Kaushik Sit

@hetc.ac.in

Assistant Professor, Electrical Engineering
Hooghly Engineering and Technology College

Kaushik Sit


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https://researchid.co/kaushik.sit

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Control and Systems Engineering, Renewable Energy, Sustainability and the Environment, Multidisciplinary
11

Scopus Publications

106

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Estimating levels of surface contamination of silicone rubber insulators by employing wavelet decomposition and machine learning-based techniques
    Kaushik Sit, Arpan Kumar Pradhan, Sovan Dalai, Biswendu Chatterjee
    Green Technology and Industry 4 0 for Sustainable Future, 2026
    An evolutionary strategy of green evaluation in the power system as well as in the era of artificial intelligence (AI) is machine learning (ML). Evolutionary techniques are employed in various fields of research. One of the important applications of evolutionary techniques is in the field of condition monitoring study of overhead line insulators. Insulators of overhead lines play a significant role in power system networks for power transmission and distribution. The power may go out completely if the overhead line insulators fail to operate. It is therefore effective to monitor overhead line insulators through surface leakage current (SLC) studies. The study employs a laboratory configuration to capture leakage current signals from multiple contaminated surfaces of silicone rubber (SiR) insulators. As a result of analyzing the SLC data, some important features about contamination levels can be extracted. Additionally, based on these characteristics, machine learning-based eco-friendly evaluation techniques are used to identify contamination classes in SiR insulators. To extract and classify features, a signal analysis tool that is wavelet decomposition and a few popular machine learning classifiers (i.e., random forest, support vector machine, Gaussian naive Bayes, and k-nearest neighbor), are employed in this work. A comparative analysis was performed to predict the degradation levels of the SiR insulators using individual ML classifiers employing advanced evaluation models in this work. This chapter determined the optimal factors that can predict the extent of dirt accumulation on SiR insulators surface.
  • Enhancing Economic Power Delivery: A Squirrel Search Algorithm Approach Addressing Realistic Power System Constraints
    Debabrata Mukharjee, Kaushik Sit, Abhik Hazra, Tanmoy Maity
    2025 IEEE 1st International Conference on Smart and Sustainable Developments in Electrical Engineering Ssdee 2025, 2025
    On complex economic dispatch (ED) issues with valve position impact, restricted practicable region, ramp borders, and manifold fuels with valve position impact, the effectiveness of the Squirrel Search Algorithm (SQSA) approach has been demonstrated. The simulation results obtained with the suggested method are comparable to those obtained with other acknowledged evolutionary methods. Based on the comparison, it appears that the advocated SQSA strategy may yield a more satisfactory response.
  • Infrared Thermal Image Aided Deep Transfer Learning Approach to Sense Contamination Grade for Non-contact monitoring of Overhead Line Insulator
    Arup Kumar Das, Shobhan Barua, Suhas Deb, Kaushik Sit, Sovan Dalai
    5th IEEE International Conference on Sustainable Energy and Future Electric Transportation Sefet 2025, 2025
    Contamination flashovers are the primary reason for insulator failure. Early and accurate prediction of insulator contamination severity can avert flashover incidents, thereby impacting the reliable operation of the electrical power system network. This paper introduces an innovative infrared image-aided deep transfer learning approach for remote monitoring of overhead line insulators. A large database is created based on the infrared image of artificially contaminated insulator samples captured at different environmental conditions. The infrared images were initially processed through a mask region- based convolutional neural network to mitigate the background effect. The infrared images corresponding to different contamination were fed to a benchmark convolutional neural network (CNN) model (Alex Net) for classification purposes. Transfer learning with a fine-tuning strategy was adopted to train the network. The result indicates that the proposed approach returns appreciably high classification performance with reduced computational time compared to existing techniques. This insight emphasizes the potential application of the proposed approach for non-contact monitoring overhead line insulators.
