scholarly journals Micro-grid Development Using Artificial Neural Network for Renewable Energy Forecast and System Control

2017 ◽  
Vol 181 ◽  
pp. 818-823 ◽  
Author(s):  
Karoly Ronay ◽  
Dorin Bica ◽  
Calin Munteanu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
System Control ◽  
Micro Grid ◽  
Artificial Neural ◽  
Energy Forecast

scholarly journals A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

2019 ◽  
Vol 9 (9) ◽  
pp. 1844 ◽  
Author(s):  
Jesús Ferrero Bermejo ◽  
Juan F. Gómez Fernández ◽  
Fernando Olivencia Polo ◽  
Adolfo Crespo Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Energy Sources ◽  
Solar Pv ◽  
Ann Models ◽  
Artificial Neural

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

scholarly journals INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

2020 ◽  
Vol 8 (5) ◽  
pp. 4047-4068
Author(s):  
Mehmet Hakan ÖZDEMİR ◽  
Murat İNCE ◽  
Batin Latif AYLAK ◽  
Okan ORAL ◽  
Mehmet Ali TAŞ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Solar Power ◽  
Short Term Memory ◽  
Renewable Energy Sources ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Renewable energy sources play an essential role in sustainable development. The share of renewable energy-based energy generation is rapidly increasing all over the world. Turkey has a great potential in terms of both solar and wind energy due to its geographical location. The desired level has not yet been reached in using this potential. Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum. In this study, data on cumulative installed solar power in Turkey in the 2009-2019 period were used. Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods were selected to predict the cumulative installed solar power for 2020 with these data. The cumulative installed power was predicted, and the results were compared and interpreted.

scholarly journals A novel artificial neural network for power quality improvement in AC microgrid

2021 ◽  
Vol 12 (4) ◽  
pp. 2151
Author(s):  
Debani Prasad Mishra ◽  
Amba Subhadarshini Nayak ◽  
Truptasha Tripathy ◽  
Surender Reddy Salkuti ◽  
Sanhita Mishra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Power Quality ◽  
Power Supply ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Sources ◽  
Artificial Neural

The microgrid concept provides a flexible power supply to the utility where the conventional grid is unable to supply. The microgrid structure is based on renewable energy sources known as distributed generators (DGs) and the power network. Nevertheless, the power quality (PQ) is a great challenge in the microgrid concept. Particularly the inclusion of renewable energy sources into the conventional grids increases the problems in the quality of power, like voltage sag/swell, oscillatory transient, voltage flickering, and voltage notching which reduces the quality and reliability of the power supply. In this paper, a microgrid is considered which consists of PV cells as DG, battery energy storage system (BESS), and a novel control strategy known as the nonlinear autoregressive exogenous model (NARX). The proposed controller is an improved artificial neural network (ANN). The various case studies like sag/swell, unbalanced condition, and voltage deviation have been simulated with the model. The comprehensive simulation results are compared with the proportional-integral (PI) controller. Hence in this paper, the robustness of the proposed controller has been studied through different situations.

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