Artificial intelligence assessment of sea salt contamination of medium voltage insulators

Transcription

1 Artificial intelligence assessment of sea salt contamination of medium voltage insulators D.S. OIKONOMOU 1 T.I. MARIS 2 L. EKONOMOU 3 oikonomoudim@gmail.com maris@teihal.gr lekonomou@hau.gr 1 Agricultural University of Athens, 75 Iera Odos Street, Athens 2 Technological Educational Institute of Chalkida, Psachna Evias 3 Hellenic American University, 12 Kaplanon Street, Athens GREECE Abstract: Sea salt contamination of overhead medium voltage insulators is the most common cause to outages in power systems installed in coastal regions. The contamination level of insulators is generally expressed by the equivalent salt deposit density (ESDD), which is the parameter that is taken into account, from almost every electric utility to diagnose the sea salt pollution severity on insulators. The periodic maintenance of insulators, which means the insulator washing, can reduce or even prevent the outages caused by the sea salt contamination. The maintenance scheduling is planned based on ESDD measurements, a process quite expensive and time consuming. The current work presents a new approach for the ESDD assessment based on artificial intelligence and more specifically artificial neural networks (ANN). A new ANN model capable to predict with accuracy the ESDD values is developed and is applied on operating medium voltage insulators presented results similar to the experimental ones. The proposed approach can be useful in the work of electrical maintenance engineers reducing maintenance time and cost. Key-Words: Artificial neural networks, Equivalent salt deposit density (ESDD), Insulators, Maintenance, Multilayer perceptron, Sea salt contamination, Weather conditions 1. Introduction It is well known that the demand of electrical power is increasing rapidly all over world. Nowadays life is almost exclusive depending on electricity and unscheduled supply interruptions can cause from a simple inconvenience up to damages, losses and panic situations. One of the most important factors that can reduce the efficiency of power systems is the failure rates caused by contaminated insulators flashovers. Contamination can take place when the environment that surrounds insulators contains diverse substances, especially saline and industrial ones. At coastal regions the insulators are mainly affected by salt particles that settle on the insulators surfaces. The winds that blow from the sea carry the salt particles. These particles are not dangerous in its dry condition but under the presence of light rain, humidity, dew or fog, the dielectric characteristics of the insulator surface are decreased, allowing the flow of leakage current between the insulator electrodes [1]. The leakage current increases itself until causes a failure of which probability and speed depends on the type and material of the insulator, the weather conditions, the voltage under which the insulator is working and most important on the type and level of contamination [2]. The contamination level of insulators is generally expressed by the equivalent salt deposit density (ESDD), and is taken into account in order to diagnose the sea salt pollution severity on insulators. Although the ESDD measurement is an expensive and time consuming process, its results is the main criterion for scheduling the maintenance (washing) of insulators that is why these measurements are still very popular and extensively used [3]. However the previous years, have been presented in the technical literature, alternative methods capable to assess the contamination of insulators. These methods are base on probabilistic assessments [4], multivariate models [5] and leakage current monitoring techniques [6]. The current work presents a new approach for the ESDD assessment based on artificial intelligence and more specifically artificial neural networks ISSN: ISBN:

2 (ANN). ANNs present to give solutions with significant results in almost every power systems problem varying from load forecasting [7] and power systems transient stability [8], to prediction of the magnetic performance of strip-wound magnetic cores [9] and evaluation of lightning performance of high voltage transmission lines [10], exploiting ANNs computational speed, ability to handle complex non-linear functions, robustness and great efficiency even in cases where full information for the studied problem is absent. The developed ANN model capable to predict with accuracy the ESDD values is developed based on actual data and is applied on operating medium voltage insulators presented results similar to the experimental ones. The proposed approach can be useful in the work of electrical maintenance engineers reducing the maintenance time and cost, offering greater efficient in distribution networks. 