Muhammad Usman Sardar

Interior Permanent Magnet Synchronous Motors (IPMSMs) are a leading technology for electric vehicles (EVs) owing to their high efficiency and power density, yet their performance is limited by torque ripple, drive losses, and sensitivity to parameter variations. This paper presents a robust rotor profile optimization (RPO) framework that integrates a modified adaptive particle swarm optimization (MAPSO) algorithm with finite-element analysis to enhance torque production and minimize torque fluctuations. Unlike conventional optimization approaches, the proposed framework incorporates sensitivity and robustness evaluation, ensuring stable performance under input uncertainties. The optimized rotor design achieves an 8.65% increase in average torque (92.45 → 100.45 Nm), a 69.61% reduction in torque ripple (22.17% → 6.74%), and a slight efficiency improvement (97.08% → 97.12%) while maintaining the same 25 kW power rating and 2250 rpm base speed. Additionally, the 6% refers to the overall or representative robustness level, confirming robustness against parameter deviations. These results demonstrate that the proposed optimization method enables reliable rotor geometry refinement within the same motor envelope, leading to higher torque density, reduced pulsations, and improved suitability for high-performance EV traction applications.

Pulse-width modulation in cable-fed motor drive systems can generate high-frequency effects, which include terminal overvoltage (TOV) stress and common mode voltage (CMV) imbalance. This work presents a grey-box model (GBM) of the cable–induction motor system that describes impedance behavior over a frequency range from low to high, up to 100 MHz. The proposed GBM includes self and mutual inductances and parasitic capacitances, with parameters identified from common-mode and differential-mode impedance spectroscopy. Analytical initial values are refined via an optimization process, avoiding finite-element simulations (FEA/FEM) while reducing the modeling effort. Compared with a conventional lumped model, the GBM lowers complexity and improves agreement with practical measurements, achieving an average frequency-domain validation error below 5% and TOV and CMV estimation errors of 4.72% and 4.19%, respectively. Time-domain tests further confirm accuracy and robustness. The GBM also locates TOV resonance frequencies associated with differential-mode anti-resonance conditions in the winding, enabling targeted analysis of TOV voltage stress, insulation degradation risk, and electromagnetic behavior in cable-fed motor drives. The model provides a flexible framework for transient simulation, parametric sensitivity analysis, and evaluation of potential fault conditions.

The rapid growth of power electronics-based devices, such as electric vehicles and renewable energy systems, has introduced nonlinear components into power systems, generating high-frequency harmonics that distort current and voltage waveforms. These distortions pose significant risks to the stability of power grids, potentially leading to equipment malfunctions, reduced efficiency, and even system failures. To address these challenges, advanced harmonic detection, suppression, and estimation techniques are required to ensure the reliable operation of modern power systems. This paper comprehensively reviews the most widely used methods for managing harmonic distortions, focusing on recent harmonic detection, suppression, and estimation advancements. Key techniques, such as Fourier analysis and wavelet transforms, are compared alongside emerging machine learning-based approaches and adaptive filtering methods, which offer enhanced accuracy in real-time and dynamic environments. Additionally, advancements in harmonic suppression technologies, including passive, active, and hybrid filtering, are discussed for their effectiveness in mitigating harmonic impacts. Furthermore, the paper explores harmonic aggregation techniques that assess the cumulative impact of multiple harmonic sources and innovative estimation models that improve harmonic quantification under complex grid conditions. With the growing integration of renewable energy and electric vehicles, this review highlights the importance of advanced harmonic management strategies to ensure the safety, efficiency, and long-term stability of power systems.

Due to its superior performance, rapid development, and environmental friendliness, the permanent magnet synchronous machine (PMSM), in all of its various design forms, is becoming a crucial component in electric vehicle (EV) applications. This research article shows the numerical implementation and design of a potential vector control for PMSM, intended for electric vehicle application and implemented analytically to achieve maximum efficiency at the lowest possible costs. Additionally, this paper contains crucial machine features, operation assumptions, and simulation validations that are used to actualize machine design. It is shown, via intensive analytical expression and discussion, that step-up changes account for 90% of the rated value, whereas step-down changes account for 10% of the rated value. By implementing advanced measures, the present research endeavors to augment the control effectiveness, operational efficiency, and dependability of PMSMs in EVs.

