Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Héctor Felipe Mateo Romero
 -- 4855 2022-10-20 11:55:21 |
2 format change
Peter Tang
 + 5 word(s) 4860 2022-10-20 13:34:34 | |
3 format change
Peter Tang
 + 4 word(s) 4864 2022-10-24 08:55:46 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Romero, H.F.M.;  Rebollo, M.�.G.;  Cardeñoso-Payo, V.;  Gómez, V.A.;  Plaza, A.R.;  Moyo, R.T.;  Hernández-Callejo, L. Artificial Intelligence Applied to Photovoltaic Systems. Encyclopedia. Available online: https://encyclopedia.pub/entry/30498 (accessed on 21 December 2024).
Romero HFM,  Rebollo M�G,  Cardeñoso-Payo V,  Gómez VA,  Plaza AR,  Moyo RT, et al. Artificial Intelligence Applied to Photovoltaic Systems. Encyclopedia. Available at: https://encyclopedia.pub/entry/30498. Accessed December 21, 2024.
Romero, Héctor Felipe Mateo, Miguel Ángel González Rebollo, Valentín Cardeñoso-Payo, Victor Alonso Gómez, Alberto Redondo Plaza, Ranganai Tawanda Moyo, Luis Hernández-Callejo. "Artificial Intelligence Applied to Photovoltaic Systems" Encyclopedia, https://encyclopedia.pub/entry/30498 (accessed December 21, 2024).
Romero, H.F.M.,  Rebollo, M.�.G.,  Cardeñoso-Payo, V.,  Gómez, V.A.,  Plaza, A.R.,  Moyo, R.T., & Hernández-Callejo, L. (2022, October 20). Artificial Intelligence Applied to Photovoltaic Systems. In Encyclopedia. https://encyclopedia.pub/entry/30498
Romero, Héctor Felipe Mateo, et al. "Artificial Intelligence Applied to Photovoltaic Systems." Encyclopedia. Web. 20 October, 2022.
Artificial Intelligence Applied to Photovoltaic Systems
Edit
This entry is adapted from the peer-reviewed paper 10.3390/app121910056

Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. Artificial intelligence (AI) is used to solve the most important problems found in photovoltaic (PV) systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training.

PV artificial intelligence MPPT forecasting parameter estimation faults detection

1. Introduction

Energy is essential in our society, being the motor of almost every sector. Fossil-fuels are historically the most important source of energy, representing 80.2% in 2019 [1] These kinds of energies have different problems; one of them is their scarcity, since they are limited resources that have been exploited for a long time. Another critical problem is the pollution caused by the burning and extraction of these fuels, which is hazardous for people [2] and the environment [3]. To solve these problems, other energy sources can be used. These alternative energies, renewable energies, have two main benefits. First of all, they are based on unlimited resources that will not run out, even with extensive exploitation. Their exploitation is also nonpolluting. Investment in these energies has been rising in the last years, even with a crisis such as the COVID-19 pandemic [1][4].
One of the most important green energies is solar energy. This energy is composed of solar, thermal and photovoltaic (PV). The latter has been found to be more useful and profitable for industry production [5][6] and has been growing steadily in recent years.
PV energy is produced by photovoltaic modules. Each module is composed of different sub-units, called solar cells, which absorb the energy emitted by the sun [7]. PV panels are usually connected in series to each other, this is known as a PV array. Each PV array is connected to a power inverter to control the production and check the performance of the array [8]. PV farms (also known as PV plants) are usually composed of many PV arrays. The maintenance of these factories is extremely complex. The production of the modules depends on different conditions, this makes mechanisms of control to optimize the production necessary. Solar modules are also vulnerable to physical defects, which can reduce or even nullify the production of one cell, or even the whole modules in the worst cases. This is usually dealt with by human labor, checking each module in a certain period of time.
Solutions to maintenance problems in PV systems have been traditionally circumscribed either to simplistic automatic supervision approaches [9] or costly direct human supervision.
In the past recent years, however, artificial intelligence (AI)-based approaches have emerged. AI techniques are being applied in almost every research field or industry to improve services or solve problems which are impossible for traditional methods [10].

2. Artificial Intelligence Applied to PV Systems

2.1. Maximum Power Point Tracking (MMPT)

The tracking of the Maximum Power Point is vital to optimize the PV systems, and it is probably the most interesting problem for research. Different techniques have been used to solve this problem, as it can be seen in Figure 1. Some classical techniques include Incremental Conductance and Perturb and Observe. Recent trends show that AI techniques are also used to solve this problem. Metaheuristics and Neural Networks were found as the most used techniques after surveying the literature.
Figure 1. Taxonomy of most used IA method for Maximum Power Point Tracking (MPPT).
A summary of the Fuzzy Logic methods applied to MPPT can be seen in Table 1.
Table 1. Fuzzy Methods for MPPT.

Method

Features

FLC [11][12][13][14][15]

FLC systems provide quick responses to changes and low oscillations near MPPT that reduce the power loss compared with traditional systems. The combination with FCN or the initial estimation of the MPP voltage further improves the results.

Type-2  [16][17][18][19]

Type-2 FL provides the methods to model and handle uncertainties, boosting the robustness of the system and hence its results.

T-S [20][21][22]

The parallel distributed control provided by the T-S FL further improves the results of FL systems, having an acceptable settling time, less oscillations and an accurate output.

Combined with other methods  [23][24][25][26][27][28]

Other methods can take advantage of the benefits of FLC systems to improve their results in MPPT.
A summary of the commented methods can be found in Table 2.
Table 2. Metaheuristic Algorithms for MPPT.

Algorithm

Features

GA [29][30][31]

Genetic Algorithms improves the results of other methods such as ANN or FPSO

PSO [32][33]

PSO is used to optimize Neural Network learning

FA [34]

This algorithm is used directly to solve MPPT. It assures fast convergence with almost zero oscillations

ABC [35][36][37]

In MPPT, this algorithm provides quick converge and accuracy in tracking.

ACO [38]

ACO is used in the learning process for adjusting weights and biases or the neural networks in other to improve its results
A summary of the analyzed models in this section can be found in Table 3.
Table 3. Neural Network Models for MPPT.

Type

Reference

Features

FeedForward Neural Network

[39]

2 networks. Each one with a single hidden layer of 20 nodes.

[29]

5 Nodes on a single layer. Data preprocessed by Genetic Algorithm.

[40]

Three hidden layers with 8,7,7 nodes, respectively. Bayesian-Regulated back-propagation for training.

