Neural Comput & Applic (2016) 27:1143–1148 DOI 10.1007/s00521-015-1969-x
An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters Lambros Ekonomou1 • Christos A. Christodoulou2 • Valeri Mladenov3
Received: 22 December 2014 / Accepted: 8 June 2015 / Published online: 23 June 2015 Ó The Natural Computing Applications Forum 2015
Abstract The paper presents an artificial neural network (ANN) software tool that has been developed in order to assess the electric field around medium-voltage surge arresters. The knowledge of the electric field around gapless metal oxide surge arresters is very useful for diagnostic tests and design procedures. For the training, validating, and testing of the ANNs, real data have been used collected from hundreds of measurements. The developed ANN software can be used by electric utilities in order to diagnose the condition of the surge arresters without stopping their operation and by laboratories and manufacturing/retail companies dealing with medium-voltage surge arresters and either face a lack of suitable measuring equipment or want to compare/verify their own measurements. Keywords Artificial neural networks Electric field Measurements Surge arresters
& Lambros Ekonomou [email protected]
Christos A. Christodoulou [email protected]
Valeri Mladenov [email protected]
Department of Electrical and Electronic Engineering, School of Mathematics, Computer Science and Engineering, City University London, London EC1V 0HB, UK
Department of Electrical Engineering, Technological Educational Institute (T.E.I.) of Central Greece, 35100 Lamia, Greece
Department of Theoretical Electrical Engineering, Technical University of Sofia, Sofia 1000, ‘‘Kliment Ohridski’’ Blvd. 8, Sofia, Bulgaria
1 Introduction The electric field around metal oxide surge arresters is a critical factor that impacts the condition of the materials, since various components of the arresters are continuously stressed by the applied high voltage and, consequently, by the developed electric field. The estimation of the electric field contributes to the initial design of the arrester and can also be regarded as an online diagnostic test procedure. In case of installed arresters, the existence of previous recorded data is necessary in order to compare the obtained results and to detect possible changes in the electric field around the arrester. If there are no available recorded data, a theoretical estimation of the expected electric field for various conditions and positions around the arresters should be useful for the estimation of the arresters’ condition. The measurement of the electric field is a complex procedure, since appropriate instruments and configuration of the system are demanded. On the other hand, theoretical simulation results require software packages and details about the geometry and the dimensions of the equipment and the materials’ characteristics. In the current work, a methodology for the computation of the electric field around metal oxide gapless surge arrester is presented, based on the artificial intelligence technique (artificial neural networks—ANN). Real recorded data, obtained by laboratory measurements, have been used in order to train the ANN. Extensive simulations have been performed using different types of ANNs, structures, learning algorithms, and transfer functions in an effort to identify the ANN models with the best generalizing ability. The best ANN model is coded in a comprehensive software tool that can be used by electric utilities, manufactures, and laboratories, which either do not
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possess suitable measuring equipment or want to compare their own measurements.
2 Electric field around surge arresters
Surge arresters are semiconductive devices that protect the electrical equipment against lightning and switching overvoltages. Main part of modern surge arresters is the ZnO varistor, which presents an intense nonlinear voltage– current characteristic (Fig. 1). In case of incoming overvoltage, arresters conduct and divert the surge current to the grounding systems. Figure 1 presents the voltage–current characteristic of a typical metal oxide gapless surge arrester, where its basic electrical characteristics are depicted. The installation position of the arresters determines the effectiveness of the arresters; an arrester generally has a limited protective zone of only a few meters to up to several tens of meters, where the protective zone is defined as the maximum separation distance for which the insulation coordination requirements are fulfilled for a given arrester protective level and coordination withstand voltage. Arresters, therefore, should be installed as close as possible to the device to be protected. Moreover, grounding resistance plays an important role, since it influences the discharge current and the residual voltage. The structure of a typical medium-voltage surge arrester without gaps includes the electrodes (high-voltage electrode and grounding electrode); the varistor (nonlinear resistor), consisting of ZnO discs; and the external insulation, consisting of polymeric or porcelain materials. The varistor and the external housing are separated by fiberglass. The ZnO nonlinear resistance and the external dielectric layer of the arrester are constantly stressed by the applied voltage and the developed electric field. The nonuniform voltage distribution along the varistor (due to parasitic capacitances) and the increased values of the electric field
continuous operating voltage
residual voltage rated voltage
Fig. 1 Voltage–current characteristic of a ZnO varistor
(influenced by the applied voltage, sharp surfaces, pollution, etc.) strain the arrester and accelerate the aging and the degradation of the materials. Figure 2 presents the voltage distribution along the nonlinear resistance. Furthermore, the measurement of the electric field can be an indicator for the arresters’ condition and therefore can be used for diagnostic and maintenance purposes [1, 2]. The electric field can be estimated either experimentally (by using appropriate field meters) or theoretically (by using software packages) [3–6]. Figure 3 presents the experimental configuration for the record of the electric field around surge arresters. The field meter is connected with the sensor through a fiber optic, in order to avoid interferences, arising from human body and metallic objects. The field meter was moved to various positions along five different axes, considering the shape and the symmetry of the arrester, and in various heights.
3 Artificial neural networks’ (ANNs) implementation Artificial neural networks are a computational tool, based on the properties of biological neural systems. They are generally made of a number of simple and highly interconnected processing elements organized in layers as shown in Fig. 4. These processing elements or neurons as they are called process information by their dynamic state response to external inputs. ANNs are capable of learning patterns by being trained with a number of known patterns. The learning process automatically adjusts the weights and thresholds of the processing elements in an effort to minimize the differences between the ANN output and the targeted output. This process is called training and is based on several different learning rules [7, 8], structures, and types. Two of the simplest ones, most commonly used powerful and effective ANNs, are the multilayer perceptron (MLP) neural network and the radial basis function neural network (RBF) [7, 8]. Both ANNs have been widely used, the last years, in the solution of many power system problems presenting very accurate results. MLP and RBF ANNs have been used for the fault location and predictive maintenance of transmission lines [9, 10], for the estimation of transmission lines’ distance protection , for lightning outage calculations and grounding resistance issues , for voltage stability monitoring , for the estimation of electric fields and the critical flashover voltage along high-voltage insulators [12, 14], and for power transformer problems such as insulation aging and fault diagnosis [15, 16]. The first step in the development of the ANN model which will be capable of efficiently assessing the electric field across medium-voltage surge arresters is to define its
Fig. 2 Voltage distribution along the nonlinear resistance
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height of varistor (h)
control board autotransformer 0...230V
sensor fiber optic field meter
Fig. 3 Laboratory configuration for the measurement of the electric field around the arrester
Fig. 4 ANN software tool’s simplified flowchart
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inputs and outputs. As inputs are selected seven parameters that significantly influence the estimation of the electric field across medium-voltage surge arresters, while as output is defined the value of the electric field. The input and output parameters are presented in Table 1. Their values were recorded during measurements that have been taken using the measurement system presented in Sect. 2. Twelve different surge arrester units have been used for the measurements (in ‘‘Appendix’’ are presented the electric field measurements that have obtained for one of these surge arresters, of polymeric insulation with height and creepage distance of 502 and 1365 mm, respectively). The measurements were extensive since they constitute a combination of all different input parameters, i.e., three different types of arresters’ insulation (T) (polymeric, porcelain, silicon), three different applying voltages (U) (nominal, MCOV, and rated), five different axes (a), six different distances (D) from the tested surge arrester (0.5, 0.8, 1.1, 1.4, 1.7, 2 m), three different heights of sensors (H) (13, 21, 29 cm), the creepage distance (dcr), and the height of the tested arresters (A) that were varied from 1200 to 1400 mm and from 480 to 520 mm, respectively, depending on the different arresters that have been used in the measurement. The output parameter of the ANN was the value of the electric field (E) around the surge arrester. For the assessment of the electric field across mediumvoltage surge arresters, both MLP and RBF neural networks have been used. For the MLP neural networks, its structure, i.e., the number of hidden layers and the number of nodes in each hidden layer, has been decided by trying varied combinations in an effort to select the structure with the best generalizing ability. The designed, trained, and tested MLP neural network models were combinations of three learning algorithms (the gradient descent, the Levenberg–Marquardt, and the gradient descent momentum with an adaptive learning rate), two transfer functions (the tan-sigmoid and the log-sigmoid), and several different structures (1–5 hidden layers and 2–50 neurons in each hidden layer). Radial basis function neural networks are three-layer networks. Each node of the hidden layer of a RBF neural
network corresponds to one basis function center. The kernel function that was used was the Gaussian function. Bias, which determines the size of the receptive field, was a free parameter. The weights in the output layer were derived using the least square error learning algorithm. At each iteration, centers were added dynamically until desired minimum square error was achieved. However, performance of the RBF neural network model critically depended upon the chosen centers, which may require an unnecessarily large RBF network to obtain a given level of accuracy and cause numerical ill conditioning [17, 18]. MATLAB Neural Network Toolbox  has been used for training all ANN models. Three thousand two hundred and forty values of input and output data were used to train and validate the ANN. These data refer to measurements taken with the field meter for the 12 surge arrester units of different insulations, heights, and creepage distances, in every possible combination of applied voltages, axis, distances from the surge arresters, and heights of the sensor. The maximum number of iterations was set to 20,000. In each training iteration, 20 % of random data, i.e., six hundred forty-eight, were removed from the training set, and a validation error was calculated for these data. The training process was repeated until a root mean square error between the actual value of electric field and the desired output value reaches either 0.5 % or the maximum number of iterations. It must be mentioned that the assessed values of the electric field were checked with the values obtained from case studies that have included in the training process and others are totally new. Having performed simulations for both types of ANNs with all possible combinations of learning algorithms, transfer functions, hidden layers and neurons in each hidden layer, it was decided that the ANN model that has presented the best generalizing ability, had a compact structure, a fast training process and consumed lower memory than all the other tried combinations was the MLP neural network with two hidden layers (23 and 29 neurons in each hidden layer), that has trained using the Levenberg–Marquardt learning rule and tan-sigmoid transfer function. The root mean square error reached 0.5 % within 17,205 iterations.
