PV Fault Detection Using CNN For Enhancing
Reliability Of Solar Power Plants
Sameer Alam
Department Of Electrical Engineering
ZHCET, AMU
ALIGARH, UTTAR PRADESH
amustudent0@gmail.com
Shivam Kaushik
Department Of Electrical Engineering
ZHCET,AMU
ALIGARH , UTTAR PRADESH
shivamkaushik909@gmail.com
Shaikh Mohd Shaique
Department Of Electrical Engineering
ZHCET, AMU
ALIGARH, UTTAR PRADESH
shaikhshaique10@gmail.com
Dr. Nidal Rafiuddin
Department of Electrical Engineering
ZHCET,AMU
nidal.rafi@gmail.com
Abstract— Solar energy presents a promising avenue for clean
and abundant electricity generation. However, issues such as
equipment malfunctions or inefficiencies can hinder its
effectiveness. This study introduces a Convolutional Neural
Networks based approach for efficient anomaly detection in
photovoltaic cells, by employing deep learning Convolutional
Neural Networks. The primary focus of this study is on
optimizing the efficiency of anomaly detection in PV cells. The
proposed project employs a comprehensive and diverse dataset
comprising large sets of infrared thermal images, encompassing
both anomalous and healthy infrared images of PV modules.
This extensive dataset is utilized to train the Convolutional
Neural Networks model, enabling precise testing of new IR
image data. The model is designed such that it differentiates
between healthy and faulty IR images from a given dataset. The
paper conducts a thorough analysis of the proposed project,
highlighting its advantages in terms of accuracy, reliability, and
efficiency. This research contributes to optimizing solar power
generation, ensuring its reliability and longevity.
Keywords—Photovoltaic (PV), Infrared (IR), Anomaly Detection,
Convolutional Neural Network (CNN), PV fault
I.
INTRODUCTION
In the pursuit of sustainable and renewable energy sources,
solar energy has emerged as a beacon of hope, offering a clean
and abundant alternative to traditional fossil fuels. There are
numerous benefits to switching to solar energy over fossil
fuels, including the fact that solar energy is evidently limitless
and emits no pollutants, while fossil fuels have a finite supply
and are prone to create a lot of pollution. Solar energy is used
in many ways for different heating applications, but the most
important use of solar energy is in electricity generation.
Evidently, electricity power demand is continuously
increasing, and conventional sources of electricity generation
are limited and not clean, so the whole world is looking
towards non-conventional renewable energy sources. Wind,
tidal, geothermal, hydro, solar, etc. are available among
renewable energy sources, but among renewable energy
sources, solar energy is being adopted very rapidly all over the
world. The initial setup cost of solar is high, but the
maintenance cost is very low as it does not use any moving
systems as compared to other energy sources [2]. The most
important thing in electricity generation from solar energy is
photovoltaic PV cells, which work on the photoelectric effect.
979-8-3503-8719-3/24/$31.00 ©2024 IEEE
The photoelectric effect is a phenomenon in which the energy
of light is directly converted into electricity with the help of a
PV cell. By using this process, sunlight is converted into
electricity, and it offers very clean and sustainable energy.
Every technology has its own set of challenges. Similarly,
photovoltaic (PV) cells also face challenges such as
manufacturing defects, weather conditions, bumps, dirt,
ageing, etc., which reduce the efficiency of the system [3]. In
all conditions that affect efficiency, we don't have control over
the weather. However, if we can identify all other faults, we
can improve efficiency by replacing or fixing them.
Identifying faults in PV cells is a significant challenge, and
ongoing research is being conducted in this field. Researchers
have achieved a lot, but there is still a lot that can be improved
through further research and development.
