The Output Power of the PV Power Plant Modeling Based on ANFIS

2014 ◽  
Vol 1006-1007 ◽  
pp. 945-954 ◽  
Author(s):  
Ling Liu ◽  
Bao Guo Tang ◽  
Kai Sun
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Power Plants ◽  
Fuzzy Inference ◽  
Engineering Application ◽  
Subtractive Clustering ◽  
Weather Factors ◽  
Inference System ◽  
Practical Engineering ◽  
Application Requirements

To find an effective and reasonable method for calculating precisely the output power of the PV power plant, adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno (TS) is proposed. Analysis of the various weather factors that affect the output power of the PV power plant, and select the appropriate input ,MATLAB as a tool ,depend on the different input variable to establish different output power of photovoltaic power plants based on the subtractive clustering the ANFIS model .Results show that all the model has a high accuracy and meet the practical engineering application requirements,by comparing models choose the optimal model.

Study on the Output Power of the PV Power Plant Model Based on ANFIS

2013 ◽  
Vol 724-725 ◽  
pp. 190-194
Author(s):  
Hai Feng Liang ◽  
Hai Hong Wang ◽  
Zi Xing Liu
Keyword(s):  
Power Plant ◽  
Output Power ◽  
Power Output ◽  
Fuzzy Inference ◽  
Engineering Application ◽  
Subtractive Clustering ◽  
Interval Estimate ◽  
Pv System ◽  
Inference System ◽  
Output Characteristics

in order to study the output power of PV plant in depth, effective and reasonable methods of modeling for PV power plant are explored and adaptive neuro-fuzzy inference system (ANFIS) based on Takagi-Sugeno (TS) model is proposed in this paper. According to the power output characteristics of PV system and a variety of factors which impact, three kinds of model of PV plant power output are established based on subtractive clustering ANFIS. After model test and calculation for confidence interval estimate of power output, the results show that the accuracy of the model is able to meet the practical engineering application requirements and the second model is optimal by comparison. In conclusion, ANFIS provides an innovative and feasible model establishment method for the power output of PV plant.

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.

Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System

2016 ◽  
Vol 5 (4) ◽  
pp. 64-82 ◽  
Author(s):  
Shereen A. El-aal ◽  
Rabie A. Ramadan ◽  
Neveen I. Ghali
Keyword(s):  
Mutual Information ◽  
Motor Imagery ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Subtractive Clustering ◽  
Imagery Task ◽  
Eeg Signals ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Maximal Relevance

Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.

Comparative Optimization on the Focusing Methods of Solar-Electric Dish Stirling System

2012 ◽  
Vol 236-237 ◽  
pp. 714-719
Author(s):  
Wei Lan ◽  
Bin Wang ◽  
Yi Ming Feng
Keyword(s):  
Power Systems ◽  
High Speed ◽  
Power Plants ◽  
Solar Power ◽  
Thermal Power ◽  
Engineering Application ◽  
Solar Thermal ◽  
Solar Thermal Power ◽  
Low Emission ◽  
Practical Engineering

Nowadays, the high-speed economic development has caused significant consumption of energy. While the circumstance is getting severer, solar energy is taken as a kind of clean, environmental friendly resource with infinite storage that has aroused a wide public concern. Photovoltaic and solar thermal are two main categories of solar applications. Because of its high conversion efficiency, low emission and flexible installation, dish Stirling solar power technology is more preferable to be used among the solar thermal area. From the view of practical engineering application, this paper illustrates multiple focusing methods of the current dish Stirling solar power systems in detail, and the comparison of these methods are given to analyze their advantages, disadvantages and their application scenarios. It can be used for the future development of dish Stirling solar power technology and applied as a reference for large dish solar thermal power plants’ installations and tests.

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