[41]

A Single hidden layer with 13 neurons. Data created by a Course Gaussian Support Vector Machine.

[24]

2-3-3-1 structure. The NN is optimized by FPSOGSA.

[42]

The topology and best weights are optimized by a PSO algorithm.

[43]

ACO is used to optimize the neural network.

Adaptive Neural Fuzzy System Interface

[25]

Bat Algorithm is used to train the network.

[44]

Crowded Plant Height Optimization is in charge of performing the learning of the network.

[45]

Combines Fuzzy Logic and Neural Networks. Three intermediate layers in which the output is based on fuzzy rules.

Recurrent Neural Network

[23]

A hidden layer and a context layer storing the results of the previous outputs of the hidden layer. A metaheuristic is used to optimize the structure and weights.

Deep Reinforcement Learning

[46]

Four networks, one for computing the policy, one for the critic and two called targets that are used to stabilize the learning procedure

2.2. Forecasting

Energy production forecasting has been an important problem, even in traditional systems, and it has been tackled with different techniques, as it can be seen in Figure 2.
Figure 2. Taxonomy of most used IA methods for forecasting.
In [47], the researchers found a system that uses Support Vector Machines (SVM) [48]. SVM is mostly used for regression. The model uses two different inputs: solar irradiance and environmental temperature, with energy production as the output. This work included the use of a parameter to tune the number of support vectors during the training. The results show a low error, with a Mean Absolute Percentage Error (MAPE) of 0.1143, but it was really intolerant at errors in the input data. The method was implemented using MATLAB. Another approach related with SVM is found in [49]. The authors propose a multi-input support vector. Three different inputs were tested. Only solar power, solar power and solar irradiance combined and finally solar power, temperature and irradiance. The best predictions were made when the third vector was used to train the network with. The model showed better results than analytical methods with a MAPE of 36%, but it was found that it was weak against changes in the climate. The method was implemented using MATLAB.
In [50], a Neural Network was used for Short-Term Forecasting. The input data were composed of the the deviation of load power and temperature of 30 days before the forecast day and the same data of 60 days before and after the forecast day in the previous year. If the forecast day is changed, the neural network needs to be retrained. The network is composed of 9 inputs nodes, 20 hidden nodes and one output neuron. The results show a Mean Absolute Percentage Error (MAPE) of 1.63% on average.
The work of [51] tries to go further, presenting a neural network of 2 hidden layers, one of 6 nodes and the second with 4. This model has nine inputs (Day, Time, Cloud Cover Index, Air Temperature, Wind speed, Air Humidity, UV index, precipitation and air pressure) and is trained using a hybrid metaheuristic, which combines PSO and GA [52]. This hybrid is faster and more robust than back-propagation for this problem.
Neural Networks have been found to be sensitive to many factors, including the architecture or the initialization of weights. Combining different NNs in an ensemble has been found to be a strategy to reduce these problems. The work of [53] tested different combinations using temperature and solar irradiance as inputs. Every combination was found to be better than using only a single NN. The data were composed of 7300 data from 365 different days. The findings were that the best architecture for forecasting is the one which uses an iterative methodology to find the outputs, forecasting one at a time with a Mean Absolute Error (MAE) of 51.48% and Mean Relative Error (MRE) of 17.24%.
The work in [54] used a fixed methodology, changing activation functions, learning rules and architecture in order to find the best neural network for their dataset. The data were acquired along a period of 70 days, obtaining 11,200 examples. The best network had 1 hidden layer with a Linear Sigmoid Activation Function. The learning rule as Conjugate Gradient [55], which uses second derivatives to determinate the weight update, inputs temperature and photovoltaic power and outputs next-day forecasting of PV power output. The validation study indicates that this network is simple and versatile and can precisely forecast with a minimum MAPE of 0.8655. The experiments were implemented using the NeuroSolutions [56].
Another problem of NN is that training can be slow since back-propagation is highly demanding. For solving this problem, the work in [57] used the extreme learning machine (ELM) technique to train the network. ELM [58] has a faster learning speed while obtaining better generalization performance, and it also optimizes the number of hidden neurons. The system is composed of three networks, one for each kind of weather. The network is trained with the PV output history and the weather history data. Based on the weather report of the next day, the model is chosen to forecast the day-ahead PV. The results show that ELM networks outperformed BP networks with a MAPE of 2.78% in the best case. The experiments were implemented using MATLAB.
Another improvement can be found in [59]; the neural network is aided by a technique known as Wavelet Transform (WT) [60]. This algorithm is specialized in isolating the spikes produced by continuous fluctuations of the PV data. It has two stages: decomposition of the input signal, which is performed before the neural networks, and reconstruction, which is performed with the output of the NN. The model used is a Radial Basic Neural Networks (RBNN) [61], which needs less computation time and is more effective than Backpropagation Neural Networks and takes as input the PV, solar irradiance and temperature of the current hour, twelve hours before and twenty hours before in order to predict the one-hour-ahead power output. The results show that the proposed model outperformed RBNN without WT for hourly PV for the horizon of 12 hours with a MAPE of 2.38% in the best case.
WT is used along other architectures as in [62]. RNNs are probed to be useful in order to predict from time series and WT deals with the fluctuations on the data provided by the meteorological time series obtained from sampling at intervals of 10 min and stored as time series. This combination proved to be able to forecast 2 days ahead more accurately than other Neural Networks.
A recent use of WT is found in [63]. This work presents a hybrid algorithm composed of WT, PSO and RBFNN used to forecast from 1 to 6 hours ahead. The inputs that are used in the model are set as Actual PV, irradiance and temperature. The WT is used to perform an data filtering on the past 15 days before the forecast day. The RBFNN is optimized by the PSO algorithm. The network performed better than the compared methods, with an MAE of 4.22% on average for a 1-hour-ahead forecast, 7.04% for a 3-hour-ahead one and 9.13% for 6-hour- ahead one.
Recurrent Neural Networks are also used in [64]. Deep Recurrent Neural Networks (DRNN), RNNs with many hidden layers, are used to forecast. These networks are capable of representing complex functions more efficiently than RNNs. The input data are composed of high-resolution time series, which are preprocessed and normalized to obtain a high-resolution time-series dataset of four different days. The architecture used was a DRNN with Long Short-Term Memory (LSMT) [65] units with two hidden layers of 35 neurons. Other models showed lower accuracies and more bias error than the proposed method that obtained an RMSE of 0.086. The experiments were implemented using MATLAB and the Keras library (now on tensorflow) in Python.
Another RNN method is found in [66]. The authors compared 5 different architectures of RNN: A basic LSTM, an LSTM with the window technique, an LSTM with time steps, an LSTM with memory between batches and stacked LSTMs with memory between batches. Two datasets of different cities were used to test the 3 models. The results show the third proposal with an RMSE of 82.15 in the first dataset and an RMSE of 136.87 in the second, which uses prior time steps in the PV series as inputs, is the most accurate and reliable, even compared with other methods such as ANN. The experiments were implemented using Keras.
The authors of [67] present an interesting modification of RNN. This work used the networks know as Echo State Network [68]. ESN presented a dynamical reservoir instead of the traditional hidden layers of RNN. Their main advantage is that only the output weights need to be trained since the reservoir and input ones are random. These networks can obtain better results than typical RNN. A restricted Boltzmann machine (RBM) [69] and principal component analysis (PCA) [70] are used in order to determine the number of reservoirs and inputs. The network parameters are found by a DFP Quasi-Newton algorithm [71]. Compared with other PV forecasting methods, the results show that the proposed model could outperform other forecasting systems with a MAPE of 0.00195%.
A complex hybrid is found in [72]. This system uses NN aided by different algorithms trained on data obtained during a year. Random Forest (RF) [73] is used to rank the different factors that affect PV in order to eliminate the less important ones. This importance degree, computed by RF, is transferred to Improved Gray Ideal Value Approximation (IGIVA) [74] as weights to determine the similar days of different climates type. The objective of this is to improve the quality of datasets. After that, the original sequence is decomposed by Complementary Ensemble Empirical Mode Decomposition (CEEMD) [75] to reduce the fluctuation of the original data. Finally, the neural network is optimized by a modification of PSO known as dynamic inertial factor particle swarm optimization (DIFPSO) [76][77]. The proposed model reduced training time and improved the forecasting accuracy with an MAE of 2.84 on sunny days, 10.12 on cloudy days and 13.01 on rainy or snowy days.
Another interesting approach is the Neuro-Fuzzy hybrid found in [78]. Fuzzy Logic is applied as a filter to the input data obtained in the energy production and weather forecast for 12 months (day, irradiance, temperature, humidity, pressure, wind speed and cloud clover) in order to speed up the system. The neural structure is composed of 7 inputs, 2 hidden layers of 9 and 5 nodes, respectively, and input. The network is trained by BP aided by a combination of PSO and GA, known as Genetic Swarm Optimization [79]. This method improved convergence speed and the predictive performance over other hourly forecast methods. The experiments were implemented using MATLAB Convolutional Neural Networks have also been applied to time-series data since they are able to learn filters that represent repeated patterns in the data without needing any prior knowledge. They also work well with noisy data. In [80], CNNs are applied for forecasting PV power using Solar Data and Electricity Data as inputs. The CNNs used the ReLu activation function, Adam optimizer and dropout to avoid overfitting. The parameters were selected by testing different architectures and choosing the most promising. The models were compared of an FFNN and an RNN of 128 hidden nodes. The results show that CNN performed similarly to LSTM and better than MLP with an MAE of 114.38.
An interesting approach mixing Big Data and Deep Learning is found in [81]. This method was used to next-day-ahead forecast in 30 min intervals. It used a multistep methodology that decomposes the forecasting problem in different subproblems. For the Big Data, Spark Apache was used. The neural Network parameters were searched using the grid search strategy. The best structure was found with 3 hidden layers with between 12 and 32 neurons. The method demonstrated that DL is suitable for big solar data since it has a linear increase in training time and performs better than other methods.
The work of [82] makes use of a new kind of Neural Network, the Dendritic Neuron Network [83], in order to forecast PV power. These kinds of neurons have 4 types of layers: synaptic layer, branch layer, membrane layer and cell-body layer. The input data (temperature and irradiance of the actual moment and the last) are transferred to the synaptic layers where they are converted by the sigmoid function and summarized to the branch layer. The results are transported to the cell-body layer for numerical judgment. This layer will transmit the data thought the axon to other neurons when the data exceed a given threshold. This new kind of network provides higher convergence speed and enhanced fitting ability. The network is also aided by WT. The results show that the model outperformed typical Feed-Forward models with an average MAPE of 10.9, with strong fluctuations and 4.55 on weak fluctuations. The experiments were run using MATLAB.
In Table 4, a summary of the reviewed models is presented.
Table 4. Models for forecasting.