Thermal images are used to identify faults in PV cells, which
capture infrared images through infrared cameras. The
working principle is based on the fact that where solar energy
is efficiently converted into electricity, there will be less heat
generation. In contrast, areas with defects or faults leading to
reduced energy conversion will accumulate heat. With the help
of infrared cameras, we can visually differentiate between
faulty and healthy sections easily. Other technologies, such as
electroluminescence images, have also been developed. In this
method, PV cells are excited with a DC supply, emitting nearinfrared light captured by a special camera. Thermal infrared
images are employed with machine learning techniques to
detect anomalies in PV cells, achieving high accuracies up to
99.7%. By analyzing temperature variations, anomalies like
hotspots and bypass substrings can be identified in PV
systems. Recent research has focused on using supervised
contrastive learning and deep learning to classify anomalies
accurately based on thermal imaging data. This integration of
thermal imaging and advanced technologies offers efficient
solutions for maintaining optimal performance in PV cells [1].
Geron [4] focusses on novel signal processing detection
methods, providing research avenues for all detection and
classification techniques. Additionally, environment-based
detection analysis is explored, contributing to the reliability of
solar power plants. The chapter emphasizes the role of
renewable energy sources (RES), particularly solar PV
systems, in reducing carbon emissions and providing
affordable energy. Vlaminck et al. [5] propose a region-based
CNN for anomaly detection in PV power plants using aerial
imagery. By emphasizing the importance of accurate
detection, they enhance the reliability and performance of
solar installations. Detecting anomalies promptly ensures
optimal operation and minimizes power loss in PV systems.
Rafiuddin et al. [6] suggested a method based on multi-depth
wavelet packet transform capable of classifying multiple
classes.
II.
METHODOLOGY
A. CNN
Artificial Neural Networks (ANNs) are computational
systems inspired by the functioning of biological nervous
systems, such as the human brain. They consist of
interconnected computational nodes, or neurons, which work
together to process information and optimize output. As
shown in Figure-1, ANNs typically include input, hidden, and
output layers, with deep learning involving multiple hidden
layers stacked upon each other [7]. This configuration in fact
aligns with the centre of several widely used ANN
architectures, including recurrent neural networks (RNNs),
restricted Boltzmann machines (RBMs), and feedforward
neural networks (FNNs).
Fig.1 Basic feedforward neural network (FNN) with an input layer, a
hidden layer, and an output layer.
Convolutional Neural Networks (CNNs) are a kind of deep
learning model that are specifically made for processing data
having a grid pattern, such as images. They are comparable to
standard ANNs. They are able to automatically learn spatial
hierarchies of features, from low-level to high-level patterns,
and are inspired by the structure of the animal visual brain.
The three primary layer types found in CNNs are convolution,
pooling, and fully linked layers [8].
The fully connected layer maps the features that have been
extracted to provide a final output, as classification. The
convolution and pooling layers handle feature extraction. As
seen in Figure-2, a convolution layer applies a kernel, a tiny
grid of parameters, at each place of the input image to enable
CNNs to process images quickly and effectively. Complexity
increases in the retrieved features as the data moves through
each layer. When training a CNN, parameters such as kernels
are optimized to minimize the discrepancy between the
outputs of the model and the labels that correspond to reality.
The most common methods for achieving this optimization
are gradient descent and backpropagation. All things
considered, CNNs perform incredibly well for tasks like image
processing because of their capacity to recognize and extract
pertinent features from incoming data [9].
Fig.2 CNN architecture used in the study
Figure-2 provides an overview of a CNN architecture and
training process flow. Convolution layers, pooling layers
(such as max pooling), and fully connected (FC) layers are the
three main parts of a CNN. Applying a loss function during
forward propagation on a training dataset is one way to assess
a model's performance with particular kernels and weights.
Following that, the weights and kernels that are adjustable are
adjusted using a gradient descent optimization algorithm and
backpropagation, with the loss value serving as the basis for
refinement. CNN designs also frequently employ the ReLU
(Rectified Linear Unit) activation function.
