scholarly journals Earth fault detection in distributed power systems on the basis of artificial neural networks approach

2021 ◽  
Author(s):  
Ali Ahmadi ◽  
◽  
Ebrahim Aghajari ◽  
Mehdi Zangeneh ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Power System ◽  
Renewable Energy Sources ◽  
Distributed Power ◽  
Earth Fault ◽  
Fault Recognition ◽  
Distributed Power System ◽  
Artificial Neural

Nowadays, the advancement of microgrids promises numerous economic and environmental advantages of renewable energies to nations and societies. The presence of decentralized energy units, however, makes serious technical challenges; for instance, criteria and procedure of fault recognition and diagnosis in this condition is entirely changing. This article, therefore, proposed a novel accurate and fast technique based on Artificial Neural Networks (ANN) for earth fault detection. A sample distributed power system considered for the proposed technique and different earth faults applied to this system consist of one phase, two phases and three phases faults. Also, any alteration of current and voltage signals of all phases is investigated at the fault occurrence moment. Analysis of simulation results demonstrates how the proposed technique could make faster responses and improve the reliability of the distributed power system by more accurate fault recognition in comparison with the other traditional methods such as the Wavelet Transformation technique. The proposed technique is likely to enhance the growth of renewable energy sources usage by decreasing operational risk factors and fault recognition delays.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ramón Fernando Colmenares-Quintero ◽  
Eyberth R. Rojas-Martinez ◽  
Fernando Macho-Hernantes ◽  
Kim E. Stansfield ◽  
Juan Carlos Colmenares-Quintero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Photovoltaic Arrays ◽  
Artificial Neural

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

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