Type

Features

Feed-Forward Neural Network

Nine inputs, 20 hidden nodes on a single layer. [50]

Nine inputs, 2 hidden layers with 6 and 4 nodes, respectively. Trained by a hybrid PSO GA algorithm. [51]

Two inputs, creates ensembles of neural networks. [53]

Two inputs, 1 hidden layer, Conjugate Gradient as learning rule. [54]

Three neural networks, one for each kind of weather. Uses Extreme Learning to optimize the parameters and architecture.  [57]

Fuzzy Logic is applied as a filter to the input data. Seven inputs, 2 hidden layers of 9 and 5 nodes, respectively. Trained by a hybrid of PSO and GA. [78]

Uses Big Data. Multistep methodology decomposes the problems into subproblems. [81]

Convolutional Neural Networks

Two inputs. Parameters are selected by testing different combinations.  [80]

Dendritic Neural Networks

Aided by WT. Provides better convergence speed and better fitting ability.  [82]

Radial Basis Network

Two inputs, aided by Wavelet Transform to preprocess the input data. [59]

High-resolution time series as input. Aided by Wavelet Transform to preprocess input data and PSO to optimize the neural network. [63]

Recurrent Neural Network

Aided by Wavelet Transform to deal with fluctuation in time series input data. [62]

Preprocessed and normalized high-resolution time series as input. Two hidden layers of 35 neurons. [64]

Tested Different RRN architectures. LSTM, which uses previous time steps, found the best one. [66]

Uses Echo State Networks aided by Restricted Boltzmann Machine, Principal Component Analysis and DFP Quasi-Newton Algorithm to optimize the network. [67]

Support Vector Machines

Two inputs. A parameter to tune the number of SVM during training. [47]

SMV compared with KNN. SMV was found to be better. [84]

Multi-input SV. Different combinations of inputs were tested. Three inputs was the best one found. [49]

