scholarly journals Recognizing animal-caused faults in power distribution systems using artificial neural networks

1993 ◽  
Vol 8 (3) ◽  
pp. 1268-1274 ◽  
Author(s):  
M.-y. Chow ◽  
S.O. Yee ◽  
L.S. Taylor
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Power Distribution ◽  
Distribution Systems ◽  
Power Distribution Systems ◽  
Artificial Neural

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.

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

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