2. Artificial neural networks (ANN) Artificial neural networks represent a parallel multilayer information processing structures. The characteristic feature of these networks are that they consider the accumulated knowledge acquired during training and respond to new events in the most appropriate manner, giving the experience gained during the training process. The model of an ANN is determined according to the network architecture, the transfer function and the learning rule. In this work a typical neural network model known as conventional multilayer perceptron model (MLP) has been used. The conventional MLP network consists of nonlinear differentiable transfer functions. The backpropagation learning rules are used to adjust the weights and biases so as to minimize the sum squared error of the network. This is achieved by continually changing the values of the network weights and biases in the direction of steepest descent with respect to error [11]. In order to train the network, a suitable number of representative examples of the relevant phenomenon must be selected so that the network can learn the fundamental characteristics of the problem. The backpropagation training may lead to a local rather than a global minimum. The local minimum that has been found may be satisfactory, but if it is not, a network with more layers and neurons may do a better job. However, the number of neurons or layers to add may not be obvious. Conventional MLP architecture is generally decided by trying varied combinations of number of hidden layers, number of nodes in a hidden layer etc. and selecting the architecture which has a better generalizing ability amongst the tried combinations [12]. Once the training process is completed and the weights and bias of each neuron in the neural network is set, the next step is to check the results of training by seeing how the network performs in situations encountered in training and in others not previously encountered. 3. Medium voltage insulators and ESDD data collection method Hellas has got more than 16,000 km coastline with 73% of them to refer to islands. The population that lives and works in these areas ascends in a percentage of 70% of the total population of Hellas. The Hellenic overhead medium voltage distribution system has got length of 88,600 km. The conductors are suspended with the use of capand-pin or suspension (disc) insulators, usually with three insulators connected in a string for each one conductor. The insulators are made of either glass or porcelain, while the last years some composite insulators are used in specific areas in order to test their effectiveness. Depending on the geographical region in which the distribution line runs, normal type insulators or fog type insulators are used. The main criterion for selecting either the one or the other type is the pollution severity of the region. The Hellenic Public Power Corporation S.A. is using in the distribution network fog type insulators in a percentage of more than 34%, while in the coastal regions (Aegean Sea Islands) the percentage of fog type insulators used, is raised to 94%. In almost all Hellenic island regions, where the salt sea contamination is intense the electric utility s employees are washing by water jet the insulators at least twice during the summer period, while there are cases (Cyclades islands), where the insulator washing can take place even every single week [13]. As has been mentioned earlier the pollution severity is quantified in terms of equivalent salt deposit density stated in units of mg/cm 2. ESDD is the equivalent amount of NaCl that would yield the same conductivity at complete dilution. According to the literature, if the value of ESDD is equal or greater than mg/cm 2, the insulators are washed [1, 14]. ISSN: ISBN:

3 ESDD measurement activities were carried out by the personnel of Hellenic Public Power Corporation S.A. weekly for a period of almost two years in Elefsina, a periphery of Attica, Hellas, utilizing two samples of cap-and-pin porcelain insulators which are commonly installed on distribution lines in that area. The samples were taken down from the scaffold and the pollutants were removed by washing the insulators using paintbrush and distilled water. Every contaminated sample for each test was washed by immersing it in distilled water and the contamination value was measured by determining the conductivity or the rate of rise of the conductivity value for the polluted water after washing the insulator [1]. Using such procedure, ESDD was determined. for selecting the structure with the best generalizing ability amongst the tried combinations. In general one hidden layer is adequate to distinguish input data that are linearly separable, whereas extra layers can accomplish nonlinear separations [11]. This approach was followed, since the selection of an optimal number of hidden layers and nodes is still an open issue. 