Dr Savarapu Chandra Shekar

@anurag.ac.in

Associate professor, Electrical and Electronics Engineering Department
Anurag Engineering College

Dr Savarapu Chandra Shekar


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https://researchid.co/savarapu

EDUCATION

Master of Technology in Power Systems (High Voltage Engineering)
Ph.D in Power systems (Micro Grids)

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Energy Engineering and Power Technology, Energy
11

Scopus Publications

74

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Design and Implementation of an Integrated Image Steganography and Detection System
    Savarapu Chandra Shekar, Mohammad Khaja Nayab Rasool, Devisri Harshini, Maddirala Krishna Mohan, Jayanth Kotapati
    Proceedings of 2nd International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2026, 2026
    Most existing image steganography systems primarily focus on either data embedding or steganalysis, treating both processes as independent tasks. This paper presents an integrated image steganography and detection framework that combines the Least Significant Bit (LSB) based data hiding with feature-based steganalysis within a single workflow. The proposed system embeds secret information into digital images while simultaneously enabling the analysis and verification of hidden content. The methodology includes image preprocessing, LSB-based encoding and decoding, and steganalysis using histogram comparison, bit-plane analysis, pixel difference evaluation, and ROC-based assessment. Experimental results show that the stego image size increased marginally from 310 KB to 312 KB, with average encoding and decoding times of 0.82 s and 0.79 s, respectively. Histogram variation remained below 0.5 %, and a 100 % decoding success rate was achieved, indicating minimal visual distortion and reliable data recovery. The results demonstrate that effective and secure data hiding can be achieved with low computational overhead. The proposed framework is suitable for lightweight and real-time secure communication scenarios, particularly in applications where low computational complexity and minimal image distortion are essential.
  • Hybrid Data Augmented Multi-Class IDS with Explainable Deep Ensemble Models and Real-Time Visualization
    Savarapu Chandra Shekar, Divvela Hema Harshini, Jayanth Sreenivas Surisetty, Challa Mahitha, Jaswanth Kandimalla
    Proceedings of 2nd International Conference on Multi Agent Systems for Collaborative Intelligence Icmsci 2026, 2026
    Intrusion detection is essential in today's network environments because of the increasing number and complexity of cyberattacks. Traditional Intrusion Detection Systems (IDS) often have difficulty identifying complex and zero-day attacks due to limited learning ability and high falsepositive rates. To tackle these issues, this work introduces a hybrid ensemble-based IDS that combines Random Forest, XGBoost, and CNN-LSTM models into a single decision framework. The proposed system examines 37 network traffic features, such as packet-level statistics, protocol flags, and behavior patterns over time, to classify benign and malicious flows accurately. Feature scaling and label encoding are used to maintain consistency, while final predictions come from a majority voting ensemble method to enhance stability and reliability. Experimental tests on network traffic datasets show strong results, achieving 97.84% accuracy, 96.51% precision, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 5. 9 2 \%}$</tex> recall, and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 6. 2 1 \%}$</tex> F1-score, with a cross-entropy loss of 0.11. The framework also has low inference latency, averaging 0.013 seconds per flow, which supports real-time deployment. A Streamlit-based application allows for CSVbased testing, manual input, attack visualization, and model confidence analysis. The results indicate that the proposed IDS successfully detects various attack types, including DoS, Probe, and R2L/U2R attacks, while keeping a low false-positive rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 1 8 \%}$</tex>. Overall, this ensemble-driven IDS provides a scalable, efficient, and practical solution for real-time network security monitoring.
  • Detection of Alcoholic using LSTM and Enhanced ANN Classification Algorithms
    Savarapu Chandra Shekar, Nidigonda Nithin Harsha, Machina Gayathri, Karnatakapu Lakshmi Abhinaya, Pepala Venkata Vamsi
    2025 6th International Conference for Emerging Technology Incet 2025, 2025
    Around the world, alcoholism is a prevalent mental illness. Drinking too much alcohol can result in alcoholism and other problems. In extreme situations, it can result in respiratory and circulatory system paralysis and inhibition, as well as death. Furthermore, there aren't many trusted methods for detecting alcoholism. EEG signals are widely used to detect alcohol, they are data derived from observing cerebral cortex fluctuations.The majority of the machine learning approaches used in current diagnostic methods depend on human centered in order to learn. We religiously use Deep Learning as a complete approach to detect EEG signals. There aren't many research that use deep learning models to classify alcohol's EEG data. After that, a CNN and a bidirectional LSTM network are employed to extract features from the denoised data. Lastly, the classification of alcohol EEG signals is carried out. The experimental findings demonstrate that the approach suggested in this work can be used to successfully detect patients with alcoholism, outperforming the majority of existing algorithms with a diagnosis accuracy of 95.32%.