The convolutional layer stands as the cornerstone within
the structure of a CNN. Consisting of neurons equipped with
adjustable weights and biases (parameters subject to training),
it plays a pivotal role. The neurons in the initial convolutional
layer establish connections solely with the pixels situated
within their respective receptive fields. Similarly, neurons
within the subsequent convolutional layer form connections
exclusively with neurons within specific local regions in the
preceding layer [10, 11]. This arrangement causes the network
to prioritise detecting basic, low-level features in its initial
layers, which are then combined to form more complex, highlevel features in subsequent layers. Consequently, the early
layers of a CNN capture fundamental image elements such as
edges, curves, and colours, while the later layers identify more
intricate features like shapes. This design mirrors the way realworld images are composed, contributing to the effectiveness
of CNNs in image recognition tasks. The neuron output in a
convolutional layer can be mathematically expressed in
eqution-1 [10].
(1)
Where Zi,j,k is the output of the neuron depends on its
location in the ith row and jth column of the k feature map in
the convolutional layer l. In this context, the horizontal stride
is represented by sw. The vertical stride is denoted by sh, the
width of the receptive field is denoted by fw, the height of the
receptive field is denoted by fh, and the number of feature
maps of the previous layer (l-1) is denoted by fn'. The location
of the neuron in the previous layer (l-1), labelled xi',j',k' can be
found in the ith row and jth column of the feature map k’ (or
channel k’, if it belongs to the input layer). The bias term of
the k feature map in the l layer is represented as bk. In addition,
the weight of the connection between any neuron k of the
feature map of the l layer and its input located in the uth row
and vth column and originating from the feature map k’ is
indicated by wu,v,k',k.
Neural networks offer advantages like fine accuracy and
faster predictions compared to traditional machine learning
algorithms. However, they come with drawbacks such as high
computational demands and the need for extensive training
data. Within neural networks, CNNs have several advantages
over fully connected deep neural networks (DNNs) for image
classification [10].
1. CNNs have fewer parameters, leading to faster training,
reduced overfitting, and requiring less training data.
2. Once CNN learns a filter for a specific feature, it can
detect that feature anywhere in the image, leveraging the
repetitive nature of image features for better generalization.
3. CNNs have an advantage over DNNs, especially when
it comes to real-world natural images, because their bottom
layers identify lower-level features, and their upper layers
integrate these features into larger, higher-level features.
Table 1. Descriptions of each 12 types of classes
Class
Description
Cell
Represents a single cell exhibiting a square
geometry that has undergone a hot-spot event.
Cell-multi
Denotes hot spots occurring in multiple cells, each
characterized by a square geometry.
Cracking
Indicates the presence of visible surface cracks on
the module.
Diode
Refers to an active bypass diode, typically
constituting 1/3 of the module.
Diode-multi
Signifies the activation of multiple bypass diodes,
typically accounting for 2/3 of the module
Hotspot
Describes the development of a thermal hotspot on
a thin-film module.
Hotspot-multi
Represents the formation of multiple thermal
hotspots on a thin-film module.
Offline-module
Denotes the entire module being subjected to
heating.
Shadowing
Refers to sunlight obstruction caused by vegetation,
man-made structures, or adjacent rows.
Soiling
Indicates the presence of dirt, dust, or other debris
on the module surface.
Vegetation
Represents panels obstructed by surrounding
vegetation.
No-anomaly
Indicates normal operation of the solar module.
B. DATASET
The dataset utilized for training comprises infrared thermal
images, focusing on two major classes: Faulty and Healthy.
Within these classes, there are distinct anomalies and normal
instances represented by thermal images of solar PV cells.
Specifically, the dataset encompasses 11 fault categories,
contributing a total of 2000 images to the Faulty class. These
anomalies include cell, cell multi, hotspot, hotspot multi,
vegetation, soiling, offline module, diode, diode multi,
cracking, and shadowing. Conversely, the Healthy class
comprises 2000 images representing PV cell units devoid of
any anomalies. These images depict the normal operational
conditions of PV cells, serving as a reference for comparison
during the training process. The descriptions for each of the
12 types are shown in Table 1 [13]. The sample images
representing non-anomalous and anomalous data are shown
in figure-3.
During training, CNN learns to distinguish between these two
classes based on the thermal patterns exhibited in the images.
