Abnormality Detection for Gas Insulated Switchgear using Self-Organizing Neural Networks

Transcription

1 Abnormality Detetion for Gas Insulated Swithgear using Self-Organizing Neural Networks Hiromi OGI, Hideo TANAKA, Yoshiakira AKIM OTO Yoshio IZUI Tokyo Eletri Power Company Computer & Communiation Researh Center 1-4-1, Irifune, Chuo-ku, Tokyo 14 Japan Phone: ext.546 Fax: Mitsubishi Eletri Corporation Industrial Systems Laboratory 8-1-1, Tsukaguhi-Honmahi, Amagasaki, Hyogo 661 Japan Phone: Fax: Keywords: Neural networks, Diagnosis, GIS Abstrat: This paper presents an Artifiial Neural Network (ANN) approah to diagnosti methods for abnormality detetion for a Gas Insulated Swithgear (GIS). An outline of the urrent tehnologies of power equipment diagnosis is initially presented followed by the proposed appliation of the Self-Organizing Neural Network (SONN) to abnormality diagnosis of GIS. Several tentative experiments through laboratory simulations for a small sized GIS are finally presented. Introdutjon Fault detetion is an important task for the reliable operation of a power system. Reent survey of the diagnosti tehnologies and power equipment reliability [I] has shown that the majority of faults are aused by the maloperation of iruit breakers or swithgears. Gas Insulated Swithgear or Gas Insulated Ciruit Breakers (GCBs) are being widely used to inrease the reliability of power system operation. In order to maintain the high reliability of operation, it is neessary to detet in advane the abnormal operation of the GIS before its propagation to a major fault. Gas Insulated Swithgear or Gas Insulated Ciruit Breakers are originally designed as maintenane free equipment. The main omponents are onealed in SF6 insulation gas and it is diffiult to examine the internal status of operation. This invisibility has neessitated the development of the Preditive Maintenane Tehnology (PMT), onerned mainly with the examination of the internal status or abnormality of operation by the external appliation of sensors. One of the major drawbaks of PMT is the ineffetiveness of the urrently available diagnosti algorithms in obtaining preise mathematial models to simulate the physial proess of the internal abnormality. Artifiial Neural Networks (ANNs), that mimi the nervous system, are finding wide appliations as potential tools for the solution of problems where onventional approahes either fail to arrive at aeptable solutions or are provide unsatisfatory performane[2]. ANNs have been suessfully applied to many problems in power systems and have promising appliations to several other related areas[3-9]. The harateristis of ANNs suh as learning, self organization, adaptation and non-linear lassifiation provide important tasks of ategory formation and lassifiation that are required to aomplish the diagnosti algorithms for PMT. The objetive of this paper is to study Kohonen's Self-Organizing Neural Network (SONN)[ 1-11] for internal abnormality detetion for GIS using sensor signal attahed outside the tank. The neural network self-organizes its internal weights based on the probability distribution of spetrum of sensor signal. The label of abnormality ategory is assigned to eah neuron after selforganization. This proess is similar to the LVQ (Leaming Vetor Quantization) algorithm. At the abnormality detetion stage, unknown spetrum is lassified as normal or abnormal status aording to the label of the nearest neuron. That is, while ategory formation is onduted by unsupervised manner, lassifiation riterion is onduted by supervised manner. In the next setion, several diagnosti tehniques for GIS are briefly reviewed. In the setion three, a short introdution to Kohonen's SONN is given. Appliation of SONN to abnormality detetion for GIS and experimental results are presented in the setion thereafter. Diagnosti Tehnjques for GIS In this setion, GIS is used as the abbreviation for gas insulated equipment. Investigation shows that breakdown due to insulation oupies a high probability of faults in the abnormal oper~tion of the GIS. The reason an be attributed to the fat that, while GIS using SF6 are designed to be ompat with high insulation apability, small partiles or mehanial un-adjustments while operation result in the gradual loss of dieletri strength and finally resulting in insulation breakdown[ I]. 1171

