Indonesian Journal of Electrical Engineering and Computer Science Vol. 32, No. 1, October 2023, pp. 413~422 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v32.i1.pp413-422  413 Journal homepage: http://ijeecs.iaescore.com Detecting surface discharge faults in switchgear by using hybrid model Yaseen Ahmed Mohammed Alsumaidaee 1 , Siaw Paw Koh 2,3 , Chong Tak Yaw 2 , Sieh Kiong Tiong 2 , Chai Phing Chen 3 1 College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia 2 Institute of Sustainable Energy, Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia 3 Department Electrical and Electronics Engineering, Universiti Tenaga Nasional (The Energy University), Kajang, Malaysia Article Info ABSTRACT Article history: Received Apr 15, 2023 Revised Jun 8, 2023 Accepted Jun 17, 2023 Switchgear plays a crucial role in power systems, providing protection and control over electrical equipment. However, tracking (surface discharge) can lead to insulation degradation and switchgear failure, necessitating reliable and effective identification of tracking defects. In this paper, we propose a hybrid one-dimension convolutional neural network long short-term memory networks (1D-CNN-LSTM) model as a solution to this problem. Data from both time domain analysis (TDA) and frequency domain analysis (FDA) are utilized for model evaluation. The model achieved error-free accuracy of 100% in both TDA and FDA during the training, validation, and testing phases. The model's performance is further assessed using performance measures and the visualization of accuracy and loss curves. The results show that the hybrid 1D-CNN-LSTM model works well to accurately find and classify surface discharge tracking defects in switchgear. The model offers precise and dependable fault identification, which has the potential to significantly enhance switchgear functionality. By enabling proactive maintenance and timely intervention, the proposed model contributes to the overall reliability and performance of switchgear in power systems. The findings of this research provide valuable insights for the design and implementation of advanced fault detection systems in switchgear applications. Keywords: 1D-CNN-LSTM Energy Surface charge Switchgear faults Tracking This is an open access article under the CC BY-SA license. Corresponding Author: Yaseen Ahmed Mohammed Alsumaidaee College of Graduate Studies (COGS), Universiti Tenaga Nasional (The Energy University) Jalan Ikram-Uniten, Kajang 43000, Selangor, Malaysia Email: eng.yassin.ahmed@gmail.com 1. INTRODUCTION In recent years, there has been a significant increase in electricity consumption, emphasizing the need for a reliable power distribution network to ensure a stable power supply for end-users [1]. A key component of this network is switchgear, which plays a crucial role in disconnecting and isolating specific buses to ensure the safety of maintenance personnel during repairs, component replacement, and fault monitoring [2]. Switchgear encompasses a range of devices, including switches, fuses, circuit breakers, isolators, relays, transformers, instruments, lightning arresters, and control panels, and is responsible for controlling and regulating electrical circuits within the power system [3], [4]. Switchgear can be classified based on insulation materials (air-insulated, oil-insulated, and gas-insulated) as well as voltage levels (low, medium, and high voltage) [5], [6]. To maintain a consistent and uninterrupted power supply, continuous monitoring and maintenance of switchgear's operational performance are crucial [7]. Malfunctioning