By analyzing the thermal characteristics associated with
faulty and healthy PV cells, the CNN becomes adept at
identifying anomalies and classifying PV cell units
accordingly. This balanced representation of both faulty and
healthy instances within the dataset ensures that the CNN
receives sufficient exposure to diverse scenarios, enabling it
to generalize well and achieve high accuracy in fault detection
and classification tasks.
C. TRAINING MODULE
((a))
(b)
Fig.3 Infrared images of PV module for (a) Non-anomalous (b) Anomalous
The training module utilizes pretrained SqueezeNet CNN
architecture. The process involves selecting and
preprocessing a dataset, initializing the model with
SqueezeNet architecture, choosing a loss function and
optimization algorithm, and iterating through training epochs
to update model parameters. Fine-tuning and hyperparameter
tuning optimize performance. Monitoring metrics like loss
and accuracy ensure effective training, resulting in a model
ready for real-world inference tasks. The pretrained
SqueezeNet model was trained on a single GPU, with adam
solver, initial learning rate of 0.0001 and mini-batch size of
128. Further, L2 regularization was set at 0.0001. During each
epoch of the training process shuffling is done to introduce
randomness into the training data, ensuring robustness and
generalization.
D. TESTING
The testing phase of the SqueezeNet involves loading the pretrained model and evaluating its performance using a separate
test dataset. By performing a forward pass, predictions are
obtained and compared with ground truth labels to assess
accuracy or other relevant metrics. Analysis of results
provides insights into the model's generalization ability and
identifies areas for potential improvement.
III.
TRAINING RESULT
The training was conducted using the SqueezeNet CNN
architecture on 4000 images categorized into two balanced
classes: faulty IR thermal images and healthy images. Each
class contained 2000 images, ensuring equal representation
for both healthy and faulty instances. The faulty class
encompassed approximately 2000 images, featuring eleven
distinct types of anomalies, including cell, cell multi, hotspot,
hotspot multi, vegetation, soiling, offline module, diode,
diode multi, cracking, and shadowing. To maintain class
balance, an equal number of instances were included for each
anomaly type. The healthy class consisted of 2000 images
devoid of any anomalies, serving as the baseline for
comparison against the faulty instances. Figure-4 and Figure5 illustrate the training progress of the SqueezeNet
considering accuracy and loss metrics respectively. Both
training accuracy and validation accuracy exhibit an
increasing trajectory, suggesting that the model effectively
learns from the training data and generalizes to validation
data. Additionally, training loss and validation loss
consistently decrease over iterations. This reduction indicates
that the model optimally adjusts its parameters during
training. This underscores the robustness of the CNN model
in learning complex patterns inherent in IR thermal images,
thereby enabling accurate classification of healthy and faulty
instances.
(a)
(b)
Fig 5. loss vs Iteration (a) Training (b) Validation
During training, the CNN model exhibited exceptional
performance, achieving a training accuracy of 99% and a
validation accuracy of 89%. Considering the complexity of
the task, this high accuracy demonstrates the efficacy of the
model in distinguishing between healthy and faulty IR
thermal images. After the completion of training, the loss
factor converged to 0.1, indicating that the model effectively
minimized the discrepancy between predicted and actual
labels during training iterations.
IV. CONCLUSION
(a)
(b)
Fig 4. Accuracy v/s Iteration (a) Training (b) Validation
In conclusion, this research presents a significant
advancement in the field of anomaly detection in photovoltaic
cells through the utilization of Convolutional Neural
Networks. The study focuses on optimizing the efficiency
and accuracy of anomaly detection in PV cells, addressing the
critical challenge of maintaining optimal performance in
solar power generation systems. By leveraging a
comprehensive dataset comprising infrared thermal images,
encompassing both healthy and anomalous instances, the
proposed CNN-based approach demonstrates remarkable
capabilities in accurately distinguishing between faulty and
healthy PV cells accuracy. The model's training process,
utilizing the SqueezeNet architecture, resulted in exceptional
performance metrics, with a training accuracy of 99% and a
validation accuracy of 89%. The effectiveness of the CNN
model in anomaly detection is evidenced by its ability to
identify various anomalies, including hotspots, vegetation,
soiling, and others, with high precision. The model's
robustness is further highlighted by its capability to
generalize well across diverse scenarios, ensuring reliable
performance in real-world applications. Overall, this research
contributes significantly to optimizing solar power
generation systems by enhancing anomaly detection
capabilities, thereby ensuring the reliability, longevity, and
efficiency of PV cells. The findings underscore the potential
of deep learning techniques, particularly CNNs, in addressing
complex challenges in renewable energy technology, paving
the way for a sustainable and greener future.
REFERENCES
1. M. Alsafasfeh, I. Abdel-Qader, and B. Bazuin, “Photovoltaic
defect classification through thermal infrared imaging using a
machine learning approach,” Progress in Photovoltaics:
Research and Applications, vol. 27, no. 10, pp. 1007–1016, Oct.
2019.
2. A. Maka and J. Alabid, “Solar energy technology and its roles
in sustainable development,” Clean Energy, vol. 6, pp. 476–483,
2022. DOI: 10.1093/ce/zkac023.
3. A. M. Soomar, A. Hakeem, M. Messaoudi, P. Musznicki, A.
Falak, and S. Czapp, “Solar Photovoltaic Energy Optimization
and Challenges,” Frontiers in Energy Research, vol. 10, article
879985, 2022. DOI: 10.3389/fenrg.2022.879985.
4. Gawre, S.K. (2022). “Advanced Fault Diagnosis and Condition
Monitoring Schemes for Solar PV Systems.” In: Bohre, A.K.,
Chaturvedi, P., Kolhe, M.L., Singh, S.N. (eds) Planning of
Hybrid Renewable Energy Systems, Electric Vehicles and
Microgrid. Energy Systems in Electrical Engineering. Springer,
Singapore. DOI: 10.1007/978-981-19-0979-5_3.
5. Vlaminck, Michiel, Rugen Heidbuchel, Wilfried Philips, and
Hiep Luong. (2022). “Region-Based CNN for Anomaly
Detection in PV Power Plants Using Aerial Imagery.” Sensors,
22(3), 1244. DOI: 10.3390/s22031244.
6. N. Rafiuddin, Y. U. Khan, and O. Farooq, “A Novel Wavelet
Approach for Multiclass iEEG Signal Classification in
Automated Diagnosis of Epilepsy,” IEEE Transactions on
Instrumentation and Measurement, vol. 71, pp. 1–10, 2022.
7. K. O’Shea and R. Nash, “An Introduction to Convolutional
Neural Networks,” ArXiv e-prints, arXiv:1511.08458, Nov.
2015.
8. D. H. Hubel and T. N. Wiesel, “Receptive fields and functional
architecture of monkey striate cortex,” Journal of Physiology,
vol. 195, pp. 215–243, 1968.
9. K. Fukushima, “Neocognitron: a self-organizing neural network
model for a mechanism of pattern recognition unaffected by
shift in position,” Biological Cybernetics, vol. 36, pp. 193–202,
1980.
10. R. Yamashita, M. Nishio, R. K. G. Do, et al., “Convolutional
neural networks: an overview and application in
radiology,” Insights Imaging, vol. 9, pp. 611–629, 2018. DOI:
10.1007/s13244-018-0639-9.
11. A. Geron, Hands-on Machine Learning with Scikit-Learn and
TensorFlow, O’Reilly Media USA, 2017.
12. S. Ioffe and C. Szegedy, “Batch normalization: accelerating
deep network training by reducing internal covariate shift,”
in Proceedings of the 32nd International Conference on
Machine Learning (PMLR), vol. 37, 2015, pp. 448–456.
13. Millendorf, M.; Obropta, E.; Vadhavkar, N. Infrared solar
module dataset for anomaly detection. In Proceedings of the
2020 International Conference on Learning Representations
(ICLR), Addis Ababa, Ethiopia, 30 April 2020