About

Prasad Enjeti is a Professor in the Department of Electrical & Computer Engineering at Texas A&M University, holding the Texas Instruments Jack Kilby Chair. He earned his Ph.D. in Electrical Engineering from Concordia University in Montreal, Canada, in 1988, his M.S. from I.I.T. Kanpur, India, in 1982, and his B.S. from Osmania University, India, in 1980. His research interests encompass advanced power electronic converters for utility interfaces of renewable energy systems such as solar, wind, fuel cells, and energy storage, as well as integrated solid state transformer concepts for grid-connected renewables and adjustable speed drive systems. He focuses on designing high-temperature power conversion systems utilizing wide band-gap semiconductor devices, power electronic intelligence at the grid edge, medium voltage power converters for large-scale renewable energy systems, and power quality enhancement for interconnected renewables. Enjeti has contributed significantly to the development of active harmonic cancellation filters, power conditioning systems for fuel cells, and microgrid technologies. He has supervised numerous Ph.D. and M.S. students, many of whom occupy prominent academic and industry positions. Recognized for his leadership and innovation, he has received multiple awards including the IEEE R. David Middlebrook Technical Achievement Award, the IEEE Power Electronics Society Inaugural Award, and the Texas A&M College of Engineering Charles W. Crawford Service Award. He is a Fellow of IEEE and holds several patents related to power conversion and harmonic filtering.

Research topics

• Computer Science
• Electrical engineering
• Engineering
• Computer Security
• Physics
• Electronic engineering
• Engineering physics

Selected publications

• Comparative Analysis of AI Models for Capacitor Current Prediction in Power Factor Correction Topologies

  2025-10-19

  articleSenior author

  This paper presents a comparative evaluation of AI-based prediction models for Root Mean Square (RMS) of DC link capacitors and harmonic currents in power factor correction (PFC) converters using only input voltage and current measurements. By eliminating the need for direct capacitor sensing, this approach enables scalable, non-invasive condition monitoring across large populations of power supplies, such as those in data centers or industrial plants. Experimental data were collected from a 90 W commercial single-phase boost PFC and a 3.6 kW totem pole PFC evaluation board over a wide input voltage range. Six regression models: XGBoost, artificial neural networks (ANN), support vector regression (SVR), random forest regression (RFR), Gaussian process regression (GPR), and a fine-tuned large language model (LLM), GPT-3.5-turbo, were trained to infer capacitor current RMS and low-order harmonics. Hyperparameters were tuned via exhaustive grid search, and model performance was assessed using mean absolute percentage error (MAPE). Results validate the feasibility of the proposed approach, paving the way for data driven PFC health monitoring without expensive instrumentation.

  PublisherDOI

• Circuit-AI: A Self-Hosted AI-Agent Language Model Framework for Control Loop Implementation and Simulation

  2025-10-12

  articleSenior author

  This paper presents an AI-Agent designed to assist with power electronics analysis, simulation, and interface with the hardware to optimize its operation employing LLaMA 3 language model, deployed locally on the NVIDIA Jetson Orin Nano Super. The system employs a self-hosted model to support simulation orchestration, control loop prototyping, and digital twin integration without requiring cloud connectivity. Modular APIs interface with simulation environments and hardware platforms, enabling more streamlined workflows. Engineers can interact with the system via natural language prompts to express design objectives, control strategies, and diagnostic tasks. This approach aims to simplify specific stages of the design process and reduce development overhead. The architecture represents a step toward evaluating the role of generative AI in power electronics workflows under resource-constrained, edge-computing conditions. The proposed system is designed to operate entirely on-device, without internet connectivity (air-gapped), making it especially well-suited for optimizing the performance of defense and industrial systems. Experimental results from the NVIDIA Jetson Orion Super hardware is discussed.

  PublisherDOI

• Methods to Enhance Cybersecurity of Multiple Inverters in Large Grid connected PV / Battery Energy Storage Systems

  2025-03-16 · 2 citations

  articleSenior author

  In this paper, methods for enhancing the cyber-security of multi-inverter systems in large-scale grid following photovoltaic (PV) and battery energy storage systems (ESS) are investigated. The study uses data from a 2.5 MW solar PV system and a 1.1 MW battery ESS, both interfaced with the grid through CenterPoint Energy Inc. at the Volkman location. The paper examines several attack scenarios, focusing particularly on False Data Injection (FDI) attacks targeting sensors within Cyber-Physical Systems (CPS). These inverters are especially vulnerable to cyber-attacks that compromise the integrity of the Programmable Logic Controller (PLC) and the transmitted sensor data. Attackers exploit vulnerabilities, including side-channel attacks on PLCs and sensor data spoofing, which threaten system security. To mitigate these risks, a dual-layer defense strategy is proposed, incorporating side-channel monitoring of the PLC and the embedding of a unique "watermark" (a low-magnitude, randomly fluctuating voltage signal) into the inverter’s DC link voltage. This watermark serves as a diagnostic tool to detect unauthorized changes in sensor data, enhancing the system’s ability to identify and respond to potential intrusions. Preliminary results from a hardware-in-the-loop (HIL) emulation, utilizing real-world data from the CenterPoint Energy solar farm, demonstrate the effectiveness of combining side-channel monitoring with watermarking in detecting previously undetectable attacks in multi-inverter systems.

  PublisherDOI

• An Isolated Three-Phase Matrix Converter Rectifier For AI Server Infrastructure

  2025-10-12

  articleSenior author

  In this paper, an isolated three-phase matrix converter-based industrial rectifier with high frequency isolation is proposed for powering AI servers. By directly modulating the input three-phase AC, the matrix converter eliminates the need for bulky DC-link capacitors typically used for intermediate energy storage. This direct conversion enhances power density and improves overall system reliability. A digital dual 4-step switching strategy is employed to optimize bidirectional switch commutation and prevent shoot-through faults, ensuring efficient and safe operation. Additionally, a high-frequency transformer provides galvanic isolation and supports efficient power transfer, further improving operational performance and load protection. A design example is presented to demonstrate a compact, robust, and reliable three-phase AC-to-DC conversion solution for demanding industrial HVDC applications targeting AI workloads. Experimental validation is carried out on a 208V, 3-phase 1 kW prototype implementing the proposed algorithm.

  PublisherDOI

• An Approach to Compensate for Low frequency DC-Link Voltage Ripple in High Power ANPC Inverter

  2025-03-16

  articleSenior author

  DC-link voltage ripple has an adverse impact on the performance of Electrical traction drives. Employing large DC-link capacitors may not be feasible in scenarios demanding high power density such as in Electric vehicle applications. This paper introduces a new technique to compensate for the DC-link voltage ripple in ANPC inverters employing a feedforward strategy. The proposed DC-link method is combined with hybrid space vector PWM (SVPWM) to effectively compensate for low frequency DC-link ripple as large as 20% by tracking voltage variation in real-time. The paper also includes derivation process for calculating the capacitor current in an ANPC inverter, which serves as the basis for selecting the appropriate capacitance value. Simulation and experiment results on a 14kW SiC ANPC inverter are presented to verify the effectiveness of the proposed technique, which successfully compensates for DC-link fluctuation.

  PublisherDOI

• A Hybrid Energy Storage System for eVTOL Unmanned Aerial Vehicles Using Supercapacitors

  2025-03-16 · 2 citations

  articleSenior author

  Electric vertical take-off and landing (eVTOL) aircraft have gained considerable interest for their potential to transform public services and meet environmental objectives. Designing an effective power supply for eVTOL is challenging due to the extreme power requirements during takeoff and landing. This work presents a power supply solution and energy management control for an all-electric hybrid energy storage system that integrates supercapacitors and batteries to enhance eVTOL endurance. The proposed system employs DC-DC converters to regulate power output from each source. This method has the potential to improve the efficiency of the energy storage system and enhances the overall lifetime of the battery system. The integration of supercapacitors reduced total energy losses by 25.24% per flight compared to the conventional battery-only system. The work is validated through simulations and hardware-in-the-loop real-time testing, demonstrating its effective performance.

  PublisherDOI

• Circuit AI for Bill of Materials, Switching Loss Optimization, Capacitor RMS Estimation, and More

  IEEE Power Electronics Magazine · 2025-02-27 · 8 citations

  article

  Circuit AI is an advanced generative AI-driven tool designed to streamline electronic design by integrating bill of materials (BOM) optimization with advanced performance analysis. Leveraging a pretrained large language model, it automates tasks such as switching loss analysis, component availability checks, and cost optimization, significantly enhancing the efficiency of BOM workflows. Additionally, Circuit AI incorporates a fine-tuned version of the pretrained model to estimate capacitor RMS currents (I cap-rms), a critical performance metric for ensuring reliable thermal operation and prolonging component lifespan in power electronics applications. By seamlessly combining BOM optimization with performance evaluation, Circuit AI supports engineers in making more efficient decisions throughout the design process, from initial selection to final production.

