Model of Restoration of Distribution Network of Electrical Energy using Artificial Neural Networks

The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

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Electrical energy demand prediction using Artificial Neural Networks and Support Vector Regression

2008 3rd International Symposium on Communications, Control and Signal Processing ◽

10.1109/isccsp.2008.4537451 ◽

2008 ◽

Cited By ~ 3

Author(s):

Gabriel I. S. Ruas ◽

Ticiano A. C. Bragatto ◽

Marcus V. Lamar ◽

Alexandre R. Aoki ◽

Silvio M. de Rocco

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Regression ◽

Energy Demand ◽

Electrical Energy ◽

Support Vector ◽

Demand Prediction ◽

Artificial Neural ◽

Energy Demand Prediction

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Implementation of artificial neural networks based AI concepts to the smart grid

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee1403411d ◽

2014 ◽

Vol 27 (3) ◽

pp. 411-424 ◽

Cited By ~ 2

Author(s):

Marko Dimitrijevic ◽

Miona Andrejevic-Stosovic ◽

Jelena Milojkovic ◽

Vanco Litovski

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Smart Grid ◽

Modern Society ◽

Electrical Energy ◽

New Paradigm ◽

Production Distribution ◽

Production And Distribution ◽

Artificial Neural

ICT and energy are two economic domains that became among the most influential to the growth of modern society. These, in the same time, due to exploitation of natural resources and producing unwanted effects to the environment, represent a kind of menace to the eco system and the human future. Implementation of measures to mitigate these unwanted effects established a new paradigm of production and distribution of electrical energy named smart grid. It relies on many novelties that improve the production, distribution and consumption of electricity among which one of the most important is the ICT. Among the ICT concepts implemented in modern smart grid one recognizes the artificial intelligence and, specifically the artificial neural network. Here, after reviewing the subject and setting the case, we are reporting some of our newest results aiming at broadening the set of tools being offered by ICT to the smart grid. We will describe our result in prediction of electricity demand and characterization of new threats to the security of the ICT that may use the grid as a carrier of the attack. We will use artificial neural networks (ANNs) as a tool in both subjects.

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Improvement of the prediction quality of electrical load profiles with artificial neural networks

Machine Learning for Cyber Physical Systems - Technologien für die intelligente Automation ◽

10.1007/978-3-662-62746-4_2 ◽

2020 ◽

pp. 13-21

Author(s):

Fabian Bauer ◽

Jessica Hagner ◽

Peter Bretschneider ◽

Stefan Klaiber

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Storage Systems ◽

Electrical Energy ◽

Optimal Management ◽

Electrical Load ◽

Linear Problems ◽

Artificial Neural ◽

And Storage

AbstractAgainst the backdrop of the economically and ecologically optimal management of electrical energy systems, accurate predictions of consumption load profiles play an important role. On this basis, it is possible to plan and implement the use of controllable energy generation and storage systems as well as energy procurement with the required lead-time, taking into account the technical and contractual boundary conditions.The recorded electrical load profiles will increase considerably in the course of the digitization of the energy industry. In order to make the most accurate predictions possible, it is necessary to develop and investigate models that take account of the growing quantity structure and, due to the significantly higher number of observations, improve the forecasting quality as far as possible.Artificial neural networks (ANN) are increasingly being used to solve non-linear problems for a growing amount of data that is affected by human and other unpredictable influences. Consequently, the model approach of an ANN is chosen for predicting load profiles. Aim of the thesis is the simulative investigation and the evaluation of the quality and optimality of a prediction model based on an ANN for electrical load profiles.

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