  • Implementation of Machine Learning Aided Discrete Meyer Wavelet Decomposition to Identify Overhead Line Insulator Surface Contaminations Based on Leakage Current Signatures
    Kaushik Sit, Swati De, Suhas Deb, Avijit Maity, Sovan Dalai, Subhendu Mondal
    2024 IEEE Silchar Subsection Conference Silcon 2024, 2024
    Overhead line insulators of power systems easily get contaminated because of atmospheric pollutants and hence allow leakage current (LC) flow throughout their surfaces. On a dry contaminated insulator surface, the leakage current is quite low, however, it increases with humidity and precipitation, leading to a higher chance of flashover. Thus, it is essential to ascertain the degree of surface contamination to enable the accurate prediction of the possibility of a flashover. The levels of such surface contamination are identified by monitoring and analyzing surface leakage current (SLC). In this regard, the SLC data of a 33-kV Silicon Rubber (SiR) insulator have been experimentally acquired for various levels of insulator surface contamination and corresponding operating voltages. Further, Discrete Meyer wavelet decomposition methods have been employed to obtain statistical features from the acquired SLC signals. These feature sets can further be utilized to detect the contamination classes of the SiR insulator samples by using SLC signals with their respective degree of contamination. For this purpose, the obtained statistical features of the SLC signals have then been further utilized to train a Machine Learning (ML) classifier named Gaussian Naïve Bayes (GNB) to determine the degree of severity of SiR insulator surface contaminations.
  • A Review on Characteristics and Assessment Techniques of High Voltage Silicone Rubber Insulator
    Kaushik Sit, Arpan Kr. Pradhan, Biswendu Chatterjee, Sovan Dalai
    IEEE Transactions on Dielectrics and Electrical Insulation, 2022
    The usage of polymeric insulators in transmission and distribution overhead lines for the last few decades has increased extensively. Silicone rubber (SiR) material is used enormously to make high voltage (HV) polymeric insulators. The SiR insulator provides insulation protection for overhead HV lines. The life span of the SiR insulators is shortened due to the aging effect. The progressive aging ultimately leads to the failure of the insulation system as well as the power supply. Therefore, a regular condition assessment of SiR insulators can help prevent the power system’s failures. A comprehensive study of the various types of aging processes of SiR insulators is reported in this article. Also, the comparative analysis of SiR insulators’ physical, chemical, and electrical properties under the influence of aging is reported here. The usefulness, limitation, and difficulties confronted in the existing methodologies are also reported in this article. This review article would be helpful to implement in future research for applying the HV overhead line insulator.
  • Machine Learning Based Wavelet Decomposition Aided Surface Contamination Prediction of Outdoor Silicone Rubber Insulators
    Kaushik Sit, Arpan Kumar Pradhan, Sovan Dalai, Biswendu Chatterjee
    2022 IEEE Calcutta Conference Calcon 2022 Proceedings, 2022
    Condition monitoring of overhead line insulators by surface leakage current (SLC) study is an effective technique. The present work uses an experimental setup to acquire the leakage current signal from various contaminated silicon rubber insulators (SiR) surfaces. Some important features associated with contamination levels are extracted from the recorded SLC data. Further, these characteristics are used to identify contamination classes of SiR insulators. A wavelet decomposition method and four machine learning classifiers (namely, Random Forest (RF), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and k-Nearest Neighbor (k-NN)) are employed for feature extraction and feature classification, respectively. A comparative analysis of individual classifiers' execution time and accuracy level is studied. This paper also identified the most appropriate characteristics for predicting the contamination class of overhead line insulators.
  • Condition Monitoring of Overhead Polymeric Insulators Employing Hyperbolic Window Stockwell Transform of Surface Leakage Current Signals
    Kaushik Sit, Arup Kumar Das, Debojyoti Mukherjee, Nasirul Haque, Suhas Deb, Arpan Kumar Pradhan, Sovan Dalai, Biswendu Chatterjee
    IEEE Sensors Journal, 2021
