Artificial Neural Network Application in Optimal Coordination of Directional Overcurrent Protective Relays in Electrical Mesh Distribution Network

2015 ◽  
Vol 785 ◽  
pp. 48-52 ◽  
Author(s):  
Osaji Emmanuel ◽  
Mohammad Lutfi Othman ◽  
Hashim Hizam ◽  
Muhammad Murtadha Othman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Time ◽  
Short Circuit ◽  
Distribution Networks ◽  
Test System ◽  
Wrong Response ◽  
Optimal Coordination ◽  
Artificial Neural ◽  
Short Circuit Fault

Directional Overcurrent relays (DOCR) applications in meshed distribution networks (MDN), eliminate short circuit fault current due to the topographical nature of the system. Effective and reliable coordination’s between primary and secondary relay pairs ensures effective coordination achievement. Otherwise, the risk of safety of lives and installations may be compromised alongside with system instability. This paper proposes an Artificial Neural Network (ANN) approach of optimizing the system operation response time of all DOCR within the network to address miscoordination problem due to wrong response time among adjacent DOCRs to the same fault. A modelled series of DOCRs in a simulated IEEE 8-bus test system in DigSilent Power Factory with extracted data from three phase short circuit fault analysis adapted in training a custom ANN. Hence, an improved optimized time is produced from the network output to eliminate miscoordination among the DOCRs.

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.

scholarly journals Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems

2021 ◽  
Author(s):  
Nathan Elias Maruch Barreto ◽  
Ciro Monteiro Baer ◽  
Mateus Jaensen Daros ◽  
Marlon Alexsandro Fritzen ◽  
Guilherme Schneider de Oliveira ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Systems ◽  
Power System ◽  
Test System ◽  
Measurement Unit ◽  
Two Phase ◽  
Advantages And Disadvantages ◽  
Phasor Measurement ◽  
Artificial Neural

This paper presents an anomalous operation detection system for power systems using the artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique is a set of simulated post-fault bus voltages and currents obtained in a sampling rate so as to emulate a phasor measurement unit network. Several types of faults are dealt with, such as three-phase to ground, two-phase, two-phase to ground and single-phase to the ground as well as line and load contingencies. All fault and steady-state simulations were performed on MATLAB using Graham Rogers’ Power System Toolbox. The artificial neural network was designed on MATLAB, using an architecture proper for pattern recognition with supervised learning and obtaining high accuracy predictions within a short amount of time. The test system used in all simulations is the IEEE 39-Bus New England Power System, which presents 10 generation units, 21 loads and three distinct areas alongside transient and sub transient models, with phasor measurement units in 14 buses. Future works are discussed, showing the possibilities for feature engineering in this type of problem, fault type detection and fault location in operation using analogous dataset and neural network structures.

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