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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Mazhar Hussain Baloch
Mehran University Engineering Sindh
Computer Science & IT Engineering

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Optimization of detection of a single line to ground fault based on ABCNN algorithm Feryal Ibrahim Jabbar; DurMuhammad Soomro; Adnan Hasan Tawafan; Mohd Noor bin Abdullah; Nur Hanis binti Mohammad Radzi; Mazhar Hussain Baloch
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v9.i4.pp623-629

Abstract

One of the most faults found in the electrical distribution network is a single line to ground fault (SLGF). It can be detected and rectified through many methods. The utilization of Peterson coil (PC), reduces the electrical arcs and make the distribution network safe from damage in contrast to the cost value. This paper focuses on the method for its detection on higher and lower values of the ground fault current (GFC). Moreover, it will identify the capacitance and earth leakage of earthling network lines as well as calculate the opposing inductance to compensate for the cause. It also presents the selfextinguishing of GFC by controlling PC through one of the novel optimization techniques called adaptive and artificial bee colony with network neural (ABCNN) to improve the algorithm's performance, like optimization efficiency, speed, solution, and iteration. As a result, the determination of the GFC equals the sound phase current. Also, the extinguishing of an electric arc results in a short time compared with classical methods. The significant advantage of this research is the increment in the system's reliability, protection of devices as well as saving in copper cost. MATLAB was used to carry out this research. For the validity, the proposed algorithm results were compared with the classical method by creating faults on separate phases also.
Optimize single line to ground fault detection in distribution grid power system using artificial bee colony Feryal Ibrahim Jabbar; Dur Muhammad Soomro; Mohd Noor bin Abdullah; Nur Hanis Mohammad Radzi; Mazhar Hussain Baloch; Asif Ahmed Rahmoon; Hassan Falah Fakhruldeen
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1286-1294

Abstract

The most common power system (PS) distribution network fault, single lineto-ground fault (SLGF), causes residual current (I res) to start an electrical arc and high voltage (HV) three times the rated voltage in other healthy phases. HV from capacitive currents (IC) damages cable insulation and PS appliances. Peterson The neutral point coil (PC) reduces (I res) and extinguishes the electric arc, but the fault current (I fault) remains below the protection devices' threshold. Operations and equipment are riskier. PC adaptive eliminates electrical arcs, making the network safer. This paper detects I faults online using Texas instrument validation in MATLAB and adaptive by artificial bee colony (ABC). This paper discusses Texas instrument fault current detection and MATLAB validation. It improves system reliability, device protection, and copper savings by thousands of tons. ABC intelligently optimizes many mathematical problems. ABC with network neural artificial intelligence (AI) improves algorithm performance (artificial bee colony network neural (ABCNN)). This new method may improve distribution network SLGF detection. This first work can work online in electrical power stations by building the (eZdsp F28335-RS232) into the program to send fault signals to the control when SLGF occurs without damaging devices, equipment, cables, or power outages.
Co-Authors Adnan Hasan Tawafan Asif Ahmed Rahmoon Dur Muhammad Soomro DurMuhammad Soomro Feryal Ibrahim Jabbar Feryal Ibrahim Jabbar Hassan Falah Fakhruldeen Mohd Noor bin Abdullah Nur Hanis binti Mohammad Radzi Nur Hanis Mohammad Radzi