    In this article, an efficient technique has been proposed to estimate the contamination level of overhead polymeric insulators. Deposition of contamination on polymeric insulator surface, is a serious issue as it often results in the flashover and even insulator failure. For estimating the severity of contamination level, surface leakage current (SLC) signals of a 11kV polymeric insulator with contaminated surface has been analyzed in time-frequency domain through hyperbolic window stockwell transform (HST). HST is more flexible than classical stockwell transform. Also, HST can able to handle both the low and high frequencies adequately. Considering the advantage, HST has been used here to estimate contamination degree from SLC signature. HST analysis of SLC signal returned a 2d complex time-frequency HS matrix. The complex time-frequency HS matrix has been separated into magnitude and phase spectrum. Based on the phase and magnitude spectrum, 15 statistical features, namely HST features has been extracted. Thereafter, 5 relevant HST features have been selected through least absolute shrinkage and selection operator (LASSO) feature selection technique. Finally, these relevant HST features are fed to four machine learning classifiers for estimation of contamination degree. It has also been observed that, the proposed framework method offered better classification accuracy compared to other standard time-frequency analysis and existing methods available in literature.
  • Mathematical Morphology aided Random Forest Classifier based High Voltage Porcelain Insulator Contamination level Classification
    Kaushik Sit, Abhinaba Chakraborty, Sovan Dalai, Biswendu Chatterjee, Arpan Kumar Pradhan
    2020 IEEE Region 10 Symposium Tensymp 2020, 2020
    In the present work, the influence of different surface contaminations on the performance of a porcelain insulator is investigated. The measurement of the Leakage current on different classes of the contaminated porcelain insulators is performed with the help of an experimental setup. Necessary features from the recorded leakage current data are thereafter identified and used for analysis purposes. The entire data analysis has been implemented at the software platform. In the reported work the authors used Mathematical Morphological function and different statistical operations for the feature extraction purpose. By using the Random Forest classifier, authors have achieved the optimal feature classification. The accuracy level of the Random Forest classifier for different classes of contamination is also reported in the paper.
  • Real time measurement and analysis of non-linearity injected by computer loads in LT distribution system using LabVIEW
    Kaushik Sit, Ajoy Kumar Chakarborty
    Proceedings of 2018 IEEE Applied Signal Processing Conference Aspcon 2018, 2018
    This paper describes the behaviour and characteristic of different computer loads which affect the L.T distribution power system. It has become necessary for finding means to tackle the current harmonics problem in distribution systems before it is too late. Computer loads are identified as one of the significant harmonics creating load. A state of the art measurement and analysis has been done using LabVIEW tools to study the behaviour of the loads.
  • Design and analysis of RFI and EMI suppressor for high frequency induction heater using filters - A comparative study
    Rahul Raman, Pradip Kumar Sadhu, Anand Kumar, Kaushik Sit
    Proceedings of the 4th IEEE International Conference on Recent Advances in Information Technology Rait 2018, 2018
    The proposed paper deals with the design and analysis of RFI and EMI suppressor for high frequency Induction Heating equipment using filters. The simulation has been done in PSIM software. The harmonics that are injected because of the high frequency switching have been a major problem in induction heating applications. These harmonics can be reduced by the connection of a low pass filter between the ac supply and the induction heating equipment. The design should be in accordance with the harmonic standards that play a very crucial role in deciding the amount of current harmonics that is allowed to flow back to the supply side. In this present work, a Semi Active Filter has been proposed that has much better harmonic attenuation ability than the existing passive filters. The THD of input current has been compared to show that the former is able to maintain much better power quality than the latter. Moreover, both of them are also compared to the system without an LPF to show the necessity and importance of low pass filters in Induction Heating Equipment.
  • A novel circuit topology of modified switched boost hybrid resonant inverter fitted induction heating equipment
    Ananyo Bhattacharya, Kaushik Sit, Pradip Kumar Sadhu, Nitai Pal
    Archives of Electrical Engineering, 2016

