# Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture

Authors: Shihao Zhou; Benren Pan; Dongbin Lu; Yiming Zhong; Guannan Wang
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. Then, a fault data set is constructed by finding effective information that can fully reflect the DC fault in the huge power grid environmental information, and the data set is screened, processed by classification feature fields, and linearly normalized. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed by introducing a deep convolutional neural network (DCNN) into the traditional generative adversarial network (GAN) for data training and DC fault diagnosis. In addition, the corresponding process is given. The proposed method and the other three methods are compared and analyzed by simulation experiments. The results show that the method proposed has the highest accuracy and smallest error loss value of 95.6% and 0.18, respectively. It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods.


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## Body

## 1. Introduction

At present, with the continuous development of UHV AC and DC Internet, the number of UHVDC substations is gradually increasing, and its importance in the power system is also increasing [1, 2]. UHVDC can effectively save costs and improve transmission capacity for long-distance power transmission. But at the same time, it also brings new problems to the safe and stable operation of power grid [3–5]. Fault diagnosis after the fault of the UHVDC is very important to the grid, which can effectively guide the relevant staff for grid fault maintenance and greatly reduce the fault time. It is an effective means to realize the smart grid self-healing function and also a research hotspot in the UHVDC industry at home and abroad [6–8].


## 2. Related Work

After a fault occurs in the UHVDC transmission system, the main changes are the electrical volume and switching volume. Therefore, collecting and analyzing the two kinds of fault symptom information is an effective means. Aiming at the problem of fault diagnosis of UHVDC transmission system, some scholars have made relevant research and achieved certain results. Wang et al. [9] used full current to replace low-frequency components to optimize the energy ratio of fault signals. By applying the energy ratio variation feature of fault sections to fault feature extraction, they proposed a fault section identification and classification method of UHVDC transmission lines based on energy ratio optimization and fuzzy logic system. However, this method is difficult to obtain clear diagnosis results in complex situations. Considering the influence of up-wave velocity and sag effect on long-distance DC overhead transmission lines, combined with traveling wave detection unit with Rogowski coil as the core, fault information, and sag coefficient, Duan et al. [10] proposed a new principle of three-terminal traveling wave ranging that is not affected by wave velocity and sag effect. However, this method needs to solve the multiple equations and the arrival time of the traveling wave head, which requires a large amount of calculation. Based on the Bergeron distribution parameter model of the line, the authors of [11] construct the corresponding fault identification method on traditional directional current method. A method for accurately identifying the fault of UHVDC is provided. However, its fault tolerance rate is low and cannot solve the uncertainty problem in power grid fault diagnosis. Aiming at the problem that traditional HVDC fault diagnosis methods are difficult to achieve remote high-resistance fault diagnosis, [12] is based on the sequence overlapping derivative (SOD) that can emphasize the feature and mutation direction of signals and the amplification of fault feature. Finally, the method in accordance with overlapping derivative transformation of voltage transient series is proposed. However, this method cannot achieve accurate fault location. By using the measured voltage and current of the DC filter to calculate the change of the capacitance value of the HVDC capacitor in real time, Lin et al. [13] propose a fault diagnosis method for detecting HVDC filter capacitor faults in the HVDC transmission system. However, this method is only applicable to the fault diagnosis of high-voltage capacitors in DC filters and has some limitations. Aiming at the problem of fault diagnosis after the fault of the HVDC cable, [14] is based on the difference between the arrival time difference of the forward traveling wave and the reverse traveling wave head after the forward and reverse DC cable faults. Finally, a VSC-HVDC directional pilot frequency protection principle based on directional traveling wave propagation time difference is proposed, which realizes the fault diagnosis of DC cables. However, this method is only suitable for fault diagnosis of DC cables and cannot be applied to fault diagnosis of overhead transmission lines in long-distance DC transmission. [15] studies from the aspects of software structure, hardware structure, DC secondary equipment modeling, and realization scheme of different module functions and designs and develops a monitoring and fault diagnosis master station system for high-voltage DC secondary equipment. In the case of faults and abnormal operation during the operation of the DC transmission system, it can quickly and accurately know the DC protection action and DC fault conditions. However, this method is aimed at the research of HVDC secondary equipment and cannot directly diagnose the fault of the transmission line itself.According to the above, a UHVDC fault diagnosis method in accordance with deep learning under cloud-edge architecture is proposed to solve the core problems of low accuracy and large error loss of current UHVDC fault diagnosis methods. In contrast with the traditional detection method, innovations of the proposed method are listed:(1)
Through the edge computing framework of “cloud” + “edge end,” a fault diagnosis architecture of the UHVDC transmission system with coordinated levels is constructed(2)
By analyzing the data sections before and after the fault and analyzing the power flow data, a fault database is constructed, and then the data are optimized through screening and preprocessing(3)
By organically combining DCNN and GAN to construct the DCGAN network structure, the accuracy and stability of fault diagnosis can be improved.


