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Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning
Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning
Rongrong, Shan;Zhenyu, Ma;Hong, Ye;Zhenxing, Lin;Gongming, Qiu;Chengyu, Ge;Yang, Lu;Kun, Yu
Hindawi Journal of Robotics Volume 2022, Article ID 9742815, 11 pages https://doi.org/10.1155/2022/9742815 Research Article Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning 1 2 3 3 1 1 Shan Rongrong , Ma Zhenyu, Ye Hong, Lin Zhenxing, Qiu Gongming, Ge Chengyu, 1 1 Lu Yang, and Yu Kun NARI Group Co., Ltd, State Grid Electric Power Research Institute, Nanjing 210000, China Zhejiang Electric Power Corporation, Hangzhou 310013, China State Grid Wenzhou Power Supply Company Ouhai Power Supply Branch, Wenzhou 325000, China Correspondence should be addressed to Shan Rongrong; firstname.lastname@example.org Received 8 February 2022; Revised 14 March 2022; Accepted 19 March 2022; Published 14 April 2022 Academic Editor: Shan Zhong Copyright © 2022 Shan Rongrong et al. 'is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In view of the poor eﬀect of most fault diagnosis methods on the intelligent recognition of equipment images, a fault diagnosis methodofdistributionequipmentbasedonthehybridmodelofrobotanddeeplearningisproposedtoreducethedependenceon manpower and realize eﬃcient intelligent diagnosis. Firstly, the robot is used to collect the on-site state images of distribution equipmenttobuildtheimageinformationdatabaseofdistribution equipment.At thesametime, therobot backgroundisused as the comprehensive database data analysis platform to optimize the sample quality of the database. 'en, the massive infrared images are segmented based on chroma saturation brightness space to distinguish the defective equipment images, and the defective equipment areas are extracted from the images by OTSU method. Finally, the residual network is used to improve the region-based fully convolutional networks (R-FCN) algorithm, and the improved R-FCN algorithm trained by the online hard example mining method is used for fault feature learning. 'e fault type, grade, and location of distribution equipment are obtained through fault criterion analysis. 'e experimental analysis of the proposed method based on PyTorch platform shows that the fault diagnosis time and accuracy are about 5.5s and 92.06%, respectively, which are better than other comparison methods and provide a certain theoretical basis for the automatic diagnosis of power grid equipment. be “allocated and used” . 'e distribution equipment will 1. Introduction have some faults under long-term operation, resulting in With the continuous improvement of social economy and abnormal temperature. 'erefore, by detecting the tem- people’s living standards, the power demand is increasing perature of the distribution equipment, the thermal fault day by day, and the scale of power system is growing day by diagnosis of the distribution equipment can be carried out day. It includes transmission and transformation networks quickly, which plays a great role in the safe operation of the power grid. of various voltage levels. 'erefore, ensuring the safe and stable operation of complex power grid is an inevitable 'e infrared image of equipment is used for fault di- requirement to ensure the economic and social develop- agnosis with high eﬃciency, accurate judgment, safety, and ment. At the same time, the economic and social devel- reliability. At the same time, it is free from electromagnetic opmenthasagreatimpactonthesecurityandeconomy,and interference, fast detection speed, and no power failure of higher reliability is required [1, 2]. As the terminal of the live equipment. 'erefore, infrared diagnosis is widely used wholepowergridoperation,distributionnetwork isthepart in the ﬁeld of equipment fault monitoring and diagnosis with the widest coverage and the largest scale in China’s technology . However, due to the characteristics of large powersystem, andit is thekey link toensure that powercan quantity and complex types of distribution equipment, if we 2 Journal of Robotics only rely on manual work in the process of data acquisition, breaker fault identiﬁcation. 'e image analysis process is analysis, and processing, the workload is relatively large, the complex, resulting in the reduction of fault identiﬁcation eﬃciency. Reference  proposes a method based on eﬃciencyislow,andtheaccuracy isrelativelylowdue tothe high dependence on manual experience. 