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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
K. V. Govardhan Rao
St. Martin’s Engineering College
Control & Systems Engineering Electrical & Electronics Engineering

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Enhanced UPS inverter control using backstepping and fuzzy neural network for improved power quality G. Anjali Devi; Swapna Ganapaneni; L. Sirisaiah; Lokesh Kotha; Subhash Manchikanti; Malligunta Kiran Kumar; T. Rakesh; K. V. Govardhan Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1069-1083

Abstract

The rapid growth of sensitive digital infrastructures and automation systems has intensified the demand for uninterrupted and high-quality power delivery. To address this critical need, this paper proposes a novel hybrid intelligent control strategy for uninterruptible power supply (UPS) inverters that integrates backstepping control, fuzzy neural network (FNN) adaptation, and sliding mode gain compensation. The proposed approach ensures superior voltage regulation and robustness under nonlinear and dynamic load conditions while minimizing dependence on predefined system parameters. The backstepping controller establishes the Lyapunov-based stability framework, the FNN adaptively estimates system uncertainties in real time, and the sliding mode gain enhances resilience against external disturbances. This synergistic control integration enables fast dynamic response, reduced harmonic distortion, and improved system efficiency compared to conventional methods. Simulation and experimental validations demonstrate that the proposed controller achieves total harmonic distortion (THD) below 3%, voltage overshoot under 2%, and enhanced transient recovery, thereby ensuring reliable power quality for critical industrial and commercial applications. The study contributes a real-time feasible, adaptive, and robust UPS inverter control architecture, marking a significant advancement in intelligent power electronics for resilient energy systems.
Advanced AI-driven battery state estimation using deep learning and ensemble models N. Sumana Keerthi; B. Jyothi; M. Sharanya; CH. Srinivas; K. Shravani; V. Sanjeeva Rao; Teerdala Rakesh; Nittala Ramchandra; Malligunta Kiran Kumar; K. V. Govardhan Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1072-1093

Abstract

Accurate estimation of the state of charge (SOC) and state of health (SOH) of lithium-ion batteries is essential for improving the performance, safety, and lifespan of electric vehicles (EVs). Traditional estimation methods often face challenges such as high computational complexity, limited adaptability to battery aging, and reduced accuracy under varying operating conditions. To overcome these limitations, this paper presents a hybrid data-driven framework that combines machine learning and deep learning techniques for SOC and SOH prediction. For SOC estimation, linear regression, recurrent neural networks (RNN), gated recurrent unit (GRU), and stacked long short-term memory (LSTM) models are employed. For SOH prediction, ensemble learning methods, including stacking regressor, tree-based pipeline optimization tool (TPOT) regressor, and hybrid GRU-LSTM models, are utilized. The proposed models are evaluated using publicly available lithium-ion battery datasets under different charge-discharge conditions. Results show that deep learning approaches achieve superior performance, with GRU-LSTM and stacked LSTM models providing highly accurate SOC estimation (R² ≈ 0.993, RMSE ≈ 0.015), while the TPOT-based ensemble model delivers near-perfect SOH prediction (R² ≈ 1.0). A web-based implementation further enables real-time battery monitoring, demonstrating the framework’s practicality for advanced battery management systems (BMS) in EV applications.
Performance analysis of multi carrier PWM techniques for a 5-phase three level NPC inverter in EV applications Venu Yarlagadda; N. Kavitha; Chava Sunil Kumar; G. Naveen; Swetha Mareddy; S. Venkata Rami Reddy; K. V. Govardhan Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1264-1274

Abstract

Multi-phase multilevel inverters have become more popular in contemporary applications due to their many benefits, which include lower switching losses, decreased common mode voltage, and reduced stress from voltage on switches. This study focuses on enhancing total harmonic distortion (THD) performance in a five-phase multilevel neutral point clamped (NPC) inverter using various multi-carrier pulse width modulation (PWM) techniques, including PD, POD, APOD, IC, PSC, and VFC. These approaches are particularly suitable for electric vehicle and industrial motor applications. Several PWM approaches were used in the SIMULINK environment to model and simulate a 5-phase, 3-level NPC inverter. In this study, the performance of load voltage THD is compared using R and RL loads connected to a multilevel inverter. Additionally, 5-phase induction motor and permanent magnet synchronous motor models are developed as loads for electric vehicle applications, and variations in torque, speed, and stator current are analyzed. The PD modulation technique showed the lowest THD among the various PWM methods, demonstrating its effectiveness in maximizing inverter performance.
Co-Authors B. Jyothi CH. Srinivas Chava Sunil Kumar G. Anjali Devi G. Naveen K. Shravani L. Sirisaiah Lokesh Kotha M. Sharanya Malligunta Kiran Kumar N. Kavitha N. Sumana Keerthi Nittala Ramchandra S. Venkata Rami Reddy Subhash Manchikanti Swapna Ganapaneni Swetha Mareddy T. Rakesh Teerdala Rakesh V. Sanjeeva Rao Venu Yarlagadda