2 A reent report on the tehnology of insulation diagnosis of eletli power equipment in utilities has shown that more than 4% of the urrent problems are aountable for the diffiulty in the detetion of abnmmal operation of GIS whih are mainly due to the inability of assessing the internal status of a GIS[l2]. PMT plays an important role in alleviating this problem. Topologial Neighborhood. Label Neuron The PMT detets small internal partial disharges, that provide signs of final insulation breakdown, with the help of sensors attahed outside to tank. This is done so as to detet the abnolltlality at an early stage in order to avoid its development to a major fault. Abnormality detetion using PMT an be divided into two major tasks, namely the development of sensors and the development of the diagnosti algorithm. Input Data Many developments have been reported for the detetion of partial disharges using sensors[l 2]. These an be lassified into the following major ategories of I) Vibration or aoustis detetion. 2) Eletroni detetion. 3) Optial or heat detetion, and 4) Gas analysis detetion. Examples of the first ategory are aeleration sensor, deteting the mehanial vibration of the tank and the ultrasoni sensor deteting the waves propagating inside the tank. Examples of the seond ategory are the monitoring of the voltage for the detetion of dieletri strength, and the use of eletrode built in the insulation spaer. Examples of the third ategory are IRTV, deteting heat produed in the tank and photosensor method, deteting the radiation of light. The last ategories to detet dissolved gases suh as SF4, SOFz and HF aused by partial disharge. It an be onluded from the above examples that there exists several tehniques for developing and seleting a suitable sensor for effiient detetion of partial disharge. During the past few years, there has been few developments in the diagnosti algorithm and proess using sensors. Most of the urrently employed diagnosti algorithms use simple "threshold systems". The basi priniple of the threshold system is as follows. "If the output of the sensor signal is less than the predefined threshold, then the GIS system is of nolltlal status, else if the output is greater than the threshold, then the system is of abnolltlal status and the equipment needs to be investigated before the abnormality develops into a major disturbane". Even though the simple threshold system is easy to implement, it is assoiated with two major drawbaks. The first drawbak deals with the diffiulty in detetion of the details of abnormality, suh as the kinds of auses or loation. The threshold system an only provide information between the normal and abnormal statuses of the equipment. The seond drawbak deals with the influene of the environmental noise on the response of the threshold system. It usually inferred that in most ases, if the output of the sensor signal is large then the GIS is in abnormal status. However the simple threshold system is apable of mislassifying the normal Fig. I A Self-Organizing Neural Network Neuron Category-1 Category-4 Feature Spae Fig.2 Self-Organization and Learning Vetor Quantization status in the presene of noise as that on the abnormal status. In order to overome these drawbaks, detetion tehniques whih are unaffeted by environmental noise need to be employed for aurate and effiient lassifiation. Self Organizing Neural Networks (SONN), whih is desribed in the following setion, promise to ahieve the above mentioned tasks. Self-Organizing Neural Networks A brief review of SONN and Learning Vetor Quantization (LV is presented in this setion. Kohonen's researh for the development of SONN and LVQ has been motivated by the following experiment. In the experiment using at visual systems, the fat was found that the order of arrangement of visual ell on retina is approximately same as the order of neuron exited by 1172

3 orresponding visual ell on visual regions on the brain. This kind of orrespondene between the arrangement of input and the arrangement of neuron is alled topologial mapping. Kohonen developed a solution to the above problem by expanding it to ompetitive learning. His idea was that the neuron topologially nearest to the most reeptive neuron also responded to the presentation of an similar input feature. In the following disussion, it will be assumed that neurons are arranged in a 2 dimensional pattern depited in figure I. The learning rule given in [1,11] is as follows. W.(t) + a(t)[x - W.