Distribution network fault section identification and fault location using artificial neural network

2018 ◽  
Author(s):  
Masoud Dashtdar ◽  
Rahman Dashti ◽  
Hamid Reza Shaker
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Location ◽  
Distribution Network ◽  
Artificial Neural ◽  
Network Fault ◽  
Fault Section Identification

scholarly journals Fault Location in Radial Distribution Network Based on Fault Current Profile and the Artificial Neural Network

2020 ◽  
Vol 20 (1) ◽  
pp. 14-21
Author(s):  
Majid Dashtdar ◽  
Masoud Dashtdar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution Systems ◽  
Fault Location ◽  
Short Circuit ◽  
Accurate Determination ◽  
Distribution Network ◽  
Variation Method ◽  
Fault Current ◽  
Artificial Neural

AbstractElectricity distribution systems are subject to a variety of faults such as permanent and transient short circuits due to the extent and multiplicity of equipment. In principle, short circuit fault causes the existing protective equipment to operate and to no electricity the various parts of the distribution network. Rapid and accurate determination of fault location, repair and recovery, it has not prevented the distribution of energy. This will satisfy consumers and prevent the losses of electricity companies. In this paper, the artificial neural network and fault current profiles are used to determine the distance of the fault, determine the type of fault and detect the short circuit. This method provides the information needed to locate the fault by sampling the current before and after the fault occurs from the SCADA system. The effect of connectivity local resistance changes and the effect of load changes on fault location were evaluated. The results show that this method is more accurate than the voltage droop profile variation method in determining the fault distance and short circuit breakdown. If only the net fault current changes profile is used, the effect of the load changes in determining the short-circuit breakdown is much less.

Effective design and development of hybrid ABC-CSO-based capacitor placement with load forecasting based on artificial neural network

2019 ◽  
Vol 39 (5) ◽  
pp. 917-930 ◽  
Author(s):  
Sarika Sharma ◽  
Smarajit Ghosh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Distribution ◽  
Power Loss ◽  
Distribution Network ◽  
Load Forecasting ◽  
Distribution Networks ◽  
Content Type ◽  
Power Loss Reduction ◽  
Artificial Neural

Purpose This paper aims to develop a capacitor position in radial distribution networks with a specific end goal to enhance the voltage profile, diminish the genuine power misfortune and accomplish temperate sparing. The issue of the capacitor situation in electric appropriation systems incorporates augmenting vitality and peak power loss by technique for capacitor establishments. Design/methodology/approach This paper proposes a novel strategy using rough thinking to pick reasonable applicant hubs in a dissemination structure for capacitor situation. Voltages and power loss reduction indices of distribution networks hubs are shown by fuzzy enrollment capacities. Findings A fuzzy expert system containing a course of action of heuristic rules is then used to ascertain the capacitor position appropriateness of each hub in the circulation structure. The sizing of capacitor is solved by using hybrid artificial bee colony–cuckoo search optimization. Practical implications Finally, a short-term load forecasting based on artificial neural network is evaluated for predicting the size of the capacitor for future loads. The proposed capacitor allocation is implemented on 69-node radial distribution network as well as 34-node radial distribution network and the results are evaluated. Originality/value Simulation results show that the proposed method has reduced the overall losses of the system compared with existing approaches.

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