  • Calories Burnt Prediction Using ANN and XGBoost Regression Algorithms from Physical Activities
    Savarapu Chandra Shekar, Dokala Vardhini Sri, Gowrineni Kyathi Chandana, Challagulla Sivanagaraju, Jatangi Sandeep Kumar
    2025 6th International Conference for Emerging Technology Incet 2025, 2025
    Predicting the number of calories burned during physical activity is essential for managing personal fitness, health monitoring, and preventing lifestyle diseases. This study offers a reliable and hybrid method for precisely estimating calories burned from physical activity data by merging Artificial Neural Networks (ANN) and the XGBoost regression algorithm. The model uses attributes including heart rate, duration, activity type, and body characteristics to improve forecast accuracy. While the gradient-boosting framework XGBoost improves generalization and addresses overfitting, the ANN component captures intricate, non-linear correlations between input characteristics. By combining these advantages, the suggested methodology outperforms independent models in terms of prediction. Results show that the hybrid model performs better than conventional regression models in terms of mean absolute error (MAE) and root mean square error (RMSE) while training and validating on a dataset of physical activity logs. The study also emphasizes how crucial feature engineering and hyperparameter adjustment are to attaining the best accuracy. With potential uses in wearable technology and healthcare platforms, the research's conclusions promote data-driven solutions for fitness tracking and tailored health management.
  • Wavelet-ANN Based Detection of Fault Location of Hybrid Renewable Energy Sources Connected Power Transmission System
    savarapu chandra shekar, T Muthamizhan, Mohammad Aijaz, D Chandra Sekhar
    International Journal of Renewable Energy Research, 2024
    The complexity of the power system increases as the hundreds of lines involved due to the penetration of conventional and renewable energy sources to meet the increased load demand. Transmitting the power for hundreds of kilometres long distances makes complexity in the network to locate the fault. Thus it is necessary to develop suitable algorithms to identify the location of the fault accurately in presence of large number of transmission lines. In this paper a novel Wavelet Artificial Neural Network (WANN) based method is developed where the Detailed coefficients (D1 coefficients) obtained from the current signals are used for training and testing ANN. The fault location is carried out in presence of renewable energy sources for various distances, fault impedances on 4-bus connected transmission system. A 4-bus transmission system is simulated using simulation software and the analysis of fault is done by using current signals of various faults with the help of wavelet multi-resolution analysis at both buses. This analysis is worked out almost within half cycle. The proposed wavelet based algorithm is tested for all fault conditions in presence of renewable energy sources with different power ratings at various distances and hence it is proved that the proposed method provided the best results for different fault impedances, fault generator capacities, fault inception angles (FIA).
  • Wavelet-ANN Based Detection of Fault Location of Renewable Energy Sources Integrated Power Transmission System
    S. Chandra Shekar, Surender Reddy Salkuti
    Green Energy and Technology, 2024
  • Wavelet-ANN Based Detection of Fault Location of 2-Bus Connected Power Transmission System
    S. Chandra Shekar, S. Jagadeesh, K. Shivashanker, K. Vinaya Sagar
    Proceedings of the 2024 International Conference on Emerging Techniques in Computational Intelligence Icetci 2024, 2024
    The complexity of the power system increases as hundreds of lines due to the penetration of conventional and non-conventional energy sources. Transmitting the power for hundreds of kilometres through large number of transmission lines makes complexity in the network to locate the fault. Thus it is necessary to develop suitable algorithms to identify the location of the fault accurately in presence of large number of transmission lines. In this paper a novel Wavelet-ANN (Artificial Neural Network) based method is developed where the Detailed coefficients (D1 coefficients) obtained from post current signals are used for wavelet analysis. The D1 coefficients obtained from wavelet analysis are used for training and testing ANN at various distances. This analysis is worked out almost within half cycle. The fault location is carried out for various distances, fault impedance's on 2-bus connected transmission system. A 2-bus transmission system is simulated using simulation software and the analysis of fault is done by wavelet analysis. The proposed wavelet based algorithm is tested for all fault conditions at the faulted area and hence it is proved that the proposed method provided the best results for different fault impedance's, fault generator capacities, fault inception angles (FIA).
