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Sensors and Materials, Vol. 33, No. 11 (2021) 3971–3982
MYU Tokyo
S & M 2738

Realization of Initiative Repair of Power Distribution Network
Based on Backpropagation Neural Network Optimization
Zhihua Guo,1 Na Li,1 Dongping Qiao,1* Hu Qiao,1 and Chao He2**
1State Grid Qinghai Electric Power Co., Ltd., Qinghai 810000, China
2School of Communication and Information Engineering,

Chongqing University of Posts and Telecommunications, Chongqing 400065, China(Received July 1, 2021; accepted October 11, 2021)

Keywords: power distribution network, fault diagnosis and prediction, backpropagation neural network,
initiative repair, intelligent sensing devices

Protection and maintenance systems are necessary for transmission systems to ensure an
efficient and reliable power supply when faults occur. However, most fault detection and location
methods rely on the electricity measurement provided by current and voltage transformers. In
addition, the initiative repair of a power distribution network is a very considerable portion of
power grid operation, and the initiative repair efficiency is crucial for power supply enterprises
to provide high-quality services. To give full attention to the value of perceived data and improve
the accuracy of fault diagnosis, the technical performance of high-speed power line carriers is
extensively investigated, and intelligent sensing devices are installed in power distribution
network lines, stations, branch boxes, table boxes, and other parts to realize the initiative repair.
To improve the accuracy and timeliness of fault diagnosis for the power distribution network, inthis paper, we propose an initiative repair and judgment mechanism based on backpropagation(BP) neural network optimization for the power distribution network. After acquiring
information data such as power consumption, the mechanism first takes advantage of the filtereddata information to train the BP neural network to predict fault information and other data.
Finally, the initiative repair of the power distribution network obtains the forecast data of the BPneural network to judge the fault of the power distribution network. The proposed mechanism
considerably shortens the time for the initiative repair and improves the traditional repair modeof the power distribution network, thereby improving the initiative repair efficiency of the power
distribution network.

1.

Introduction

Owing to the complexity of the power distribution network, both power distribution andprotection are rigorous challenges for engineers. The basic function of protection is to ensure thesupply continuity of the whole system and minimize equipment damage by detecting faults.(1) A
good protection system must ensure the reliability of the system by protecting the operation of*Corresponding author: e-mail: syyxzzbjb@126.com**Corresponding author: e-mail: d170101004@stu.cqupt.edu.cn

https://doi.org/10.18494/SAM.2021.3515

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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the system against all types of failure at all locations in the network. The system must be secureand reliable. In the power distribution network, overload, overvoltage, power fluctuations, andother reasons will cause a fault. When a fault occurs, the protective device starts the circuit
breaker operation, disconnecting the power to the faulty part. In general, a circuit breaker is oneof the main pieces of equipment in the network fault protection scheme. This protection must be
studied and judged before overcurrent and overvoltage, which can cause damage to theconnected equipment, such as transformers. Therefore, rapid fault detection can help protect
equipment, allowing damage to be reduced before disconnecting a faulty line. On the other
hand, accurate fault location can help power company workers troubleshoot persistent failures
and locate areas where failures often occur, thereby reducing the frequency and duration ofoutages. Thus, while fault detection and location schemes have been developed, various
algorithms are still being developed to perform this task more accurately and efficiently.
Owing to its different topology and operation characteristics, the fault location of the power
distribution network still faces many challenges. The traditional fault location method of thepower distribution network includes the visual inspection of power line patrol. For undergroundcabling systems, faults are also located by a dedicated cable test vehicle. However, these
traditional fault location methods cannot quickly locate the fault. The increasing dependence ofthe power distribution network on electricity requires a reliable supply of electricity. Recently,
regulators have required utilities to meet system performance benchmarks.(2) Therefore, thisparadigm has driven the demand for intelligent distribution systems, which are very critical anduseful for the detection, location, and classification of a power distribution network.
In recent years, various fault detection schemes have been developed for the transmission
network, but the accurate location of faults has not been considered, and the faults in the powertransmission network have not been classified. Therefore, to improve the service quality of
power supply, enhance the efficiency and accuracy of fault analysis, and further transform
distribution services from passive to active, the initiative repair of the power distribution
network based on the backpropagation (BP) neural network is proposed. The power distribution
network achieves an initiative repair mechanism, a fusion energy management, control, andelectricity information acquisition system, distribution automation, an equipment operationslean management 2.0 system, a geographical information system, fault indicator data of themaster station system, and BP neural network prediction. The realized information is used forthe transformation of the traditional repair mode, which contributes to realizing active andefficient fault investigation and initiative repair.

