IMPLEMENTATION OF THE DUAL-BODY INTELLIGENT INSPECTION ROBOT IN SUBSTATION BASED ON DATA MINING ALGORITHM

Transcription

1 IMPLEMENTATION OF THE DUAL-BODY INTELLIGENT INSPECTION ROBOT IN SUBSTATION BASED ON DATA MINING ALGORITHM Janfeng WU, Chengzh MA, Png XU, Sume GUO, Ku LAI, Ta HU, Ln QIAO Aress:Jangmen Power Supply Bureau of Guangong Power Gr Co.,Lt, Guangong, , Chna ABSTRACT: base on the nformaton ntegraton platform of ntellgent substaton an the relevant algorthms of ata mnng, the research s carre out for the mage ata processng of ntellgent nspecton system. An mage feature extracton metho for substaton equpment base on mprove watershe an Krawtchouk nvarant moment an gray-level co-occurrence matrx (GLCM) s propose, an base on the HSIM functon, an mprove DENCLUE clusterng metho s propose to complete the fnal equpment clusterng entfcaton. When the nspecton robot of substaton performs the nspecton task, the collecte mage content of the fel equpment s fferent ue to the change of shootng poston. Amng at the problem that the tratonal mage processng metho of fxe-pont shootng can t meet the mage processng requrements of substaton equpment, frstly, the mprove watershe s use to realze the segmentaton of equpment object n the patrol mage. Then, base on the segmentaton, Krawtchouk nvarant moment an GLCM are use to realze the feature extracton n the presence of shelter an affne transformaton. Fnally, base on the mprove DENCLUE clusterng metho of Hsm functon, the target recognton s acheve. It s prove that ths metho can effectvely entfy the equpment uner the nfluence of affne transformaton an shelter of substaton equpment mage, an effectvely etect outlers for abnormal objects n the mage, so as to mprove the target recognton ablty of equpment state montorng. KEYWORDS: ntellgent substaton; ata mnng; ntellgent patrol INTRODUCTION As the global economy an populaton grows, so oes the nee for energy. The power supply moe wth fossl energy as the man boy s facng problems such as the graual epleton of nonrenewable resources an the ncreasng envronmental polluton. Developng an utlzng clean energy, mprovng energy effcency, mprovng power gr safety an relablty, an optmzng resource allocaton are the best ways to solve energy crss an envronmental problems. Smart power gr base on avance energy technology, communcaton technology, automatc control technology, sensor measurement technology an analytcal ecson-makng technology has become an nevtable tren of the evelopment of power nustry []. At present, the systems such as veo montorng, ntellgent nspecton, safety an fre control n the substaton are stll nepenent from each other, wthout access to an ntegrate nformaton platform, becomng an nformaton slan. A lot of manual analyss an processng of 02 such nformaton s requre, whch s not conucve to the ntellgent management of the substaton. Deployng more avance applcatons of auxlary systems n the ntegrate applcaton server an makng full use of nformaton ntegraton platform to ntegrate solate slan nformaton can effectvely solve ths problem. Most of the auxlary proucton system nformaton n the substaton s unstructure ata such as forms, mages, veos, etc., an real-tme montorng wll generate a large amount of hstorcal ata. The common ata analyss an processng metho can t meet the actual eman. The ata mnng technology of extractng useful nformaton quckly an effectvely s the key to the ntegraton of the panoramc nformaton platform of substaton. Data mnng technology can extract nterestng patterns an knowlege from large amounts of ata [2]. It s a multscplnary fel that ntegrates ata warehouse, artfcal neural network, machne learnng, genetc algorthm an