  • Wavelet-ann based fault location identification in micro grid inter connected transmission system
    S. Chandra Shekar*, , G. Ravi Kumar, S.V.N.L. Lalitha, , and
    International Journal of Recent Technology and Engineering, 2019
    This paper presents a novel protection scheme for the protection of transmission system with microgrid (MG) having of wind energy, solar PV energy and fuel cell sources. MGs provide environmental, economical benefits for the end consumers, power usages and society. However, transmission line and MGs poses major technical challenges. Protection system must respond both MG and utility grid failures. Technical challenges of MG protection are to respond to main and MG faults. A MG model is designed and it is connected to a transmission line. Later, for detection and classification of faults wavelet Analysis (WT) is used. Faults are detected by the fault indices and compared with defined threshold value. The location of fault is done by artificial neural networks (ANN) on MG connected transmission system using detailed (D1) coefficients of energy current signals. This proposed algorithm is tested and more effective for the detection, classification and location of faults on MG interconnected transmission system. This algorithm is accurate and independent of fault inception angle (FIA), fault impedance and fault distance on line.
  • Renewable energy integrated multi-terminal transmission system using wavelet based protection scheme
    Savarapu Chandra Sekhar, G. Ravi Kumar, S.V.N.L Lalitha
    International Journal of Power Electronics and Drive Systems, 2019
    The power plants behavior is crucial under faulted conditions and responses on protection systems. Major microgrid (MG) protection problem is the problem between the fault current in utilitygrid mode and microgrid mode. As conventional protection system doesn’t offer solution for all MG protection challenge, but it needs advanced protection strategy. Protection system must response to both the utilitygrid and MG faults. Fast response of protection is necessary as early as possible if the fault is occurs on utilitygrid and if the fault is occurs on MG, the protection scheme must seperate the small possible portion of MG to remove the fault. This work presents a typical MG protection scheme using digital relaying and satellite communication with wavelet detailed D&lt;sub&gt;1&lt;/sub&gt;-coefficients of mother wavelet Bior 1.5. This research work is done for the detection, discrimination and locality of faults at distributed generators (DG’s) integration in multi-terminal transmission system. The algorithm tested under various faults with fault inception angles (FIA), fault impedances and fault distance of feeder line.
  • A transient current based micro-grid connected power system protection scheme using wavelet approach
    S. Chandra Shekar, G.Ravi Kumar, S.V.N.L Lalitha
    International Journal of Electrical and Computer Engineering, 2019
    Micro-grids comprise Distributed Energy Resources (DER’s) with low voltage distribution networks having controllable loads those can operate with different voltage levels are connected to the micro-grid and operated in grid mode or islanding mode in a coordinated way of control. DER’s provides clear environment-economical benefits for society and consumer utilities. But their development poses great technical challenges mainly protection of main and micro grid. Protection scheme must have to respond to both the main grid and micro-grid faults. If the fault is occurs on main grid, the response must isolate the DER’s from the main grid rapidly to protect the system loads. If the fault ocuurs within the micro-grid, the protection scheme must coordinate and isolates the least priority possible part of the grid to eliminate the fault. In order to deal with the bidirectional energy flow due to large numbers of micro sources new protection schemes are required. The system is simulated using MATLAB Wavelet Tool box and Wavelet based Multi-resolution Analysis is considered. Wavelet based Multi-resolution Analysis is used for detection, discrimination and location of faults on transmission network. This paper is discussed a transient current based micro-grid connected power system protection scheme using Wavelet Approach described on wavelet detailed-coefficients of Mother Biorthogonal 1.5 wavelet. The proposed algorithm is tested in micro-grid connected power systems environment and proved for the detection, discrimination and location of faults which is almost independent of fault impedance, fault inception angle (FIA) and fault distance of feeder line.
  • Wavelet based fault analysis of hybrid energy source micro-grid connected multi terminal transmission system protection scheme
    Journal of Advanced Research in Dynamical and Control Systems, 2018