2.

Current Research on Initiative Repair of Power Distribution Network

Recently, the initiative repair project has been transformed into process management, thestandardization in management level, and a militarized form in terms of on-site initiative
repair.(3) To streamline the process of users’ fault reporting to initiative repair, the command
platform of the power distribution network under the initiative repair project is fully utilized.
Through the communication between the mobile transceiver equipped by the initiative repair
team and the initiative repair command platform, the initiative repair of the power distribution

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network is supplemented.(4) From the analysis of the current initiative repair process, thelimitations of the power distribution network are as follows:(1) After the occurrence of a power failure, it will take a long time from the user’s perception of
the fault to the call for initiative repair, which highlights the weakness that the power supply
enterprise cannot actively perceive the occurrence of a fault and cannot carry out theinitiative repair.
(2) At present, power supply enterprises have systems such as distribution automation, which canmonitor the running state of the power grid in real time and locate faults. However, thetraditional passive repair mode fails to take advantage of this state awareness, resulting in a
waste of resources.
To sum up, the traditional model of passive repair, which is reported by electricity customers,
can no longer meet the demand of current customers for high service quality nor does it makeuse of the current smart grid monitoring capability of power supply enterprises. Therefore, it isnecessary to develop an approach based on the big data perception and intelligent processing of
the power distribution networks,(5) and to propose the initiative repair model based on the BPneural network, which derives the development of the power distribution network. Its flow chartof active rush repair is shown in Fig. 1. By adding two new links of “repair report and early
warning” and “work order analysis”, the initiative of the original process has been improved anda closed loop of the rush repair process has been formed.

3.

Technical Structure of Data Acquisition and Analysis

To train the prediction model of the BP neural network, it is necessary to extract and process
the big data generated in the operation of the power grid. From the definition of the big dataindustry chain, the key technologies of big data include not only core technologies such as data
analysis technology but also important technologies such as data management, data processing,and data visualization. The technical architecture diagram is shown in Fig. 2.

Fig. 1.

(Color online) Realization of initiative repair of power distribution network.

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Fig. 2.

(Color online) Technology architecture.

Data extraction technology: To facilitate analysis, data are mainly designed for business.
Thus, it is necessary to regularly extract a large number of various types of data stored in the
database of large-scale information systems and remove the incomplete data to apply it toelectricity analysis.(6) The data of this system are mainly derived from engineering production
management, customer service telephone repair, real-time monitoring, grid line acquisition,
monitoring assistive acquisition, history failure acquisition, application data, and so forth. After
extraction, cleaning, and transformation, four processes are used for BP neural network model
establishment and troubleshooting.
Data analysis technology: The comprehensive early warning and judgment of faults find andexpress regularities in massive data. Major methods for fault prediction include classification,clustering, correlation, and so forth.(7) In this paper, the BP neural network is mainly used for
fault analysis and prediction. This method can greatly enhance the operation and maintenance ofthe power distribution network.
Data service technology: To improve the speed of data access, cache memory is establishedin the data service database, various data analysis output interfaces are set up to provide various
data access services, and all types of fault that may occur in the current power grid equipment
can be displayed in the client in real time and intuitively.
Data presentation techniques: The full-type and multidimensional display of the data is a new
grid fault risk in the geographic information system.
System security: Security is the lifeline of the information system.(8) To effectively prevent
attacks of illegal intruders and hackers, and protect the security of the system, a complete
information security system is established by employing internal and external network
separation systems in accordance with business characteristics.

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Technical safety and management safety are the two main components of an application
security system. Technical security design includes application, data, system, and network
security, and application security is the center of business security protection.(9) The safety
management includes security personnel, safety management system, safety technology
improvement, safety assessment, supervision, and so forth.(10)4.