RECENT SCHOLAR PUBLICATIONS

  • Estimating levels of surface contamination of silicone rubber insulators by employing wavelet decomposition and machine learning-based techniques
    K Sit, AK Pradhan, S Dalai, B Chatterjee
    Green Technology and Industry 4.0 for Sustainable Future, 246-285 , 2026
    2026.0
  • Infrared Thermal Image Aided Deep Transfer Learning Approach to Sense Contamination Grade for Non-contact monitoring of Overhead Line Insulator
    AK Das, S Barua, S Deb, K Sit, S Dalai
    2025 IEEE 5th International Conference on Sustainable Energy and Future … , 2025
    2025.0
  • AI Based Wireless Electricity Consumption Monitoring Device
    PRKPDPSM Dr. Swetha Shekarappa G, Dr. Senbagavalli M, Dr. Kaushik Sit, Dr ...
    IN Patent 474933-001 , 2025
    2025.0
  • Enhancing Economic Power Delivery: A Squirrel Search Algorithm Approach Addressing Realistic Power System Constraints
    D Mukharjee, K Sit, A Hazra, T Maity
    2025 IEEE 1st International Conference on Smart and Sustainable Developments … , 2025
    2025.0
    Citations: 1
  • Implementation of Machine Learning Aided Discrete Meyer Wavelet Decomposition to Identify Overhead Line Insulator Surface Contaminations Based on Leakage Current Signatures
    K Sit, S De, S Deb, A Maity, S Dalai, S Mondal
    2024 IEEE Silchar Subsection Conference (SILCON 2024), 1-6 , 2024
    2024.0
  • Condition assessment of overhead line insulators using advanced techniques
    K Sit
    Jadavpur university, Kolkata, West Bengal , 2023
    2023.0
  • Machine Learning Based Wavelet Decomposition Aided Surface Contamination Prediction of Outdoor Silicone Rubber Insulators
    K Sit, AK Pradhan, S Dalai, B Chatterjee
    2022 IEEE Calcutta Conference (CALCON), 332-336 , 2022
    2022.0
    Citations: 1
  • A Review on Characteristics and Assessment Techniques of High Voltage Silicone Rubber Insulator
    K Sit, AK Pradhan, B Chatterjee, S Dalai
    IEEE Transactions on Dielectrics and Electrical Insulation 29 (5), 1889 - 1903 , 2022
    2022.0
    Citations: 52
  • A Hybrid particulate matter (PM) emission Control Device having Electrostatic Precipitator and a High-frequency Induction Heating coil for diesel engine and method for the same
    S Kaushik, S Pradip Kumar, B Arijit, K Bhaumik, C Moumita, C Sudipta, ...
    IN Patent 400,850 , 2022
    2022.0
  • Condition monitoring of overhead polymeric insulators employing hyperbolic window stockwell transform of surface leakage current signals
    K Sit, AK Das, D Mukherjee, N Haque, S Deb, AK Pradhan, S Dalai, ...
    IEEE Sensors Journal 21 (9), 10957-10964 , 2021
    2021.0
    Citations: 35
  • Mathematical morphology aided random forest classifier based high voltage porcelain insulator contamination level classification
    K Sit, A Chakraborty, S Dalai, B Chatterjee, AK Pradhan
    2020 IEEE Region 10 Symposium (TENSYMP), 98-101 , 2020
    2020.0
    Citations: 9
  • Real time measurement and analysis of non-linearity injected by computer loads in LT distribution system using LabVIEW
    K Sit, AK Chakarborty
    2018 IEEE Applied Signal Processing Conference (ASPCON), 326-331 , 2018
    2018.0
  • Design and analysis of RFI and EMI suppressor for high frequency induction heater using filters—A comparative study
    R Raman, PK Sadhu, A Kumar, K Sit
    2018 4th International Conference on Recent Advances in Information … , 2018
    2018.0
    Citations: 4
  • A novel circuit topology of modified switched boost hybrid resonant inverter fitted induction heating equipment
    A Bhattacharya, K Sit, PK Sadhu, N Pal
    Archives of Electrical Engineering (ISSN: 1427-4221) 65 (4), 815-826 , 2016
    2016.0
    Citations: 4
  • 246 Estimating levels of surface contamination of silicone rubber insulators by employing wavelet decomposition and machine learning-based techniques
    K Sit, A Kumar Pradhan, S Dalai, B Chatterjee
    Green Technology and Industry 4.0 for Sustainable Future, 246-285 , 0

