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N H Nik Ali
Universiti Tenaga Nasional

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Development process and testing of partial discharge detection device on medium voltage XLPE cable Mohamad Izmir Farhan Mohamad Radzi; N H Nik Ali; Azrul Mohd Ariffin; Muhamad Safwan Abd Rahman; Norhidayu Rameli; Mohamad Radzi Ahmad; Ali Syari’ati Mohd Salleh
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1297-1305

Abstract

High voltage assets play a vital role in providing uninterrupted power to the consumers and any slight problems experienced by the assets may cause losses in millions of dollars to businesses. Therefore it is of utmost importance to monitor the health of high voltage assets. This research presents the development process of a partial discharge (PD) device that is able to detect PD acoustic waves for monitoring high voltage assets purposes. Medium voltage cross-linked polyethylene (XLPE) cable was used which was introduced with spherical void defects at the joints of the cable that functioned to produce PD acoustic waves. Outcome of the development processes provides the finished design of the PD sensing device, known as partial discharge detection (PDD) device. The functionality of the PDD device was also assessed through controlled experimentations, and they proved to be successful. Pure PD waveform captured by the ultrasonic sensor was similar when compared to a HFCT sensor’s pure PD waveform. The PDD device is a small and affordable, and is opened to various improvements such as integrating artificial intelligence (AI) unto the device, and one day may replace most existing bulky and expensive PD sensing devices that are readily available in the market.
Electric field bridging pattern of pre-breakdown and breakdown condition in transformer oil Nur Badariah Ahmad Mustafa; N H Nik Ali; H. Zainuddin; Marizuana Mat Daud; Farah Hani Nordin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1210-1218

Abstract

Transformer is considered as one of the most important equipment in electrical power system networks. However, most problems occurred in transformer were related to the defects and weakness of the insulation systems. The oils used in transformer act as coolant and insulation purposes hence maintaining the dielectric strength of the transformer. In this work, electric field bridging pattern is observed from pre-breakdown and breakdown condition. The electric field bridging formation was recorded in the experimental setup and images were captured per frame. 193 images were randomly chosen from the whole video frames where 102 images were the pre-breakdown images and 91 images were the breakdown images. This system comprises of four stages: (i) a preprocessing stage to mark the electrodes tips and background subtraction; (ii) a segmentation stage to extract the electric field bridging formation in region of interest; (iii) a feature extraction stage to extract electric field bridging using feature descriptors, area, minor-axis and major-axis length   (iv) a classification stage to identify the pre-breakdown and breakdown condition. System performance was evaluated using support vector machine (SVM), k-nearest neighbour (k-NN) and random forest (RF) and SVM provided the most promising accuracy that was 99%. The results show that the combination of three feature descriptors, area, minor-axis and major-axis length are the best features combination in identifying the transformer oil condition. In future work, further studies will be conducted to investigate the pattern of pre- and post-breakdown due to some similarity found in image pattern. Due to that, more feature descriptors will be identified to find a unique pattern between pre- and post-breakdown condition
Co-Authors Ali Syari’ati Mohd Salleh Azrul Mohd Ariffin Farah Hani Nordin H. Zainuddin Marizuana Mat Daud Mohamad Izmir Farhan Mohamad Radzi Mohamad Radzi Ahmad Muhamad Safwan Abd Rahman Norhidayu Rameli Nur Badariah Ahmad Mustafa