Hardware design for MAS power distribution restoration using neural networks

2015 ◽  
Author(s):  
Mohamad A. Mashta ◽  
M. A. Choudhry ◽  
Ali Feliachi
Keyword(s):  
Neural Networks ◽  
Power Distribution ◽  
Hardware Design

Artificial Neural Networks to Improve Current Harmonics Identification and Compensation

2010 ◽  
pp. 256-290 ◽  
Author(s):  
Patrice Wira ◽  
Djaffar Ould Abdeslam ◽  
Jean Mercklé
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Digital Signal Processor ◽  
Power Distribution ◽  
Distribution System ◽  
Control Laws ◽  
Identification Schemes ◽  
Learning Capabilities ◽  
Artificial Neural ◽  
The One

Artificial Neural Networks (ANNs) have demonstrated very interesting properties in adaptive identification schemes and control laws. In this work, they are employed for the on-line control strategy of an Active Power Filter (APF) in order to improve its performance. Indeed, neural-based approaches are synthesized to design adaptive and efficient harmonic identification schemes. The proposed neural approaches are employed for compensating for the changing harmonic distortions introduced in a power distribution system by unknown nonlinear loads. The implementation of the ANNs has been optimized on a digital signal processor for real-time experiments. The feasibility of the implementation has been validated and the neural compensation schemes exhibit good performances compared to conventional approaches. By their learning capabilities, ANNs are able to take into account time-varying parameters such as voltage sags and harmonic content changes, and thus appreciably improve the performance of the APF compared to the one obtained with traditional compensating methods.

Voltage Instability Detection Using Neural Networks

2011 ◽  
pp. 1596-1602
Author(s):  
Adnan Khashman ◽  
Kadri Buruncuk ◽  
Samir Jabr
Keyword(s):  
Neural Networks ◽  
Power Systems ◽  
Power Distribution ◽  
Distribution System ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Detection System ◽  
Nonlinear Problems ◽  
Voltage Instability ◽  
Voltage Output

The explosive growth in decision-support systems over the past 30 years has yielded numerous “intelligent” systems that have often produced less-than-stellar results (Michalewicz Z. et al., 2005). The increasing trend in developing intelligent systems based on neural networks is attributed to their capability of learning nonlinear problems offline with selective training, which can lead to sufficiently accurate online response. Artificial neural networks have been used to solve many problems obtaining outstanding results in various application areas such as power systems. Power systems applications can benefit from such intelligent systems; particularly for voltage stabilization, where voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. This article presents an intelligent system which detects voltage instability and classifies voltage output of an assumed power distribution system (PDS) as: stable, unstable or overload. The novelty of our work is the use of voltage output images as the input patterns to the neural network for training and generalizing purposes, thus providing a faster instability detection system that simulates a trained operator controlling and monitoring the 3-phase voltage output of the simulated PDS.

scholarly journals Avaliação do Processo para Embarcar uma Rede Neural Baseada em YOLO Utilizando um Acelerador de Hardware Dedicado

2020 ◽  
Author(s):  
Isabel Costa ◽  
Elias Silva Jr ◽  
Antônio Rodrigues ◽  
Leandro Angeloni ◽  
Edmilson Dias
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Object Detection ◽  
Power Distribution ◽  
Deep Neural Networks ◽  
Aerial Images ◽  
Great Progress ◽  
Operational Networks

Object Detection is a challenging task in computer vision, but Deep Neural Networks (DNN) have made great progress in this area. This work presents the process and the results obtained in the attempts to embed a YOLO V3 model in a Neural Compute Engine, the Movidius Stick. Experiments were carried out with a Tensorflow model that is converted to Movidius (using OpenVINO) including an evaluation of the Movidius stick connected to a Raspberry Pi3. The application uses aerial images of power distribution towers captured by a drone. Although there are some fully operational networks for Neural Compute Engines, there are some difficulties in porting new networks to the platform, with gains in performance, but with losses in accuracy.

Component Stress Evaluation in an Electrical Power Distribution System Using Neural Networks

2010 ◽  
pp. 21-30 ◽  
Author(s):  
Miguel A. Sanz-Bobi ◽  
Rodrigo J. A. Vieira ◽  
Chiara Brighenti ◽  
Rafael Palacios ◽  
Guillermo Nicolau ◽  
...  
Keyword(s):  
Neural Networks ◽  
Power Distribution ◽  
Distribution System ◽  
Electrical Power ◽  
Stress Evaluation ◽  
Power Distribution System
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