Materials and Methods

For different subclasses, the category and characteristics of data are not the same, anddifferent training sets are required to train the fault category. Different test sets are used to
predict the fault, and the failure incidence is calculated. The model is applied to the establishmentof a model of the BP neural network, thereby achieving the early study of the fault. Bysummarizing the causes of historical failures, the cause of the fault is divided into four major
categories, namely, external human factors, operation and maintenance accidents, natural
disasters, and power grid equipment failures. Each type of fault can be divided into several
subclasses.
The power grid equipment faults can be divided into the ring network cabinet fault, cablefault, distribution room fault, switch cabinet fault, tower fault, arrester fault, insulator fault, andso forth. The external human factors can be divided into construction damage, vehicle accidents,
human theft, and so forth. Natural disaster factors can be divided into lightning, wind, flood,
debris flow, and snow backlog. Operation and maintenance accidents can be divided into small
animal damage, tree barrier, negligent inspection, overload voltage, and so forth.
4.1

Study and judge model

As shown in Fig. 3, the faults detection model mechanism includes the detection of faults
reported by the customer service center and those automatically perceived by the system. On thebasis of the 95598 customer repair information, electricity information acquisition system, andpower distribution automation, the information is collected, extracted, cleaned, transformed, andloaded. In addition, the specific fault information is integrated to analyze the relationship among
multiple faults, the probability of equipment failure, and the planned power failure information,and to comprehensively determine the fault type.
4.2

Study and judge model procedure(1) Collect fault-related messages from the customer service center, electricity informationacquisition system, and distribution automation platform, and remove bad data using data
quality assessment.
(2) The fault information is input into the BP neural network for model training and then used in
the advanced prediction of power distribution network faults in the future. Filtering scheduled
power outages caused by the user generates a repair, resulting in an urgent repair work order.
(3) Retrieve and analyze the fault stations corresponding to the repair work order according to3976

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Fig. 3.

(Color online) Fault study and judge model.

the fault messages of telephone repair by users, and merge the redundant urgent repair workorders in the same station area.
(4) The 95598 customer service telephone started the work order, and fault prediction sensing theactive work order may be repeated, so the system conducts comprehensive research andjudgment on the fault information and filters the repeated fault information to avoid repeatedorders and increased workload. The system detection work order is related to the user repair
work order, users are notified in various ways, the priority is given to complete the user repair
work order, and the system detection work order schedule is completed.
4.3

User initiative repair warning based on BP neural network

In view of equipment faults, we established the fault prediction model of the power gridequipment itself from real-time monitoring data of equipment, operation data, equipment
manufacturer, equipment operating life, and historical fault times of equipment, and according
to historical fault information. For example, for ring network cabinet equipment, real-timemonitoring data include temperature, humidity, local discharge, cable temperature, bus pile headtemperature, arrester discharge times, smoke concentration, and other dimensions; operation
data include voltage, current, and other dimensions; equipment manufacturer includes
manufacturer name, equipment batch, and other dimensions. The collected information is shownin Table 1.
The BP neural network is one of the most widely used and successful neural networks atpresent. It has a simple structure, strong plasticity, and good ability to approximate nonlinear
mapping.(11,12) Therefore, it is very suitable for the initiative repair research and judgment of thepower distribution network.

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Table 1
Collected Information.
NumberVariable
1
Air temperature
3
Partial discharge
5
Bus pile head temperature
7
Smoke concentration9
Voltage
11
Manufacturer no.
13
Service life of equipment

Fig. 4.

Number
2
4
6
8
10
12
14

Variable
Air humidity
Wire temperature
Discharge times of lightning rod
Concentration of flammable gas
Electric current
Equipment production batch
Frequency of equipment failure(Color online) BP neural network architecture.