'erefore, auto- feature model for single-phase grounding fault in active matic image acquisition and analysis of distribution distribution network system, which transforms the solution equipment are of great signiﬁcance to ensure the safety and of nonlinear feature model into single-objective optimiza- stability of distribution network . tion of feature entropy, which can well identify single-phase In recent years, the automatic inspection technology of fault, but the identiﬁcation eﬀect of equipment with feature power distribution room has been popularized, and various type is not ideal. automaticrobotsandUAVshavemadegreatprogressinthe With the continuous development of computer original data acquisition stage. However, the accurate and technology and the rapid development of 5G communi- eﬃcient processing of collected image data is still in its cation technology, machine learning algorithm has been infancy.Howtoextractthefeaturesofinterestfrominfrared widely used this year, especially the deep learning algo- rithm has certain advantages in the ﬁeld of fault identi- images for power distribution equipment recognition is a problem to be solved . Among them, the deep learning ﬁcation and classiﬁcation. Reference  proposes algorithmhasmadegreatachievementsinimageprocessing, artiﬁcial neural network algorithm to identify the insu- speechrecognition,andtextanalysis.Byestablishingadeep- lator stateand uses single-layerand multilayerperceptron seatedneuralnetwork,high-levelfeaturesareextractedfrom artiﬁcial intelligence algorithm to classify the conditions low-level features layer by layer, so as to achieve the eﬀect of of distribution insulators. 'is technology can make the target classiﬁcation and recognition . Compared with the automatic inspection of electrical system more accurate manually designed feature extraction method, the distrib- and eﬃcient, but it lacks high reliable database for sup- uted features obtained by deep learning network model can port. Reference  proposed a Mask R convolution better express the essence of data [8, 9]. 'erefore, in order neural network method and used transfer learning and to improve the eﬃciency of thermal fault detection of dis- dynamic learning rate algorithm to realize eﬃcient rec- ognition of annotated image data sets, but it relied too tributionequipment,improvetheintelligenceofpowergrid, reduce the labor cost of detection, and reduce the false much on graphics annotation and lacked practical ap- detection rate, a fault diagnosis method of distribution plication value. In , appropriate traveling wave time- equipment based on the hybrid model of robot and deep frequency characteristic parameters of fault current are learning is proposed, which eﬀectively ensures the safe and selected as the input of adaptive depth belief network reliable operation of distribution equipment. model to obtain the fault type, but only considering the fault current characteristics as the basis, the reliability needs to be further improved. 2. Related Research Basedontheaboveanalysis,aimingattheproblemssuch as the complexity and diversity of smart grid distribution At present, there are many researches on fault diagnosis of distribution equipment at home and abroad, which can be equipment and the unsatisfactory eﬀect of most existing image recognition methods, a distribution equipment fault divided into traditional fault identiﬁcation and classiﬁcation methods and machine learning based identiﬁcation and diagnosis method based on robot and deep learning hybrid model is proposed. Its innovations are summarized as classiﬁcation methods . Among them, the traditional fault identiﬁcation and classiﬁcation methods mainly in- follows: clude fuzzy clustering, discrete wavelet transform, and (1) In order to obtain the image information of distri- chaotic algorithm. For example,  proposed an infrared bution equipment more comprehensively, the pro- image segmentation algorithm based on intuitionistic fuzzy posed method introduces the robot to construct the clustering algorithm based on spatial distribution infor- corresponding image knowledge database, which mation, which is suitable for power equipment. It can well provides the basis for fault classiﬁcation and fault suppress the strong interference of nontarget objects in location. infrared image to image segmentation, but the method is (2) In order to locate the equipment defect area in the more traditional and has poor segmentation eﬀect for infrared image of distribution equipment, the pro- complex intelligent power grid equipment. Reference  posed method performs threshold segmentation on proposed an anomaly detection method based on spatial the infrared image in hue saturation value (HSV) clustering applied by auxiliary feature vector and density space and uses OTSU method to extract the noise. 'e auxiliary feature vector of each conditional equipmentdefectarea,soas toimprovetheaccuracy variable is constructed for clustering to identify normal data of subsequent fault diagnosis. patterns and diﬀerent types of anomalies. Reference  (3) Aiming at the problem that the deep learning al- proposed a data mining driving scheme based on discrete gorithm is prone to gradient disappearance and wavelet transform to realize high impedance fault detection gradient explosion, the proposed method uses the in active distribution network, but the universality of the residual network to improve the region-based fully method is not high. Reference  proposed a method to convolutional networks (R-FCN) algorithm and obtain the vibration characteristics of circuit breaker based appliesittothelearningoffaultyequipment,soasto on time-frequency and chaos analysis to realize circuit Journal of Robotics 3 obtainthe fault type and location with high accuracy Robot and further improve the safety of equipment. Defect backstage system 3. Proposed Method 3.1. Construction of Image Information Database of Distri- System data Fault information Model base analysis center bution Equipment Based on Robot Inspection. 