4. Design of the Proposed ANN The goal is to develop an artificial neural network architecture that could assess the ESDD value (pollution severity) on sea salt contaminated medium voltage insulators. Five parameters that play important role to the sea salt contamination of the insulators were selected as the inputs to the artificial neural network, while as an output the ESDD value was considered. These data constitute actual collected data and have been supplied from the National Meteorological Authority of Hellas [15] and the Hellenic Public Power Corporation S.A. [16]. Fig. 1 presents the proposed ANN model architecture for the assessment of ESDD on medium voltage insulators. It is clearly shown the five input parameters: wind velocity, temperature, month, rainfall, humidity and the output one: ESDD value. It must be mentioned that the authors have selected only those five input parameters for the developed ANN model, although there are also others that affect the sea salt contamination of the insulators. The reason was that there were not available records of data for any other parameters. As it has mentioned earlier each ANN model is determined according to its structure, the transfer function and the learning rule, which are used in an effort to learn the network the fundamental characteristics of the examined problem. The learning rules and the transfer functions are used to adjust the network s weights and biases in order to minimize the sum-squared error. The structure of the networks i.e. the number of hidden layers and the number of nodes in each hidden layer, is generally decided by trying varied combinations Fig. 1: The proposed ANN model architecture for the assessment of the ESDD value on medium voltage insulators. Table 1. Designed MLP ANN Models Structure - 1 to 4 hidden layers - 2 to 50 neurons in each hidden layer Learning Algorithm - Gradient Descent - Quasi-Newton - Levenberg- Marquardt - Random Order - Linear Incremental Transfer Function - Hyperbolic Tangent Sigmoid - Logarithmic Sigmoid - Hard-Limit - Competitive In this work several different multilayer perceptron models were designed and tested in order to be identified the model with the best generalizing ability. These were combinations of four different learning algorithms, five different transfer ISSN: ISBN:

4 functions and several different structures consisted of 1 to 4 hidden layers with 2 to 50 neurons in each hidden layer (table 1). 5. ANN Training, Validation and Testing The MATLAB neural network toolbox [17] was used to train the neural network models. Ninety six values of each input and output data, referring to almost every week of a two-year period, were used to train and validate the neural network models. In each training iteration 20% of random samples were removed from the training set and validation error was calculated for these data. The training process was repeated until a root mean square error between the actual output and the desired output reaches the goal of 1% or a maximum number of epochs, it was set to 15,000, is accomplished. Finally, the ESDD value was checked with the number obtained from situations encountered in the training and others which have not been encountered. Following the training, validation and testing process of all possible combinations of structure, transfer function and learning algorithm, it was selected and used further to assess the ESDD value of sea salt contaminated medium voltage insulators the model, that presented the best generalizing ability, had a compact structure, a fast training process and consumed low memory. This ANN model had the following characteristics: three hidden layers, with eleven, twenty eight and eighteen neurons in each hidden layer respectively, random order incremental learning algorithm and logarithmic sigmoid transfer function. Samples ESDD values (mg/cm 2 ) Actual measurement ANN assessment Fig. 2: Comparison of the actual ESDD values and ANN assessment results for the five contaminated medium voltage insulators. The selected ANN model has been used in order to assess the sea salt contamination of five different cap-and-pin porcelain insulators from the area of Chalkida, Hellas of known sea salt contamination. Fig. 2 presents the comparison of the actual ESDD values and these obtained using the designed ANN model. It is obvious that the results obtained according to the proposed ANN method are very close to the actual ones, something which clearly implies that the proposed ANN model is well working and has an acceptable accuracy. 