Item Type Article
Authors Mao, Mingxuan;Feng, Xinying;Xin, Jihao;Chow, Tommy W. S.
Citation Mao, M., Feng, X., Xin, J., & Chow, T. W. S. (2022). A Convolutional Neural Network Based Maximum Power Point Voltage
Forecasting Method for Pavement PV Array. IEEE Transactions on Instrumentation and Measurement, 1–1. https://doi.org/10.1109/
tim.2022.3227552 Eprint version Post-print
DOI 10.1109/tim.2022.3227552
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Download date 2023-12-19 18:24:02
Link to Item http://hdl.handle.net/10754/686363
A Convolutional Neural Network Based Maximum Power Point Voltage Forecasting Method for
Pavement PV Array
Mingxuan Mao, Xinying Feng, Jihao Xin, and Tommy W. S. Chow, Fellow, IEEE
Abstract—The shadows formed by fast-moving vehicles on a pavement PV array exhibit complex dynamic random distribution characteristics, which can cause a dynamic multipeak PV curve.
Dynamic vehicle shadow will cause the reduction in pavement PV power, so the question is how to maximize the power in such conditions by operating at different maximum power point (MPP) quickly and continually. To address this issue, this paper proposes a maximum power point voltage forecasting method based on convolutional neural network (CNN). This method inputs the environmental information of pavement PV array into the proposed CNN model for learning and then uses this model to forecast the maximum power point voltage. Finally, simulation and experimental test with ResNet, MLP and CNN methods are carried out and the comparison results show that this model can accurately predict the maximum power point voltage of pavement PV array under different vehicle shading conditions.
Index Terms—Pavement PV array, convolutional neural network (CNN), vehicle shadow image, maximum power point voltage forecasting model,feature extraction
I.INTRODUCTION
ITH the development of photovoltaic (PV) technology, applying PV approaches to transportation problems has become a new trend in the PV industry, and pavement PV arrays have been developed.
However, shadows on pavement PV array are subject to the distribution of vehicles, which have strong dynamic random distribution characteristics, and seriously affect the power quality of the pavement PV array. In this case, the PV output characteristic curve is nonlinear and has multiple peaks, which seriously affect the output efficiency [1]. Many traditional maximum power point tracking (MPPT) algorithms, such as the perturbation and observation method [2], conductance increment method [3], etc. cannot effectively distinguish between local peaks and global peaks and cannot achieve tracking of global maximum power points under the complex shading conditions. The speed of traditional MPPT is relatively slow in the situation of fast-changing partial shading.
This work was supported in part by the National Natural Science Foundation of China under Grant 52107177 and Grant 62073272, in part by the International Postdoctoral Exchange Fellowship Program under Grant 2020045. (Corresponding author: Mingxuan Mao)
M. X. Mao is with the School of Electrical Engineering, Chongqing University, Chongqing 400044, China, and also with the Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, U.K. (e-mail: [email protected]).
To find an MPPT method suitable for partial shading conditions, researchers have proposed many improved traditional algorithms and intelligent algorithms. Among them, a short-circuit current combined with a disturbance observation method is proposed in [4], which can respond quickly to environmental changes and effectively reduce the disturbance of a system at the maximum power point. In references [5-6], researchers studied the application of artificial neural networks (ANNs) and fuzzy control algorithms to MPPT. Although these methods can effectively address the nonlinear characteristics of the PV curve to a certain extent, they are restricted by the processing ability of the algorithm. Additionally, the essence of MPPT is the optimization problem of complex functions, and swarm intelligence optimization algorithms, including the particle swarm optimization (PSO) algorithm [7], immune firefly algorithm (IFA) [8], and whale optimization algorithm (WOA) [9]. Hayder et al. [10] studied different improved particle swarm MPPT algorithms, which can achieve accurate tracking after global optimization, but these methods have oscillating output power during optimization.
The research and application of machine learning and deep learning algorithms in PV systems have increased considerably in recent years, and researchers have conducted certain research and exploration tasks. Early researchers used support vector machines (SVMs) with weather forecasting to predict the power generation of PV arrays [11]. Traditional machine learning methods like SVM have the advantages of good sample set robustness and insensitive to kernel selection. However, the major shortcomings of using SVM and other traditional ways are that it is difficult to implement large-scale training samples and solve the problem of multi-classification (classification of data sets with labels of more than two categories).
