Design of an artificial neural network for the estimation of the flashover voltage on insulators

This work attempts to apply an artificial neural network in order to estimate the critical flashover voltage on polluted insulators. The artificial neural network uses as input variables the following characteristics of the insulator: diameter, height, creepage distance, form factor and equivalent salt deposit density, and estimates the critical flashover voltage. The data used to train the network and test its performance is derived from experimental measurements and a mathematical model. Various cases have been studied and their results presented separately. Training and testing sets have been modified for each case.     

Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines

Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150 kV and 400 kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be used by electric power utilities as useful tools for the design of electric power systems, alternative to the conventional analytical methods.     

A fuzzy logic optimization methodology for the estimation of the critical flashover voltage on insulators

The objective of this paper is to present a new methodology for predicting the critical flashover voltage of polluted insulators based on fuzzy logic. The prediction contains not only the estimated value, but also the respective confidence interval based on the re-sampling method. Various parameters, such as the number and the base width of the triangular membership functions used for the fuzzification process, etc., are assigned different values in order to optimize the estimation of the critical flashover voltage. Additionally, different methods for training the fuzzy system are applied and compared for their appropriateness in accurately predicting the critical flashover voltage.     

Assessment of surge arrester failure rate and application studies in Hellenic high voltage transmission lines

The use of transmission line surge arresters to improve the lightning performance of transmission lines is becoming more common. Especially in areas with high soil resistivity and ground flash density, surge arresters constitute the most effective protection mean. In this paper a methodology for assessing the surge arrester failure rate based on the electrogeometrical model is presented. Critical currents that exceed arresters rated energy stress were estimated by the use of a simulation tool. The methodology is applied on operating Hellenic transmission lines of 150 kV. Several case studies are analyzed by installing surge arresters on different intervals, in relation to the region's tower footing resistance and the ground flash density. The obtained results are compared with real records of outage rate showing the effectiveness of the surge arresters in the reduction of the recorded failure rate. The presented methodology can be proved valuable to the studies of electric power systems designers intending in a more effective lightning protection, reducing the operational costs and providing continuity of service.     

An architecture for the intelligent detection and alleviation of overloaded transmission lines

A new control strategy is proposed that offers intelligent solutions to the problem of alleviating overloaded lines. The subsystems used consist of artificial neural networks, a holographic associative memory, and a knowledge based expert system.