2004 hternational Conference on Solid Dielecfrics, Toulouse,Frnnce, July 5-9, 2004
Neural Networks In Prediction Of Accelerated Thermal Ageing Effect on OiVPaper Insulation Tensile Strength L.Mokhnache (I), P. Verma ('),
k Bouhskeur (3),
(')University of Batna, Faculty of Engineering, Dept of Electrical Engineering, L.S.P-LE, Batna, Algeria
[email protected] ( 2 ) Thapar Institute of Engineering& Technology, PATIALA-147 004, India (')Ecole Nationale Polytechnique, High voltage Lab.,Avenue Hacene Badi, El-Harrach, Algiers, Algeria
INTRODUCTION Power transformers are one of the most expensive and strategically important components of any electric power transmission system and loss of a transformercan have an enormous impact on continuity and reliability of supply and also a cost. Prediction is an important subject for power systems economy point of view. It allows solving economical problems of energy and making maintenance in a simplest way. As ageing experiments are costly and time consuming, the aim of this paper is to reduce the ageing experiment time and predict accelerated thermal ageing stress for the oil/paper insulation. Experimental data has heen carried out in the Indian's Institute of Science high voltage laboratory. The paper ageing has been done in air and in oil for an interval of ageing temperature ranging from 12OoC to 160T and for an ageing period of 4000 hours. Having the database of the full accelerated thermal ageing interval of the insulation, we have trained the net in the aim to predict insulation characteristicsat longest intervals. In this way, we have trained the net within an interval less than the used one in experiment, to predict insulation characteristics corresponding to the full experimental interval. This allows comparing predicted and experimental properties and validate the prediction.
Experiments have been made to measure respectively electrical and mechanical properties of aged samples of oil and paper. Test Cell shown in Figure 1 has been designed for accelerated thermal ageing on oil-paper. The oil taken conforms to the requirements of relevant Indian national specification IS-335 (1993) 121 and the unaged elechical grade paper that of relevant specification IS-9935(1985) 131.The oil and paper were supplied by transformer manufacturer .The seven layers of paper were wrapped on to the copper conductor. The paper sample was dried in the vacuum (Itorr) at 70'C for IO hours, then impregnated with oil. The oil was taken as f r s h in this study. Sufficient quantity of oil and paper were put in test cell in the ratio of (20:l) proportion by weight [4]. The oil was aged with and without paper whereas paper was aged in air and oil. The capacity of the test cell was 3 litters. The test cell was kept in the air circulated oven for accelerated ageing at 12OoC, 140'C and 160'C. The temperature control of the oven was within f 2°C. Oil samples from the test cell were taken out periodically i.e. at 160'C (50, 100, 200, 400, 800 and 1600 hrs), 140'C (100,200,400, 800 and 2000), 120'C (300,600, 1200, 2400 and 4000). After each cycle of ageing, the oil samples were tested for breakdown voltage (BDV) (with an without paper) and the paper for tensile strength.
OIWDAPER THERMAL AGEING It has been established in [I] that the failure rate of transformers is highest for the insulation thermal ageing constraint of oil and paper insulation. Cellulose and hydrocarbon oil have been used as transformers insulation for many years. Cellulose has been proven to have desirable electrical, chemical and physical properties, and at the same time, it has low cost. The polymeric chains of solid cellulose insulation contain a large number of anhydroglucose rings and weak C-0 molecular bonds and glycosidic bonds which are thermally less stable than the hydrocarbon bonds in oil, and which decomposes at lower temperature.
Fig. 1 Test Cell 0-7803-83486/04/$20.00 022004 IEEE.