With the rapid development of neural networks and deep learning, the forecasting research methods related to PV power generation or PV arrays are presented. In [12-14], long short- term memory (LSTM) was used for ultrashort-term PV power forecasting. Wang et al. [15] adopted the method of combining
X. Y. Feng is with the School of Electrical Engineering, Chongqing University, Chongqing 400044, China. (e-mail: [email protected]).
J. H Xin is with the Resilient Computing and Cybersecurity Center, KAUST, Thuwal 23955, Saudi Arabia. (e-mail: [email protected]).
T. W. S. Chow is with the Electrical Engineering Department, City University of Hong Kong, Hong Kong (e-mail: [email protected]).
W
dual-mode cuckoo search (DMCS) and wavelet neural networks (WNNs) to predict PV power generation. In addition, convolutional neural networks (CNNs) in deep learning have multilayer learning structures and the ability to process big data.
They can efficiently extract the feature information of image data and have been widely used in image recognition and classification [16]. In [17-18], a CNN was applied to analyze sky or cloud images and then obtain the irradiation to predict the maximum power point. Moreover, a combination of CNNs and LSTM networks was proposed for short-term PV power prediction [19-20]. The Elman neural network (ENN) has also been used to predict the working voltage of a PV maximum power point and to carry out the subsequent MPPT control task [21]. Some traditional MPPT methods have been combined with neural networks [22-23], such as ANNs and the fuzzy logic
controller (FLC). In addition, some hybrid methods that combine hill climbing (HC) algorithms and neural networks have been proposed for tracking the MPP of PV arrays [24].
Roy et al. [25] compared the effects of the Levenberg- Marquardt algorithm, the Bayesian regularization algorithm and the scaled conjugate gradient algorithm in MPPT.
Therefore, machine learning and deep learning algorithms have been applied in MPPT. However, the algorithms proposed in the above literatures need to combine neural networks with other algorithms to forecast the MPP voltage under partial shading conditions, which increases the overall forecasting time so that they then cannot provide online real-time control to maximize pavement PV power. A summary of previously proposed global MPPT based on different optimization algorithms and neural networks is shown in Table I.
TABLE I
THE ADVANTAGES AND DISADVANTAGES OF MPPT MODELS MPPT
methods Ref. No. Advantages Disadvantages
ANN [5][6] Excellent output characteristic and smaller oscillation
Fast tracking speed without iteration process
A lot of data for training required
Additional sensors required PSO [7][10] Effectively avoid the local maximum and track the global
maximum power point
Multiple iterations required
There is power oscillation during tracking IFA [8] Restrain the oscillation of voltage and power
Reduce the tracking time and the steps of iterations
The complexity of the algorithm increases
WOA [9] Simple mechanism and easy deployment
Fewer parameters to be controlled
Possibility of premature convergence
ENN [21] A better steady state and stable response is achieved with reduced fluctuations near the MPP
Slow training speed
Unable to avoid the local maximum GA-FLC-
ANN
[22] Suitable for various scenarios Complex structure
A large amount of data is required for deployment ANFIS-PSO [23] No extra sensor required for measurement of irradiance and
temperature variables
Low MPPT tracking period, zero steady-state error
The structure of the algorithm is complex
ANN-HC [24] Reducethe number of iterations
Small number of samples required
Low accuracy with dissimilar features
ANN-LM [25] Enhance applicability for large-scale applications
Good performance in the overall data processing
Complex structure
A lot of data required
In addition, in the application of pavement PV arrays, the shadows formed by vehicles change rapidly and randomly, and traditional methods cannot predict the voltage at the maximum power point in a short time, so it drives the need of developing a new voltage prediction method using pavement PV array &
vehicle shadow images.
Based on the above analysis, we use CNN for the prediction of the maximum power point voltage and propose a rapid forecasting method for determining the maximum power point voltage of pavement PV arrays based on a CNN. By converting the irradiation and temperature into an image describing the state of the pavement PV array, we input it into the CNN model to learn and train the characteristics of the pavement PV array.
