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 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 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.

scholarly journals Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 351 ◽  
Author(s):  
Promphak Dawan ◽  
Kobsak Sriprapha ◽  
Songkiate Kittisontirak ◽  
Terapong Boonraksa ◽  
Nitikorn Junhuathon ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Fuzzy Inference ◽  
Photovoltaic System ◽  
Mean Square ◽  
Fuzzy Inference Systems ◽  
Swarm Optimization ◽  
Pv Systems ◽  
Neuro Fuzzy ◽  
Inference Systems

The power output forecasting of the photovoltaic (PV) system is essential before deciding to install a photovoltaic system in Nakhon Ratchasima, Thailand, due to the uneven power production and unstable data. This research simulates the power output forecasting of PV systems by using adaptive neuro-fuzzy inference systems (ANFIS), comparing accuracy with particle swarm optimization combined with artificial neural network methods (PSO-ANN). The simulation results show that the forecasting with the ANFIS method is more accurate than the PSO-ANN method. The performance of the ANFIS and PSO-ANN models were verified with mean square error (MSE), root mean square error (RMSE), mean absolute error (MAP) and mean absolute percent error (MAPE). The accuracy of the ANFIS model is 99.8532%, and the PSO-ANN method is 98.9157%. The power output forecast results of the model were evaluated and show that the proposed ANFIS forecasting method is more beneficial compared to the existing method for the computation of power output and investment decision making. Therefore, the analysis of the production of power output from PV systems is essential to be used for the most benefit and analysis of the investment cost.

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.

Switched Capacitor-Coupled Inductor DC–DC Converter for Grid-Connected PV System using LFCSO-Based Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 29 (12) ◽  
pp. 2050201 ◽  
Author(s):  
J. K. Mohan Kumar ◽  
H. Abdul Rauf ◽  
R. Umamaheswari
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Switched Capacitor ◽  
Lévy Flight ◽  
Pv System ◽  
Partial Shading ◽  
Levy Flight ◽  
Inference System ◽  
Neuro Fuzzy

In this paper, the Levy flight-based chicken swarm optimization (LFCSO) is proposed to follow the highest power of grid-joined photovoltaic (PV) framework. To analyze the grid-associated PV framework, the characteristics of current, power, voltage, and irradiance are determined. Because of the low yield voltage of the source PV, a big advance up converter with big productivity is required when the source PV is associated with the matrix power. A tale great advance up converter dependent on the exchanged capacitor and inductor is illustrated in this paper. The LFCSO algorithm with the adaptive neuro-fuzzy inference system is used to generate the control pulses of the transformer-coupled inductor DC–DC converter-less switched capacitor. While using the switched capacitor-coupled inductor, the voltage addition is expanded in the DC–DC converter and the power of PV is maximized. Here, the normal CSO algorithm is updated with the help of Levy flight functions to generate optimal results. To get the accurate optimal results, the output of the proposed LFCSO algorithm is given as the input of the ANFIS technique. After that, the optimal results are generated and they provide the pulses for the system. The working guideline is analyzed and the voltage addition is derived with the utilization of the proposed technique. From that point forward, it predicts the exact maximum power of the converter according to its inputs. Under the variety of solar irradiance and partial shading conditions (PSCs), the PV system is tested and its characteristics are analyzed in different time instants. The proposed LFCSO with ANFIS method is actualized in Simulink/MATLABstage, and the tracking executing is examined with a traditional method such as genetic algorithm (GA), perturb and observe (P&O) technique–neuro-fuzzy controller (NFC) and fuzzy logic controller (FLC) technique.

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.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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