'etraditional knowledge base equipment status is usually determined by manual analysis. 'e workload is huge and error-prone, which aﬀects the judgment of system status, resulting in potential safety Knowledge database hazards. 'erefore, the robot is used for patrol inspection to Database obtain the status image of distribution equipment and build the corresponding information base for the analysis of Production Robot data system equipment status, so as to ﬁnd the faulty equipment in time Online and ensure the reliable operation of power grid . 'e monitoring construction process of distribution equipment image in- formation base based on robot inspection is shown in Figure 1: Construction process of image information database for Figure 1. distribution equipment. 'e basic data sources of the database mainly include production system, online monitoring system, and robot (1) 'e data of the three-party platform includes the backgroundinspectionsystem.'erelevant data ofthestate information required in the database structure table. quantity of power equipment mainly comes from the power After eliminating the redundant information, the production management system (PMS), which can provide integrated data in a uniﬁed format can be obtained, the real-time operation condition, historical operation state, and the defect alarm data can be located and re- historical maintenance record, historical test data, equip- trieved quickly. ment account, equipment parameters, and other informa- tionoftheequipment.'eonlinemonitoringsystemmainly (2) 'e fault information base is mainly taken from the defectsystemrecordsandcontainsalargenumberof relies on various sensors on each power equipment for real- time monitoring. 'e robot background inspection system relevant equipment fault cases, including fault characteristics, solutions, expert opinions, and can not only provide the observation of some state quan- manufacturer records. At the same time, the tities, but also carry out corresponding state evaluation and maintenance record database and equipment ac- analysis for diﬀerent equipment states according to the count database are used to build a comprehensive automatic state evaluation system. In addition, the data databaseoffaultinformation,soastoscreenthefault composition of the system includes infrared temperature inspection points. measurement, visible light reading, and telemetry reading. 'e robot inspection cycle generally refers to the in- (3) 'eknowledgeinformationbaseistheengineforthe spection plan formulated by the distribution network op- system to evaluate the equipment status and judge eration inspection center, and two inspection robots the fault. 'e internal rules at all levels provide the complete the tasks of infrared temperature measurement logicalbasisforthesystemtojudgethefault.'ekey and data transcription of equipment in the area . At the is knowledge acquisition, that is, collecting and same time, the robot background uses the threshold out of mining the knowledge at all levels to enrich the limit judgment method to automatically evaluate the knowledge base. equipmentstatus.Inordertoensuresuﬃcientchargingtime of the robot and avoid the daily patrol and infrared tem- perature measurement period, the special patrol at night is 3.2. Defect Feature Extraction of Distribution Equipment. set in the nonbusy working period of the robot every day, When extracting the defect features of distribution equip- with the upper limit of one time. 'e data reports collected ment, it is necessary to perform threshold segmentation on by the special patrol at night and infrared temperature theinfraredimageinHSVspace,separatetheinfraredimage measurement are included in the database for screening and background from irrelevant equipment and defective preprocessing. equipment, and then extract the equipment defect area . In addition, the background of the inspection robot is equipped with a system server, which includes data analysis software terminal, data exchange server, data storage server, 3.2.1. OTSU 4reshold Segmentation. OTSUisconsideredto dataoperationserver,andothermodules.'edataexchange be one of the best algorithms in image threshold segmen- server is responsible for collecting and classifying the pro- tation. 'e threshold segmentation process of OTSU algo- rithm is as follows: ﬁrstly, the image is processed in gray duction system, online monitoring system, and robot patrol data into the storage server. 'ere are three-party databases, level, the number of pixels in the whole image is counted, fault information base and knowledge information base in and the probability distribution of each pixel in the whole the data storage server. image is calculated; then, the gray level is traversed and 4 Journal of Robotics searchedinthewholeimage,andtheinterclassprobabilityof classiﬁcation . 