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

Due to their efficiency and control capabilities, induction motors fed with inverters have become prevalent in various industrial applications. However, ensuring the reliable operation of the motor and diagnosing faults on time are crucial for preventing unexpected failures and minimizing downtime. This paper systematically analyzes condition monitoring and practical diagnostic techniques for inverter-fed motor drive systems. This study encompasses a thorough evaluation of different methods used for condition monitoring and diagnostics of induction motors, with the most crucial faults in their stator, rotor, bearings, eccentricity, shaft currents, and partial discharges. It also includes an assessment of their applicability. The presented analysis includes a focus on the challenges associated with inverter-fed systems, such as high-frequency harmonics, common-mode voltages causing the bearing currents, and high voltage gradients (dv/dt) due to fast switching frequency, which can impact the motor operation, as well as its faults analysis. Furthermore, this research explores the usefulness and efficiency of various available diagnostic methods, such as motor current signature analysis and other useful analyses using advanced signal processing techniques. This study aims to present findings that provide valuable insights for developing comprehensive condition monitoring strategies, and practical diagnostic techniques that enable proactive maintenance, enhanced system performance, and improved operational reliability of inverter-fed motor drive systems.

Currently, there is a growing interest in the topic of power quality across the entire electric power system. The power system is a complex network of modules with a broad distribution area, designed to meet the demand for electric power. The power system is susceptible to a range of disturbances arising from either dynamic consumer load behavior or faults. The power system necessitates a precise and advanced stability and protection mechanism. Ensuring the safe and reliable operation of the power system is a significant challenge, with voltage stability and fault protection being key areas of concern. The present research study proposes a technique to augment the stability of a grid-connected system that comprises of several generating sources through the utilization of FACTS devices, specifically static synchronous compensator (STATCOM). The technology has been designed to enable the monitoring of supervisory control of devices, while the distribution system offers decision-making tools to enhance system performance. A fault was generated between the time interval of 1-1.04 sec duration, and the simulation waveform was subsequently observed. The present study entails a simulation-based comparison between a power system without a STATCOM devise and one equipped with the STATCOM. The analysis results of the transient voltage stability of HV AC transmission lines utilizing STATCOM.

Model predictive control (MPC) is a powerful tool for the control of permanent magnet synchronous motors. However, conventional MPC permits using a single voltage vector during one control interval. This results in higher current distortions and large torque ripples. Sensitivity to control parameters is another issue associated with conventional MPC. The duty cycle suggests using an active vector and a null vector during one sampling interval. The method needs excessive computational and prediction effort. Furthermore, a necessary zero vector as the second vector might not give the optimal results. To overcome the problems of computational burden, this paper proposes that a reference voltage vector can be calculated and used to determine the voltage vector to be used for the next interval. This reduces the computational effort to a minimum. Furthermore, it is proposed that the second vector can either be active or null. To overcome the problem of parameter dependence, an electromotive force is calculated on basis of previous values. Simulations have been carried out to verify the efficacy of the proposed method.

Power systems rely on the voltage regulation to function properly. The majority of equipment, apparatus, electrical machinery, consumer appliances, and so on are intended to operate at a certain voltage. Wide voltage fluctuations may lead to operational problems, malfunctions, and performance degradation. Customers should be supplied with electricity at a relatively consistent voltage. Voltage and frequency in an A.C current system must be kept constant, whereas voltage in a D.C system must be kept constant to operate properly. The major concern is to maintain a stable voltage at the load end of the power-system. The research work in this paper improves the voltage stability of the three-phase inverter by using a PID discrete controller with a synchronous reference frame (d-q) control system. The voltage stability is investigated under various loading conditions using the software MATLAB/Simulink. The simulation results demonstrate the accuracy of the proposed method for controlling the output voltage.