6. Conclusions The paper describes an artificial neural network method for the assessment of the sea salt contamination of medium voltage insulators. Actual input and output data collected from the Hellenic distribution network were used in the training, validation and testing process. The developed ANN model applied on contaminated insulators of known contamination, presenting great accuracy. The proposed ANN approach can be useful in the studies of electrical maintenance engineers since it can give knowledge not only about the contamination behavior and level but also the critical months and exposure periods of the year, resulting in a more effective maintenance policy References: [1] A.S. Ahmad, P.S. Ghosh, S.S. Ahmad, S.A.K. Aljunid, Assessment of ESDD on high voltage insulators using artificial neural network, Electric Power Systems Research, Vol. 72, 2004, pp [2] G. Montoya-Tena, R. Harnandez-Corona, I. Ramirez-Vazquez, Experiences on pollution level in Mexico, Electric Power Systems Research, Vol. 76, 2005, pp [3] L. An, X. Jiang, Z. Han, Measurement of equivalent salt deposit density (ESDD) on a suspension insulator, IEEE Trans. Dielectr. Electr. Insul., Vol. 9, No. 4, 2002, pp [4] K. Naito, Y. Mizuno, W. Naganawa, A study on probabilistic assessment of contamination flashover of high voltage insulator, IEEE Trans. Power Deliv., Vol. 10, No. 3, 1995, pp ISSN: ISBN:

5 [5] H. Ahmad, M.Y. Bin Ibrahim, A multivariate model for equivalent salt deposit density ESDD distribution prediction for the maintenance of polluted insulator, CIGRE Symposium, Bangkok, [6] I. Ramirez, J.L. Fierro, Criteria for the diagnostic of polluted ceramic insulators based on the leakage current monitoring technique, Conf. on Electrical Insulation and Dielectric Phenomena, Austin, TX, USA, [7] B. Kermanshahi, H. Iwamiya H, Up to the year 2020 load forecasting using neural nets, Electric Power Energy Systems, Vol. 17, 2002, pp [8] A.D.P. Lotufo, M.L.M. Lopes, C.R. Minussi, Sensitivity analysis by neural networks applied to power systems transient stability, Electric Power Systems Research, Vol. 77, No. 7, 2007, pp [9] G.K. Miti, A.J. Moses, Neural network-based software tool for predicting magnetic performance of strip-wound magnetic cores at medium to high frequency, IEE Proc-Sci. Meas. Technol., Vol. 151, No. 3, 2004, pp [10] L. Ekonomou, I.F. Gonos, I.A. Stathopulos, Application and comparison of several artificial neural networks for evaluating the lightning performance of high voltage transmission lines, 14 th International Symposium on High-Voltage Engineering (ISH 2005), Beijing, China, paper B-04, [11] K. Hornik, Some new results on neural network approximation. Neural Networks, Vol. 6, 1993, pp [12] R. Lippmann, An introduction to computing with neural nets. IEEE ASSP Magazine, Vol. 4, No. 2, 1987, pp [13] I. Alexandri, E. Fournarakis, G. Georgantzis, D. Stavropoulos, Feasibility investigation of medium voltage composite insulators use in distribution networks, 2003 CIGRE Greek National Committee Conference, Athens, Greece, 2003, pp [14] B.A. Adami, A practical application of reliability management with RTV silicon coating at 500 kv substation, 2005 World Congress & Exhibition on Insulators, Arresters & Bushings, Hong Kong, [15] Data supplied from the National Meteorological Authority of Hellas, [16] PPC S.A., Annual electrical energy s statistical and economical data. Athens, Hellenic Public Power Corporation S.A., 2006 [17] H. Demuth, M. Beale, Neural Network Toolbox: For use with MATLAB, The Math Works, Dimitrios S. Oikonomou was born on September 28, 1982 in Athens, Greece. He received his degree in Natural Resources Management and Agricultural Engineering in 2008 from the Agricultural University of Athens in Greece. He is currently a research assistant in the Agricultural University of Athens. His research interests concern artificial neural networks, modelling, simulation, electrical motors and electrical conversion. Theodoros I. Maris was born in Arta, Greece in He received his diploma in Electrical Engineering and his Ph.D. from the School of Engineering of University of Patras in Greece in 1984 and 1994 respectively. He became teaching assistant ( ) at the Department of Electrical Engineering of the Technological Educational Institute of Chalkida and thereafter Assistant Professor ( ) and Associate Professor (since 2005). His research interests concern electric energy systems, direct current interconnections, high voltage direct current converters, electrical drives, photovoltaic inverters, transmission and distribution lines and artificial neural networks. Lambros Ekonomou was born on January 9, 1976 in Athens, Greece. He received a Bachelor of Engineering (Hons) in Electrical Engineering and Electronics in 1997 and a Master of Science in Advanced Control in 1998 from University of Manchester Institute of Science and Technology (U.M.I.S.T.) in United Kingdom. In 2006 he graduated with a Ph.D. in High Voltage Engineering from the National Technical University of Athens (N.T.U.A.) in Greece. In June 2007, he was appointed Assistant Professor in the Hellenic American University, while he has worked with various companies including Hellenic Public Power Corporation S.A. and Hellenic Aerospace Industry S.A. His research interests concern high voltage engineering, transmission and distribution lines, lightning performance, lightning protection, stability analysis, control design, reliability, electrical drives and artificial neural networks. ISSN: ISBN: