Prasad. N. Enjeti

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. This paper 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 algorithm based on the motor model derived from FEA parameter estimation, utilizing two 3-level active neutral point clamped (ANPC) inverters with half-bridge 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 rpm 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%.

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.

A robust approach for cybersecurity of industrial control systems (ICSs) that utilize programmable logic controllers (PLCs) to control critical industrial processes is demonstrated in this paper. For example, industrial water/chemical tank-based system, the liquid level sensor measurement output data may be compromised or manipulated by attackers, this can cause tank overflow, unregulated, or even malfunction. We proposed a general-purpose method called Dynamic Watermarking (DW) to secure ICSs. The basic idea of DW is that it adds a private random signal watermark on the control signal from the controller, then this watermark signal propagates through the plant and adequately converted then comes back to the attack detector. The attack detector at the actuator side can detect Man-in-the-middle (MiTM) or Masquerade attack on the cascading system in real time. The proposed method is experimentally tested and validated with several cyber-attack scenarios on a laboratory scale water tank level control system controlled by an Allen-Bradley Micro820 PLC.

In this paper an active defense mechanism for detecting cyber-attacks on a grid-tied PV system is discussed. The proposed defense mechanism introduces a unique random signal termed as “watermark” into the control inputs of the grid-tied inverter system. The watermark signal is then shown to propagate via the switching inverter and its components and appear in all of the measurement data employed to control the system. The proposed approach employs the measured data to construct a system identification model, followed by two statistical tests (Test-1 and Test-2) that are designed to accurately flag cyber-attacks. The proposed approach is shown to rapidly detect any malicious manipulation of measurement data such as false data injection, record and replay etc. from malicious actors that would like to gain control of the the grid tied inverter and/or disturb its operation to potentially cause greater damage to the grid. The robustness of the proposed active defense mechanism is tested and validated with several attack scenarios implemented on a 3kW laboratory grid tied PV system

Grid-connected inverter systems that are important for increased utilization of renewable energy are vulnerable to cyberattacks. Malicious agents can attack a system by providing false measurements of sensed signals, such as power available/generated, or the grid current to sabotage the inverter control system. To defend against such attacks, we propose a technique of dynamic watermarking that superimposes a small secret signal onto the actuator signal and raises an alarm when the purported measurement data returned from the sensors do not contain appropriate transformations of the secret watermark. In this article, a robust attack detector is introduced, which does not require either knowledge of the variance of the ambient sensor noise or a model of the system. We develop an additional test that is not only impervious to sensor noise variance but can also be used to distinguish malicious attacks from sensor unreliability due to changing levels of sensor noise. We also show that a data-driven system identification of the switching inverter system can be employed in the dynamic watermarking tests. We present both simulation and experimental results from a grid-connected photovoltaic (PV) system, which show that the resulting robust dynamic watermarking method can detect malicious attacks on inverter control systems.

In this article, we address the cyber-security problem of industrial control systems (ICSs) when their sensor measurements may be compromised due to an attacker who has intercepted those measurements via a network. We introduce a general-purpose method “Dynamic Watermarking (DW)” to detect potential cyber-intrusions on speed sensor measurements within industrial control systems, which deploy an adjustable speed drive (ASD) to control a critical process. The DW method is injecting a random private low-amplitude signal with a zero mean Gaussian distribution, “watermark”, into one of the input phase voltages powering the ASD system. The watermark signal propagates through the system including pulse width modulation (PWM) power conversion stage and motor, then ultimately appears in the speed sensor measurements. By deploying two statistical DW tests with two proper thresholds, the system can detect potential cyber-intrusions or unobservable cyber-attacks such as replay attacks and false data injection attacks (FDIA). The DW method tested on a laboratory-scale ASD system experimentally to protect the system against cyber-intrusions. This system, powered by a commercial PWM drive operating at 208 V, 3-phase, and 3.7 kW, served as our experimental platform.

This paper proposes a modular three-phase diode rectifier with high frequency isolation and selective harmonic elimination in input currents. The proposed system consists of modular AC-AC converters with 20 kHz transformer isolation on the primary side followed by a 3-phase diode rectifier to produce a high quality DC output voltage. A new hybrid pulse width modulation scheme is proposed that combines the high frequency 20kHz transformer operating frequency with a low frequency selective elimination of 5th, 7th, 11th and 13th current harmonics in the 60Hz input currents resulting in near sinusoidal waveform.Therefore; high quality input current is achieved in open loop. The secondary side consists of a three-phase diode rectifier with high quality output, suitable for ev-charging/switch mode power supplied in data centers etc. The output DC voltage is shown to be regulated via a simple duty cycle control of the high frequency (20 kHz) without affecting the harmonic elimination features in the input currents. The proposed approach is modular and can adapt to higher input voltages. Simulation results are discussed which explore all the features of the proposed high performance AC to DC rectifier. Select results are verified experimentally on a 120V rms line-to-line, 3-phase, 1kW laboratory prototype system.¹¹This article is the updated version of [1] and include more details about the output DC voltage regulation, the design of input (AC) and output (DC) filter, additional experimental results among other changes

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