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All Journal IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics International Journal of Engineering, Science and Information Technology
Deepak Kumar
National Institute of Technology, Hamirpur (HP)
Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Electric Power System Monitoring and Theft Detection using Power Line Communication Awasthi, Minakshi; Kumar, Amit; Kumar, Deepak; Jeet Pal, Indra
International Journal of Engineering, Science and Information Technology Vol 2, No 2 (2022)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.024 KB) | DOI: 10.52088/ijesty.v2i2.254

Abstract

Electric power theft is a serious concern in the world irrespective of being major revenue losses and developing a nation. More than one-third part of the electricity generated power is lost due to electric theft, power loss, and inefficiencies in the distribution system. Interdicted or illegal utilization of electricity has not only affected economically but also obstructs the design and modeling phases of the power system. Due to electric theft, providing wrong data input values for power system analysis and difficult to load forecasting. In this paper, an inventive Simulink model is designed to detect and monitoring of electric power theft in power system distribution networks through Power Line Communication (PLC). Electric power theft was detected with variance amendment in the amplitude of carrier signal with a narrow band. PLC technique is utilized for data communication over the power line. A narrow band power line carrier signal which has high frequency transferred in power line alongside with power frequency signal. The deviation in the amplitude of the transmitted carrier signal is monitored at the regular time- intervals and the stealing of electricity can be distinguished by the computing of distinction change within the amplitude of the carrier signal. In a normal case, the signal present fixes pattern and waveform, but in the case of power theft, the signal shows some variation and disturbance in a within waveform pattern. A pattern recognition and monitoring approach is used for direct power theft in the PLC model. The Simulink model is performed on MATLAB software to analyze the performance and efficient results that satisfy the proposed Simulink model.
Power System Restoration Using Multilayer Perceptron Kumar, Deepak
International Journal of Engineering, Science and Information Technology Vol 1, No 1 (2021)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (811.256 KB) | DOI: 10.52088/ijesty.v1i1.35

Abstract

In recent years, power systems are being operated nearer to their limits due to economic competition and deregulation. Also, nowadays the challenge is to include large and ever increasing amounts of decentralized generated power into the existing transmission network and at the same time comply with the electricity market transmission demands. Both factors increase the risk of blackout. After which, power needs to be restored as quickly and reliably as possible and, accordingly, detailed power system restoration plans are required. The multilayer perceptron network is chosen for a more precise examination.
Enhancing manufacturing efficiency: leveraging CRM data with Lean-based DL approach for early failure detection Kalluri, Venkata Saiteja; Malineni, Sai Chakravarthy; Seenivasan, Manjula; Sakkarai, Jeevitha; Kumar, Deepak; Ananthan, Bhuvanesh
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8757

Abstract

In the pursuit of enhancing manufacturing competitiveness in India, companies are exploring innovative strategies to streamline operations and ensure product quality. Embracing Lean principles has become a focal point for many, aiming to optimize profitability while minimizing waste. As part of this endeavour, researchers have introduced various methodologies grounded in Lean principles to track and mitigate operational inefficiencies. This paper introduces a novel approach leveraging deep learning (DL) techniques to detect early failures in manufacturing systems. Initially, realtime data is collected and subjected to a normalization process, employing the weighted adaptive min-max normalization (WAdapt-MMN) technique to enhance data relevance and facilitate the training process. Subsequently, the paper proposes the utilization of a triple streamed attentive recalling recurrent neural network (TSAtt-RRNN) model to effectively identify Leanbased manufacturing failures. Through empirical evaluation, the proposed approach achieves promising results, with an accuracy of 99.23%, precision of 98.79%, recall of 98.92%, and F-measure of 99.2% in detecting early failures. This research underscores the potential of integrating DL methodologies with customer relationship management (CRM) data to bolster early failure detection capabilities in manufacturing, thereby fostering operational efficiency and competitive advantage.
Selection of smooth motion profile for a tube locator module of an inspection device Perumalsamy, G.; Visweswaran, P.; Kumar, Deepak; Winston, S. Joseph; Murugan, S.
IAES International Journal of Robotics and Automation (IJRA) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v11i3.pp181-195

Abstract

The Prototype Fast Breeder Reactor steam generators inspection system has seven modules. In this, tube locator module is a planar serial two-link robotic arm, which is used to place the eddy current probe above the steam generators tube hole in the tube sheet region. The trajectory planning of the two-link robotic arm is one of the important tasks, so the peak velocity, peak acceleration, peak jerk of various motion profiles for a given distance has to be selected properly for smooth motion and to avoid actuator saturation. The fifth-order polynomial gives lower acceleration and velocity than the jerk-limited S-curve. In this paper, the comparison of peak values of kinematic variables (velocity, acceleration, and jerk) for different motion profiles has been presented.
Co-Authors Ananthan, Bhuvanesh Awasthi, Minakshi Jeet Pal, Indra Kalluri, Venkata Saiteja Kumar, Amit Malineni, Sai Chakravarthy Murugan, S. Perumalsamy, G. Sakkarai, Jeevitha Seenivasan, Manjula Visweswaran, P. Winston, S. Joseph