2.3. Parameter Estimation

Finding the parameters of the PV models is vital to simulate their behavior and to optimize their production. This problem is simplified by finding the unknown parameters in order to optimize the output power. Different techniques, most of them metaheuristics, have been used to solve this problem, as can be seen in Figure 3.
Figure 3. Taxonomy of most used metaheuristics for parameter estimation.
Metaheuristics are the most used techniques to estimate PV parameters. Different kinds of algorithms have been evaluated in recent years. The work in [85] compares different evolutionary algorithms, comparing Genetic Algorithms [86], Particle Swarm Optimization [87] and differential evolution [88]. DE is an evolutionary algorithm similar to Genetic Algorithms but which uses real numbers to codify the problem, this solves the problem of GA when it comes to converging speed. The fitness function was computed as the sum of the absolute errors in current and voltage. The findings showed that the best results were given by DE and the worst ones by GA. The authors also implemented different hybrids: Tabu Search [89] assisted differential evolution to avoid falling in local minimums, PSO assisted DE in which PSO is activated after 5 generations of DE and DE assisted by Tabu Search where DE is used to search for the optimal solution in a subset of the whole search space, while TS is used to move the local search within the global space. These hybrids performed better than the originals and provided more stability. DE assisted TS and provided the best results, and it was the fastest.

2.4. Defects Detection

Finding defects on the surface of the PV cells is a problem completely related to computer vision. As observed in the bibliography, the most used technique for photographing the images is electroluminescence. The datasets are usually private, but there are some exceptions. It can be seen in Figure 4 the most used techniques for detecting defects.
Figure 4. Most used IA method for defect detection.
Classical approaches as found in [90], which tried to detect defects in the solar modules using image processing techniques. In order to segment the different modules, they used the first derivative of the statistic curve in order to find the division line between each chip. After that, they used another technique, the otsu method, to obtain a binary image. Finally, the algorithm tries to identify the state of the module using the geometry of the resulting image. This algorithm produced interesting results, with a recognition rate of 80% on cracked modules, 95% on fragmented and 99% on good state modules. The recognition was also quite fast. The algorithms were implemented and applied via MATLAB.
Another approach is found in [91]. This method combines the image processing techniques with Support Vector Machines. The dataset featured 13,392 samples of EL images of solar cells. The images are preprocessed in order to reduce spatial noises and to accurately highlight crack pixels in images. After that, binary processing is performed, and finally, the features are extracted from the image. These features are used by different SVMs in order to classify the cells. The results present that the SVM with penalty parameter weighting is the best SVM, with a correct detection rate of 91%, with specificity and accuracy of more than 97%. The experiments were run in MATLAB.
In [92], the author compare Convolutional Neural Networks with SVM. The SVM is trained with data from the ELPV dataset, composed of 2624 EL images of solar cells, obtained by finding the features of the images using different feature descriptors. The CNN used was a pretrained VGG19 with the upper layers changed and trained with the examples. The models were tested with both monocrystalline and polycrystalline modules. The results show that both classifiers were useful for visual inspection, both with an average accuracy of 82.4%. The algorithms were implemented in Python, using Keras for the Neural Network.
The work in [93] presented a similar approach using SVM and CNN. The CNN was composed of two convolutional layers with leaky-relu and max-pooling. The convolutional part was aided by two leaky-relu dense layers and the output layer. The SVM was trained with different features extracted from the images. The dataset was built with 90 images of full-sized commercial modules that were segmented afterward, obtaining 540 cells. The results show similar behavior in both methods, with an accuracy of 98%. The article also tackled unsupervised learning, trying to cluster the images by two features. This resulted in a model that was able to assign the correct label in 66% of cases.The algorithms were implemented in Python, using Tensorflow and OpenCV.
The work found in [94] presents a CNN with 13 convolutional layers, an adaptation of the VGG16 architecture. The dataset was obtained by photographing solar modules of 6 × 12 cells with an EL camera. The network was trained using oversampling and data augmentation in order to reduce the error. The results show that the network performed the best when both oversampling and data augmentation were presented with a Balance Error Rate of 7.73% on binary classification problems of quick convergence. The method was implemented with Keras. The preprocessing was performed with OpenCV.
The authors of [95] present new models that are trained not only with images with cracks but also with corrosion. The images were obtained by photographing modules with the EL technique and performing segmentation afterward, obtaining 5400 images. The models are SVM and CNN. The CNN is composed of two convolutional layers. The SVM parameters are optimized by a grid search. The results show a precision of 99%, an improvement over other methods. The experiments were conducted via Keras and Tensorflow.
A variation of convolutional networks is found in [96]. A multichannel CNN is presented. This network has different convolutional layers for each kind of input. This network also can use inputs of different sizes. After each convolutional layer, a dense layer is applied. Finally, a final dense layer combines all the previous data in order to classify the image. This multichannel CNN improves the feature extraction of single-channel CNNs. The dataset was made by 8301 different EL images of cells. The results show a 96.76% accuracy, much more than the 86% presented by single-channel CNNs. The algorithms were implemented in Python using Keras.
The model presented in [97] is composed of six convolutional layers using different regularization techniques such as batch optimization. The dataset used was the ELPV dataset, with 2624 images. The resulting network is a light architecture that achieved high performance using few parameters with an accuracy of 93%.The experiments were run on Tensorflow.
In order to further improve the results, a new approach is presented in [98]. The authors use Fully Convolutional Neural Networks. An FCNN is a CNN without any dense layer. The model used is the U-net, which has been used previously in biomedical image problems with low data. This dataset was composed of 542 EL images. It is composed of 21 convolutional layers of different sizes. The results show that it was better to accept a slight decrease in the performance in order to improve the speed of the system. The algorithms were implemented in python using Keras and Tensorflow.
Wavelet Transform is used in [99]. This method combines two kinds of WT: Discrete WT and Stationary WT in order to extract textural and edge features from solar cells that have been previously preprocessed. The dataset was composed of 2300 EL images. Finally, two different classifiers are used: An SVM and an FFNN. The best model was the FFNN with 93.6% accuracy, over the 92.6% presented by the SVM.
Another Neural Network used is the Complementary Attention Network in [100]. The CAN is composed of a channel-wise attention subnetwork connected with a spatial attention subnetwork. This CAN can be grouped with any CNN, Fast R CNN [101] being the one chosen by the authors. Two datasets were used, one composed of 2029 images and another of 2129 EL images. The network was used for classification and detection, obtaining an accuracy of 99.17% for classification and a mean average precision of 87.38%. The network was faster and had similar parameter numbers to other commercial methods. The algorithms were implemented using Python.
A very interesting approach is presented in [102]. This method is Deep-Feature-Based, extracting features through convolutional neural networks that are classified afterward for classification algorithms such as SVM, KNN or FNN. The particularity of this system is that it used features from different networks. These features are combined using minimum redundancy and maximum relevance for feature selection. The dataset used was the ELPV dataset, with 2624 images. The selected CNNs for feature extraction are Resnet-50, VGG-16, VGG-19 and DarkNet-19. The best method was found with SVM, selecting 2000 features with an accuracy of 94.52% in two-class classification and 89.63% in four-class classification.
In the Table 5, a summary of the reviewed models is presented.
Table 5. Models for detection of faults.