## 3. Proposed Method

### 3.1. Cloud-Edge Collaboration Architecture

Firstly, the fault diagnosis methods of HVDC system are sorted out from the hierarchical structure to provide guidance for system design and application.Based on the edge computing framework of “cloud” + “edge end,” the fault diagnosis scenario of HVDC transmission system is studied in detail. The fault diagnosis method of HVDC includes three layers, as shown in Figure1.Figure 1
Four-layer hierarchical structure of fault diagnosis system.Three-layer structure shown in Figure1 consists of the data acquisition layer, edge prediction layer, and cloud diagnosis layer from bottom to top. The concepts and key technologies of each level are analyzed:(1)
Data acquisition layer: the data acquisition layer provides all the data support for the warning system, which data comes from the supervisory control and data acquisition (SCADA) and other monitoring systems including the integrated monitoring system, the infrared temperature measurement system of the valve hall, and the infrared temperature measurement system of the site and the robot inspection system. Collected data are from a wide range of sources, and the data are heterogeneous. Therefore, it is necessary to carry out special data collection and data fusion. The collected data can be consumed by the upper modules of the fault diagnosis system.(2)
Edge prediction layer: the work of equipment parameter prediction is transferred to one end closer to the site by edge computing, which can reduce the amount of computation. In order to achieve more accurate prediction, comprehensive equipment state information needs to be considered in the process of power equipment parameter prediction. In this paper, the prediction method based on data sharing is adopted to achieve more accurate prediction by constructing the edge side power equipment parameter prediction model and integrating the data of other field areas.(3)
Cloud diagnosis layer: a central server inside the main control room acts as a “cloud” server to process information gathered after a device fault. Based on the edge prediction layer, the cloud diagnosis layer analyzes the parameter trend of the device in the future period and adopts the method of device status evaluation to realize device fault diagnosis. At the same time, with the assistance of fault information management, the marked misdiagnosis information is filtered to realize intelligent fault diagnosis of equipment.(4)
Each layer of the framework is intrinsically related, and each layer transmits information through industrial bus. The data acquisition layer provides data support for the upper edge prediction layer, including the relevant data collected by each monitoring system. The edge prediction layer predicts the parameters of the UHVDC system and transmits the prediction results and existing historical data to the cloud diagnosis layer for the generation of fault diagnosis information by its internal model. All levels coordinate with each other to form a fault diagnosis method for the UHVDC transmission system.