NEURAL NETWORKS IN PREDICTION In this application, the used neural network is the radial basis function Gaussian one (RBFG). The RBFG networks constitute efficient models for the function generalization. The use of the Gaussian function allows benefiting from its local characteristic to facilitate the training and improve the generalization. The most important advantage of this technique is that the network has always one hidden layer [5]. Giving a set of input and output data xi, yi i=lJ ...& the state of the bidden unit j will be denoted by:
where: c,,:i=l, ...n.andj=l,..., maretheRBFGcenters o,,: define the widths of the Gaussians. The RBFG centers are vectors of n dimensions; they can be selected from the training data by some mechanisms cited in [6]. In ow case, training with the ROM, we have used a simple technique that consists of an arrangedent of these centers in a regular trellis in order to cover uniformly the data space input. The network response y’ is given by:
To predict a future value ywl of a set of measured data y. i=1, ...,n, the algorithm will be trained on a set of samples having the form (y,,y,+,), i=l, ...n-1. AAer this training, the weights of the net are updated so as when the network receives the value y. its response will be Y , + ~ .This procedure is repeated until the prediction of all values is obtained. RESULTS AND DISCUSSION We present in figure 2 the effect of thermal aging on tensile strength of paper aged in oil and in air for three aging temperatures 120, 140 and 160OC. The learning time is the maximum aging time for each temperature: 1600h for the temperature 16OoC, 2000h for 1 4 0 T and 4000h for 120°C. The prediction is done respectively for a period of 800 h, 1000h, 2000h for 16OoC, 14OoC, 120oc.
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The chosen network is trained by Random Optimization Method (ROM), which is based on the generation of random sequences in the aim to move the synaptic weights in the space to find an optimum. It is more suitable in some cases where the error function has several minima. Notice that the ROM converges to a global minimum with a probability equal to 1 in a compact set. The idea of this algorithm is to attribute to the net weights a set of a white noise sequence and then compute the network output. If the target output is less than the previous error, the new weights are kept; otherwise, the previous weights are kept until the desired error is obtained.
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Fig.2 Prediction of tensile &ength of paper aged in air and in lmnsfonner oil under different temperatures ranping from 120 to 160°C
In figure 3 (a,b,c), it can be seen that initially the oiul breakdown voltage (BDV) decreases and then increases upon further ageing. This trend may be due to moisture in the oil, which is removed afier continued ageing. Moisture is recognized to be “enemy number one” for transformer insulation [fl. 4- p a p r - c w a h o u t paper
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REFERENCES
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[l] MSA Minhas and al, “Failures in power system transformers and appropriate monitoring techniques”, High voltage Engineering symposium, published by IEE N”467, PP. 1.94.S23, August 1999.
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lc ) Fig. 3 BDV of Oil versus aging time for different aging temperatures. (a)160°C - (b)14OoC (c)120°C
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In figure 3, the learning time is the same as in the case of figure 2 and the prediction period is respectively Noh, SOOh, lOOOh for 160°C, 14OoC,12O0C.For illuStrarion,we present in figure 4 results where the RBFG net predicts the BDV till 7000 hours.
[4] Saha, T.K Hilli, D.J.T. Le, T.T. “Investigations into effective methods for assessing the condition of insulation in aged power transformers”. IEEE Transactom on power deliveiy, 13(4), ppI214-1222, 1998. [5] Mokhnache, A. Boubakeur, A. Feliachi, ‘Thermal Ageing Prediction of Transformer Oil and PVC of High Voltage Cables Using Neural Networks’, IEE Proceedings - Science, Measurement and Technology pp: 107-112, Vol. 150, Issue 03, May 2003.
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[6] S.Khemaissia, AS. Morris,’’ Review of networks and choice of radial basis function networks for system identification”, Technologies Avancies, N”6, pp 55-85, 1994.
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[2] IS: 335 - 1993 W e w Insulating Oil”(Bureau of Indian Standard). [3] IS: 9335 (Part iii Sectionl), “Cellulosic Papers for Electrical purposes” (Bureau of Indian Standard).
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The ageing process of insulation can be monitored hy several properties measurements, which need long test period to have a complete database to predict the dielectric behaviour of the HV insulation. The main purpose of insulation monitoring is to limit stoppage of electric service by giving the information on dielectric deterioration leading to a failure of HV equipment. Prediction of transformer oiYpaper thermal ageing may help considerablyto improve its maintenance. When the oil is in contact with paper, the BDV of oil shows increasing @endfor short period. This study will help to assess the health of transformer and therefore, appropriate action can be taken well in advance to save the life of msformers.
[7] 1. Fofana,. V. Wasserberg, H. Borsi, and E. Gockenbach, ‘‘Challenge of mixed insulating liquids for use in high- voltage transformers, part-2: Investigations of mixed liquid impregnated paper insulation”. IEEE Electrical imulaiion magazine, 18(4), pp 5-16,2002,
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