2 other technologes. It s wely use n power, communcaton, fnance, mecal, an other nustres. The tren of ntellgent evelopment of substatons s unmanne or less manne. Affecte by the angerous work area, severe weather contons an long nspecton tme, the manual nspecton work s heavy an the effcency s low, whch becomes one of the unfavorable factors for the stable operaton an proucton of substaton equpment. Inustral robots are hghly aaptable to harsh envronments an have graually replace the tratonal manual nspecton, provng relable onste vsual ata for operatng conton agnoss an ntellgent mantenance of substaton equpment. For the large amount of mage nformaton returne by the nspecton robot, the ntellgent mage processng technology s neee to mne the mage features of the target equpment, separate the evces n the fel mage contanng complex scenes, an then entfy the segmente mages, etermne the type of equpment that each part of the mage belongs to, an fnally juge whether there are scharge, ol leakage, amage an other phenomena n the process of equpment operaton accorng to the mage recognton results, so as to acheve the correct evaluaton of equpment state. 2 LITERATURE REVIEW At present, the applcaton of ata mnng technology to power systems manly nclues equpment fault agnoss, power loa forecastng, an operatonal state optmzaton. Transformer fault agnoss s stll a hot ssue n ata mnng applcatons. Accorng to the concentraton of ssolve gas n the ol of the current transformer, the state of the transformer operaton s juge. Fan et al. [3] use the gray clou moel to agnose the transformer fault. Frstly, the correlaton egree between each parameter an the stanar fault type s calculate, then the assessment weght of each gas s etermne through machne learnng, the fnal fault type s juge by combnng the two, an goo results are obtane; Lu et al. [4] use the fuzzy clusterng metho of weghte kernel to juge the fault state of transformer. The stanar fuzzy clusterng uses Euclean stance to juge the stance between the clusterng pont an the cluster center. The Gaussan kernel functon s use to replace the Euclean stance, whch mproves the accuracy reucton cause by more mensons of Euclean stance; Wang et al. [5] conucte a smulaton stuy on the classfcaton of transformer fault wth fferental evoluton algorthm an neural network. Frstly, the target functon of the cluster s optmze wth fferental evoluton, the output of the cluster s trane as the nput of the neural network, an fnally the neural network s use as the classfer of transformer fault agnoss; n the fel of power loa precton, Arozero et al. [6] frstly extracte the rule of loa sequence through wavelet transform, then use the fuzzy grey correlaton clusterng metho to select the hstorcal ate smlar to the weather contons of the object to be teste, an fnally precte the loa of that ate base on BP neural network; Zhu et al. [7] selecte typcal ata samples from the comprehensve loa of fferent tme peros n a ay, an revse the aly loa precton moel through clusterng analyss; Jnku et al. [8] montore an agnose the equpment of the substaton through nfrare mage segmentaton, an acheve target segmentaton accorng to the color fferences of fferent temperatures of the nfrare mage. When ths metho s use to ve evces wth smlar colors, the effect s not obvous, an the algorthm complexty s hgh; Yang et al. [9] explane the applcaton of template-base mage classfcaton an recognton metho n substaton, but there was a lack of scusson on the affne transformaton of equpment mages ue to fferent shootng locatons an angles. 3 SEGMENTATION AND EXTRACTION OF IMAGE OBJECTS 3. Segmentaton metho base on mprove watershe Fel mages collecte by the nspecton robot often contan multple evces, an there s mutual occluson between the evces. Correctly segmentng the equpment n the mage s a necessary prerequste for the ntellgent nspecton of the substaton. Image segmentaton s an mage processng metho that separates the ponts or regons n a gtal mage wth the same or smlar features from other regons. Watershe segmentaton algorthm s a transformaton base on mathematcal morphology. Its man ea s to regar the grayscale mage as a natural terran. The pxels wth hgh lumnance values are regare as mountan peaks, an the low lumnance values are low valleys. Durng the vson process, water s njecte nto the bottom of the valley. As the water potental keeps rsng, t wll floo the nearby mountan peak, so as to establsh a watershe on the top of the mountan to 03

