Salih Baris Ozturk

This study presents an Artificial Neural Network (ANN)-based adaptive switching frequency control strategy for series Direct current (DC) motor drives used in battery-powered mining locomotives, aiming to extend the lifetime of critical power-electronic components such as Insulated Gate Bipolar Transistors (IGBTs) and DC bus capacitors. In embedded systems for electric traction, two dominant degradation factors, motor current ripple and IGBT temperature fluctuation, significantly shorten component lifetimes. Conventional fixed switching frequencies impose a trade off: higher frequencies reduce current ripple but increase IGBT losses and temperature, while lower frequencies yield the opposite effect. Consequently, an adaptive variable switching frequency control algorithm is proposed to perform real-time decision making by predicting the optimal switching frequency that minimizes both motor current ripple and IGBT thermal fluctuations. The proposed algorithm was trained with a dataset acquired from current sensors, NTC temperature sensors, and a potentiometer defining the target current (PWM duty). Performance comparisons with a fixed frequency demonstrate that the ANN-driven approach maintains an average current ripple of less than 5% (average) and 10% (maximum), while the lifetime of the IGBT and capacitors improves. A fairness index was defined to quantify the relative lifetime improvement of the IGBT and capacitor, revealing that the proposed variable frequency switching model enhances the overall system performance by up to 13 times compared to fixed-frequency operation. These results confirm that the integration of embedded machine learning and adaptive control algorithms can substantially enhance the durability and efficiency of power-electronic systems in real-time industrial applications.

This paper presents the design and implementation of a high-efficiency, full silicon carbide (SiC)-based center-tapped phase-shifted full-bridge (PSFB) converter for NiCd battery charging applications in railway systems. The converter utilizes SiC MOSFET modules on the primary side and SiC diodes on the secondary side, resulting in significant efficiency improvements due to the superior switching characteristics and high-temperature tolerance inherent in SiC devices. A nanocrystalline-cored center-tapped transformer is optimized to minimize voltage stress on the rectifier diodes. Additionally, the use of a nanocrystalline core provides high saturation flux density, low core loss, and excellent permeability, particularly at high frequencies, which significantly enhances system efficiency. The converter also compensates for temperature fluctuations during operation, enabling a wide and adjustable output voltage range according to the temperature differences. A prototype of the 10-kW, 50-kHz PSFB converter, operating with an input voltage range of 700–750 V and output voltage of 77–138 V, was developed and tested both through simulations and experimentally. The converter achieved a maximum efficiency of 97% and demonstrated a high power density of 2.23 kW/L, thereby validating the effectiveness of the proposed design for railway battery charging applications.

Power electronic systems are rapidly evolving; thus, effective prototyping methods are required to test control algorithms and assess system performance prior to hardware implementation. This research suggests a Model-In-The-Loop (MIL), Software-In-The-Loop (SIL), and Processor-In-The-Loop (PIL) methodologies-based complete prototyping strategy for flyback converters using the TI F28069M Launchpad kit. These techniques can be combined to evaluate control strategies accurately and quickly, speeding up design cycles and enhancing system reliability. The proposed prototyping platform is presented in this work, along with a thorough explanation of each prototyping stage and its associated advantages. The effectiveness of the suggested approach for 50W rated power flyback converter in terms of quick algorithm development, system simulation, real-time control implementation and controller design accuracy is analyzed and shown by experimental results. The results show that the performance of the designed controller for the flyback converter is almost the same as in MIL and SIL implementation in terms of the overshoot and settling time in the reference voltage tracking. On the other hand, in the PIL implementation, the overshoot performance of the controller deviates by 1.18% more than in SIL and MIL implementation. These also confirm that the flyback control system's performance is reliable and effective during all phases of development.

