  
Investment Planning ◽  
Smart Meters ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

Navigational path analysis of mobile robots using an adaptive neuro-fuzzy inference system controller in a dynamic environment

2010 ◽  
Vol 224 (6) ◽  
pp. 1369-1381 ◽  
Author(s):  
D R Parhi ◽  
M K Singh
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Inference ◽  
Dynamic Environment ◽  
Fuzzy Logic System ◽  
Inference System ◽  
The Neural Network ◽  
Neuro Fuzzy ◽  
Logic System

This article focuses on the navigational path analysis of mobile robots using the adaptive neuro-fuzzy inference system (ANFIS) in a cluttered dynamic environment. In the ANFIS controller, after the input layer there is a fuzzy layer and the rest of the layers are neural network layers. The adaptive neuro-fuzzy hybrid system combines the advantages of the fuzzy logic system, which deals with explicit knowledge that can be explained and understood, and those of the neural network, which deals with implicit knowledge that can be acquired by learning. The inputs to the fuzzy logic layer include the front obstacle distance, the left obstacle distance, the right obstacle distance, and target steering. A learning algorithm based on the neural network technique has been developed to tune the parameters of fuzzy membership functions, which smooth the trajectory generated by the fuzzy logic system. Using the developed ANFIS controller, the mobile robots are able to avoid static and dynamic obstacles and reach the target successfully in cluttered environments. The experimental results agree well with the simulation results; this proves the authenticity of the theory developed.

scholarly journals An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
M. Ajay Kumar ◽  
N. Srikanth
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Pulse Width ◽  
Fuzzy Logic Controller ◽  
Pulse Width Modulation ◽  
Fuzzy Inference ◽  
Light Transmission ◽  
Inference System ◽  
Hvdc Light ◽  
Neuro Fuzzy

AbstractIn HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

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