  PublisherDOI

• Circuit-AI: An Advanced Large Language Model (LLM) Based AI-Agent for Bill of Materials (BoM) Optimization, Circuit Simulations & Design

  2025-10-19 · 2 citations

  articleSenior author

  This paper introduces Circuit-AI, an AI-driven design assistant that leverages large language models (LLMs) to enhance efficiency in power electronics design. By integrating natural language processing with engineering workflows, Circuit-AI streamlines critical tasks such as Bill of Materials (BoM) optimization and circuit simulations through tools like LTspice and MATLAB. The platform automates component lookup, selection, verification, and simulation setup, reducing design time and minimizing human errors. Experimental evaluations demonstrate its ability to accelerate decision-making, improve design accuracy, and facilitate seamless interaction between engineers and simulation tools. By bridging AI and power electronics, Circuit-AI offers a scalable solution for both professionals and emerging engineers.

  PublisherDOI

• Comprehensive Evaluation of Cyber Attacks on Grid-Connected Smart Inverters

  2025-03-16 · 1 citations

  article

  This paper presents an in-depth study of cyberattacks on a grid-connected solar photovoltaic (PV) inverter, with a focus on denial-of-service (DoS) attacks. Four distinct scenarios are explored: (a) data flooding attacks, (b) man-in-the-middle (MITM) attacks, (c) compromised web server attacks, and (d) side-channel attacks. Preliminary laboratory experiments were conducted on a 3.8 kW grid-connected PV inverter powered by a PV emulator, illustrating the system’s responses to each attack type. The results demonstrate the potential for DoS incidents and service interruptions. Future work will expand on this analysis and include a discussion of various mitigation strategies.

  PublisherDOI

• Parameter Estimation and Control of Axial Flux Permanent Magnet Motors for Electric Aircraft: Evaluating Axial Force Implications in Field-Oriented Control Methods

  IEEE Journal of Emerging and Selected Topics in Power Electronics · 2025-03-17 · 3 citations

  article

  Axial flux permanent magnet (AFPM) motors are emerging as a promising technology for electrifying transportation due to their high specific power, torque, and efficiency. A model of a motor is developed, demonstrating that the inductance matrix of this AFPM motor differs from typical machines primarily due to the fractional slot concentrated winding (FSCW). This article provides a comprehensive parameter estimation of the ASCEND motor utilizing finite element analysis (FEA) for parameter estimation across a wide range of operating conditions and a field-oriented control (FOC) algorithm based on the motor model derived from FEA parameter estimation, utilizing two three-level active neutral point clamped (ANPC) inverters with half-bridge silicon carbide (SiC) modules. A tailored approach to current trajectory, incorporating maximum torque per amp (MTPA) and maximum torque per loss (MTPL) strategies, is developed to optimize torque acquisition and assess the impact of current variations on axial forces imposed on the rotor. The application of the negative d-axis current reduces axial forces by approximately 11% with MTPA and 24% with MTPL at speed of 5000 r/min and peak torque. Implementing the control system with a similar motor topology shows that the model aligns well with experimental results, with speed and torque discrepancies of less than 3%.

  PublisherDOI

Frequent coauthors

• I.J. Pitel

  36 shared

• Harish S. Krishnamoorthy

  University of Houston

  33 shared

• L. Palma

  27 shared

• Frede Blaabjerg

  Aalborg University

  21 shared

• Jaewon Kim

  Decision Systems (United States)

  20 shared

• Jorge Ramos-Ruiz

  Alpha and Omega Semiconductor (United States)

  20 shared

• Le Xie

  Texas A&M University

  18 shared

• P. R. Kumar

  Texas A&M University

  17 shared

Education

• Ph.D, Electrical Engineering

  Concordia University

  1987

Awards & honors

• Fellow, Institute of Electrical and Electronics Engineers (I…
• Best Paper Award, Power Conversion Intelligent Motion (PCIM)…
• R. David Middlebrook Technical Achievement Award
• Inaugural recipient, IEEE Power Electronics Society – 2012
• Texas A&M University College of Engineering Charles W. Crawf…