3 prevent the water from flowng nto other areas. When the water potental excees all the peaks, the algorthm termnates an the segmentaton s complete. The marker watershe mprovement metho base on foregroun an backgroun solves the uner-segmentaton problem generate by the mnmum value mergng algorthm. The basc ea of ths algorthm s to frst use other methos (such as threshol segmentaton metho or texture segmentaton metho) to etermne the backgroun an foregroun target s general segmentaton area an mark them separately. Common morphologcal treatments nclue corroson, expanson, openng an closng. 3.2 Image feature extracton base on gray-level co-occurrence matrx an Krawtchouk nvarant moments Due to fferences n proucton processes, raw materals, uses, an envronmental contons, varous types of equpment n substatons have unque local texture nformaton, makng t possble to entfy substatons base on texture features. Gray-level co-occurrence matrx s a kn of characterstc strbuton whch has rectonal rectvty an escrbes the nformaton relaton of gray value between pxels. It s the basc metho for analyzng local patterns an arrangement rules of mages. Gray-level co-occurrence matrx s well escrbe n the extracton of texture features of mage targets, but t s also as senstve to nose an llumnaton as other text-base feature analyss methos. When Gaussan nose s use to egenerate the mage base on the fferent target local textures of the mage, Gaussan nose wll have a huge mpact on the gray-level co-occurrence matrx of each object s local texture features. The texture features of the orgnal mage are completely submerge by the nose strbuton. To elmnate the nfluence of Gaussan nose on the statstcs of gray-level co-occurrence matrx, the output mage s automatcally ajuste accorng to the local varance of the mage through the aaptve flter to make the matrx statstcs close to the orgnal mage statstcs. The prncple of Wener aaptve flter s as follows: let f (x, y) be the lumnance functon at the pont (x, y) n the mage to be processe, an η s the M* N neghborhoo of the pxel n the mage. After aaptve flterng, the lumnance value of the pont n η s: 2 2 v g,, 2 x y f x y () Where x, x, yη, v 2 are the varance of the nose n the mage, μ s the mean of the pxels lumnance n η, an σ 2 s the varance of the pxels lumnance values n η. Krawtchouk nvarant moment technque s aopte to compensate for the naccuracy of clusterng results cause by nose nterference n the gray-level co-occurrence matrx statstcs. At the same tme, n the case that the effect of nvarant moment processng s not eal, such as the target area s smlar an the contour fference s not sgnfcant, the text-base statstc characterstc can also be use as an effectve bass for recognton. The mage uner Gaussan nose s fltere by the characterstc quantty of Krawtchouk nvarant moments, an nvarant moments have obvous extracton effect on contour features such as target shape an area. However, ue to the nterference of occluson, llumnaton, foregn matter an other factors, the mage of substaton equpment can t meet the actual nees by only relyng on contour features to entfy the target. Therefore, an mage feature extracton metho for substaton equpment s propose by combnng Krawtchouk moment nvarant wth gray level symbotc matrx statstc. Therefore, an mage feature extracton metho for substaton equpment base on the combnaton of Krawtchouk nvarant moment an gray-level co-occurrence matrx statstcs s propose. The contour feature of the object s recognze frst, an then the result of the recognton s correcte wth the texture feature to acheve the fnal recognton of the evce object. 4 IMPROVED DENSITY CLUSTERING ALGORITHM BASED ON HSIM FUNCTION The man purpose of substaton ntellgent nspecton system s to montor the fel equpment to evaluate whether the equpment operaton s normal. Normally, the evce runs normally most of the tme, an an abnormal phenomena s a small probablty event. The montorng metho base on mage recognton s manly to extract the characterstc quantty of each target object n the mage after segmentaton, an juges whether the target object belongs to a normal runnng state or an abnormal state accorng to the characterstc quantty. In the hgh-mensonal ata space, the smlarty comparson algorthm represente by geometrc stance s not effectve n clusterng analyss because of the sparsty an nose of objects. For two objects wth hgher smlarty, snce the varaton of a few of the characterstc elements s more obvous, the calculaton of the 04

4 geometrc stance causes other small changes to be covere up. On the contrary, the robustness of smlarty measurement n hgh-mensonal ata space can be mprove f the majorty of features wth hgh smlarty can be strengthene whle the mnorty of features wth rastc changes can be weakene. Base on the Manhattan stance, Yang et al. propose the Hsm functon by focusng on the same or smlar features among objects, an usng the mean value metho to weaken the features wth rastc changes [0]: Hsm X x y, M (2) Where X= (x, x 2,, x ) an Y= (y, y 2,, y ) are two objects n -mensonal ata space. Accorng to the mportance of each feature element to the clusterng result, a weght value w s set for each feature element. The larger the weght, the smaller the nfluence on the overall smlarty, then equaton (2) becomes: Hsm X w x y, M (3) In formula (3), there are two promnent problems n Hsm functon:. when x-y =0, the weght w s nval; when x - y >>0, an x - y / x 0, the smlarty of the feature elements can t be fathfully reflecte. To solve the above two efcences, formula (3) s mprove to: an the comparson results are more consstent wth the actual stuaton. 5 APPLICATION OF IMAGE SEGMENTATION AND CLUSTERING ANALYSIS OF SUBSTATION EQUIPMENT The expermental ata are obtane from mages of the ensmeter an nsulate sleeve of the 500kV hgh voltage sulfur hexafluore crcut breaker at fferent tmes taken by a 500kV transformer substaton nspecton robot. 5. Watershe segmentaton of mage equpment target Fgure -6 shows the grey-scale map of the fel mage collecte at t-t6. It can be seen from fgure -6 that the target object of the fel mage at fferent moments has affne transformaton, mutual occluson, foregn boy coverage an other phenomena. Fgure -3 shows the grey-scale map of nsulate sleeve an enstometer taken at fferent tmes (t-t3). Fgure 4 shows the greyscale map when there s occluson on the nsulate sleeve at t4; fgure 5 shows the grey-scale map of the sngle equpment (enstometer) mage at t5; fgure 6 shows the grey-scale map of the sngle equpment (nsulate sleeve) at t6. Accorng to the grey-scale map of the equpment mage at t-t6 n fgure -6, watershe transform s performe wth ths metho. The segmentaton results are shown n fgure 7-2. Hsm X Y hsmx, y, (4) hsm x, y w w x y w x y 2x y, x y, x 0ory 0 other (5) After the algorthm s mprove, both the absolute fference of the orgnal algorthm s preserve an the relatve varaton fference between each vector element s fully consere, 05