Type

Features

Accuracy

Dataset Size

Image Processing Techniques

Segmentation + obtention of binary image + classification. [90]

from 80% to 99%

—

SVM + Image Processing Techniques

Images are preprocessed and features are extracted from the image. These features are used in an SVM with penalty parameter weighting. [91]

97%

13,392

SVM and CNN

Pretrained VGG19 using different feature descriptors. Similar results for both methods. [92]

82.4%

2624

CNN is composed of 2 layers using leaky-relu. SVM trained with different features extracted from the images. Similar behavior in both models. [93]

98%

540

CNN is composed of 2 convolutional layers. SVM parameters optimized by search grid. [95]

96%.

2840

CNN

Thirteen convolutional layers, an adaptation of VGG16. Uses oversampling and data augmentation. [94]

Uses a different measurement

5400

Multichannel CNN. Accepts inputs of different sizes. Improves the feature extraction of single-channel CNN. [96]

96.76%

8301

Six convolutional layers. Regulation techniques such as batch optimization. [97]

93%

2624

Fully Convolutional Neural Network. Pretrained u-net, composed of 21 convolutional layers. [98]

Uses a different measurement

542

CNN aided by a Complementary Attention Network, composed of a channel-wise attention subnetwork connected with a spatial attention subnetwork. Usable with different CNNs. [100]

99.17%

2300

WT+ SVM and FFNN

Combines discrete WT and stationary WT to extract features and SVM and FFNN to classify them. [99]

93.6%

2029

CNN + SVM, KNN, etc.

Extracts features from different networks, combining them with minimum redundancy and maximum relevance for feature selection. Uses Resnet-50, VGG-16, VGG-19 and DarkNet-19. [102]