RECENT SCHOLAR PUBLICATIONS

  • Hybrid Data Augmented Multi-Class IDS with Explainable Deep Ensemble Models and Real-Time Visualization
    SC Shekar, DH Harshini, JS Surisetty, C Mahitha, J Kandimalla
    2026 Second International Conference on Multi-Agent Systems for … , 2026
    2026.0
  • Design and Implementation of an Integrated Image Steganography and Detection System
    SC Shekar, MKN Rasool, D Harshini, MK Mohan, J Kotapati
    2026 Second International Conference on Multi-Agent Systems for … , 2026
    2026.0
  • Detection of Alcoholic using LSTM and Enhanced ANN Classification Algorithms
    SC Shekar, NN Harsha, M Gayathri, KL Abhinaya, PV Vamsi
    2025 6th International Conference for Emerging Technology (INCET), 1-6 , 2025
    2025.0
    Citations: 1
  • Calories Burnt Prediction Using ANN and XGBoost Regression Algorithms from Physical Activities
    SC Shekar, DV Sri, GK Chandana, C Sivanagaraju, JS Kumar
    2025 6th International Conference for Emerging Technology (INCET), 1-5 , 2025
    2025.0
    Citations: 1
  • Wavelet-ANN based detection of fault location of hybrid renewable energy sources connected power transmission system
    T Muthamizhan, M Aijaz, DC Sekhar
    International Journal of Renewable Energy Research (IJRER) 14 (3), 551-562 , 2024
    2024.0
    Citations: 11
  • Wavelet-ANN Based Detection of Fault Location of 2-Bus Connected Power Transmission System
    SC Shekar, S Jagadeesh, K Shivashanker, KV Sagar
    2024 International Conference on Emerging Techniques in Computational … , 2024
    2024.0
    Citations: 1
  • Wavelet-ANN Based Detection of Fault Location of Renewable Energy Sources Integrated Power Transmission System
    SC Shekar, SR Salkuti
    Energy and Environmental Aspects of Emerging Technologies for Smart Grid … , 2024
    2024.0
    Citations: 3
  • Renewable energy integrated multi-terminal transmission system using wavelet based protection scheme
    SC Sekhar, GR Kumar, S Lalitha
    International Journal of Power Electronics and Drive Systems 10 (2), 995-1002 , 2019
    2019.0
    Citations: 7
  • A transient current based micro-grid connected power system protection scheme using wavelet approach.
    SC Shekar, GR Kumar, S Lalitha
    International Journal of Electrical & Computer Engineering (2088-8708) 9 (1) , 2019
    2019.0
    Citations: 39
  • TRANSMISSION LINE PROTECTION SCHEME IN PRESENCE OF MICROGRID USING NEURO-WAVELET ANALYSIS
    DSVNLL S.CHANDRA SHEKAR, Dr.G.RAVI KUMAR
    International Journal of Pure and Applied Mathematics 120 (6), 551-565 , 2018
    2018.0
  • Wavelet based transient fault detection and analysis of microgrid connected power system
    SC Shekar, GR Kumar, S Lalitha
    Int. J. Power Syst 1, 46-52 , 2016
    2016.0
    Citations: 8
  • Wavelet Based Multi-Terminal Transmission Line Protection with MicroGrid
    SC Shekar, GR Kumar, S Lalitha
    WSEAS Transactions on Power Systems 11, 133-138 , 2016
    2016.0
    Citations: 1
  • Simulation of Series, Parallel Configured 7 level Switched Capacitor Inverter with Inductive Load
    SCS Dosapati.Firoj Kumar
    International Journal of Advanced and Innovative Research 3 (8), 233-237 , 2014
    2014.0
  • Improvement of Power Quality Using Transformerless Cascaded STATCOM under Balanced / Un-Balanced Supply Conditions with Non- Linear Loads
    SCS Mohammad.yaseen
    International Journal of Advanced and Innovative Research 3 (8), 205-210 , 2014
    2014.0
  • Intensification of a Distribution System using Sinusoidal Pulse Width Modulation by D-STATCOM for Voltage Sag and Swell
    SCS M.Tejaswini
    International Journal of Advance Engineering and Research Development 1 (8 … , 2014
    2014.0
  • ELEVEN–LEVEL CASCADED H-BRIDGE INVERTER FED INDUCTION MOTOR USING PHASE SHIFT PWM TECHNIQUE
    VNV Kanamarlapudi, CS Savarapu
    2013.0
  • Wavelet-ANN Based Detection of Fault Location of Hybrid Renewable Energy Sources Connected Power Transmission System
    SC Shekar, T Muthamizhan, M Aijaz, DC Sekhar
    Citations: 2
  • Modeling and Control of MPPT Based Grid Connected Wind-PV Hybrid Generation System
    SC Shekar, KR Ram
  • Wavelet Analysis of Faulty Terminal and Faulty Phase Identification of 4-Bus Transmission System
    SC SHEKAR
  • Impact of Distributed Generation on Reliability Evaluation of Distribution System
    S Paleti, R Thumu, SC Shekar

