A Review of the Modeling of Advanced Protection Scheme for a 33/11kv Injection Substation Using Artificial Neural Network

• Author(s): Simeon Ebahe Okachi ; Emmanuel Nkpeh Eneji ; James Eko Akpama ; Sampson Akem Bendor; Vincent Nsed Ogar ; Eunice Ene Ijeoma; Godwin Acha Obule
• Paper ID: 1705683
• Page: 145-157
• Published Date: 19-04-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 10 April-2024

Abstract

Fault occurrences in a distribution network often take time to detect and locate. This delay may lead to major or multiple faults. Even when the fault location is finally resolved, isolating the fault can present other challenges. It is not unusual for an entire network to be shut down in order to isolate a minor fault. This can impose severe negative impacts on the supplier as well as the consumers. This report endeavors to design a timely and reliable electric power fault detection and location for a 33/11kV injection substation. The network is modeled and simulatedin the MATLAB/Simulink environment. The trainingtesting and evaluation of the intelligent locator isdone based on a multilayer perceptron feed forward artificial neural network with back propagationalgorithm. Neural network pre-processing refers to the steps taken to prepare and clean input data before it is fed into a neural network for training or inference. The learning process involves the algorithm making predictions based on the input data and comparing these predictions to the actual outputs.The modeling in this research first identified fault conditions, to be sure that a fault has occurred to begin with. Then the scheme should also be able to classify the fault to establish what type of fault that has occurred. And finally, the scheme should localize the fault, to a particular line or lines, and the approximate distance from the detection point.

Keywords

Artificial Neural Network, Transmission/Distribution Networks, Injection Substation and Modeling of Advanced Protection Scheme.

Citations

IRE Journals:
Simeon Ebahe Okachi , Emmanuel Nkpeh Eneji , James Eko Akpama , Sampson Akem Bendor; Vincent Nsed Ogar , Eunice Ene Ijeoma; Godwin Acha Obule "A Review of the Modeling of Advanced Protection Scheme for a 33/11kv Injection Substation Using Artificial Neural Network" Iconic Research And Engineering Journals Volume 7 Issue 10 2024 Page 145-157

IEEE:
Simeon Ebahe Okachi , Emmanuel Nkpeh Eneji , James Eko Akpama , Sampson Akem Bendor; Vincent Nsed Ogar , Eunice Ene Ijeoma; Godwin Acha Obule "A Review of the Modeling of Advanced Protection Scheme for a 33/11kv Injection Substation Using Artificial Neural Network" Iconic Research And Engineering Journals, 7(10)