94.52%

2624

References

  1. Adib, R.; Zervos, A.; Eckhart, M.; David, M.E.A.; Kirsty, H.; Peter, H.; Governments, R.; Bariloche, F. Renewables 2021 Global Status Report. In REN21 Renewables Now; 2021; Available online: https://www.iea.org/reports/renewables-2021 (accessed on 10 September 2022).
  2. Kampa, M.; Castanas, E. Human health effects of air pollution. Environ. Pollut. 2008, 151, 362–367.
  3. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049.
  4. Eroğlu, H. Effects of Covid-19 outbreak on environment and renewable energy sector. Environ. Dev. Sustain. 2021, 23, 4782–4790.
  5. Danowitz, A. Solar Thermal vs. Photovoltaic. 2010. Available online: http://large.stanford.edu/courses/2010/ph240/danowitz2/ (accessed on 10 September 2022).
  6. GreenMatch. Differences Between Solar PV and Solar Thermal; GreenMatch: Copenhagen, Denmark, 2016; Available online: https://www.greenmatch.co.uk/blog/2015/04/solar-panels-vs-solar-thermal (accessed on 10 September 2022).
  7. Markvart, T.; Castañer, L. Principles of Solar Cell Operation. In Practical Handbook of Photovoltaics: Fundamentals and Applications; Elsevier: Amsterdam, The Netherlands, 2003; pp. 71–93.
  8. Satpathy, R.; Pamuru, V. Solar PV Power: Design, Manufacturing and Applications from Sand to Systems; Elsevier: Amsterdam, The Netherlands, 2020; pp. 1–493.
  9. Yetayew, T.T.; Workineh, T.G. A Comprehensive Review and Evaluation of Classical MPPT Techniques for a Photovoltaic System. In Advances of Science and Technology; Springer: Berlin/Heidelberg, Germany, 2021; Volume 384, pp. 259–272.
  10. Pallathadka, H.; Ramirez-Asis, E.H.; Loli-Poma, T.P.; Kaliyaperumal, K.; Ventayen, R.J.M.; Naved, M. Applications of artificial intelligence in business management, e-commerce and finance. Mater. Today Proc. 2021, in press.
  11. Mahmoud, A.M.A.; Mashaly, H.M.; Kandil, S.A.; Khashab, H.E.; Nashed, M.N.F. Fuzzy logic implementation for photovoltaic maximum power tracking. In Proceedings of the 26th Annual Conference of the IEEE-Industrial-Electronics-Society, Nagoya, Japan, 22–28 October 2000; pp. 735–740.
  12. Hui, J.; Sun, X. MPPT Strategy of PV System Based on Adaptive Fuzzy PID Algorithm. In Life System Modeling and Intelligent Computing Pt I, Proceedings of the International Conference on Life System Modeling and Simulation/International Conference on Intelligent Computing for Sustainable Energy and Environment, Wuxi, China, 17–20 September 2010; Springer: Berlin/Heidelberg, Germany, 2010; Volume 97, pp. 220–228.
  13. Kottas, T.L.; Karlis, A.D.; Boutalis, Y.S. Fuzzy Cognitive Networks for Maximum Power Point Tracking in Photovoltaic Arrays. Fuzzy Cogn. Maps Adv. Theory Methodol. Tools Appl. 2010, 247, 231–257.
  14. Adly, M.; El-Sherif, H.; Ibrahim, M. Maximum Power Point Tracker for a PV Cell using a Fuzzy Agent adapted by the Fractional Open Circuit Voltage Technique. In Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ 2011), Taipei, Taiwan, 27–30 June 2011; pp. 1918–1922.
  15. Qiao, X.; Wu, B.; Deng, Z.; You, Y. MPPT of photovoltaic generation system using fuzzy/PID control. Electr. Power Autom. Equip. 2008, 28, 92–95.
  16. Altin, N. Interval type-2 fuzzy logic controller based Maximum Power Point tracking in photovoltaic systems. Adv. Electr. Comput. Eng. 2013, 13, 65–70.
  17. Kececioglu, O.F.; Gani, A.; Sekkeli, M. Design and Hardware Implementation Based on Hybrid Structure for MPPT of PV System Using an Interval Type-2 TSK Fuzzy Logic Controller. Energies 2020, 13, 1842.
  18. Altin, N. Single phase grid interactive PV system with MPPT capability based on type-2 fuzzy logic systems. In Proceedings of the International Conference on Renewable Energy Research and Applications, ICRERA 2012, Istanbul, Turkey, 18–21 September 2012.
  19. Verma, P.; Garg, R.; Mahajan, P. Asymmetrical interval type-2 fuzzy logic control based MPPT tuning for PV system under partial shading condition. ISA Trans. 2020, 100, 251–263.
  20. Allouche, M.; Dahech, K.; Chaabane, M. Multiobjective maximum power tracking control of photovoltaic systems: T-S fuzzy model-based approach. Soft Comput. 2018, 22, 2121–2132.
  21. Zayani, H.; Allouche, M.; Kharrat, M.; Chaabane, M. T-S fuzzy Maximum Power Point tracking control of photovoltaic conversion system. In Proceedings of the 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2015, Monastir, Tunisia, 21–23 December 2016; pp. 534–539.
  22. Dahmane, M.; Bosche, J.; El-Hajjaji, A.; Davarifar, M. T-S implementation of an MPPT algorithm for photovoltaic conversion system using poles placement and H performances. In Proceedings of the 3rd International Conference on Systems and Control, ICSC 2013, Algiers, Algeria, 29–31 October 2013; pp. 1116–1121.
  23. Azali, S.; Sheikhan, M. Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for Maximum Power Point tracking. Appl. Intell. 2016, 44, 88–110.
  24. Duman, S.; Yorukeren, N.; Altas, I.H. A novel MPPT algorithm based on optimized artificial neural network by using FPSOGSA for standalone photovoltaic energy systems. Neural Comput. Appl. 2018, 29, 257–278.
  25. Sarkar, R.; Kumar, J.R.; Sridhar, R.; Vidyasagar, S. A New Hybrid BAT-ANFIS-Based Power Tracking Technique for Partial Shaded Photovoltaic Systems. Int. J. Fuzzy Syst. 2021, 23, 1313–1325.
  26. Naveen; Dahiya, A.K. Implementation and Comparison of Perturb Observe, ANN and ANFIS Based MPPT Techniques. In Proceedings of the International Conference on Inventive Research in Computing Applications, ICIRCA 2018, Coimbatore, India, 11–12 July 2018; pp. 1–5.
  27. Arora, A.; Gaur, P. Comparison of ANN and ANFIS based MPPT Controller for grid connected PV systems. In Proceedings of the 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, New Delhi, India, 17–20 December 2016.
  28. Padmanaban, S.; Priyadarshi, N.; Bhaskar, M.S.; Holm-Nielsen, J.B.; Ramachandaramurthy, V.K.; Hossain, E. A Hybrid ANFIS-ABC Based MPPT Controller for PV System with Anti-Islanding Grid Protection: Experimental Realization. IEEE Access 2019, 7, 103377–103389.
  29. Kulaksiz, A.A.; Akkaya, R. Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system. Turk. J. Electr. Eng. Comput. Sci. 2012, 20, 241–254.
  30. Kulaksiz, A.A.; Akkaya, R. A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive. Sol. Energy 2012, 86, 2366–2375.
  31. Larbes, C.; Cheikh, S.M.A.; Obeidi, T.; Zerguerras, A. Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system. Renew. Energy 2009, 34, 2093–2100.
  32. Azab, M. Optimal power point tracking for stand-alone PV system using particle swarm optimization. In Proceedings of the IEEE International Symposium on Industrial Electronics, Bari, Italy, 4–7 July 2010; pp. 969–973.
  33. Alshareef, M.; Lin, Z.; Ma, M.; Cao, W. Accelerated Particle Swarm Optimization for Photovoltaic Maximum Power Point Tracking under Partial Shading Conditions. Energies 2019, 12, 623.