(t)] W(t+I)= IJ p IJ IJ { W;j(t) where, ifi,jen 1 it) if i,j ~ N 1 it) W;i(t) weight vetor of neuron at (i,j) position at epoh t xp a(t) pth input feature vetor adaptation gain at epoh t N 1 it) neighborhood set of neuron at (l,j) at epoh t The most reeptive neuron, i.e., the neuron most near to the input feature is defined by equation (2). The neighbourhood parameter is usually defined as the Eulidean distane. Several other distane measures suh as the inner produt an be employed sine all omponents of the feature vetor onsidered as positive in this paper. As the learning proeeds, the value of the adaptation gain and the neighborhood redue to zero in order to obtain stabilized onvergene. In the simulation, the initial value of adaptation gain a1 o was set to.9. 1 a(t) = a t (3) 1 (4) N 1 J(t) = N - t The initial value of neighborhood No was set to half of neuron grid size and that also redues to zero by the following equations. In eah epoh, learning rule (I) is applied to every presentation of the input feature vetor and the error, defined in equation (5), is alulated. In the simulation, the onvergene riterion is defined by the maximum epoh number instead of error value, that is set as a value of 2. E =.!. L,~xP - wjjf 2 p Atually speaking, learning rule (1) is the rule that redues the error defined by (5). The gradient by weight vetor (5) is given in equation (6). Equation (6) is the equivalent to the differential form of learning rule ( 1 ). ~W.(t) =-VwE IJ ~ (1) (2) (5) (6) The essene of self-organization is equivalent to the lustering algorithm shown in Figure 2 used for abnormality detetion. In the above figure, the data in the feature spae are indiated as small irles. The data with the same pattern are derived from the same auses. The larger irles indiate the neuron of SONN. SONN ategorizes the data into four different types. Thus, eah ategory of data indiates the ause of abnormality. The learning algorithm assigns the neurons in the feature spae aording to the probability distribution of input data while preserving the network topology. Eah neuron has his own neighborhood region in the feature spae. In other words, feature spae (or vetor spae) is quantized into regions. The name LVQ is derived from this harateristi. The neuron is the representative of eah region and is assigned the label of one of the abnormal ategories. Kohonen's LVQ algorithm is used for the fine adjustment of the boundary of eah region. We guess, however, that strit LVQ algorithm is unneessarily and self-organization and assignment of the label to eah neuron is enough for abnormality detetion of this ase. This is beause strit LVQ is devised for fine adjustment of the boundary of eah ategory. This is effetive for the ase where the boundary is omplex. The experiments omparing the performane of SONN for 5x5 and IOx I neurons (Fig. IQ and II) show similar results. This indiates there exists no-omplex boundary and thus strit LVQ has little effet for inreasing the lassifiation performane. SONN has examples applied to seurity field[8,9] in the power system. Appliation of Self-Oreanjzine Neural Networks to Abnormality Detetion for GIS Large quantity of experimental data is usually needed for evaluating the performane of the neural network. However. it is very diffiult to obtain enough data from GIS operating the field. Therefore experiments were onduted in the fatory to simulate atual fault onditions that would be expeted to our in the GIS while in operation. Figure 3 shows the simulated normal (Figure3. I) and various possible abnormalities that our in a GIS. In the above figure, the junk is a small thin alurninum wire that simulates the presene of the abnormal partile. Three pratial onditions are simulated orresponding to the relative loation of the junk in the GIS tank, whih are lassified as I) partile stiking to entral ondutor (Figure 3.2), 2) partile stiking on the bottom of the tank (Figure 3.3), and 3) partile floating in the tank (Figure 3.4). One type of abnormality is the ontat failure between ondutors(figure 3.5). The gap length is seleted as a small value of less than.1 mm. Another type of abnormality is ontat failure between metal fittings (Figure 3.6), whih an be either due to loose fittings or due to ageing and vibration of the tank. 1173

