S. M. Imrat Rahman

Virtual integration of geographically dispersed laboratories through real-time co-simulation presents powerful capabilities for co-simulating massive complex systems but it is hindered by communication delays that compromise accuracy and stability. This challenge is particularly concerning for real-time power system co-simulation, where delays can induce synchronization loss and limit dynamic and transient studies. This study proposes an adaptive, multi-parameter model-free framework for predicting and compensating delays in co-simulated systems, addressing this critical issue. This framework leverages the improved damping impedance method interface algorithm and an adaptive, parameter-tuning predictor system that predicts and compensates for delays without requiring complex interface signal transformation, processing, decomposition, reconstruction, phase estimation, system models, and no human interference. The proposed approach is validated using a joint experiment between two laboratories at Clemson, SC, USA and Greenville, SC, USA, with the Damping Impedance Method as an Interface Algorithm between the two partitioned subsystems. The coupling errors, state tracking errors, and residual complex power are used as evaluation metrics for the proposed delay compensation. This approach enhances co-simulation accuracy and stability, facilitating reliable dynamic and transient analyses.

Power electronics building block (PEBB) concept involves integrating fundamental components into functional blocks that can be stacked, extending converter power ratings for all-electric ships (AESs). This modular approach reduces costs, size, weight, design complexity, and maintenance. PEBBs can be realized as modular multi-level converters (MMCs), which offer advantages like modularity, low switching losses, minimal voltage/current quantization, high reliability, and efficiency. However, effective switching control methods are crucial to balance capacitor voltages and suppress circulating currents. This paper proposes an improved nearest level control (NLC) method that employs smoothed trapezoidal reference signals instead of sinusoidal references, aiming to enhance capacitor voltage balancing, suppress circulating currents, and improve the output power quality of PEBBs in AESs. The proposed NLC method is analyzed in real-time for an N-level PEBB connected to an induction machine (IM) with variable speed and torque load. The real-time verification is conducted in the Typhoon HIL606 digital real-time simulator (DRTS). The results validate the feasibility and effectiveness of the proposed NLC method for a three-phase N-level PEBB concept for AESs.

In recent years, data-driven methods have shown promise in diagnosing various open circuit fault (OCF) modes in inverter drive applications. However, existing studies primarily evaluate the reliability of these methods based solely on classification accuracy, neglecting critical real-time factors, such as computational delays and data transfer latency associated with the data-driven approach and communication protocols, respectively, which can affect real-time operational reliability. This article addresses these gaps by proposing a universally applicable reliability score criterion that integrates classification accuracy with system timing profiles. In addition, a model predictive control strategy employing active thermal management (ATM) is applied to the drive system, enabling a detailed analysis of the impact of OCF modes on the junction temperature of mosfets. Moreover, a novel feature extraction dataset is introduced, leveraging resistive/conduction loss data from the ATM scheme without requiring signal preprocessing. The proposed reliability score quantification and the dataset's diagnostic potential are validated using various data-driven classifiers. The most reliable classifier, achieving a 99.95% diagnosis accuracy, is further tested under diverse operating conditions using a control hardware-in-the-loop setup on an OPAL-RT testbed and Raspberry Pi.

Inverter-based electric drive systems (EDS) in electric vehicles (EVs) are susceptible to various common cyber and physical anomalies (CPAs), such as cyber-attacks (CAs) and open-circuit faults (OCFs) in power switches. Most existing data-driven diagnostic schemes for motor drives, solely rely on residual and 3-phase output current signals and are prone to misclassification due to overlapping data features associated with these anomalies. Moreover, these methods concentrate on detecting either CAs or OCFs independently, lacking a unified approach that can be universally applied to both types of anomalies. Therefore, in this work, a physics-informed machine learning (PIML) approach is proposed which can effectively distinguish between CAs and OCFs in EDS. In this regard, the unique features associated with stator current, resulting from various CPAs, are used to estimate the stator voltage characteristics through a synchronous motor mathematical model. The proposed voltage-based features characterizing better correlation via distinctive EDS transients in response to these anomalies are further used as the system’s prior information by a machine learning (ML) classifier. Moreover, active-thermal management (ATM)-based model predictive control (MPC) is utilized in the control layer due to its benefits in device-level thermal cycling and power quality. In addition, various variations in electrical, mechanical, and thermal characteristics due to CPAs in EDS have also been analyzed. The presented approach is evaluated for the US06 standard drive cycle in real-time via controller-hardware-in-the-loop (CHIL) experiment for various case study scenarios, resulting in a classification accuracy of 98.92%.

Recently, the thermal management of power electronic converters has gained significant attention due to the continuous trend of developing very compact power electronic converters with high power density. With the evolution of power semiconductor devices, high operating temperatures and large thermal cycles have become possible, necessitating a significant improvement in thermal system designs. Researchers have made significant efforts to develop effective thermal management systems to improve the reliability and lifetime of power electronic converters. This article intends to present a thorough review of thermal management systems employed in power electronics cooling. The applied thermal management techniques have been reviewed from the perspective of electrical parameter regulation and heat dissipation control. Regulation of electrical parameters involves active thermal control, which is a method for controlling junction temperature and thermal cycling of power semiconductor devices. The active thermal control implementation processes reviewed in this article consist of increasing overload capacity, manipulating switching and conduction losses, employing modified modulation processes, balancing thermal stress at the converter level, and controlling thermal stress at the system level. Control of heat dissipation can be achieved through direct and indirect cooling of power electronic converters with air or liquid as the coolant. The effectiveness and implementation methods of these cooling techniques, such as channel cooling, phase change material-based cooling, immersion cooling, jet impingement and spray cooling, are reviewed in this paper. Moreover, performance-enhancing ideas and challenges for these techniques are discussed. The primary objective of this review paper is to bridge the existing gap in the literature by offering a comprehensive comparison of commonly employed thermal management techniques.