5 Fgure. Grey-scale map of evce at t Fgure. 2 Grey-scale map of evce at t2 Fgure. 3 Grey-scale map of evce at t3 Fgure. 4 Grey-scale map of evce at t4 Fgure. 5 Grey-scale map of evce at t5 Fgure. 6 Grey-scale map of evce at t6 Fgure. 7 Segmentaton result at t Fgure. 8 Segmentaton result at t2 06

6 Fgure. 9 Segmentaton result at t3 Fgure. 0 Segmentaton result at t4 Fgure. Segmentaton result of enstometer at t5 Fgure. 2 Segmentaton result of nsulate sleeve at t6 Fgure 7-9 shows the target mage of the enstometer an the nsulate sleeve at t-t3 after the fel mage s segmente by watershe transformaton. The left pcture s enstometer mage, an the rght pcture s the mage of nsulate sleeve. Fgure 0 shows the segmentaton result at t4, wth the enstometer mage on the left, nsulate sleeve mage on the mle an shelter mage on the rght. Fgure shows the segmentaton results of enstometer at t5 an fgure 2 shows the segmentaton results of nsulate sleeve at t Krawtchouk nvarant moment an texture characterstc quantty of the extractng target Accorng to the segmentaton results of mage targets at fferent tmes n fgure 7-2, the statstcal characterstc quanttes of Krawtchouk nvarant moments an gray-level co-occurrence matrx of local texture are calculate respectvely. Table shows the extracton results of Krawtchouk nvarant moment characterstc quanttes of the target object after mage segmentaton at tme t - t6. The target object at tme t-t6 n Table s the segmente target object after the watershe transformaton at fferent tmes n Fgure 3.8. Table 2 shows the gray-level co-occurrence matrx statstcs of local texture. For the convenence of calculaton, all the characterstc quanttes n the table have been normalze. 07

7 Table The characterstc quantty of Krawtchouk nvarant moment of the target after segmentaton Target object The characterstc quantty of Krawtchouk nvarant moment Q20 Q02 Q2 Q2 Q30 Q03 Denstometer at t Insulate sleeve at t Denstometer at t Insulate sleeve at t Denstometer at t /73 Insulate sleeve at t Denstometer at t Insulate sleeve at t Shelter at t Denstometer at t Insulate sleeve at t Table 2 The statstcal characterstc quantty of gray-level co-occurrence matrx of the target after segmentaton Target object The characterstc quantty of gray-level co-occurrence matrx Angular secon Moment of moment nerta Correlaton Entropy Inverse fference moment Denstometer at t Insulate sleeve at t Denstometer at t Insulate sleeve at t Denstometer at t Insulate sleeve at t Denstometer at t Insulate sleeve at t Shelter at t Denstometer at t Insulate sleeve at t Densty clusterng base on mprove HSIM functon Enter the samples contane n tables an 2 nto the DENCLUE moel. In the moel, the learnng rate σ=0.8, the ensty attracton pont threshol =0.5, σ=0.5. When the ensty attracton pont s at the t5 ensty meter an the t2 nsulaton sleeve, the calculate return cluster s C = {,3,5,7,0}, C2 = {2,4,6,8,}, C3 = {9}, wheren, the gtal elements n the set are the evce target orer n tables an 2, an the elements n the C3 set on t satsfy f ˆ x*, so t s juge to be an outler, whch s consstent wth the actual stuaton. Ths proves that ths metho can effectvely entfy the equpment target when the affne transformaton an the abnormal object occluson appearng n the mage taken by the nspecton robot an can realze the outler etecton of the abnormal object an locate the abnormal state of the equpment operaton. 6 CONCLUSION The relate contents of mage recognton of 08 substaton equpment base on cluster analyss are emphatcally ntrouce. The metho of extractng local texture features base on the statstcs of graylevel co-occurrence matrx s ntrouce, an ts efects are analyze. An the metho of extractng mage features by combnng Krawtchouk nvarant moments wth the statstcs of gray-level cooccurrence matrx s propose; then the mprove HSIM functon s propose to mprove the smlarty crteron n the ensty estmaton functon; fnally, takng the fel equpment mage ata capture by the actual substaton nspecton robot as an example, the propose mage segmentaton metho, mage object feature extracton metho an clusterng analyss metho are verfe by experments. The results are n lne wth the actual stuaton, whch proves the feasblty an effectveness of the metho n the mage processng of the substaton ntellgent nspecton equpment an can prove strong support for the applcaton of ntellgent nspecton, fault agnoss an mantenance n the substaton.

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