Finally, the effectiveness of this method is verified by the simulation and experiment test.
The main contributions of this paper can be summarized as follow:
i. A novel maximum power point voltage prediction method for pavement PV array without current and voltage sensors is proposed, designed, and implemented based on CNN. As a result, it has the ability of tracking the global MPP with a minimum amount of computation under complex, dynamical vehicle shadow conditions.
ii. Compared with existing MPPT methods, the CNN prediction method can obtain maximum power point by only one calculation and therefore avoids the transient time of current and voltage during iterations.
iii. The optimized CNN model is compared with other neural network models to verify the superiority of the proposed method through simulation and hardware experiment using Python programming.
The rest of the paper is arranged in the following Sections.
Section II analyses the output characteristic curve of pavement PV array. Section III presents the principle of the proposed
MPP voltage prediction method based on CNN. In Section IV, the performance of the proposed prediction method is verified through simulation and hardware experiments. Finally, the conclusion of this paper is given in Section V.
II.OUTPUT CHARACTERISTIC ANALYSIS OF THE PAVEMENT PV ARRAY
A. Solar PV cell engineering model
A PV cell is the smallest unit in a PV array. It is essential to establish a PV cell model that is accurate and practical when studying the MPPT control of a PV power system. A PV cell single diode equivalent circuit [26] is the most widely used mathematical model for PV cells, and its structure is shown in Fig. 1.
Fig. 1. Equivalent circuit of PV cells.
In Fig. 1, 𝑉 is the output voltage of the PV cell, 𝐼𝑝ℎ is the photogenerated current,𝐼d is the current flowing through the diode, 𝐼 is the PV cell output current, and 𝑅s and 𝑅sh are the equivalent series resistance and parallel resistance, respectively. According to Kirchhoff’s current law, the I-V equation of PV cells can be obtained as:
0 exp s 1 s
ph
sh
q V IR V IR
I I I
AkT R
(1) where 𝐼0 is the reverse saturation current; 𝑞 is the unit charge, which is 1.6 × 10−19 C; 𝑘 is the Boltzmann constant, which is 1.39 × 10−23 J/K; 𝐴 is the quality factor of the diode; and 𝑇 is the absolute temperature, which is 300 K.
In real engineering simulations, for the convenience of calculation, the following parameter models can be used [27]:
1
2
1 exp 1
sc
oc
I I C V
C V
(2)
1
2
1 m exp m
sc oc
I V
C I C V
(3)
1
2 m 1 ln 1 m
oc sc
V I
C V I
(4) where 𝐼𝑠𝑐 is the short-circuit current, 𝑉𝑜𝑐 is the open circuit voltage, 𝐼𝑚 is the peak power point current, and 𝑉𝑚 is the peak power point voltage.
B. Output characteristic analysis of a pavement PV array with vehicle shadows
The output power of PV arrays is closely related to irradiation and temperature. The output characteristics of road
PV arrays are also related to the vehicle position on the road, so the output characteristics have strong random characteristics.
To obtain the environmental information of the road PV array, the environmental information can be converted into a two- dimensional image.
Fig. 2. The experimental system of pavement PV array with the vehicle shadow.
To obtain actual experimental data, this paper builds an experimental system for pavement PV array, as shown in Fig.
2. The system consists of 1 × 8 PV arrays to simulate road PV arrays, a host computer and an MP-170 I-V curve checker. The MP-170 I-V curve checker can record the I-V curve and PV curve of the PV pavement in the current situation. During the experiment, a vehicle model was used to simulate actual road traffic conditions, and the corresponding image data and the voltage of the maximum power point were recorded.
Fig. 3. P-V curves in the experimental system of pavement PV array with the vehicle shadow.
The corresponding vehicle position and corresponding PV curve can be obtained through the experimental equipment, and the results are shown in Fig.3. The figure shows the process of two vehicles passing through the dual lanes in turn and the corresponding PV curve. The red dots on the curve are the corresponding positions of the maximum power point. It can be seen from the figure that as two vehicles pass through the road PV array, the PV curve will change from a single peak to multiple peaks, and the voltage at the maximum power point will also change with the number of vehicles.