  34. Sundareswaran, K.; Peddapati, S.; Palani, S. MPPT of PV systems under partial shaded conditions through a colony of flashing fireflies. IEEE Trans. Energy Convers. 2014, 29, 463–472.
  35. soufyane Benyoucef, A.; Chouder, A.; Kara, K.; Silvestre, S.; Sahed, O.A. Artificial bee colony based algorithm for Maximum Power Point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl. Soft Comput. J. 2015, 32, 38–48.
  36. Bilal, B. Implementation of Artificial Bee Colony algorithm on Maximum Power Point Tracking for PV modules. In Proceedings of the 8th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2013, Bucharest, Romania, 23–25 May 2013.
  37. Oshaba, A.S.; Ali, E.S.; Elazim, S.M.A. PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load. Neural Comput. Appl. 2017, 28, 353–364.
  38. Moreira, H.S.; Silva, J.L.D.S.; Prym, G.C.; Sakô, E.Y.; dos Reis, M.V.G.; Villalva, M.G. Comparison of Swarm Optimization Methods for MPPT in Partially Shaded Photovoltaic Systems. In Proceedings of the 2nd International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, 9–11 September 2019.
  39. Habibi, M.; Yazdizadeh, A. New MPPT Controller Design for PV Arrays Using Neural Networks (Zanjan City Case Study). In Proceedings of the 6th International Symposium on Neural Networks, Wuhan, China, 26–29 May 2009; Volume 5552, pp. 1050–1058.
  40. Veligorskyi, O.; Chakirov, R.; Vagapov, Y. Artificial neural network-based Maximum Power Point tracker for the photovoltaic application. In Proceedings of the 2015 1st International Conference on Industrial Networks and Intelligent Systems, INISCom 2015, Tokyo, Japan, 2–4 March 2015; pp. 133–138.
  41. Farayola, A.M.; Hasan, A.N.; Ali, A. Efficient photovoltaic mppt system using coarse gaussian support vector machine and artificial neural network techniques. Int. J. Innov. Comput. Inf. Control. 2018, 14, 323–339.
  42. Al-Majidi, S.D.; Abbod, M.F.; Al-Raweshidy, H.S. A particle swarm optimisation-trained feedforward neural network for predicting the Maximum Power Point of a photovoltaic array. Eng. Appl. Artif. Intell. 2020, 92, 103688.
  43. Babes, B.; Boutaghane, A.; Hamouda, N. A novel nature-inspired Maximum Power Point tracking (MPPT) controller based on ACO-ANN algorithm for photovoltaic (PV) system fed arc welding machines. Neural Comput. Appl. 2021, 34, 299–317.
  44. Pachaivannan, N.; Subburam, R.; Padmanaban, M.; Subramanian, A. Certain investigations of ANFIS assisted CPHO algorithm tuned MPPT controller for PV arrays under partial shading conditions. J. Ambient. Intell. Humaniz. Comput. 2021, 12, 9923–9938.
  45. Mlakic, D.; Nikolovski, S. ANFIS as a method for determinating MPPT in the photovoltaic system simulated in MATLAB/Simulink. In Proceedings of the 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016, Opatija, Croatia, 30 May–3 June 2016; pp. 1082–1086.
  46. Avila, L.; Paula, M.D.; Trimboli, M.; Carlucho, I. Deep reinforcement learning approach for MPPT control of partially shaded PV systems in Smart Grids. Appl. Soft Comput. 2020, 97, 106711.
  47. Leone, R.D.; Pietrini, M.; Giovannelli, A. Photovoltaic energy production forecast using support vector regression. Neural Comput. Appl. 2015, 26, 1955–1962.
  48. Kecman, V. Support Vector Machines—An Introduction; Springer: Berlin/Heidelberg, Germany, 2005; Volume 177, p. 605.
  49. Nageem, R.; Jayabarathi, R. Predicting the Power Output of a Grid-Connected Solar Panel Using Multi-Input Support Vector Regression. Procedia Comput. Sci. 2017, 115, 723–730.
  50. Senjyu, T.; Takara, H.; Uezato, K.; Funabashi, T. One-hour-ahead load forecasting using neural network. IEEE Trans. Power Syst. 2002, 17, 113–118.
  51. Caputo, D.; Grimaccia, F.; Mussetta, M.; Zich, R.E. Photovoltaic Plants Predictive Model by means of ANN trained by a Hybrid Evolutionary Algorithm. In Proceedings of the World Congress on Computational Intelligence (WCCI 2010), Barcelona, Spain, 18–23 July 2010.
  52. Gandelli, A.; Grimaccia, F.; Mussetta, M.; Pirinoli, P.; Zich, R.E. Development and validation of different hybridization strategies between GA and PSO. In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, 25–28 September 2007; pp. 2782–2787.
  53. Rana, M.; Koprinska, I.; Agelidis, V.G. Forecasting solar power generated by grid connected PV systems using ensembles of neural networks. In Proceedings of the International Joint Conference on Neural Networks, Killarney, Ireland, 12–17 July 2015.
  54. Ehsan, R.M.; Simon, S.P.; Venkateswaran, P.R. Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron. Neural Comput. Appl. 2016, 28, 3981–3992.
  55. Johansson, E.; Dowla, F.; Goodman, D. Backpropagation Learning for Multilayer Feed-Forward Neural Networks Using the Conjugate Gradient Method. Int. J. Neural Syst. 2011, 2, 291–301.
  56. NeuroDimension, Inc. Neurosolutions. Available online: http://www.neurosolutions.com/neurosolutions/ (accessed on 10 September 2022).
  57. Li, Z.; Zang, C.; Zeng, P.; Yu, H.; Li, H. Day-ahead Hourly Photovoltaic Generation Forecasting using Extreme Learning Machine. In Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, China, 8–12 June 2015; pp. 779–783.
  58. Huang, G.B.; Zhu, Q.Y.; Siew, C.K. Extreme learning machine: A new learning scheme of feedforward neural networks. In Proceedings of the IEEE International Conference on Neural Networks—Conference Proceedings, Budapest, Hungary, 25–29 July 2004; Volume 2, pp. 985–990.
  59. Mandal, P.; Madhira, S.T.S.; haque, A.U.; Meng, J.; Pineda, R.L. Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Procedia Comput. Sci. 2012, 12, 332–337.
  60. Mallat, S.G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11, 674–693.
  61. Park, J.; Sandberg, I.W. Approximation and Radial-Basis-Function Networks. Neural Comput. 1993, 5, 305–316.
  62. Capizzi, G.; Napoli, C.; Bonanno, F. Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting. IEEE Trans. Neural Netw. Learn. Syst. 2012, 23, 1805–1815.
  63. Wen, Y.; AlHakeem, D.; Mandal, P.; Chakraborty, S.; Wu, Y.K.; Senjyu, T.; Paudyal, S.; Tseng, T.L. Performance Evaluation of Probabilistic Methods Based on Bootstrap and Quantile Regression to Quantify PV Power Point Forecast Uncertainty. IEEE Trans. Neural Netw. Learn. Syst. 2020, 31, 1134–1144.
  64. Alzahrani, A.; Shamsi, P.; Dagli, C.; Ferdowsi, M. Solar Irradiance Forecasting Using Deep Neural Networks. Procedia Comput. Sci. 2017, 114, 304–313.
  65. Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780.
  66. Abdel-Nasser, M.; Mahmoud, K. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl. 2019, 31, 2727–2740.
  67. Yao, X.; Wang, Z.; Zhang, H. A novel photovoltaic power forecasting model based on echo state network. Neurocomputing 2019, 325, 182–189.