In critical applications of electrical machines, ensuring validity and safety is paramount to prevent system failures with potentially hazardous consequences. The integration of machine learning (ML) technologies plays a crucial role in monitoring system performance and averting failures. Among various motor types, permanent magnet synchronous motors (PMSMs) are widely favored for their versatile speed range, enhanced power density, and ease of control, finding applications in both industrial settings and electric vehicles. This study focuses on the detection and classification of the percentage of broken magnets in PMSMs using a pre-trained AlexNet convolutional neural network (CNN) model. The dataset was generated by combining finite element methods (FEMs) and short-time Fourier transform (STFT) applied to stator phase currents, which exhibited significant variations due to diverse broken magnet structures. Leveraging transfer learning, the pre-trained AlexNet model underwent adjustments, including the elimination and rearrangement of the final three layers and the introduction of new layers tailored for electrical machine applications. The resulting pre-trained CNN model achieved a remarkable performance, boasting a 99.94% training accuracy and 0.0004% training loss in the simulation dataset, utilizing a PMSM with 4% magnet damage for experimental validation. The model’s effectiveness was further affirmed by an impressive 99.95% area under the receiver operating characteristic (ROC) curve in the experimental dataset. These results underscore the efficacy and robustness of the proposed pre-trained CNN method in detecting and classifying the percentage of broken magnets, even with a limited dataset.

Maximum Power Point Tracking (MPPT) systems enable photovoltaic (PV) panels to work at their Maximum PowerPoint (MPP). To do this, several algorithms have been developed, including conventional, intelligent, and meta-heuristic. Once a partial shading condition (PSC) occurs, more than one peak emerges in the power-voltage curve of photovoltaic arrays. Under PSCs, conventional algorithms get stuck at the local maximum point and fail to reach the global maximum point. Being an alternative method, Particle Swarm Optimization (PSO) algorithm has been frequently employed for MPPT systems under PSCs. This algorithm has some parameters that affect its performance to reach the global MPP of the PV panel. Therefore, with well-tuned parameters, the effectiveness of the PSO will increase for the different PSCs. In this study, the effects of the cognitive learning and social learning parameters of the PSO algorithm are investigated under different PSCs. To achieve this, an MPPT system, including a boost-type DC-DC converter, is created in MATLAB®/Simulink®. Simulation studies show that the PSO algorithm fails to track global MPP with constant cognitive and social learning parameters under changing partial shading conditions. Furthermore, the results show that these two parameters affect the time to reach the MPP of the PSO algorithm.

Silicon carbide (SiC) metal oxide semiconductor field effect transistors (MOSFETs) have many advantages compared to silicon (Si) MOSFETs: low drain-source resistance, high thermal conductivity, low leakage current, and high switching frequency. As a result, Si MOSFETs are replaced with SiC MOSFETs in many industrial applications. However, there are still not as many SiC modules to customize for each application. To meet the high-power requirement for custom applications, paralleling discrete SiC MOSFETs is an essential solution. However, it comes with many technical challenges; inequality in current sharing, different switching losses, different transient characteristics, and so forth. In this paper, the detailed MATLAB^®/Simulink^® Simpscape model of the SiC MOSFET from the datasheet and the simulation of the half-bridge circuit are investigated. Furthermore, this paper proposes the implementation of the four-paralleled SiC MOSFET half-bridge circuit with an improved symmetric gate driver layout. Moreover, a unique laminated busbar connected directly to the printed circuit board (PCB) is proposed to increase current and thermal capacity and decrease parasitic effects. Finally, the experimental and simulation results are presented using a 650 V SiC MOSFET (CREE) double-pulse test (DPT) circuit. The voltage overshoot problems and applied solutions are also presented.

A Single-Ended Primary-Inductor Converter (SEPIC) converter with an Inductor-Capacitor (LC) regenerative snubber is proposed to reduce Electromagnetic Interference (EMI) for Electric Vehicle (EV) applications. The switching energy is transferred through a capacitor to an inductor which is coupled to SEPIC inductors. This technique reduces the number of components and also returns some of switching energy to SEPIC converter. The mathematical analysis and optimization of LC snubber with respect to number of turns is also presented. Spice simulations and experimental results are provided to verify its performance. The proposed LC regenerative snubber reduces the peak voltage by 16 V on the switching transistor during the switching transient. It is also indicated that 8 dB reduction is achieved in the EMI measurements at ringing frequency and 10 dB reduction at high frequency band.