III.PROPOSED MAXIMUM POWER POINT VOLTAGE PREDICTION
METHOD BASED ON CNN
A. Maximum power point voltage prediction model based on CNN
CNNs have achieved great success in the field of vision and are mostly used to extract the spatial features of images. CNN is mainly composed of convolutional layers, pooling layers and fully connected layers and obtains global high-level features by
gradually extracting local features [28]. A fully connected layer is the simplest structure to fit equations, where all neurons in adjacent layers are connected. The output of one fully connected layer can be expressed as:
T
y f W x b (5) where 𝑦 represents the output and 𝑊 represents the weight of each neuron.
Convolutional layers are used to obtain global high-level features by gradually performing convolution. The operation, named convolution, is expressed as
1 ,
n n n
j i i j j
i
x f x k b
(6)where 𝑥𝑗𝑛 and 𝑥𝑖𝑛−1 represent the feature maps of the 𝑛𝑡ℎand previous convolutional layers, respectively; 𝑘𝑖,𝑗represents the convolution kernel used in the feature map between the 𝑖𝑡ℎand 𝑗𝑡ℎ layers; 𝑏𝑗𝑖 represents the bias of the neuron; 𝑓(•) stands for the activation function.
A pooling layer is used to compress the model size and enhance the generalization ability. A pooling layer can be expressed as:
1
1
n n n n
j j j j
x f down x b (7) where 𝑑𝑜𝑤𝑛(•) represents the pooling method adopted by the pooling layer and is also known as “downsampling”. Popular choices are maximum pooling or average pooling. 𝛽𝑗𝑛−1 indicates the weight of product operation.
To effectively extract the feature information of a pavement PV array with shadows, this paper designs a CNN structure with 2 layers of convolution and 2 fully connected layers. The main network layers are shown in Table II.
TABLE II LAYERS OF CNN
ID Layer Activation Function
1 Convolution ReLU
2 Pooling MaxPool
3 Convolution ReLU
4 Pooling MaxPool
5 Flatten —
6 Full-Connection ReLU
7 Full-Connection ReLU
8 Output —
B. Comparison with other models
Usually, a simple model is less accurate but faster. Ideally, a complex model can obtain a better fit but may encounter vanishing gradient problems. ResNet [29] forms a residual block by adding shortcut connections, as shown in Fig. 4, which ensures that the input data are fed to distant layers.
Fig. 4. Residual Block.
We compared our 4-layer CNN (2 convolutional layers and
2 fully connected layers) with a 4-layer multilayer perceptron (MLP) [30] and 18-layer ResNet, as shown in Table III. During testing, the prediction time of ResNet was 20 times that of the CNN, and the training time was also double that of the CNN.
TABLE III
COMPARING WITH OTHER NEURAL NETWORK MODELS Time of 6k data MLP[30] ResNet[29] CNN
Prediction time 25.3ms 436ms 22.3ms
Training time 1.2s 2.57s 1.2s
By balancing the training cost, prediction time and performance, our CNN model can obtain satisfactory results in a short time.
C. Maximum power point voltage prediction model based on CNN
To better improve the MPPT control efficiency under fast- shading conditions, based on the above comparison results, this paper proposes a CNN-based pavement PV maximum power point voltage forecasting algorithm. This model collects the irradiation and temperature of a PV cell in the pavement PV array, converts them into a 2-dimensional matrix according to the position and value, and inputs the matrix into the CNN model to forecast the maximum power point voltage. Its structure is shown in Fig. 5.
Fig. 5. Flow chart of the proposed method.
The proposed model structure is shown in Fig. 6.
Fig. 6. CNN structure diagram.
The samples in this paper are set to 1 kW/m2, the temperature is 25°C, and the vehicle position is generated by MATLAB; under the same temperature conditions, the state of the PV panel can be divided into two cases: shaded and unshaded. After normalizing the temperature and irradiation on the panel, the elements in the conversion matrix also have only two states. The conversion matrix can be input into the CNN for training, the shadow position of the vehicle is analyzed, and the maximum power point voltage is forecasted by the CNN.