As shown in Fig. 4, n represents the number of nodes in the input layer, l defines the numberof neurons in the hidden layer, m is the number of neurons in the output layer, wji represents theconnection right between the jth neuron in the hidden layer and the ith input node, and wkj is theconnection right between the kth neuron in the output layer and the jth neuron in the hidden
layer. Then, the output of the hidden layer isn=
H j f (∑ w ji X i − a j=), j 1, 2, ..., l ,

(1)

i =1

where aj represents the threshold of the jth neuron in the hidden layer and f(·) is a nonlinear
activation function: f(x) = 1/(1 + e−x). Therefore, the actual output of the output neuron isn*H=f ( ∑ w jk ∗ bk ), =j 1, 2, ..., l ,jk =1

where bk represents the threshold of the kth neuron in the output layer.
The prediction error can be obtained from the target and actual outputs of the network.

(2)

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=
ek

1 m
∑ (Ok − Yk )2
2 k =1(3)

In the learning process, the weight and threshold should be adjusted continuously to reduce
ek. The weight update expression is defined as follows.
w=
wkj + η Ok (1 − Ok )(Yk − Ok ) H j ,=j 1, 2, ⋅⋅⋅, l ;=k 1, 2, ⋅⋅⋅, mkj
mw=
w ji + η H j (1 − H j ) X i ∑ wkj Ok (1 − Ok )(Yk − Ok ),=i 1, 2, ⋅⋅⋅, n ;=j 1, 2, ⋅⋅⋅, l
ji

(4)

k =1

The threshold update expression is defined as follows.

bk= bk + η Ok (Ok − 1)(Yk − Ok ), k= 1, 2, ⋅⋅⋅, mma=a j + η H j ( H j − 1)∑ wkj Ok (1 − Ok )(Yk − Ok ), =j 1, 2, ⋅⋅⋅, l
j

(5)

k =1

Here, η defines learning efficiency, and its value varies from 0 to 1.

5.

Results and Discussion

The numbers of layers and nodes in the hidden layer directly affect the performance of the BPneural network.(13) To improve the prediction accuracy, on the basis of the existing results, thegolden section(14) and gradient descent methods are further used to optimize the number ofhidden layer nodes in the neural network.(15) The calculation steps are as follows:
According to the number of input nodes (ni) and the number of output nodes (no), thefollowing formula is used to determine the number of nodes in the hidden layer:1
ni no + no (ni2 + ni ) − 1
2nh =.
ni + no(6)

Assuming that 2k × nh (k = 1, 2, ...) is regarded as the number of hidden layer nodes, selecting
a small number of learning samples, and using a specific training method, we can start training
learning samples according to the activation function in Eq. (1) and calculate the error. In
Eq. (7), erra represents the error of the network with the number of nodes a in the hidden layer
under certain conditions, and Da−b represents the error difference between the network with thenumber of nodes a in the hidden layer and the network with the number of nodes b (a < b) in thehidden layer under certain conditions, i.e.,Sensors and Materials, Vol. 33, No. 11 (2021)

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Da −b =

erra − errb
.
erra

(7)

We can calculate the degree of error reduction when the numbers of hidden layer nodes are
2k × n and 2k+1 × n, and find the range with the largest error, which is denoted as (m1, m2).
Considering n = 0.618 × (m1 – m2) + m1, we determine the corresponding degrees of error
reduction Dm1 − n and Dn − m2 in the hidden layer nodes with n.
If Dm1 − n and Da are related to the required error accuracy, generally optional 0.2 < Da < 0.4,
then n = m1. If m2 – m1 < λ × m1, the calculation is completed, otherwise Da = 0.618 × Da; if
Dn − m1 ≤ Da, then n = m2, Da = 0.618 × Da; if m2 – m1 < λ × m1, the computation is finished.
After several steps of the above method, the number of nodes in the hidden layer of the
optimized BP neural network can be calculated, which can be applied to the initiative repair andearly warning.
According to 4659 original data samples collected, the fault prediction model of the existing
BP neural network is trained. Then, a verification set was established for testing, and the resultsof comparing the verification data with the real data are shown in Fig. 5.
The optimized BP neural network is used to predict the faults of the power distribution
network. As shown in Fig. 5, the average prediction accuracy can reach 94.5%, basically meeting
the requirements of fault prediction.
The decision tree, BP neural network, and support vector machine (SVM) algorithm wereused to eliminate and predict the fault risk level, and the accuracy and recall rate of the fourcases of prediction are shown in Table 2. Level 1 represents the failure of the power distribution
network, i.e., slight; level 2 means important; level 3 means severe. As shown in Table 2, theaccuracy rate, recall rate, and comprehensive classification performance of the BP neuralnetwork algorithm in this paper are 90.5, 80.9, and 84.2%, respectively. Although the predicted
recall rate of the decision tree algorithm for level 1 is 100%, the recall rate for level 2 is 58.8%,

Fig. 5.