4 Tank of GIS y Central ondutor...!oo("gj~ OCuGJ l Normal 4 Floating junk 1 Normal -1{UG:Jd 1 4 Floating junk Ds 2 Condutor 5 Small gap between ondutors 2 Condutor 5 Small gap between ondutors Bad ontat ---I I L.J 3 Tank 6 Insuffiiently fixed metal fitting Fig.3 Examples of Experimental Abnonnalites 3 Tank 6 Insuffiiently fixed metal fitting Fig. 5 Examples of Original Sensor Signal 6HZ 1 Normal 4 Floating junk I Time Average I + Amplitude Normalization i Input to Neural Network 51:Ji 1~ 8~.1 2 Condutor 5 Small gap between ondutors J Hi 3Tank 6 Insuffiiently fixed metal fitting Fig.6 Examples of Time Averaged Spetrum of the Sensor Signal Fig. 4 Sampling of Aelaration Sensor and Preproessing of samples is best for the experiments, it has been limited to 853 sample points due to hardware limitations. However, thi s sampling rate overs the whole frequeny limitation of the sensor These abnormalities ause partial disharge inside the tank, that and reates no problem for simulations onduted later. Sampled ause vibrations in the insulated gas. These vibrations ause the data for one yle is transformed to 124 points spetrum by Fast mehanial vibration of the tank, that an be deteted as the Fourier Transform (FFT). Initially these spetrums are averaged hange in aeleration with the help of the aeleration sensor for one yle to anel the noise. Next, normalization is performed attahed outside the tank. Aeleration sensors are employed in to redue 124 dimensional vetor to a 64 dimensional vetor and this experiment due to their ompat size, light weight and make the spetrum suitable as an input feature vetor of unit portability in omparison with other sensors. length. Manipulating I 24 dimensional vetor is both omputationally umbersome and time onsuming and annot be Figure 4 shows the two stages of sampling of the sensor signal used for fast abnormality detetion. It was onluded that 64 and preproessing. The sensor signal is sampled at a rate of 853 vetor dimension is enough for the present objetive of samples per yle (1/6 seond for 6Hz). Although 124 points abnormality detetion. It is possible to improve the perfonnane 1174

5 Gas Insulated Swithgear FFT and other normalization Self-Organizing Neural Network Metal junk Aelaration sensor 1 Normal 2 Condutor 3 Tank 4 Floating junk 5Small gap 6 Metall fitting A A A A A A "" " "... "... "... A A A A A A "..." A A A A A A "... A A A A A A A... " A A A A... A A " ~ "... " "... "..:...:...: :..:""...: Fig. 8 An Example of Learned 2 Dimensional Self-Organizing Neural Network ( 5 x 5 neurons ) Fig. 7 The Arhiteture of Self-Organizing Neural Network for larger dimensions, however, this require more omputation time and needs further study. Figure 5 shows the examples of original sensor signal for both the normal and abnormal status. In the above figure horizontal axis indiates time sale of 1/6 seond interval and vertial axis indiates amplitude of sensor signal. The amplitude for the ases of 'normal', 'ondutor ', 'tank ' and 'floating junk' is of the order of hundreds, while that of 'small gap between ondutors' is of the order of ten thousand and amplitude for 'insuffiiently~fixed metal fitting' is of the order of thousands. These examples show that the order of amplitude does not always orrespond to an abnormality status. This observation implies that though the simple threshold system works well for most of the ases, it is also apable of performing mislassifiation. In other words, observation of amplitude or its orresponding value is not enough for aurate abnormality detetion. It is neessary to obtain additional features in addition to the amplitude. Figure 6 shows the examples of spetrum for both the normal and abnormal status. Eah status orresponds to the sensor signal desribed in Figure 5. These spetrums are obtained after time average but before normalization of dimension and vetor length. It is easier to distinguish between the normal and abnormal status from the spetrum patterns rather than the original sensor signals. This is the reason why the spetrum patterns are employed as input feature vetors to the SONN. Figure 7 shows the omplete arhiteture of an abnormality detetion system using SONN. The above figure also illustrates the abnormality due to the ase of metal junk fixed on entral ondutor. Simulation experiments were onduted in the following fashion. The number of experimental data was approximately one hundred. Fig. 9 An Example of Learned 2 Dimensional Self-Organizing Neural Network ( 1 x 1 neurons ) Six data for normal status, 55 data for ondutor and so on. Eah data is transformed into an input feature vetor of 64 dimensional unit vetor. During learning, all of the learning data were presented to the network for eah epoh and learning rule of equation (l) was applied for eah learning data. After onvergene, all of the learning data were again presented to the network together with the assignment of the label of eah neuron. Eah neuron searhes the nearest learning data aording to equation (2) and is assigned the abnormal status orresponding to the label of the learning data as a label. Figures 8 and 9 shows the examples of labeled 5x5 and loxlo SONN respeti vely. The number of auses orresponds to the number in the Figure 3. Beause of the ompetitive learning with 1175