'e ﬂow of HSV based defect region the image foreground and background at the current gray extraction algorithm is shown in Figure 2. leveliscalculated;ﬁnally,thethresholdcorrespondingtothe When processing the infrared image of defective variance between classes and within classes is calculated by equipment, ﬁrst merge the similar pixels corresponding to the given objective function. the area with the same temperature, and segment the image Suppose there are D gray levels in the image, in which according to the threshold of the three components of the the number of pixels with gray value of i is N and the total defective areaintheHSV colorspace toextract thedefective number of pixels in the image is N. 'en, the average gray area.'en,thediscretedefectregionsareconnectedthrough value of the whole image is the closed operation in mathematical morphology, and the threshold segmentation of the original image is carried out D−1 by OTSU method to separate the power equipment and the μ � i . (1) background region. Finally, the defect area is found in the i�0 binary image separated by OTSU method; that is, the de- According to the gray characteristics of the image, the fective power equipment is separated from other areas, so as image is divided into foreground B and background B . 0 1 to achieve the purpose of extracting defective power 'en, p (T) and p (T) represent the probability of oc- 0 1 equipment and facilitate the identiﬁcation and diagnosis of currence of foreground B and background B when the 0 1 power equipment types and fault types. threshold is T, respectively. 'e calculation is as follows: p (T) � , 3.3. Fault Diagnosis of Distribution Equipment Based on Deep (2) i�0 Learning Hybrid Model p (T) � 1 − p (T). 1 0 3.3.1. Defect Training Based on Deep Learning Hybrid Model. R-FCN algorithm architecture mainly includes backbone 'en,themeanvaluesofforeground B andbackground network, region proposal network (RPN), and region of B are interest (ROI) subnet . When fault diagnosis of power ⎧ ⎪ i N /N distribution equipment is carried out, ﬁrst input the col- ⎪ i�0 μ (T) � , ⎪ 0 ⎪ lected infrared image of power equipment into convolution p (T) neural network and extract the convolution feature map of (3) ⎪ infrared image. In this process, deeper and more abstract ⎪ T μ − i N /N i�0 ⎪ image features can be extracted by using a larger backbone ⎩ μ (T) � . network (ResNet 101) to improve the recognition accuracy p (T) . 'en, the feature map is sent to the RPN network to 'e interclass variance with threshold T in the gray generate anchors, which are marked with foreground and histogram is calculated as follows: background, and the foreground area with high score is 2 2 selectedastherecommendedareaROIs.'eseROIsaresent to the ROI subnet for further training, and 300 recom- σ (T) � p (T)μ (T) − μ + p (T)μ (T) − μ . 0 1 B 0 1 mended windows are generated for each infrared image of (4) power equipment. At the same time, the characteristic map of the full convolution layer is calculated with the multilayer 'e optimal threshold is deﬁned as the T value corre- convolution kernel to generate a position sensitive score sponding to the maximum variance between classes, which map. 'e ROI and Score Maps are input into the later is calculated as Softmax layer for vote. 'rough the Softmax layer for 2 2 classiﬁcation, the ROI with the highest score is ﬁnally ob- σ (T) � max σ (T). (5) B B 0≤T≤D−1 tained,thatis,thelocationandtypeoftheobjectlocatedand recognized. 'e architecture of R-FCN algorithm is shown in Figure 3. 3.2.2. Defect Region Extraction Based on HSV. In order to (1) Residual Network. When the depth of the deep learning improve the accuracy of equipment fault image classiﬁca- network reaches a certain degree, the problems of gradient tion,thedefectregionandbackgroundintheinfraredimage disappearance and gradient explosion often appear during of fault power equipment are separated by using the defect training.Inordertosolvethisproblem,theresidualnetwork region segmentation algorithm based on HSV. Since it is (ResNet) is used to improve the R-FCN algorithm; that is, impossible to determine the defect type only by analyzing the residual network is selected as the backbone network. thefaultarea,itisnecessarytosegmentthedefectareabased 'e residual element is essentially the mapping residual on mathematical morphology according to the location of required for ﬁtting through these stacked layers. Suppose the defect area. 'rough this method, the defective power thatthenetworkmappingis H(x)andtheresidualmapping equipment and the background area in the infrared image function of the network is F(x), F(x) � H(x) − x. 'e so- are separated, so as to reduce the interference of the called residual is the diﬀerence between the observed value background area in the infrared image on the defect type Journal of Robotics 5 it only needs to make F(x) � 0 to get H(x) � x, so as to Start avoid the disappearance and explosion of gradient. 'e input x and output x of the m-th residual unit m m+1 Initialize the image clustering center point so that the distance S of are expressed as follows: each clustering center point is evenly distributed in the image. x � f h x + δ x , ω , m+1 m m m M−1 In each 3 × 3 select the pixel with the smallest gradient in the (6) neighborhood as the clustering center in the neighborhood. x � x + δ