### 3.2. Edge Layer Fault Feature Screening and Preprocessing

The information of power grid frequency, voltage, and line power before and after the occurrence of UHVDC faults is huge. In order to effectively diagnose the faults, it is necessary to pick out the effective information which means finding the bus, unit, and line information affected by the DC faults from the huge power grid environment information [16, 17]. The information that can fully reflect the DC fault is the fault feature screened out.The real-time power flow data of DC operation at different times are selected to simulate DC faults. The simulation is carried out forM times and M groups of data sections before and after the faults are obtained. According to the analysis of power flow data before and after DC faults, the AC and DC power transmission system has N nodes, and DC nodes are D. The variation of active and reactive power between node n1 and n2, n1,n2=1,2,3,…,N before and after occurrence of DC locking fault is (1)ΔPn1n2=Pn1n21−Pn1n20,(2)ΔQn1n2=Qn1n21−Qn1n20.In (1) and (2), Pn1n21 and Qn1n21 is the active and reactive power transmission values after fault, respectively. Pn1n20 and Qn1n20 is the active and reactive power transmission values n1n2 before the fault, respectively.The voltage amplitude changes of then1, n1=1,2,3,…,N AC node and the n3, n3=1,2,3,…,D DC node before and after a DC fault occurs are shown as follows.(3)ΔUn1=Un11−Un10,(4)ΔUn3=Un31−Un30.In (3) and (4), Un11, Un31, Un10, and Un30 represent the voltage values of AC and DC node after and before fault, respectively.If the variations of active and reactive power, voltage in AC-DC power grid are less than the threshold value in more thanM simulation results, the corresponding data of active power and reactive power variation and voltage amplitude variation do not reflect the DC fault feature and are directly removed. The remaining data is still M groups. The average value of the same type of data is taken after removing the maximum and minimum values, and then the ratio can be obtained by dividing the average value by the data before the fault. Taking ΔPn1n2 as an example, the ratio solving process is shown as follows:(5)Pn1n2a=∑ΔPn1n2−ΔPn1n2max−ΔPn1n2minM−2Pn1n20c.Here,ΔPn1n2max is the maximum value of the active power change. ΔPn1n2min is the minimum value of the active power change, and c represents the branch number of the fault line adjacent to the node n1.Considering that the electrical information of the nodes and branches that are closer to the DC fault point can reflect fault feature more easily, the ratios ofPn1n2a, Qn1n2a, Un1a, and Un3a are sorted from large to small. The larger the ratio is, the more affected the fault is. If none of them is greater than the threshold value, the corresponding original data ΔPn1n2, ΔQn1n2, ΔUn1, ΔUn3, Pn1n20, Qn1n20, Pn1n21, and Qn1n21 are retained.After reading the filteredM groups fault feature data, data preprocessing is required to use the deep learning method for training learning [18, 19]. Each group of feature data before and after faults are sorted out, and the fault feature of each screened bus voltage, active power, reactive power, and frequency are listed as feature fields. If the data field value is invalid, it must change the invalid value to the average value of the M group fault characteristic data. In addition, there are classification feature fields, which are generally converted into N fields when there are N categories.After processing the classified feature fields, it is necessary to standardize the feature fields so that all the values are in the same interval, and the numerical feature fields have common standards. It improves the accuracy of the trained model. Standardization scales data so that it falls into a small, specific range. As the feature distribution is not changed, the data will not lose its characteristic information [20, 21]. In order to remove the unit limit of the data, it is necessary to transform them into dimensionless pure values, so that the features of different dimensions can be numerically comparable to some extent. The accuracy of classifier and the iteration speed of gradient descent can be improved to obtain the optimal solution. In addition, when some distance calculation algorithms are involved, standardization can make each feature make the same contribution to the result and effectively reduce the loss of accuracy. The standardization is carried out through linear normalization of data which means that data are mapped uniformly to the interval [0,1] [22]. The specific process is shown as follows:(6)Yg=Y−YminYmax−Yminsmax−smin+smin.Here,Y represents the sample value. Yg is the linear normalization result. Ymax is the maximum value of the sample. Ymin is the lowest sample value. smax−smin indicates the zoom range. smin indicates the scaling minimum value.


### 3.3. Cloud Layer Fault Feature Training and Diagnosis Based on Deep Learning

#### 3.3.1. Fault Characteristic Training

Generative adversarial network (GAN) adopts the idea of binary game. The probability distribution of data can be learned autonomously through adversarial learning between generator and discriminator, which is usually suitable for AC and DC fault diagnosis. However, GAN has some defects in practical application. For example, it is sometimes unstable in the training process, which leads to the generator being unable to learn the probability distribution of real data and thus generating meaningless data [23]. In order to solve this problem, the deep convolutional neural network (DCNN) is introduced for improvement, and deep convolutional generative adversarial network (DCGAN) is generated. DCNN and GAN are organically combined.DCNN is generally used in supervised fields, but rarely in unsupervised fields. Introducing DCNN with strong feature extraction ability into GAN can help improve its stability and generate better data. The model structures of DCGAN discriminator and generator are shown in Figure2.Figure 2
Model structures of the discriminator and generator in DCGAN.From Figure2, it can be seen that batch normalization is added to all layers except the input layer in the discriminator and generator models. In the neural network parameter learning, the gradient back-propagation method is mainly used, and the batch normalization processing method can effectively promote the gradient propagation between each layer and accelerate the convergence speed of the network. In addition, the batch normalization method can prevent the model from overfitting. It makes the generative model have good generalization ability and avoid generating a large number of identical samples.The generator in DCGAN adopts a deconvolution model. In the generator, tanh activation function is used in the output layer, and ReLU function is used in other layers. It can effectively avoid the problem of gradient disappearing and accelerate the training speed of the model. The tanh activation function and the ReLU activation function are shown in (7) and (8), respectively.(7)tanhx=expx−exp−xexpx+exp−x,(8)ReLUx=0,x≤0x,x>0.The discriminator model removes the fully connected layer based on the convolutional network, and the activation function ReLU is replaced by LeakyReLU. The activation function of LeakyReLU is shown as follows:(9)LeakyReLUx=0.2x,x≤0x,x>0.ADAM is used as the optimizer of the DCGAN model, and the initial learning rate of generator is set to 0.0002 and the initial learning rate of discriminator is set to 0.0001, which can ensure that the loss value of discriminator is reduced less than 0 in the process of training parameters. Otherwise, the DCGAN model will fail to reach Nash equilibrium [24].The discriminator adopts the cross entropy for the loss function of real data and generated data. It is shown as follows:(10)L=tlogf+1−tlog1−f.Here,t represents the real data category. f represents the predicted data distribution.The cross-entropy expression of the discriminator for the generated data is shown as follows.(11)Cf=−log1−Sx.Here,x represents the value of the generated sample output by the discriminator, and S represents the activation function. The formula represents the cross entropy between 0 and the predicted data of the sample generated by the discriminator.The cross-entropy expression of the discriminator for real data is shown as follows:(12)Ct=−logSy.Here,y represents the value of the generated sample output by the discriminator. This formula represents the cross entropy between the predicted data of the real sample by the discriminator and 1.