6 -~ ' o :e C> (.) SONN(5x5) I /o of Training Data Fig. 1 Reognition Rate of Self-Organizing Neural Network -~ rn :e C> U. of 5 x 5 Neurons SONN(1Ox1) ,.--.--,..--, i ""~1'--"...,...--" /o of Training Data Fig. 11 Reognition Rate of Self-Organizing Neural Network of 1 x 1 Neurons neighborhood, the same auses are assigned to the nearby neuron in the 2 dimensional topology. This result an also be ontemplated from the Figure 2. Idential auses. result in the formation of similar spetrum patterns. In the feature spae (also termed as spetrum spae) the idential auses result in the approximate distribution of neurons in the neighborhood with the weights of the neuron orresponding to the luster enters while preserving the 2 dimensional network topology. Comparison of Figures 8 and 9 shows the similarity of lass distribution. Classes I and 6 are widely separated and oupy diagonal loations, while lasses 1 and 4 are losely loated. Classes 4 and 6 are also losely loated. However, further study is needed to understand the distribution of the topology and the positions of eah lass. Learning time of the SONN was of the order of a few minutes simulated on SUN Spare Station 37 (l 7MIPS) that is omparatively faster than Bak Propagation (BP) learning. In the previous work presented in[6,7] BP was employed that required more than 1 hour for learning. Figure 1 and 11 show results of the performane evaluation of the SONN. The network size onsidered is of 5x5 for Figure JO and!ox 1 for Figure 11. The horizontal axes represents the perentage of data used for learning from the whole experimental data. For instane, the number '8' on the horizontal axes implies that 8% of experimental data was used for learning while the remaining 2% of data were used for evaluation. The definition of the terms 'orret' and 'inorret' ourring in the above figures are defined as follows. If the label of the nearest neuron to the evaluating data is the same as that of learning data, it is lassified as orret, i.e., after learning, eah neuron is assoiated with the label orresponding to either 'normal' or 'abnormal' status as shown in Figure 8 and Figure 9. The nearest neuron is obtained from equation (2). If the label of this nearesl neuron is as same as the ause of the evaluating data, then the lassifiation is defined as 'orret'. On the other hand, if the label of the nearest neuron is different from that of evaluating data, it is lassified as 'inorret'. Investigation on above figures explains that approximately 8% orretion rate an be ahieved with the present experimental data. and slightly higher orretion rate an be obtained if more learning data is available. Comparison between Figure 1 and Figure 1.1 shows that similar results an be obtained for different network sizes. The number of neurons for the. 1Ox1 network is four times larger than that of the 5x5 network, i.e., SONN with 25 (5x5) neurons is found to suitable for satisfatory perforrpane ompared to that of the SONN with 1 (1Oxl) neurons for this experiment. The orretion rate of 8% needs further study to improve the lassifiation performane. The fator responsible for dereasing the orretion rate is the data for lass four orresponding to the abnormal ase of 'floating junk'. The junk piee, simulated by means of a small thin aluminium wire, moves around the inside of the tank. When the junk is near to the ondutor, the abnormal status is lassified as that of '' (lass 2) and when it is near the bottom of the tank it is lassified as 'tank ' (lass 3). The lassifiation rate is mainly inherent in the data other than neural network arhiteture. This observation also suggests that the same lassifiation rate an be expeted if all of the 1% of the data is used for training the neural network. Further study needs to be arried out to improve its performane. One suh possible strategy is the use of sensor fusion that employs more than two kinds of sensor signals for effiient abnormality 1176

