Vasilios P. Androvitsaneas

Performance of ground enhancing compounds during the year

Grounding resistance could be reduced significantly with the usage of ground enhancing compounds in lightning protection systems. This paper presents the results of a series of field measurements on commercially-available ground enhancing compounds. It is the aim of this study to assess the behavior of ground enhancing compounds, which are widely used in grounding systems, in order to decrease the grounding resistance value. It is well known that most of the rise of potential of the grounding rod is determined by the soil resistivity surrounding the grounding rod and the magnitude of the applied current. As a result, the lowest feasible grounding resistance value is desirable, in order to provide the lowest impede path for fault currents to be dispersed into the earth, in the shortest time possible. For this purpose, five grounding rods were driven, each one of them, in different ground enhancing compounds. The measurement results are presented in relation to time and to rainfall. Furthermore, several thoughts, comments and proposals are presented about: a) the use feasibility of ground enhancing compounds, b) the choice of the suitable compound, in relation with the cost and the achieved grounding resistance value, c) the time and weather conditions influence on ground enhancing compound behavior.

Estimation of ground enhancing compound performance using Artificial Neural Network

Grounding system constitutes an essential part of the protection system of electrical installations and power systems against lightning and fault currents. Therefore, it is of paramount importance that engineers ensure as low values for grounding resistance as possible, during the designing phase as well as the lifecycle of the grounding system. A widely used technique of reducing the grounding resistance value, in case of high soil resistivity values, or lack of adequate space for the installation of grounding systems, is the use of ground enhancing compounds. This paper presents a methodology, for the evaluation of grounding resistance, under various meteorological conditions, of grounding systems embedded in natural soil as well as in ground enhancing compounds, using Artificial Neural Network (ANN). The ANN training is based on field measurements that have been performed in Greece during the last year. As a matter of fact, this is a first step to develop a new method for estimating variations of grounding resistance value.

Wavelet Neural Network for ground resistance estimation

This paper presents the results of a computational approach for the ground resistance of grounding systems, used for the safe operation of electrical installations, substations and power transmission lines and aspires to build a forecasting model for the ground resistance values. The proposed model consists of a Wavelet Neural Network, which has been trained and validated by field measurements, performed for the last three years. Several grounding rods, encased in ground enhancing compounds and natural soil, have been tested, so that a wide data set for the training of the network can be obtained, covering various soil conditions. The input variables of the network are the soil resistivity within various depths of the tested field, varying with respect to time and the rainfall height during the year. This work introduces the wavelet analysis in the field of ground resistance estimation and attempts to take advantage of the benefits of artificial intelligence.

Computational Intelligence Techniques for Modelling the Critical Flashover Voltage of Insulators: From Accuracy to Comprehensibility

This paper copes with the problem of flashover voltage on polluted insulators, being one of the most important components of electric power systems. Α number of appropriately selected computational intelligence techniques are developed and applied for the modelling of the problem. Some of the applied techniques work as black-box models, but they are capable of achieving highly accurate results (artificial neural networks and gravitational search algorithms). Other techniques, on the contrary, obtain results somewhat less accurate, but highly comprehensible (genetic programming and inductive decision trees). However, all the applied techniques outperform standard data analysis approaches, such as regression models. The variables used in the analyses are the insulator’s maximum diameter, height, creepage distance, insulator’s manufacturing constant, and also the insulator’s pollution. In this research work the critical flashover voltage on a polluted insulator is expressed as a function of the aforementioned variables. The used database consists of 168 different cases of polluted insulators, created through both actual and simulated values. Results are encouraging, with room for further study, aiming towards the development of models for the proper inspection and maintenance of insulators.

Intelligent Data Analysis in Electric Power Engineering Applications

This chapter presents various intelligent approaches for modelling, generalization and knowledge extraction from data, which are applied in different electric power engineering domains of the real world. Specifically, the chapter presents: (1) the application of ANNs, inductive ML, genetic programming and wavelet NNs, in the problem of ground resistance estimation, an important problem for the design of grounding systems in constructions, (2) the application of ANNs, genetic programming and nature inspired techniques such as gravitational search algorithm in the problem of estimating the value of critical flashover voltage of insulators, a well-known difficult topic of electric power systems, (3) the application of specific intelligent techniques (ANNs, fuzzy logic, etc.) in load forecasting problems and in optimization tasks in transmission lines. The presentation refers to previously conducted research related to the application domains and briefly analyzes each domain of application, the data corresponding to the problem under consideration, while are also included a brief presentation of each intelligent technique and presentation and discussion of the results obtained. Intelligent approaches are proved to be handy tools for the specific applications as they succeed to generalize the operation and behavior of specific parts of electric power systems, they manage to induce new, useful knowledge (mathematical relations, rules and rule based systems, etc.) and thus they effectively assist the proper design and operation of complex real world electric power systems.

Ground Resistance Estimation Using Feed-Forward Neural Networks, Linear Regression and Feature Selection Models

This paper proposes ways for estimating the ground resistance of several grounding systems, embedded in various ground enhancing compounds. Grounding systems are used to divert high fault currents to the earth. The proper estimation of the ground resistance is useful from a technical and also economic viewpoint, for the proper electrical installation of constructions. The work utilizes both, conventional and intelligent data analysis techniques, for ground resistance modelling from field measurements. In order to estimate ground resistance from weather and ground data such as soil resistivity, rainfall measurements, etc., three linear regression models have been applied to a properly selected dataset, as well as an intelligent approach based in feed-forward neural networks,. A feature selection process has also been successfully applied, showing that features selected for estimation agree with experts’ opinion on the importance of the variables considered. Experimental data consist of field measurements that have been performed in Greece during the last three years. The input variables used for analysis are related to soil resistivity within various depths and rainfall height during some periods of time, like last week and last month. Experiments produce high quality results, as correlation exceeds 99% for specific experimental settings of all approaches tested.