#### 3.3.2. Fault Diagnosis of UHVDC Transmission

All data are split into training dataset and validation dataset in a ratio of 4∶1. Before the training of the DCGAN model, the evaluation method of the model should be set, and the evaluation index should be set as accuracy. The number of training cycles and training data items are set in each batch. After the completion of each training cycle, the accuracy and error are calculated after the training cycle. The UHVDC fault diagnosis decision-making process is shown in Figure3, where the threshold value of accuracy is represented by A0.Figure 3
UHVDC fault diagnosis and decision-making flow chart.The specific process of the UHVDC fault diagnosis method based on deep learning under the cloud-edge architecture is as follows:(1)
Generate fault data sets based on the data collected by SCADA system and other monitoring systems in the data acquisition layer.(2)
Modeling of fault feature. Real-time power flow data of DC operation at different moments are selected for DC fault simulation, and data sections before and after faults are obtained by simulation.(3)
Fault feature screening of the edge layer. the information that can fully reflect DC fault is found in the huge power grid environment information, such as bus, unit, and line information, and the fault feature is screened out.(4)
Data preprocessing. For each screened fault feature such as bus voltage, active power, reactive power, and frequency, the classification feature field is processed, and the feature field is linearly normalized.(5)
The training dataset and the validation dataset are trained separately in the DCGAN model, and the fault diagnosis results of the model are output.(6)
The accuracy rate and error loss value of DCGAN model fault diagnosis are calculated according to the fault diagnosis result data. If the accuracy rate or error loss value does not satisfy the threshold, the training will continue until the accuracy rate and error loss value meets the threshold. And, the corresponding fault scheduling decision can be generated.


## 3.1. Cloud-Edge Collaboration Architecture

Firstly, the fault diagnosis methods of HVDC system are sorted out from the hierarchical structure to provide guidance for system design and application.Based on the edge computing framework of “cloud” + “edge end,” the fault diagnosis scenario of HVDC transmission system is studied in detail. The fault diagnosis method of HVDC includes three layers, as shown in Figure1.Figure 1
Four-layer hierarchical structure of fault diagnosis system.Three-layer structure shown in Figure1 consists of the data acquisition layer, edge prediction layer, and cloud diagnosis layer from bottom to top. The concepts and key technologies of each level are analyzed:(1)
Data acquisition layer: the data acquisition layer provides all the data support for the warning system, which data comes from the supervisory control and data acquisition (SCADA) and other monitoring systems including the integrated monitoring system, the infrared temperature measurement system of the valve hall, and the infrared temperature measurement system of the site and the robot inspection system. Collected data are from a wide range of sources, and the data are heterogeneous. Therefore, it is necessary to carry out special data collection and data fusion. The collected data can be consumed by the upper modules of the fault diagnosis system.(2)
Edge prediction layer: the work of equipment parameter prediction is transferred to one end closer to the site by edge computing, which can reduce the amount of computation. In order to achieve more accurate prediction, comprehensive equipment state information needs to be considered in the process of power equipment parameter prediction. In this paper, the prediction method based on data sharing is adopted to achieve more accurate prediction by constructing the edge side power equipment parameter prediction model and integrating the data of other field areas.(3)
Cloud diagnosis layer: a central server inside the main control room acts as a “cloud” server to process information gathered after a device fault. Based on the edge prediction layer, the cloud diagnosis layer analyzes the parameter trend of the device in the future period and adopts the method of device status evaluation to realize device fault diagnosis. At the same time, with the assistance of fault information management, the marked misdiagnosis information is filtered to realize intelligent fault diagnosis of equipment.(4)
Each layer of the framework is intrinsically related, and each layer transmits information through industrial bus. The data acquisition layer provides data support for the upper edge prediction layer, including the relevant data collected by each monitoring system. The edge prediction layer predicts the parameters of the UHVDC system and transmits the prediction results and existing historical data to the cloud diagnosis layer for the generation of fault diagnosis information by its internal model. All levels coordinate with each other to form a fault diagnosis method for the UHVDC transmission system.