MOST CITED SCHOLAR PUBLICATIONS

  • A transient current based micro-grid connected power system protection scheme using wavelet approach.
    SC Shekar, GR Kumar, S Lalitha
    International Journal of Electrical & Computer Engineering (2088-8708) 9 (1) , 2019
    2019.0
    Citations: 39
  • Wavelet-ANN based detection of fault location of hybrid renewable energy sources connected power transmission system
    T Muthamizhan, M Aijaz, DC Sekhar
    International Journal of Renewable Energy Research (IJRER) 14 (3), 551-562 , 2024
    2024.0
    Citations: 11
  • Wavelet based transient fault detection and analysis of microgrid connected power system
    SC Shekar, GR Kumar, S Lalitha
    Int. J. Power Syst 1, 46-52 , 2016
    2016.0
    Citations: 8
  • Renewable energy integrated multi-terminal transmission system using wavelet based protection scheme
    SC Sekhar, GR Kumar, S Lalitha
    International Journal of Power Electronics and Drive Systems 10 (2), 995-1002 , 2019
    2019.0
    Citations: 7
  • Wavelet-ANN Based Detection of Fault Location of Renewable Energy Sources Integrated Power Transmission System
    SC Shekar, SR Salkuti
    Energy and Environmental Aspects of Emerging Technologies for Smart Grid … , 2024
    2024.0
    Citations: 3
  • Wavelet-ANN Based Detection of Fault Location of Hybrid Renewable Energy Sources Connected Power Transmission System
    SC Shekar, T Muthamizhan, M Aijaz, DC Sekhar
    Citations: 2
  • Detection of Alcoholic using LSTM and Enhanced ANN Classification Algorithms
    SC Shekar, NN Harsha, M Gayathri, KL Abhinaya, PV Vamsi
    2025 6th International Conference for Emerging Technology (INCET), 1-6 , 2025
    2025.0
    Citations: 1
  • Calories Burnt Prediction Using ANN and XGBoost Regression Algorithms from Physical Activities
    SC Shekar, DV Sri, GK Chandana, C Sivanagaraju, JS Kumar
    2025 6th International Conference for Emerging Technology (INCET), 1-5 , 2025
    2025.0
    Citations: 1
  • Wavelet-ANN Based Detection of Fault Location of 2-Bus Connected Power Transmission System
    SC Shekar, S Jagadeesh, K Shivashanker, KV Sagar
    2024 International Conference on Emerging Techniques in Computational … , 2024
    2024.0
    Citations: 1
  • Wavelet Based Multi-Terminal Transmission Line Protection with MicroGrid
    SC Shekar, GR Kumar, S Lalitha
    WSEAS Transactions on Power Systems 11, 133-138 , 2016
    2016.0
    Citations: 1
  • Hybrid Data Augmented Multi-Class IDS with Explainable Deep Ensemble Models and Real-Time Visualization
    SC Shekar, DH Harshini, JS Surisetty, C Mahitha, J Kandimalla
    2026 Second International Conference on Multi-Agent Systems for … , 2026
    2026.0
  • Design and Implementation of an Integrated Image Steganography and Detection System
    SC Shekar, MKN Rasool, D Harshini, MK Mohan, J Kotapati
    2026 Second International Conference on Multi-Agent Systems for … , 2026
    2026.0
  • TRANSMISSION LINE PROTECTION SCHEME IN PRESENCE OF MICROGRID USING NEURO-WAVELET ANALYSIS
    DSVNLL S.CHANDRA SHEKAR, Dr.G.RAVI KUMAR
    International Journal of Pure and Applied Mathematics 120 (6), 551-565 , 2018
    2018.0
  • Simulation of Series, Parallel Configured 7 level Switched Capacitor Inverter with Inductive Load
    SCS Dosapati.Firoj Kumar
    International Journal of Advanced and Innovative Research 3 (8), 233-237 , 2014
    2014.0
  • Improvement of Power Quality Using Transformerless Cascaded STATCOM under Balanced / Un-Balanced Supply Conditions with Non- Linear Loads
    SCS Mohammad.yaseen
    International Journal of Advanced and Innovative Research 3 (8), 205-210 , 2014
    2014.0
  • Intensification of a Distribution System using Sinusoidal Pulse Width Modulation by D-STATCOM for Voltage Sag and Swell
    SCS M.Tejaswini
    International Journal of Advance Engineering and Research Development 1 (8 … , 2014
    2014.0
  • ELEVEN–LEVEL CASCADED H-BRIDGE INVERTER FED INDUCTION MOTOR USING PHASE SHIFT PWM TECHNIQUE
    VNV Kanamarlapudi, CS Savarapu
    2013.0
  • Modeling and Control of MPPT Based Grid Connected Wind-PV Hybrid Generation System
    SC Shekar, KR Ram
  • Wavelet Analysis of Faulty Terminal and Faulty Phase Identification of 4-Bus Transmission System
    SC SHEKAR
  • Impact of Distributed Generation on Reliability Evaluation of Distribution System
    S Paleti, R Thumu, SC Shekar