This maximum power point voltage prediction algorithm for the pavement PV array is mainly composed of a CNN model, which is used for the extraction of the shading and temperature distribution characteristic information of the pavement PV
array. The CNN voltage prediction model is divided into two phases: the training phase and the prediction phase. In the training phase, the CNN uses data samples for feature extraction and continuously adjusts according to the results to obtain an accurate voltage prediction model; in the prediction phase, real-time data are input into the trained CNN model for voltage value prediction.
IV.RESULTS AND DISCUSSIONS
A. Python simulation results
To obtain training samples, we first build a PV power simulation model to work under different shade conditions and temperatures and record the voltage corresponding to the maximum power point for each PV cell’s irradiation value.
Under standard test conditions, the performance parameters of the entire PV array are as follows: the open circuit voltage is 242.7 V, the short circuit current is 18.9 A, the maximum power point voltage is 203.6 V, the maximum power point current is 17.7 A, and the maximum power is 3613.4. In the simulation test, the shading situation is simulated according to the irradiation of the PV cells in different areas. By using this method, a total of 7,800 sets of data are generated, and a representative set of data is shown in Fig. 7(a)-(b).
(a) Irradiation transformation matrix of single pavement PV panel under vehicle shadow
(b) Irradiation transformation matrix of pavement PV array on two-lane PV highway with side road under vehicle shadow
(c) Distribution diagram of the maximum power point of the pavement PV array
Fig. 7. Digital feature extraction process of pavement PV array.
Our networks run on an AMD R7 CPU with TensorFlow 2.3.0. We use 7800 data samples with random shading generated by PVMismatch, an open source simulator by SunPower Ltd. The data are divided into a training set and a test set at a ratio of 3:1. To verify the distribution of the test samples, 100 samples are randomly selected from the simulation results to check the maximum power point voltage distribution of the pavement PV array, and the results are shown in Fig. 7 (c). It can be seen that the maximum power point voltage is roughly distributed between 100 and 200 V, and there is no situation where the data are too clustered or out of the actual range.
Therefore, the 7800 samples of data generated by the simulation can be used for follow-up research.
To evaluate model’s performance, we calculate three metrics: the average absolute error (MAE), mean square error (MSE) and mean square logarithm error (MSLE). The smaller the three error indexes are, the better the prediction performance of the model. The calculation formula of the three indexes is as follows:
1 N '
t t
t
y y
MAE N
(8)
2 1
'
N
t t
t
y y
MSE N
(9)
21
log ' 1 log 1
N
t t
t
y y
MLSE N
(10) In the formula, 𝑦𝑡 represents the theoretical value of the maximum power point voltage at time t, 𝑦𝑡′ represents the predicted value of the PV maximum power point voltage at time t, and N represents the size of the test set.
Fig. 8. CNN training process.
In the experimental system, considering that the irradiation characteristics and image characteristics of the pavement PV array contain a small amount of information, the CNN model adopts a structure of two layers of convolution and two fully connected layers. According to the size of the pavement PV array, the size of the input layer of the CNN model is 16×60, and the pooling layer adopts 2×2 MaxPool and uses ReLU as the activation function. Furthermore, to increase the
generalization ability of the model, we introduce a dropout strategy, which is set to 0.2 during training. In this paper, the Adam learning algorithm is used for model convergence. We choose MAE as the loss function, the learning rate is set to 0.001, and the number of epochs is set to 100. With the hyperparameter settings, the training process is shown in Fig. 8.
Fig. 8 shows that with continuous training, the values of the training loss and verification loss decrease synchronously, indicating that the model is not overfitted.
(a) Training curve of different convolution kernel sizes
(b) Training curve of different FC units
Fig. 9. Training curve of MAE.
During the training and learning process of the CNN, different settings of hyperparameters have a great impact on the prediction accuracy of the model. To obtain the optimal model structure, this paper tests and compares different convolution kernel sizes and numbers of fully connected layer neurons.
TABLE IV
PERFORMANCE COMPARISON OF DIFFERENT CONVOLUTION KERNEL SIZES
Kernel Size MAE MSE MSLE
3&3 6.10 73.58 0.0038
3&5 5.27 54.58 0.0025
5&5 4.71 43.88 0.0020
5&7 5.26 52.39 0.0025
7&7 4.36 36.59 0.0016
To compare the performance of the model with different convolution kernel sizes, when the default number of neurons in the fully connected layer is 64&64, the two convolutional layers are set to 3&3, 3&5, 5&5, 5&7 and 7&7 for training comparison. As seen from Fig. 9a, as the size of the convolution kernel increases, the difference in the convergence curves of the CNN model training gradually decreases. Among them, the final convergence values of the models with convolution kernel
sizes of 5&5, 5&7 and 7&7 are similar. From Table IV, it can be concluded that the model with the convolution kernel size of 7&7 has the best overall performance compared to other structural test performance data, and its MAE value is 4.36, MSE value is the lowest at 36.89, and MSLE value is the lowest at 0.0016.
TABLE V
PERFORMANCE COMPARISON OF DIFFERENT CONVOLUTION KERNEL SIZES
FC Neurons MAE MSE MSLE
64*64 4.36 36.59 0.0016
128*64 3.51 23.37 0.0010
128*128 3.52 23.18 0.0010
256*128 2.73 13.86 0.00059
256*256 2.54 11.13 0.00049
512*256 2.73 13.00 0.00053
512*512 2.65 12.48 0.00052
In the next step, we compare the performance of the CNN model when the numbers of neurons in the 2-layer fully connected layer are different. Under the premise that the default convolution kernel size is 7&7, the number of two fully connected layers is set and trained according to Table V. From the model training convergence curve shown in Fig. 9b, it can be seen that the more neurons there are, the stronger the model information processing capability and the better the convergence performance. However, its final convergence value will be closer to a fixed value. Table V shows that the model with 256&256 neurons has the best performance compared to the test data of other structures. Its MAE value is reduced to 2.54, MSE value is reduced to 11.13, and MSLE value is reduced to 0.00049. Considering that as the number of neurons increases, the model structure will become more complicated, and overfitting may occur. Therefore, the number of neurons will no longer be tested. Considering the structure of the model in this paper, the number of neurons in the fully connected layer is finally chosen to be 256&256.
Fig. 10. Prediction result with the proposed CNN method.
By analyzing the above experimental results, this paper establishes a CNN voltage prediction model with a convolution kernel size of 7&7 and a fully connected layer neuron size of 256&256. In Fig. 10, we use the CNN model to predict the voltage under 50 groups of different conditions and compare the model’s predicted voltage value with the theoretical value.
The test results show that the proposed CNN-based fast prediction model for the maximum power point voltage of the
pavement PV array can effectively predict the maximum power point voltage of the pavement PV array under different vehicle partial shading conditions.
B. comparison and analysis
To better compare the prediction results of various methods, the concept of error voltage is introduced, which represents the difference between the predicted result and the actual maximum power point voltage. The error voltage can be expressed by the following equation:
EV R P
U U U (11) where 𝑈𝐸𝑉 represents the value of error voltage; 𝑈𝑅 represents the real value of MPP voltage; 𝑈𝑃 represents the prediction value of MPP voltage.
To check the prediction effect of the maximum power point voltage prediction model, the proposed CNN prediction method is compared with ResNet [29] and MLP [30] in the same dataset, and the results are shown in Fig. 11.
Fig. 11. MPP voltage prediction curves with real value and three methods.
Fig. 11 shows MPP voltage prediction result and error curves with real value and 3 methods. From left part of Fig. 11, it can be seen that CNN has the best prediction effect and has the highest degree of fitting with the actual value, while MLP and ResNet are worse. Moreover, error voltage in the right part of Fig. 11 is the MPP voltage prediction error which represents the difference between the predicted value and the real value.
From the right part of Fig. 11, we can see that the error voltage of the CNN maximum power point voltage prediction model is also the smallest among the neural networks. Therefore, it can be considered that the proposed prediction method has certain advantages compared with other neural networks in dealing with pavement PV array.
In order to verify the computational efficiency of the CNN based maximum power point voltage prediction method for pavement PV arrays, MAE, MSE, MSLE and the prediction
time of each algorithm are recorded in Table VI. It can be seen that due to the simple structure of MLP and CNN, their prediction speed is the fastest; while ResNet has the largest number of layers and the most complex structure, its prediction speed is the slowest. The comparison of MAE, MSE and MSLE shows that the prediction effect of the CNN prediction algorithm is the best.
TABLE VI
PERFORMANCE COMPARISON OF NEURAL NETWORKS IN SIMULATION
Neural networks MLP ResNet CNN
Average prediction time 0.0364 ms 0.5238 ms 0.1236 ms
MAE 9.5477 11.7561 4.3596
MSE 168.8378 282.4076 59.8715
MSLE 0.0080 0.0144 0.0032
C. Experimental validation
In previous research, a CNN-based maximum power point voltage prediction model for pavement PV array was obtained, and the model was simulated and verified. Therefore, on this basis, this section conducts experiments on the proposed prediction algorithm to verify its feasibility. The engineering parameters of the experimental equipment in this paper are shown in Table VII.
TABLE VII
PARAMETERS OF PAVEMENT PV ARRAY
Parameter Value
Maximum power 160 W
Maximum power current 1.1 A Maximum power voltage 142 V Short circuit current 1.2 A Open circuit voltage 168 V
The experimental data come from manual trials on the experimental system for pavement PV array in Fig. 2, and experimental images were collected from June 19, 2022 to June 21, 2022. To ensure the test effect, different vehicle positions were photographed at different irradiation levels, the blurred and low irradiation images were filtered, and a total of 226 images were filtered out. Due to the small amount of experimental data, to prevent overfitting, this paper uses sharpening and adding filters for image augmentation, which can effectively prevent overfitting. After image augmentation, the pavement PV array data set has a total of 1356 images, as shown in Fig. 12.
Fig. 12. The augmented data set.
Through the augmented dataset, we input the test data to ResNet, MLP and the proposed CNN models, and the
prediction results are shown in Fig. 13. We can see from the left part of Fig. 13 that ResNet and MLP perform poorly in the prediction of the real pavement PV array & vehicle shadow images. In addition, the right part of Fig. 13 also indicates that the error voltage of CNN is smaller than that of ResNet and MLP in most cases. Therefore, the experimental results verify that the proposed method not only outperforms the other two comparison methods, but also shows good robustness and stability of predicting MPP voltage on the real pavement PV array & vehicle shadow images.
Fig. 13. MPP voltage prediction curves with real value and three methods on the real pavement PV array & vehicle
shadow images.
After recording the prediction time, MAE, MSE and MSLE of the three methods, the results are shown in Table VIII. Since the prediction time is shorter than the acquisition time of the image sensor, the forecasting performance should be determined by the forecasting accuracy. It can be seen from Table VIII that the prediction accuracy of CNN is the highest, indicating that the maximum power point voltage prediction algorithm proposed in this paper has the highest prediction accuracy. Comprehensively considering the prediction accuracy and speed of the 3 prediction algorithms in the experiment, it can be considered that CNN prediction method can effectively predict the maximum power point voltage of pavement PV array.
TABLE VIII
PERFORMANCE COMPARISON OF NEURAL NETWORKS IN THE EXPERIMENT
Neural networks MLP ResNet CNN
Average prediction time 0.1037 ms 0.5429 ms 0.2790 ms
MAE 5.2571 5.6827 3.7622
MSE 46.8733 65.4052 33.4117
MSLE 0.0027 0.0038 0.0020
V.CONCLUSION
In this paper, we propose a prediction model method for the maximum power point voltage based on CNN. First, the CNN voltage prediction model is constructed by comparing the performance of the different neural network algorithms with the different convolution kernel sizes. The image information of the pavement PV array is then used as the input, and we use the CNN model to forecast its maximum power point voltage in a short amount of time. Finally, a series of simulation and experimental tests are done, and the prediction results of the MPP voltage under different shading conditions show that the proposed prediction model can effectively predict the maximum power point voltage of pavement PV array in a robust way.
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