## 3.2. Edge Layer Fault Feature Screening and Preprocessing

The information of power grid frequency, voltage, and line power before and after the occurrence of UHVDC faults is huge. In order to effectively diagnose the faults, it is necessary to pick out the effective information which means finding the bus, unit, and line information affected by the DC faults from the huge power grid environment information [16, 17]. The information that can fully reflect the DC fault is the fault feature screened out.The real-time power flow data of DC operation at different times are selected to simulate DC faults. The simulation is carried out forM times and M groups of data sections before and after the faults are obtained. According to the analysis of power flow data before and after DC faults, the AC and DC power transmission system has N nodes, and DC nodes are D. The variation of active and reactive power between node n1 and n2, n1,n2=1,2,3,…,N before and after occurrence of DC locking fault is (1)ΔPn1n2=Pn1n21−Pn1n20,(2)ΔQn1n2=Qn1n21−Qn1n20.In (1) and (2), Pn1n21 and Qn1n21 is the active and reactive power transmission values after fault, respectively. Pn1n20 and Qn1n20 is the active and reactive power transmission values n1n2 before the fault, respectively.The voltage amplitude changes of then1, n1=1,2,3,…,N AC node and the n3, n3=1,2,3,…,D DC node before and after a DC fault occurs are shown as follows.(3)ΔUn1=Un11−Un10,(4)ΔUn3=Un31−Un30.In (3) and (4), Un11, Un31, Un10, and Un30 represent the voltage values of AC and DC node after and before fault, respectively.If the variations of active and reactive power, voltage in AC-DC power grid are less than the threshold value in more thanM simulation results, the corresponding data of active power and reactive power variation and voltage amplitude variation do not reflect the DC fault feature and are directly removed. The remaining data is still M groups. The average value of the same type of data is taken after removing the maximum and minimum values, and then the ratio can be obtained by dividing the average value by the data before the fault. Taking ΔPn1n2 as an example, the ratio solving process is shown as follows:(5)Pn1n2a=∑ΔPn1n2−ΔPn1n2max−ΔPn1n2minM−2Pn1n20c.Here,ΔPn1n2max is the maximum value of the active power change. ΔPn1n2min is the minimum value of the active power change, and c represents the branch number of the fault line adjacent to the node n1.Considering that the electrical information of the nodes and branches that are closer to the DC fault point can reflect fault feature more easily, the ratios ofPn1n2a, Qn1n2a, Un1a, and Un3a are sorted from large to small. The larger the ratio is, the more affected the fault is. If none of them is greater than the threshold value, the corresponding original data ΔPn1n2, ΔQn1n2, ΔUn1, ΔUn3, Pn1n20, Qn1n20, Pn1n21, and Qn1n21 are retained.After reading the filteredM groups fault feature data, data preprocessing is required to use the deep learning method for training learning [18, 19]. Each group of feature data before and after faults are sorted out, and the fault feature of each screened bus voltage, active power, reactive power, and frequency are listed as feature fields. If the data field value is invalid, it must change the invalid value to the average value of the M group fault characteristic data. In addition, there are classification feature fields, which are generally converted into N fields when there are N categories.After processing the classified feature fields, it is necessary to standardize the feature fields so that all the values are in the same interval, and the numerical feature fields have common standards. It improves the accuracy of the trained model. Standardization scales data so that it falls into a small, specific range. As the feature distribution is not changed, the data will not lose its characteristic information [20, 21]. In order to remove the unit limit of the data, it is necessary to transform them into dimensionless pure values, so that the features of different dimensions can be numerically comparable to some extent. The accuracy of classifier and the iteration speed of gradient descent can be improved to obtain the optimal solution. In addition, when some distance calculation algorithms are involved, standardization can make each feature make the same contribution to the result and effectively reduce the loss of accuracy. The standardization is carried out through linear normalization of data which means that data are mapped uniformly to the interval [0,1] [22]. The specific process is shown as follows:(6)Yg=Y−YminYmax−Yminsmax−smin+smin.Here,Y represents the sample value. Yg is the linear normalization result. Ymax is the maximum value of the sample. Ymin is the lowest sample value. smax−smin indicates the zoom range. smin indicates the scaling minimum value.


