Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Kolluru, Pavan Kumar
Unknown Affiliation
Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of innovative deep learning techniques in smart power systems Devi, Odugu Rama; Kolluru, Pavan Kumar; Shaik, Nagul; Trinadh Naidu, Kamparapu V. V. Satya; Mohan, Chunduri; Mohana Rai, Pottasiri Chandra; Bhukya, Lakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp723-731

Abstract

The integration of deep learning techniques into smart power systems has gained significant attention due to their potential to optimize energy management, enhance grid reliability, and enable efficient utilization of renewable energy sources. This research article explores the enhanced application of deep learning techniques in smart power systems. It provides an overview of the key challenges faced by traditional power systems and presents various deep learning methodologies that can address these challenges. The results showed that the root mean square errors (RMSE) for the weekend power load forecast were 18.4 for the random forest and 18.2 for the long short-term memory (LSTM) algorithm, while 28.6 was predicted by the support vector machine (SVM) algorithm. The authors' approach provides the most accurate forecast (15.7). After being validated using real-world load data, this technique provides reliable power load predictions even when load oscillations are present. The article also discusses recent advancements, future research directions, and potential benefits of utilizing deep learning techniques in smart power systems.
Securing electric vehicle charging stations from adversarial cyber attacks using hybrid detection models Jaladanki, Ravindra Babu; Kolluru, Pavan Kumar; Shaik, Nagul; Trinadh Naidu, Kamparapu V V Satya; Veeraiah, Duggineni; Pradhan, Anita; N. P. Sairam, Rallabandi Ch. S.
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11144

Abstract

Electric vehicle charging infrastructure (EVCI) has become essential. However, these infrastructures are increasingly vulnerable to cyber threats, particularly through spoofing and adversarial attacks on charging ports. This paper introduces a robust anomaly detection framework leveraging long short-term memory (LSTM) based autoencoders to identify anomalies in electric vehicle (EV) charging port current magnitudes. A simulated EVCI setup is developed in MATLAB/Simulink to capture charging behaviors under normal and adversarial scenarios. To generate adversarial data, the fast gradient sign method (FGSM) is employed. The reconstructed outputs from the LSTM-autoencoder (LSTM-AE) are statistically compared to real-time observations using the Kolmogorov–Smirnov (KS) test to detect anomalies. The framework achieves a high detection accuracy of 98.5%, demonstrating strong resilience against cyber-injected data anomalies and setting a foundation for enhanced EVCI cybersecurity.
Co-Authors Bhukya, Lakshmi Devi, Odugu Rama Jaladanki, Ravindra Babu Mohan, Chunduri Mohana Rai, Pottasiri Chandra N. P. Sairam, Rallabandi Ch. S. Pradhan, Anita Shaik, Nagul Trinadh Naidu, Kamparapu V V Satya Trinadh Naidu, Kamparapu V. V. Satya Veeraiah, Duggineni