Table 1 Artificial neural network input and output parameters Input variables
Type of insulation (T)
Value of the electric field (E)
Applied voltage (U) Axes (a) Distance from surge arrester (D) Height of sensor (H) Creepage distance (dcr) Height of arrester (A)
4 The proposed ANN software tool The designed ANN model that was trained for the assessment of the electric filed around metal oxide surge arresters has been coded in the software tool presented in the flowchart of Fig. 4. The software tool that is very simple in use and flexible in any modifications and changes can be suitable for electric utilities, manufacturers, and laboratories involved in the surge arresters electric field tests and do
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not possess the necessary measurement equipment. The developed software tool is organized as follows: (a) (b) (c) (d) (e) (f) (g) (h) (i)
Introduction of the of arrester’s insulation type (T). Introduction of the applied voltage (U). Introduction of the axis (a). Introduction of the distance from the surge arrester (D). Introduction of the height of the sensor (H). Introduction of the creepage distance (dcr). Introduction of the height of the arrester (A). Assessment of the electric field. Option of another assessment.
The presented software tool is capable of producing accurate results only for the cases and the parameters that have been used for the training of the selected ANN model, i.e., the three types of arrester’s insulation, the three applied voltages, the five different axes, the six distances from the surge arrester, the three different heights of sensors, creepage distances from 1200 to 1400 mm, and heights of arresters from 480 to 520 mm. For the assessment of the electric field of surge arresters with different parameters, a new ANN model must be trained, and a new software tool must be developed .
1147 Table 2 Examined case studies No.
Varying parameters T
5 Results The developed ANN software tool for the assessment of the electric field across medium-voltage surge arresters has been applied to 10 different case studies (different insulation types, applying voltage, axis, distance from the surge arrester, height of sensor, creepage distance, and height of arrester) of known produced electric field (measured using the measurement system presented in Sect. 2), which were not part in the training, validating, and testing processes in order to verify its accuracy. The varying parameters of these 10 case studies are shown in Table 2. Figure 5 presents a comparison of the electric field values obtained for the 10 case studies using the measurement system (real measured data) and the proposed ANN software tool. In the same figure are compared the results obtained using a simulations tool (PC Opera) for the same 10 case studies. The comparison using the proposed ANN software tool electric field with the real measured values and the simulated ones clearly shows that the developed ANN software tool is working well, is efficient, and has an acceptable accuracy. The percentage errors between real measured electric field values and the values obtained using the simulation tool and the ANN software tool are presented in Fig. 6. It is clear that the developed ANN tool presents a relative error that is much lower than this of the simulation tool, something that makes clear the usefulness of the current work.
Fig. 5 Comparison of electric field for each case study
Fig. 6 Electric field’s relative error for real measured, ANN and simulation assessed values
6 Conclusions The paper describes the development of an artificial neural network software tool capable of assessing the electric field around medium-voltage surge arresters. This piece of information is very useful for the design, testing, and maintenance procedures of medium-voltage surge
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arresters. The developed software tool provides an alternative, more efficient and accurate methodology compared to the existing simulation methods for the assessment of this critical magnitude. Real measured data have been used for the training of the selected artificial neural network model, while different ANN types, structures, learning algorithms, and activation functions have been tried. The selected ANN model that had the best generalizing ability and fast training process has been coded in a very userfriendly comprehensive software tool in order to be used by electric utilities, laboratories, and manufacturing/ retail companies dealing with medium-voltage surge arresters which either do not possess the necessary measuring equipment or want to compare/verify their own measurements.
Appendix See Table 3. Table 3 Electric field measurements obtained across a polymeric surge arrester with height of 502 mm and creepage distance of 1365 mm H (mm) D (mm)
13 21 E (V/m)
H (mm) D (mm)
13 21 E (V/m)
2 Axis 3
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