Objective

2

Global clean energy development has promoted the annual installed capacity of photovoltaic (PV)

and the following failure rate of PV panel has been increasing as well. Traditional PV panel fault detection relies on manual inspection

which has low efficiency and high false detection rate. Currently

the intelligent PV panel fault detection method is more practical based on machine vision. To solve PV panel fault detection

many studies used convolutional neural networks (CNNs)

and these models are based on supervised learning. However

supervised learning method is not adequate due to the lack of negative samples. Our research is focused on training a semi supervised network that detects anomalies using a dataset that is highly biased towards a particular class

i.e.

only use positive samples for training. The generative adversarial network (GAN) can produce good output through the mutual game learning of two modules (at least) in the framework

including the generative model and the discriminant model. Conditional GAN introduces condition variable via generator and discriminator. This additional condition variable can be used to guide the generated data by generator. The gradient descent is challenged more while the number of layers of neural network increasing. Most activation or weight execution methods are challenged in the context of gradient disappearance

gradient explosion and non-convergence. To improve the generalization performance of the deep convolution network

gradient centralization (

$$ {\rm{GC}}$$

) is applied on conditional GAN to resist over fitting through regularizing weight space and output feature space.

Method

2

To complete the PV panel fault detection with efficiency and accuracy

we use a semi supervised anomaly detection model which combines the adversarial auto-encoders and the conditional GAN. First

a novel model is constructed based on semi supervised generative countermeasure network. Second

three loss functions are defined to optimize individual sub-networks

i.e.

adversarial loss

encoder loss

and contextual loss. This model use smooth L1 loss to define the loss because it can optimize the advantages of L1 loss and L2 loss to speed up the training of the model. Next

the objective function

$$ L$$

is obtained by the weighted sum of the three loss functions. Third

the original optimization function is added with

$$ {\rm{GC}}$$

to regularize the weight and output space for over fitting prevention. To reduce the error between the reconstructed image and its input original image

the generator network is used to learn the data distribution of the positive sample in the PV panel dataset

and the discriminator network is used for adversarial training. The generator can capture the training data distribution within PV panel image and its latent space vector both. Fourthly

the generator will first encode it into latent space vector for testing when the normal PV panel image is put into the trained model

and then decode it into the reconstructed image. The illustration of reconstructed image generated by the model is equivalent to the input image. Thanks to the data distribution learned by the generator

the error between the input normal PV panel image and its reconstructed image is smaller than the threshold

which is defined by the anomaly model. However

the reconstructed image is not similar to the input image when the abnormal PV panel image is put into the model. The error between the input image and its reconstructed image is bigger than the threshold due to no learned data distribution by the generator. Finally

the model can detect abnormal PV panel via the existing error between the input image and its reconstructed image.

Result

2

This dataset of the PV panel image is collected by Zhejiang Power Plant and photographed by unmanned aerial vehicle (UAV). The original image size is 3 840 × 2 048 pixels. The color and pattern of PV panel image are single and regular. The original images are cut into the size of 32 × 32 pixels and the following 32 000 sub images are obtained because dividing a large image into several small images has no negative impact on the model. The training set is randomly selected from the total sample by 80%

and the test set is the remaining 20%

that is

the size of the training set is 25 600

and the size of the test set is 6 400. Because the image size is 32 × 32 pixels and the color image is reflected in three channels

the input size of the encoder is 32 × 32 × 3

the convolution kernel of 4 × 4 is used. The first three layers of convolution are filled with 0 edges and the number is 1

and the step size of convolution kernels is 2. In the last step

1 × 100 convolution space is used to fill the final volume. The structure of decoder and encoder is symmetrical. The first layer of transposed convolution has no filling

and the last three layers are filled with 1 and 2 steps. The first three layers transpose the convolution layer

and then add batch standardization layer and ReLU activation function layer. The reconstructed image with the size of 32 × 32 × 3 is as the output. Then

the normal PV panel image is used as input of the semi supervised anomaly detection model as the original image of the positive sample

then the model is trained. The momentum parameter is set to 0.999

the learning rate is set to 0.000 2

and the batch size is set to 64. After the training of the model

the test PV panel image is input into the trained semi supervised anomaly detection model

and its reconstructed image is generated by the generator network. The error is calculated between the original PV panel image and its reconstructed image. Finally

the model can clarify the PV panel is normal or abnormal in terms of the error is less than the adaptive threshold or not. Our method is compared to the pretrained Visual Geometry Group 16-layer network(VGG16)

anomaly generative adversarial network(AnoGAN)

GANomaly

etc

and the area under curve (AUC) is improved by 0.12

0.052 and 0.033

respectively.

Conclusion

2

In this semi supervised anomaly detection model

a large number of positive samples are needed to be included in the training samples with no labels. The semi supervised anomaly detection model does not need a large number of negative examples in comparison with supervised learning

which solves the problem of the lack of negative examples. Furthermore

the proposed model combines the advantages of

$$ {\rm{GC}}$$

and ganomaly to make the PV panel anomaly detection results more accurate.