  68. Gallicchio, C.; Micheli, A. Deep Echo State Network (DeepESN): A Brief Survey. arXiv 2017, arXiv:abs/1712.04323.
  69. Hinton, G.E. A Practical Guide to Training Restricted Boltzmann Machines. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 7700 Lecture N.; Springer: Berlin/Heidelberg, Germany, 2012; pp. 599–619.
  70. Jolliffe, I.T. Principal Component Analysis; Springer: Berlin/Heidelberg, Germany, 2002.
  71. Davidon, W. Variable Metric Method for Minimization; Technical Report; Argonne National Laboratory (ANL): Lemont, IL, USA, 1959.
  72. Niu, D.; Wang, K.; Sun, L.; Wu, J.; Xu, X. Short-term photovoltaic power generation forecasting based on random forest feature selection and CEEMD: A case study. Appl. Soft Comput. J. 2020, 93, 106389.
  73. Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32.
  74. Behzadian, M.; Otaghsara, S.K.; Yazdani, M.; Ignatius, J. A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 2012, 39, 13051–13069.
  75. Yeh, J.R.; Shieh, J.S.; Huang, N.E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2011, 2, 135–156.
  76. Li, H.; Tan, Q. A BP neural network based on improved particle swarm optimization and its application in reliability forecasting. Res. J. Appl. Sci. Eng. Technol. 2013, 6, 1246–1251.
  77. Jiao, B.; Lian, Z.; Gu, X. A dynamic inertia weight particle swarm optimization algorithm. Chaos Solitons Fractals 2008, 37, 698–705.
  78. Quan, D.M.; Ogliari, E.; Grimaccia, F.; Leva, S.; Mussetta, M. Hybrid model for hourly forecast of photovoltaic and wind power. IEEE Int. Conf. Fuzzy Syst. 2013.
  79. Grimaccia, F.; Mussetta, M.; Zich, R.E. Genetical swarm optimization: Self-adaptive hybrid evolutionary algorithm for electromagnetics. IEEE Trans. Antennas Propag. 2007, 55, 781–785.
  80. Koprinska, I.; Wu, D.; Wang, Z. Convolutional Neural Networks for Energy Time Series Forecasting. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 8–13 July 2018.
  81. Torres, J.F.; Troncoso, A.; Koprinska, I.; Wang, Z.; Martínez-Álvarez, F. Big data solar power forecasting based on deep learning and multiple data sources. Expert Syst. 2019, 36, e12394.
  82. Zhang, T.; Lv, C.; Ma, F.; Zhao, K.; Wang, H.; O’Hare, G.M. A photovoltaic power forecasting model based on dendritic neuron networks with the aid of wavelet transform. Neurocomputing 2020, 397, 438–446.
  83. Jiang, T.; Wang, D.; Ji, J.; Todo, Y.; Gao, S. Single dendritic neuron with nonlinear computation capacity: A case study on XOR problem. In Proceedings of the 2015 IEEE International Conference on Progress in Informatics and Computing, PIC 2015, Nanjing, China, 18–20 December 2016; pp. 20–24.
  84. Wolff, B.; Lorenz, E.; Kramer, O. Statistical learning for short-term photovoltaic power predictions. Stud. Comput. Intell. 2016, 645, 31–45.
  85. Siddiqui, M.U.; Abido, M. Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms. Appl. Soft Comput. J. 2013, 13, 4608–4621.
  86. Goldberg, D.E. Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed.; Addison-Wesley Longman Publishing Co., Inc.: Boston, MA, USA, 1989.
  87. Clerc, M. Particle Swarm Optimization; Wiley: Hoboken, NJ, USA, 2010.
  88. Opara, K.R.; Arabas, J. Differential Evolution: A survey of theoretical analyses. Swarm Evol. Comput. 2019, 44, 546–558.
  89. Glover, F.; Laguna, M. Tabu Search; Springer: New York, NY, USA, 1997.
  90. Nian, B.; Fu, Z.; Wang, L.; Cao, X. Automatic detection of defects in solar modules: Image processing in detecting. In Proceedings of the 6th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2010, Chengdu, China, 23–25 September 2010.
  91. Anwar, S.A.; Abdullah, M.Z. Micro-crack detection of multicrystalline solar cells featuring shape analysis and support vector machines. In Proceedings of the 2012 IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2012, Penang, Malaysia, 23–25 November 2012; pp. 143–148.
  92. Deitsch, S.; Buerhop-Lutz, C.; Sovetkin, E.; Steland, A.; Maier, A.; Gallwitz, F.; Riess, C. Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images. Mach. Vis. Appl. 2018, 32, 84.
  93. Karimi, A.M.; Fada, J.S.; Liu, J.; Braid, J.L.; Koyuturk, M.; French, R.H. Feature Extraction, Supervised and Unsupervised Machine Learning Classification of PV Cell Electroluminescence Images. In Proceedings of the IEEE 7th World Conference on Photovoltaic Energy Conversion, WCPEC 2018—A Joint Conference of 45th IEEE PVSC, 28th PVSEC and 34th EU PVSEC, 2018, Waikoloa, HI, USA, 10–15 June 2018; pp. 418–424.
  94. Bartler, A.; Mauch, L.; Yang, B.; Reuter, M.; Stoicescu, L. Automated detection of solar cell defects with deep learning. In Proceedings of the 26th European Signal Processing Conference (EUSIPCO), Rome, Italy, 3–7 September 2018; pp. 2035–2039.
  95. Karimi, A.M.; Fada, J.S.; Hossain, M.A.; Yang, S.; Peshek, T.J.; Braid, J.L.; French, R.H. Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification. IEEE J. Photovoltaics 2019, 9, 1324–1335.
  96. Ying, Z.; Li, M.; Tong, W.; Haiyong, C. Automatic Detection of Photovoltaic Module Cells using Multi-Channel Convolutional Neural Network. In Proceedings of the 2018 Chinese Automation Congress, CAC 2018, Xi’an, China, 30 November–2 December 2018; pp. 3571–3576.
  97. Akram, M.W.; Li, G.; Jin, Y.; Chen, X.; Zhu, C.; Zhao, X.; Khaliq, A.; Faheem, M.; Ahmad, A. CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 2019, 189, 116319.
  98. Balzategui, J.; Eciolaza, L.; Arana-Arexolaleiba, N. Defect detection on Polycrystalline solar cells using Electroluminescence and Fully Convolutional Neural Networks. In Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020, Honolulu, HI, USA, 12–15 January 2020; pp. 949–953.
  99. Mathias, N.; Shaikh, F.; Thakur, C.; Shetty, S.; Dumane, P.; Chavan, D.S. Detection of Micro-Cracks in Electroluminescence Images of Photovoltaic Modules. SSRN Electron. J. 2020.
  100. Su, B.; Chen, H.; Chen, P.; Bian, G.; Liu, K.; Liu, W. Deep Learning-Based Solar-Cell Manufacturing Defect Detection with Complementary Attention Network. IEEE Trans. Ind. Inform. 2021, 17, 4084–4095.
  101. Girshick, R. Fast R-CNN. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 13–16 December 2015; pp. 1440–1448.
  102. Demirci, M.Y.; Beşli, N.; Gümüşçü, A. Efficient deep feature extraction and classification for identifying defective photovoltaic module cells in Electroluminescence images. Expert Syst. Appl. 2021, 175, 114810.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Computer Science, Artificial Intelligence; Green & Sustainable Science & Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Héctor Felipe Mateo Romero
,
Miguel Ángel González Rebollo
,
Valentín Cardeñoso-Payo
,
Victor Alonso Gómez
,
Alberto Redondo Plaza
,
Ranganai Tawanda Moyo
,
Luis Hernández-Callejo
View Times: 1.9K
Revisions: 3 times (View History)
Update Date: 24 Oct 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback