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. Keywords: Electrical power systems, Electric power distribution, Smart grid, Power system modelling and simulation, Steady State, Power flow, Matpower, Matlab, Modelling, Simulatio

Machine Learning Techniques for the Prediction of the Magnetic and Electric Field of Electrostatic Discharges

The magnetic and electric fields of electrostatic discharges are assessed using the Naïve Bayes algorithm, a machine learning technique. Laboratory data from electrostatic discharge generators were used for the implementation of this algorithm. The applied machine learning algorithm can be used to predict the radiated field knowing the discharge current. The results of the Naïve Bayes algorithm are compared to a previous software tool derived by Artificial Neural Networks, proving its better outcome. The Naïve Bayes algorithm has excellent performance on most classification tasks, despite its simplicity, and usually is more accurate than many sophisticated methods. The proposed algorithm can be used by laboratories that conduct electrostatic discharge tests on electronic equipment. It will be a useful software tool, since they will be able to predict the radiating electromagnetic field by simply measuring the discharge current from the electrostatic discharge generators

Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles

This paper reviews protection schemes for meshed distribution networks. It gives emphasis to the increasing penetration of electric vehicles, their charging patterns, and to the increasing value of distributed generators, especially from renewables. It includes a preliminary analysis on system planning with electric vehicles that is studied probabilistically and a more detailed analysis of the expected changes introduced by these new loads. Finally, a real time hardware-in-the-loop review analysis for protection systems and the open source networks available for protection studies from several sources are also provided. This work could be useful as a collective review of the recent bibliography on protection for meshed networks, giving emphasis to electric vehicles and their real time simulation

Machine Learning Techniques for the Prediction of the Magnetic and Electric Field of Electrostatic Discharges

The magnetic and electric fields of electrostatic discharges are assessed using the Naïve Bayes algorithm, a machine learning technique. Laboratory data from electrostatic discharge generators were used for the implementation of this algorithm. The applied machine learning algorithm can be used to predict the radiated field knowing the discharge current. The results of the Naïve Bayes algorithm are compared to a previous software tool derived by Artificial Neural Networks, proving its better outcome. The Naïve Bayes algorithm has excellent performance on most classification tasks, despite its simplicity, and usually is more accurate than many sophisticated methods. The proposed algorithm can be used by laboratories that conduct electrostatic discharge tests on electronic equipment. It will be a useful software tool, since they will be able to predict the radiating electromagnetic field by simply measuring the discharge current from the electrostatic discharge generators

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

The data depicted to this article represent a real electricity distribution network of the Hellenic Electricity Distribution system. This network has been chosen due to its moderate penetration of renewable sources and its capability in part of time to demonstrate reverse power flows. It a rural line of fifty-five kilometers (55km) total length, which is 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-amperes (12 MVA). The source code is ready to perform load flow simulation on Matpower for the maximum observed load power on the half production for renewables. The simulation results are also provided

Replication Data for: "Simulated correlation between Representative Concentration Pathways and Paris Agreement."

This data set includes simulated results for the Representative Concentration Pathways 2.6 and 2.0, 1.0

On the Computation of the Voltage Distribution along the Non-Linear Resistor of Gapless Metal Oxide Surge Arresters

The voltage distribution along the non-linear resistance of metal oxide surges is of great importance for their proper operation, since the non-uniform potential distribution results in higher thermal stresses of the varistor discs near the high voltage electrode, leading to a faster ageing of the discs at the top and, consequently, a downgrade in arrester effectiveness and reliability or even failures. The current work deals with the examination of the voltage distribution along the non-linear resistance of medium voltage metal oxide gapless surge arresters, using an appropriate computer tool, discussing configuration that improve the voltage distribution. Moreover, the impact of various factors on the voltage distribution is examined. The extracted results can contribute to the more efficient design of modern metal oxide gapless surge arresters, in an effort to ensure their reliable operation to protect the electrical equipment against lightning surges