## 3.3. Cloud Layer Fault Feature Training and Diagnosis Based on Deep Learning

### 3.3.1. Fault Characteristic Training

Generative adversarial network (GAN) adopts the idea of binary game. The probability distribution of data can be learned autonomously through adversarial learning between generator and discriminator, which is usually suitable for AC and DC fault diagnosis. However, GAN has some defects in practical application. For example, it is sometimes unstable in the training process, which leads to the generator being unable to learn the probability distribution of real data and thus generating meaningless data [23]. In order to solve this problem, the deep convolutional neural network (DCNN) is introduced for improvement, and deep convolutional generative adversarial network (DCGAN) is generated. DCNN and GAN are organically combined.DCNN is generally used in supervised fields, but rarely in unsupervised fields. Introducing DCNN with strong feature extraction ability into GAN can help improve its stability and generate better data. The model structures of DCGAN discriminator and generator are shown in Figure2.Figure 2
Model structures of the discriminator and generator in DCGAN.From Figure2, it can be seen that batch normalization is added to all layers except the input layer in the discriminator and generator models. In the neural network parameter learning, the gradient back-propagation method is mainly used, and the batch normalization processing method can effectively promote the gradient propagation between each layer and accelerate the convergence speed of the network. In addition, the batch normalization method can prevent the model from overfitting. It makes the generative model have good generalization ability and avoid generating a large number of identical samples.The generator in DCGAN adopts a deconvolution model. In the generator, tanh activation function is used in the output layer, and ReLU function is used in other layers. It can effectively avoid the problem of gradient disappearing and accelerate the training speed of the model. The tanh activation function and the ReLU activation function are shown in (7) and (8), respectively.(7)tanhx=expx−exp−xexpx+exp−x,(8)ReLUx=0,x≤0x,x>0.The discriminator model removes the fully connected layer based on the convolutional network, and the activation function ReLU is replaced by LeakyReLU. The activation function of LeakyReLU is shown as follows:(9)LeakyReLUx=0.2x,x≤0x,x>0.ADAM is used as the optimizer of the DCGAN model, and the initial learning rate of generator is set to 0.0002 and the initial learning rate of discriminator is set to 0.0001, which can ensure that the loss value of discriminator is reduced less than 0 in the process of training parameters. Otherwise, the DCGAN model will fail to reach Nash equilibrium [24].The discriminator adopts the cross entropy for the loss function of real data and generated data. It is shown as follows:(10)L=tlogf+1−tlog1−f.Here,t represents the real data category. f represents the predicted data distribution.The cross-entropy expression of the discriminator for the generated data is shown as follows.(11)Cf=−log1−Sx.Here,x represents the value of the generated sample output by the discriminator, and S represents the activation function. The formula represents the cross entropy between 0 and the predicted data of the sample generated by the discriminator.The cross-entropy expression of the discriminator for real data is shown as follows:(12)Ct=−logSy.Here,y represents the value of the generated sample output by the discriminator. This formula represents the cross entropy between the predicted data of the real sample by the discriminator and 1.


### 3.3.2. Fault Diagnosis of UHVDC Transmission

All data are split into training dataset and validation dataset in a ratio of 4∶1. Before the training of the DCGAN model, the evaluation method of the model should be set, and the evaluation index should be set as accuracy. The number of training cycles and training data items are set in each batch. After the completion of each training cycle, the accuracy and error are calculated after the training cycle. The UHVDC fault diagnosis decision-making process is shown in Figure3, where the threshold value of accuracy is represented by A0.Figure 3
UHVDC fault diagnosis and decision-making flow chart.The specific process of the UHVDC fault diagnosis method based on deep learning under the cloud-edge architecture is as follows:(1)
Generate fault data sets based on the data collected by SCADA system and other monitoring systems in the data acquisition layer.(2)
Modeling of fault feature. Real-time power flow data of DC operation at different moments are selected for DC fault simulation, and data sections before and after faults are obtained by simulation.(3)
Fault feature screening of the edge layer. the information that can fully reflect DC fault is found in the huge power grid environment information, such as bus, unit, and line information, and the fault feature is screened out.(4)
Data preprocessing. For each screened fault feature such as bus voltage, active power, reactive power, and frequency, the classification feature field is processed, and the feature field is linearly normalized.(5)
The training dataset and the validation dataset are trained separately in the DCGAN model, and the fault diagnosis results of the model are output.(6)
The accuracy rate and error loss value of DCGAN model fault diagnosis are calculated according to the fault diagnosis result data. If the accuracy rate or error loss value does not satisfy the threshold, the training will continue until the accuracy rate and error loss value meets the threshold. And, the corresponding fault scheduling decision can be generated.