Fault prediction accuracy of power distribution network.

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Table 2Comparison of results of different classification algorithms.
MethodReference
Index
Level 1
Precision75
Decision tree
(16)
Recall100
Precision
82.6
BP neural
Proposed
network
Recall90.5
Precision
83
SVM
(17)
Recall92.9

Level 2
90.9
58.8
83.3
78.4
86.4
78.4

Level 3
73.7
85.7
85
80.9
85.7
85.7

Accuracy
78.9
—
84.2
—
85.1
—

indicating that the adaptability of level 2 is very poor. Experimental results show that theclassification performance of the fault level prediction method is better than that of the decision
tree method.

6.

Conclusions

From the background of the comprehensive construction for the ubiquitous electric Internetof Things, both failure research study and judgment location, and initiative repair of the power
distribution network based on the BP neural network have been actively carried out. Through thefull sensing data collection of the power distribution network and reliance on the distribution
topology relationship, the fault logic algorithm is studied, which realizes not only the precise
fault location of a single household, but also the intelligent fault identification of customers,
precise policy and initiative repair report, and timely notification of power failure information.
The quality and efficiency of fault processing and customer satisfaction are significantly
improved. On the basis of the characteristics of state awareness, we designed a power distribution
network based on the BP neural network to elucidate the initiative repair mechanism. The
method can give full attention to the technical advantage of the BP neural network method, andcompared with the traditional fault analysis method, this method realizes the initiative repair of
the power distribution network, greatly shortens the time of troubleshooting, and improves theefficiency and accuracy of the fault repair research. Moreover, the accuracy of fault diagnosis is
improved. This method effectively promotes the transformation of the initiative repair mode of
the power distribution network from passive to active and significantly improves the service
quality perceived by customers.

Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant
51577028).

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About the Authors
Zhihua Guo received his B.S. degree in Electronic Instruments andMeasuring Technique from Changchun University of Science and Technology,
China in 1997. He joined State Grid Qinghai Electric Power Co., Ltd., where
he is currently a senior engineer. His research interests are in power marketingand power metering. (gzh7028@163.com.cn)

Na Li received her B.S. degree from Changchun University of Technology,
China, and Master’s degree from the University of Electronic Science andTechnology of China (UESTC), China, in 2003 and 2012, respectively. She
joined State Grid Qinghai Electric Power Co., Ltd., where she is currently asenior engineer focusing on power engineering technology. Her researchinterests are in online loss and electric energy metering and collection.
(lena110@126.com.cn)
Dongping Qiao received his B.S. degree in Computer Application from
Northwest Institute of Telecommunication Engineering, China, in 1987. He
joined State Grid Qinghai Electric Power Co., Ltd., where he is currently asenior engineer focusing on industrial engineering. His research interests arein power operation management and power marketing. (syyxzzbjb@126.com)

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Hu Qiao received his B.S. degree in Computer Science and Technology from
Lanzhou University, China in 2001. He joined State Grid Qinghai ElectricPower Co., Ltd., where he is currently a senior engineer focusing on industrial
engineering. His research interests are in electricity price and electricity andenergy metering. (qiaohu4251@qh.sgcc.com.cn)

		Chao He received his B.S. and M.S. degrees from Chongqing Three Gorges
University in China in 2014 and 2017, respectively. Now, he is currently
pursuing his Ph.D. degree from School of Communication and Information
Engineering, Chongqing University of Posts and Telecommunications. His
research interests are in MEC, VNE, and FiWi broadband access network.
(d170101004@stu.cqupt.edu.cn)