Optimized Design of Artificial Neural Network based Global MPPT for PV System under Partial Shading Conditions

Expanding the number of photovoltaic (PV) systems integrated into a grid raises many concerns regarding protection, system safety, and power quality. In order to monitor the effects of the current harmonics generated by PV systems, this paper presents long-term current harmonic distortion prediction models. The proposed models use a multilayer perceptron neural network, a type of artificial neural network (ANN), with input parameters that are easy to measure in order to predict current harmonics. The models were trained with one-year worth of measurements of power quality at the point of common coupling of the PV system with the distribution network and the meteorological parameters measured at the test site. A total of six different models were developed, tested, and validated regarding a number of hidden layers and input parameters. The results show that the model with three input parameters and two hidden layers generates the best prediction performance.

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Artificial Neural Network Based Maximum Power Point Tracking for PV System

2019 Chinese Control Conference (CCC) ◽

10.23919/chicc.2019.8865275 ◽

2019 ◽

Cited By ~ 1

Author(s):

Al-wesabi Ibrahim ◽

Min Ding ◽

Xiaoling Jin ◽

Xianzhong Dai ◽

Mohammad Abu Sarhan ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Maximum Power Point Tracking ◽

Maximum Power ◽

Pv System ◽

Maximum Power Point ◽

Point Tracking ◽

Power Point Tracking ◽

Artificial Neural ◽

Power Point

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Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

Journal of Solar Energy Engineering ◽

10.1115/1.4030693 ◽

2015 ◽

Vol 137 (4) ◽

Cited By ~ 8

Author(s):

Ammar Mohammed Ameen ◽

Jagadeesh Pasupuleti ◽

Tamer Khatib ◽

Wilfried Elmenreich ◽

Hussein A. Kazem

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Measured Data ◽

Mean Bias Error ◽

Output Current ◽

Pv System ◽

Proposed Model ◽

Artificial Neural ◽

Predicted Values

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system’s output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model’s accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, −0.21 A (−4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

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Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling

Materials ◽

10.3390/ma12183037 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3037 ◽

Cited By ~ 3

Author(s):

Xiaobo Xu ◽

Xiaocheng Zhang ◽

Zhaowu Huang ◽

Shaoyou Xie ◽

Wenping Gu ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Mean Bias Error ◽

Neural Network Modeling ◽

Bias Error ◽

Pv System ◽

Shunt Resistance ◽

Pv Module ◽

Pv Modules ◽

Artificial Neural

In the photovoltaic (PV) field, the outdoor evaluation of a PV system is quite complex, due to the variations of temperature and irradiance. In fact, the diagnosis of the PV modules is extremely required in order to maintain the optimum performance. In this paper, an artificial neural network (ANN) is proposed to build and train the model, and evaluate the PV module performance by mean bias error, mean square error and the regression analysis. We take temperature, irradiance and a specific voltage for input, and a specific current value for output, repeat several times in order to obtain an I-V curve. The main feature lies to the data-driven black-box method, with the ignorance of any analytical equations and hence the conventional five parameters (serial resistance, shunt resistance, non-ideal factor, reverse saturation current, and photon current). The ANN is able to predict the I-V curves of the Si PV module at arbitrary irradiance and temperature. Finally, the proposed algorithm has proved to be valid in terms of comparison with the testing dataset.

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The Optimized Design of Artificial Neural Network Model

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol6.issue22.90 ◽

2012 ◽

Vol 6 (22) ◽

pp. 794-800

Author(s):

Wenying ZHANG ◽

Jinyu GUAN ◽

Suqin FENG

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Optimized Design ◽

Artificial Neural

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Evaluation of the Use of Artificial Neural Networks in Solar Energy

Solar Concentrating Modules With Louvered Heliostats - Advances in Environmental Engineering and Green Technologies ◽

10.4018/978-1-7998-4276-7.ch007 ◽

2021 ◽

pp. 182-210

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Set Theory ◽

Optimization Problems ◽

Search Algorithm ◽

Pv System ◽

Solar Insolation ◽

Pv Panel ◽

Artificial Neural ◽

Artificial Neural Network Ann

Artificial neural network (ANN) is initially used to forecast the solar insolation level and followed by the particle swarm optimisation (PSO) to optimise the power generation of the PV system based on the solar insolation level, cell temperature, efficiency of PV panel, and output voltage requirements. Genetic algorithm is a general-purpose optimization algorithm that is distinguished from conventional optimization techniques by the use of concepts of population genetics to guide the optimization search. Tabu search algorithm is a conceptually simple and an elegant iterative technique for finding good solutions to optimization problems. Simulated annealing algorithms appeared as a promising heuristic algorithm for handling the combinatorial optimization problems. Fuzzy logic algorithms set theory can be considered as a generation of the classical set theory. The artificial neural network (ANN)-based solar insolation forecast has shown satisfactory results with minimal error, and the generated PV power can be optimised significantly with the aids of the PSO algorithm.

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