## 3.3.1. Fault Characteristic Training

Generative adversarial network (GAN) adopts the idea of binary game. The probability distribution of data can be learned autonomously through adversarial learning between generator and discriminator, which is usually suitable for AC and DC fault diagnosis. However, GAN has some defects in practical application. For example, it is sometimes unstable in the training process, which leads to the generator being unable to learn the probability distribution of real data and thus generating meaningless data [23]. In order to solve this problem, the deep convolutional neural network (DCNN) is introduced for improvement, and deep convolutional generative adversarial network (DCGAN) is generated. DCNN and GAN are organically combined.DCNN is generally used in supervised fields, but rarely in unsupervised fields. Introducing DCNN with strong feature extraction ability into GAN can help improve its stability and generate better data. The model structures of DCGAN discriminator and generator are shown in Figure2.Figure 2
Model structures of the discriminator and generator in DCGAN.From Figure2, it can be seen that batch normalization is added to all layers except the input layer in the discriminator and generator models. In the neural network parameter learning, the gradient back-propagation method is mainly used, and the batch normalization processing method can effectively promote the gradient propagation between each layer and accelerate the convergence speed of the network. In addition, the batch normalization method can prevent the model from overfitting. It makes the generative model have good generalization ability and avoid generating a large number of identical samples.The generator in DCGAN adopts a deconvolution model. In the generator, tanh activation function is used in the output layer, and ReLU function is used in other layers. It can effectively avoid the problem of gradient disappearing and accelerate the training speed of the model. The tanh activation function and the ReLU activation function are shown in (7) and (8), respectively.(7)tanhx=expx−exp−xexpx+exp−x,(8)ReLUx=0,x≤0x,x>0.The discriminator model removes the fully connected layer based on the convolutional network, and the activation function ReLU is replaced by LeakyReLU. The activation function of LeakyReLU is shown as follows:(9)LeakyReLUx=0.2x,x≤0x,x>0.ADAM is used as the optimizer of the DCGAN model, and the initial learning rate of generator is set to 0.0002 and the initial learning rate of discriminator is set to 0.0001, which can ensure that the loss value of discriminator is reduced less than 0 in the process of training parameters. Otherwise, the DCGAN model will fail to reach Nash equilibrium [24].The discriminator adopts the cross entropy for the loss function of real data and generated data. It is shown as follows:(10)L=tlogf+1−tlog1−f.Here,t represents the real data category. f represents the predicted data distribution.The cross-entropy expression of the discriminator for the generated data is shown as follows.(11)Cf=−log1−Sx.Here,x represents the value of the generated sample output by the discriminator, and S represents the activation function. The formula represents the cross entropy between 0 and the predicted data of the sample generated by the discriminator.The cross-entropy expression of the discriminator for real data is shown as follows:(12)Ct=−logSy.Here,y represents the value of the generated sample output by the discriminator. This formula represents the cross entropy between the predicted data of the real sample by the discriminator and 1.


## 3.3.2. Fault Diagnosis of UHVDC Transmission

All data are split into training dataset and validation dataset in a ratio of 4∶1. Before the training of the DCGAN model, the evaluation method of the model should be set, and the evaluation index should be set as accuracy. The number of training cycles and training data items are set in each batch. After the completion of each training cycle, the accuracy and error are calculated after the training cycle. The UHVDC fault diagnosis decision-making process is shown in Figure3, where the threshold value of accuracy is represented by A0.Figure 3
UHVDC fault diagnosis and decision-making flow chart.The specific process of the UHVDC fault diagnosis method based on deep learning under the cloud-edge architecture is as follows:(1)
Generate fault data sets based on the data collected by SCADA system and other monitoring systems in the data acquisition layer.(2)
Modeling of fault feature. Real-time power flow data of DC operation at different moments are selected for DC fault simulation, and data sections before and after faults are obtained by simulation.(3)
Fault feature screening of the edge layer. the information that can fully reflect DC fault is found in the huge power grid environment information, such as bus, unit, and line information, and the fault feature is screened out.(4)
Data preprocessing. For each screened fault feature such as bus voltage, active power, reactive power, and frequency, the classification feature field is processed, and the feature field is linearly normalized.(5)
The training dataset and the validation dataset are trained separately in the DCGAN model, and the fault diagnosis results of the model are output.(6)
The accuracy rate and error loss value of DCGAN model fault diagnosis are calculated according to the fault diagnosis result data. If the accuracy rate or error loss value does not satisfy the threshold, the training will continue until the accuracy rate and error loss value meets the threshold. And, the corresponding fault scheduling decision can be generated.