MOST CITED SCHOLAR PUBLICATIONS

  • A Review on Characteristics and Assessment Techniques of High Voltage Silicone Rubber Insulator
    K Sit, AK Pradhan, B Chatterjee, S Dalai
    IEEE Transactions on Dielectrics and Electrical Insulation 29 (5), 1889 - 1903 , 2022
    2022.0
    Citations: 52
  • Condition monitoring of overhead polymeric insulators employing hyperbolic window stockwell transform of surface leakage current signals
    K Sit, AK Das, D Mukherjee, N Haque, S Deb, AK Pradhan, S Dalai, ...
    IEEE Sensors Journal 21 (9), 10957-10964 , 2021
    2021.0
    Citations: 35
  • Mathematical morphology aided random forest classifier based high voltage porcelain insulator contamination level classification
    K Sit, A Chakraborty, S Dalai, B Chatterjee, AK Pradhan
    2020 IEEE Region 10 Symposium (TENSYMP), 98-101 , 2020
    2020.0
    Citations: 9
  • Design and analysis of RFI and EMI suppressor for high frequency induction heater using filters—A comparative study
    R Raman, PK Sadhu, A Kumar, K Sit
    2018 4th International Conference on Recent Advances in Information … , 2018
    2018.0
    Citations: 4
  • A novel circuit topology of modified switched boost hybrid resonant inverter fitted induction heating equipment
    A Bhattacharya, K Sit, PK Sadhu, N Pal
    Archives of Electrical Engineering (ISSN: 1427-4221) 65 (4), 815-826 , 2016
    2016.0
    Citations: 4
  • Enhancing Economic Power Delivery: A Squirrel Search Algorithm Approach Addressing Realistic Power System Constraints
    D Mukharjee, K Sit, A Hazra, T Maity
    2025 IEEE 1st International Conference on Smart and Sustainable Developments … , 2025
    2025.0
    Citations: 1
  • Machine Learning Based Wavelet Decomposition Aided Surface Contamination Prediction of Outdoor Silicone Rubber Insulators
    K Sit, AK Pradhan, S Dalai, B Chatterjee
    2022 IEEE Calcutta Conference (CALCON), 332-336 , 2022
    2022.0
    Citations: 1
  • Estimating levels of surface contamination of silicone rubber insulators by employing wavelet decomposition and machine learning-based techniques
    K Sit, AK Pradhan, S Dalai, B Chatterjee
    Green Technology and Industry 4.0 for Sustainable Future, 246-285 , 2026
    2026.0
  • Infrared Thermal Image Aided Deep Transfer Learning Approach to Sense Contamination Grade for Non-contact monitoring of Overhead Line Insulator
    AK Das, S Barua, S Deb, K Sit, S Dalai
    2025 IEEE 5th International Conference on Sustainable Energy and Future … , 2025
    2025.0
  • AI Based Wireless Electricity Consumption Monitoring Device
    PRKPDPSM Dr. Swetha Shekarappa G, Dr. Senbagavalli M, Dr. Kaushik Sit, Dr ...
    IN Patent 474933-001 , 2025
    2025.0
  • Implementation of Machine Learning Aided Discrete Meyer Wavelet Decomposition to Identify Overhead Line Insulator Surface Contaminations Based on Leakage Current Signatures
    K Sit, S De, S Deb, A Maity, S Dalai, S Mondal
    2024 IEEE Silchar Subsection Conference (SILCON 2024), 1-6 , 2024
    2024.0
  • Condition assessment of overhead line insulators using advanced techniques
    K Sit
    Jadavpur university, Kolkata, West Bengal , 2023
    2023.0
  • A Hybrid particulate matter (PM) emission Control Device having Electrostatic Precipitator and a High-frequency Induction Heating coil for diesel engine and method for the same
    S Kaushik, S Pradip Kumar, B Arijit, K Bhaumik, C Moumita, C Sudipta, ...
    IN Patent 400,850 , 2022
    2022.0
  • Real time measurement and analysis of non-linearity injected by computer loads in LT distribution system using LabVIEW
    K Sit, AK Chakarborty
    2018 IEEE Applied Signal Processing Conference (ASPCON), 326-331 , 2018
    2018.0
  • 246 Estimating levels of surface contamination of silicone rubber insulators by employing wavelet decomposition and machine learning-based techniques
    K Sit, A Kumar Pradhan, S Dalai, B Chatterjee
    Green Technology and Industry 4.0 for Sustainable Future, 246-285 , 0