7 disrimination. In the Figure 11, BP(MNN) represents the result of neural network using bakpropagation onduted for the same experimental setup desribed in [6,7]. If enough learning data is available, BP ahieves similar. orretion rate as SONN. BP, however, ahieves poor performane due to the. limited availability of learning data This differene of apability may be derived from the differene of mathematial struture of SONN and BP. In other words, SONN reates luster enters of eah ategory in the feature spae while BP must learn every boundary of ategories. Creating luster enters is possible even for a few number of learning data. Estimation of the exat boundary, The proposed method are still at a preliminary stage. On-line learning in the real field, validation for other types of GIS and ombinations with other sensors are the next issue. The authors expet that ANNs inluding SONN will have large potential for pratial diagnosis in the near future and are urrently designing the portable prototype system. The authors are onduting the field tests to evaluate the system in the next few years. Referenes 1) IEE of Japan, "The report on diagnosti tehnologies and reliability on swithgears", Vol.29, Jan ) DARPA, "DARPA Neural Network Study", AFCEA however, is diffiult unless enough number of learning data is available. International Press, Fairfax, Virginia, ) T. S. Dillon: et al., "Short Term Load Foreasting Using Compared to BP, SONN has the advantages of fast learning and Adaptive Pattern Reognition and Self Organizing higher orretion rate for small number of learning data. On the Tehniques", Pro. of the 5th PSCC, pp.1-16, other hand, BP may have the advantages suh that BP an learn exat ategory boundary if large number of learning data is available. BP an produe analog value for lassifiation results that may be interpreted as the probability of ause estimation. In the pratial appliation, it is neessarily to learn the data in the real field. This requirement gives rise to the advantage of SONN beause of its fast learning apabilities. It is important to note that the simple threshold system an never be used to ondut suh kind of abnormality lassifiation. The proposed SONN struture is valid for all GIS. The learned weight of SONN, however, is valid only for the one used in the simulations or with similar mehanial struture. However, sine major parts of the GIS are designed in aordane to a standard speifiation and onsist of a ombination of these parts, both of the struture and weight of SONN will be generalized for other similar GIS onfigurations. Coplusjop GIS are widely used power systems due to the main advantages of ompatness, exellent insulation strength and reliability of operation. The enlosed struture of the GIS has prompted the requirement of the PMT and related effiient and aurate diagnosti algorithms other than onventional threshold system. The ANN, that has the apabilities of ategory formation and lassifiation, is one of the potential methodologies to establish required diagnosti algorithm. In this paper, the SONN is applied to abnormality detetion for GIS. In the experiment, we have obtained the results suh as (1) SONN an perform abnormality lassifiation with more than 8% orretion rate for the experimental data. (2) SONN an perform similar orretion rate for small number of learning data that BP an perform only poor results. (3) The learning time of SONN is a few minutes. It is dramatially faster than that of BP ) M. A. El-Sharkawi, et al., "Artifiial Neural Networks as Operator Aid for On-Line Stati Seurity Assessment of Power Systems", Pro. of loth PSCC, pp , August, ) H. Tanaka, et al.,"design and Evaluation of Neural Network for Fault Diagnosis", Pro. of the Seond Symposium on Expert Systems Appliation to Power Systems, pp , July, ) H. Ogi, et al.,"preventive Maintenane System for Gas Insulated Swithgear using an Artifiial Neural Network", Pro. of the Third Symposium on Expert Systems Appliation to Power Systems, pp , April, ) H. Ogi, et al., "Fault Diagnosis System for GIS using an Artifiial Neural Network", Pro. of the First International Forum on Appliations of Neural Networks to Power Systems, pp , July, ) H. Mori, et al.,"an Artifiial Neural-Net Based Method for Estimating Power System Dynami Stability Index", Pro. of the First International Forum on Appliations of Neural Networks to Power Systems, pp , July, ) D. Niebur, et al. "Power System Stati Seurity Assessment Using the Kohonen Neural Network Classifier", Pro. of IEEE Power Industry Computer Appliation Conferene, pp , May, 1991.Vol.42, Jan ) T. Kohonen, "The Self-Organizing Map", Pro. of IEEE, Vol.78, No.9, pp , Sep II) T. Kohonen, "Self-Organization and Assoiative Memory", 3rd edition, Springer Verlag, Berlin, ) IEE of Japan, "The report on the tehnology of insulation diagnosis of eletri power equipment on operation", Vol. 42, Jan