## 4. Experiment and Analysis

For the fault diagnosis results, the overall accuracy rate and error loss value of line fault diagnosis, and the accuracy of fault diagnosis after pole-to-ground fault and pole-to-pole short circuit fault occurred in different lines are the evaluation criteria. The UHVDC fault diagnosis method based on deep learning under cloud-edge architecture is compared with the fault diagnosis methods in [9, 11, 13].The overall accuracy and overall error value of line fault diagnosis for different fault diagnosis methods are compared and analyzed. The results are shown in Table1.Table 1
Accuracy and error value of different fault diagnosis methods.
MethodAccuracy (%)Error lossProposed method95.60.18Ref. [9]89.70.32Ref. [11]85.90.37Ref. [13]80.80.41From Table1, it can be known that the accuracy of the HVDC transmission fault diagnosis method approached in this paper is the highest at 95.6%, which is 5.9%, 9.7%, and 14.8% higher than the other three fault diagnosis methods. In addition, the error loss value is the smallest at 0.18. Compared with the other three fault diagnosis methods, the error loss value is reduced by 0.14, 0.19, and 0.23, respectively.According to the situation of single-pole grounding faults in different positions of the same line, the accuracy of different fault diagnosis methods can be seen in Table2.Table 2
Accuracy of different methods when single-pole grounding fault occurs at different locations of lines.
LocationMethodsProposed method (%)Ref. [9]Ref. [11] (%)Ref. [13] (%)0 km92.684.280.373.530 km96.391.486.781.5100 km94.889.284.980.3150 km94.288.684.380.1It can be seen from Table2 that when pole-to-ground faults occurs at different locations of the HVDC transmission line, the UHVDC fault diagnosis method approached in this paper has the highest accuracy. The fault diagnosis accuracy rates at 0 km, 30 km, 100 km, and 150 km from the head of the line are 92.6%, 96.3%, 94.8%, and 94.2%, respectively. From Tables 1 and 2, it can be known that the proposed fault diagnosis method has better diagnostic performance.According to the situation of pole-to-ground fault and pole-to-pole short circuit fault in different lines, the accuracy of different fault diagnosis methods is compared in Figures4 and 5.Figure 4
Diagnostic accuracy of different algorithms for pole-to-ground faults.Figure 5
Diagnosis accuracy of different algorithms for pole-to-pole short-circuit fault.From Figure5, it can be seen that, in the case of different types of DC transmission line faults, the accuracy of the proposed method is the highest compared to the other three methods. For different UHVDC transmission lines, the diagnostic accuracy of pole-to-ground fault and pole-to-pole short-circuit fault is 96.1% and 97.2%, respectively. The reason is that the introduction of a deep convolutional neural network into the traditional GAN network, which enhances the stability of the model during the training process, obtains a more realistic data probability distribution through more effective data. And, it improves the accuracy of model fault diagnosis and basically is not affected by the fault type and line.


## 5. Conclusion

Aiming at the fault diagnosis of the UHVDC system, a fault diagnosis method for UHVDC transmission based on deep learning under cloud-edge architecture is proposed. And, the simulation and comparison analysis are conducted between the method and other three methods. It is clear from the results that the fault diagnosis method based on the edge computing framework of “cloud” + “edge end” can effectively improve the diagnosis efficiency. The speed and accuracy of model training can be improved effectively by screening fault data sets, classifying feature fields, and linear normalization. Introducing DCNN into GAN for data training and DC fault diagnosis can greatly improve the stability and accuracy of fault diagnosis and reduce error loss. Future work will focus on the lightweight and configurable fault diagnosis model and reduce the resource consumption of the model and how to improve the calculating speed based on ensuring the accuracy of diagnosis.



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*Source: 1592426-2022-05-13.xml*