Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.

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Applications of ANFIS in Loss of Excitation Faults Detection in Hydro-Generators

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2016040104 ◽

2016 ◽

Vol 5 (2) ◽

pp. 63-79 ◽

Cited By ~ 6

Author(s):

Mohamed Salah El-Din Ahmed Abdel Aziz ◽

Mohamed El Samahy ◽

Mohamed A. Moustafa Hassan ◽

Fahmy El Bendary

Keyword(s):

Design Process ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Impedance Measurements ◽

Line Voltage ◽

Artificial Intelligent ◽

Phase Current ◽

Neuro Fuzzy ◽

Generator Terminal

This article presents a new methodology for Loss of Excitation (LOE) faults detection in Hydro-generators using Adaptive Neuro Fuzzy Inference System. The proposed structure was trained by data from simulation of a 345kV system under different faults conditions and tested for various loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the article. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R and X) and the generator RMS Line to Line voltage and Phase current (Vtrms and Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other techniques. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process. The obtained results are very promising.

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Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

10.20944/preprints202009.0377.v1 ◽

2020 ◽

Author(s):

Randall Claywell ◽

Nadai Laszlo ◽

Felde Imre ◽

Amir Mosavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf ◽

Inference System ◽

Diffuse Fraction ◽

Testing Procedures

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

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Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System

Energies ◽

10.3390/en12020289 ◽

2019 ◽

Vol 12 (2) ◽

pp. 289 ◽

Cited By ~ 47

Author(s):

Majid Dehghani ◽

Hossein Riahi-Madvar ◽

Farhad Hooshyaripor ◽

Amir Mosavi ◽

Shahaboddin Shamshirband ◽

...

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Coupled Model ◽

Grey Wolf ◽

Hydropower Generation ◽

Grey Wolf Optimization ◽

Inference System ◽

Neuro Fuzzy ◽

Dam Reservoirs ◽

Thiessen Polygons

Hydropower is among the cleanest sources of energy. However, the rate of hydropower generation is profoundly affected by the inflow to the dam reservoirs. In this study, the Grey wolf optimization (GWO) method coupled with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the hydropower generation. For this purpose, the Dez basin average of rainfall was calculated using Thiessen polygons. Twenty input combinations, including the inflow to the dam, the rainfall and the hydropower in the previous months were used, while the output in all the scenarios was one month of hydropower generation. Then, the coupled model was used to forecast the hydropower generation. Results indicated that the method was promising. GWO-ANFIS was capable of predicting the hydropower generation satisfactorily, while the ANFIS failed in nine input-output combinations.

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Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

10.21203/rs.3.rs-77142/v1 ◽

2020 ◽

Author(s):

Randall Claywell ◽

Laszlo Nadai ◽

Imre Felde ◽

Amir Mosavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf ◽

Inference System ◽

Diffuse Fraction ◽

Testing Procedures

Abstract The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

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Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

10.31219/osf.io/6qybp ◽

2020 ◽

Author(s):

Randall Claywell ◽

Laszlo Nadai ◽

Felde Imre ◽

Amir Mosavi

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Evaluation Criteria ◽

Absolute Error ◽

Grey Wolf Optimizer ◽

Multi Layer Perceptron ◽

Grey Wolf ◽

Inference System ◽

Diffuse Fraction ◽

Testing Procedures

The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.

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Loss of Excitation Faults Detection in Hydro-Generators Using an Adaptive Neuro Fuzzy Inference System

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v1.i2.pp300-309 ◽

2016 ◽

Vol 1 (2) ◽

pp. 300 ◽

Cited By ~ 4

Author(s):

Mohamed Salah El-Din Abdel Aziz ◽

Mohamed Elsamahy ◽

Mohamed Moustafa ◽

Fahmy Bendary

Keyword(s):

Design Process ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Impedance Measurements ◽

New Approach ◽

Line Voltage ◽

Artificial Intelligent ◽

Neuro Fuzzy ◽

Generator Terminal

<em>This paper presents a new approach for Loss of Excitation (LOE) faults detection in Hydro-generators using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a 345kV system under various faults conditions and tested for different loading conditions. Details of the design process and the results of performance using the proposed technique are discussed in the paper. Two different techniques are discussed in this article according to the type of inputs to the proposed ANFIS unit, the generator terminal impedance measurements (R & X) and the generator RMS Line to Line voltage and Phase current (Vtrms & Ia). The two proposed techniques results are compared with each other and are compared with the traditional distance relay response in addition to other technique. The results show that the proposed Artificial Intelligent based technique is efficient in the Loss of Excitation faults (LOE) detection process and the obtained results are very promising</em>.

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