L. Ekonomou

Estimation of wind turbines optimal number and produced power in a wind farm using an artificial neural network model

One of the most significant issues in the design of a new wind farm is the estimation of optimal number of wind turbines that has to be installed in it. The goal of every wind farm designer is the production of the maximum possible power, minimizing the installation cost. The cost can be significantly reduced using the minimum required number of wind turbines for specific power production, occupying at the same time the least possible acreage of land. In this work an artificial neural network (ANN) model is developed which has the ability to estimate the optimal number of wind turbines and the total produced power in a wind farm. The ANN model’s results are compared with those of earlier studies that have followed other approaches, proving that the ANN model is well working and has an acceptable accuracy. The proposed model can be useful in the studies of wind farm designers as a supportive tool for the estimation of the optimal number of wind turbines in a wind farm.

Probability of backflashover in transmission lines due to lightning strokes using Monte-Carlo simulation

A method, which estimates the lightning performance of high voltage transmission lines based on the Monte-Carlo simulation technique, is described in this paper. The average number of faults which occur in a transmission line, dividing them in single-phase and three-phase faults, as well as the average grounding resistances of the transmission lines are calculated. The method is applied, on several operating Greek transmission lines, showing good correlation between predicted and field observation results. The proposed method can be used as a useful tool in the design of electric power systems, aiding in the right insulation dimensioning of a transmission line.

Modeling of the grounding resistance variation using ARMA models

Abstract This study addresses the problem of modeling the variation of the grounding resistance during the year. An AutoRegressive Moving Average (ARMA) model is fitted (off-line) on the provided actual data using the Corrected Akaike Information Criterion (AICC). The developed model is shown to fit the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on line/adaptive modeling is required. In both cases, and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise is necessary. In this paper, a new method based on the multi-model partitioning theory which is also applicable to on line/adaptive operation, is used for the solution of the above mentioned problem. The simulations show that the proposed method succeeds in selecting the correct ARMA model order and estimates the parameters accurately in very few steps and even with a small sample size. For validation purposes the method introduced is compared with three other established order selection criteria presenting very good results. The proposed method can be extremely useful in the studies of electrical engineer designers, since the variation of the grounding resistance during the year affects significantly power systems performance and must be definitely considered.

A Review of Techniques to Counter Spam and Spit

This paper studies the most important techniques with which to challenge the problem of unsolicited e-mails (spam) and unsolicited messages in Internet telephony (spit). First an introduction to the problem of spam demonstrates the importance (economic and technological) of finding a solution. Then we analyze the most important techniques that exist to counter the problem. After that we concentrate on a new problem: spam using new internet telephony technologies (spit). This problem, even if existing only theoretically until now, very soon will be one of the main factors affecting the broad use of VoIP. We analyze the most important methods and techniques of countering spit. Finally, we mentione differences between spam and spit and state some useful conclusions.

Estimation of the electromagnetic field radiating by electrostatic discharges using artificial neural networks

Abstract An artificial neural network (ANN) model and more specifically a feedforward multilayer network, which uses the powerful backpropagation learning rule, is addressed in order to estimate the electric and magnetic field radiating by electrostatic discharges (ESDs). Plenty of actual measurements, carried out in the High Voltage Laboratory of the National Technical University of Athens are used in training, validation and testing processes. The developed ANN can be a necessary tool for laboratories involved in ESD tests, either facing a lack of suitable measuring equipment or for laboratories which want to compare their own measurements. This is extremely useful for the laboratories involved in the ESD tests according to the current IEC Standard [International Standard IEC 61000-4-2: Electromagnetic Compatibility (EMC), Part 4: Testing and measurement techniques, Section 2: Electrostatic discharge immunity test, Basic EMC Publication, 1995.], since the forthcoming revised version of this Standard will almost certainly include measurements of the radiating electromagnetic field during the verification of the ESD generators. The authors believe that the proposed ANN will be extensively used, since the produced electromagnetic field radiating by electrostatic discharges, can be calculated very easily and accurately by simply measuring the discharge current.

Design of artificial neural network models for the prediction of the Hellenic energy consumption

Energy consumption predictions are essential and are required in the studies of capacity expansion, energy supply strategy, capital investment, revenue analysis and market research management. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability to handle complex non-linear functions, robustness and great efficiency, even in cases where full information for the studied problem is absent. In this paper, several ANN models were addressed to identify the future energy consumption. Each model has been constructed using different structures, learning algorithms and transfer functions in order the best generalizing ability to be achieved. Actual input and output data were used in the training, validation and testing process. A comparison among the developed neural network models was performed in order the most suitable model to be selected. Finally the selected ANN model has been used for the prediction of the Hellenic energy consumption in the years ahead.

Economic And Social Characteristics Of Albanian Immigrant Entrepreneurs In Greece

Greece has experienced rapid growth in immigrant and refugee populations since 1990. Although most are immigrants from Albania and throughout the Balkan region, some immigrant and refugee groups arriving in Greece also come from the former Soviet Union, Southeast Asia and Africa. Some of these newcomers have started small businesses in their quest to become economically self-sufficient, serve the consumer needs of fellow newcomers, and integrate into community life. The purpose of this research is two-fold: to review the extant literature on social and economic factors influencing immigrant entrepreneurship in Greece, and to determine characteristics and business profiles of Albanian immigrant-owned small businesses within the municipality of Attiki — the location of Athens, Greeces capital city and largest urban center.

An artificial neural network software tool for the assessment of the electric field around metal oxide surge arresters

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.

A Heuristic Combinatorial Optimization Algorithm for Load-Leveling and Peak Demand Reduction using Energy Storage Systems

Abstract A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup.

An open data repository for steady state analysis of a 100-node electricity distribution network with moderate connection of renewable energy sources

The data of this article represent a real electricity distribution network on twenty kilovolts (20 kV) at medium voltage level of the Hellenic electricity distribution system [1]. This network has been chosen as suitable for smart grid analysis. It demonstrates moderate penetration of renewable sources and it has capability in part of time for reverse power flows. It is suitable for studies of load aggregation, storage, demand response. It represents a rural line of fifty-five kilometres (55 km) total length, a typical length for this type. It serves forty-five (45) medium to low voltage transformers and twenty-four (24) connections to photovoltaic plants. The total installed load capacity is twelve mega-volt-ampere (12 MVA), however the maximum observed load is lower. The data are ready to perform load flow simulation on Matpower [2] for the maximum observed load power on the half production for renewables. The simulation results and processed data for creating the source code are also provided on the database available at http://dx.doi.org/10.7910/DVN/1I6MKU.