ECTI Transactions on Electrical Engineering, Electronics, and Communications

Experimental Validation of Standards Compliant Inductive Wireless EV Charging Station with Misalignments and Installation Dependent Performance

2025-09-29T23:55:07+07:00

This paper presents the design, implementation, and evaluation of a prototype inductive wireless power transfer (IPT) system for electric vehicle (EV) charging stations, developed to comply with international standards of IEC 61980-1 and SAE J2954. The system was tested under various coil misalignment conditions (in x, y, and z axes) and three installation configurations—on-ground, in-ground, and underground—to assess its performance and feasibility for practical deployment as a charging station.

Experimental results demonstrate that the IPT system delivers a maximum power output of 11.2 kW, with IPT’s efficiency reaching 90% and DC-to-DC overall system efficiency at 87% during high-current operation of 70 A. The prototype maintained stable performance under realistic misalignment conditions of up to ±160 mm in both lateral and longitudinal directions. In charging tests, the system successfully charged an EV battery from 40% to 95% state of charge within 110 minutes, while supplying power to both the battery and auxiliary loads.

These findings confirm the technical feasibility and reliability of the proposed IPT-based EV charging station. The system satisfies key requirements of the referenced standards and demonstrates strong potential for implementation in Thailand’s emerging EV charging infrastructure.

Impact of DC-link Voltage Ripple on Induction Motor Drives Utilizing Cascaded H-bridge Multilevel Inverter

2025-09-01T20:07:56+07:00

This article investigates the impact of DC-link voltage ripple on the performance of the 5-level CHB IM drive, especially focusing on torque ripple and speed variations. Each H-bridge cell of the CHB inverter obtains an isolated DC power supply from a single-phase full-bridge rectifier. Particular capacitance values are utilized on the output side of rectifiers to visibly demonstrate the different amounts of ripple in the DC-link voltage. In this study, the motor speed is consistently held at 500 rpm throughout the operating range, while the load torque is considered in both no-load and with-load conditions. The IPD level-shifted technique is employed to produce the switching signals of the 5-level CHB IM drive, which is based on the FOC strategy. The validation of the drive system performance influenced by the quality of the DC-link voltage waveform is entirely verified by simulation results.

Optimal Scheduling of Electric Bus Fleets Using PSO-RNN for Enhanced Battery Life and Efficiency

2025-04-30T12:05:10+07:00

This paper presents an ideal strategy for electric bus fleets (EBFs) using a Particle Swarm Optimization-Recurrent Neural Network (PSO-RNN) approach, by a focus on enhancing battery life by considering battery capacity fade. The proposed method addresses the nonlinear nature of battery degradation and formulates the EBF scheduling problem as a multi-stage decision process. The PSO-RNN model is utilized to determine the optimal scheduling strategy that reduces the sum of battery alternateson the running life of the electrical vehicle (Buses). This study is conducted using an urban public transit system as a case study, with scenarios considering five and seven different working loads. Results demonstrate that the optimal scheduling strategy significantly reduces battery capacity loss and the sum of alternates, leading to lower operational costs and extended battery life. The efficacy of the intended method is validated by comparing the battery capacity fading process and replacement frequency under both scheduled and unscheduled scenarios.

Improvement of Line Stability Indices with Solar Photovoltaic Integration in Power System Network

2025-08-14T04:12:28+07:00

The stability of power systems is challenged by the rise of renewable energy sources, fast load changes, and increased consumption. The voltage stability index (VSI) is crucial for assessing power supply stability and triggering responses to voltage instability. This paper uses fast voltage stability index (FVSI), line stability factor (Lqp), and line stability index (Lmn) to evaluate the IEEE 14-bus and 118-bus systems. With solar photovoltaic generator (SPVG) integration at the most critical bus, these indices assess system stability under nominal and varied reactive power conditions. The main goal of the paper is to mitigate critical line severity by integrating PV systems, using Newton-Raphson load flow analysis in PSAT/MATLAB. Results show significant improvements in line indices with SPVG, notably reducing critical line
severity for the IEEE 14-bus (lines 5-1) and 118-bus (lines 44-43) systems.

Comparative Study of Switching Technique for Vienna Rectifier at 50 kW-350kW

2025-07-02T10:21:22+07:00

This research presents a comparison of switch control techniques for the Vienna Rectifier between Pulse width modulation (PWM) and Space Vector Pulse Width Modulation (SVPWM) for electric vehicle charging systems with power ratings between 50–350 kW. Focusing on studying the impact on power quality from the supply side, including of Total Harmonic Distortion (THD) and Power Factor (PF). The system simulation was conducted on the MATLAB/Simulink platform, with the design of a PI controller to generate control signals for the circuit switch, particularly in the case of PWM techniques. The experimental results show that the output voltage remains constant at 800 V, and the harmonic distortion of the input current can be maintained below 5%, which complies with the IEEE 519-2022 standard. Additionally, stability tests of the system were conducted under sudden load changes by varying the load power at 50 kW, 100 kW, 250 kW, and 350 kW, respectively. The test results show that the output voltage remains stable at 800 V with THD values of 4.794%, 2.4%, 1.0%, and 0.7065%, respectively. Compared with the SVPWM technique, the PWM technique has a lower THD for the input current, and the output voltage ripple signal is smaller when the power level exceeds 250 kW, reflecting better system performance under a wide range of load conditions. Additionally, the PWM technique uses fewer resources for system control.

Bi-Level DG Optimization in Distribution Networks with TOU Pricing and Demand Response Using MPA

2025-09-16T18:47:45+07:00

This study proposes a bi-level optimization model for the optimal allocation of distributed generation (DG) in active distribution networks, taking into account timeof- use (TOU) electricity pricing and demand response (DR) behavior. The upper level represents the decisionmaking of the distribution system operator (DSO), aiming to minimize power losses, voltage deviations, and DG investment costs. At the lower level, responsive consumers optimize their hourly demand profiles to minimize electricity costs under TOU tariffs, subject to behavioral and operational constraints. To enable tractable computation, the original bi-level structure is reformulated into a single-level nonlinear programming problem through the application of Karush-Kuhn-Tucker (KKT) optimality conditions. The resulting model is highly constrained and non-convex. This makes it suitable for solutions using metaheuristic approaches. In this work, the Marine Predators Algorithm (MPA) is adopted due to its superior global search capability and convergence characteristics. Numerical simulations on the IEEE 33-bus and 69-bus test systems confirm the
effectiveness of the proposed approach in reducing both energy losses and operational costs. The results highlight the practical applicability of the MPA-based framework for intelligent DG planning in smart grids under dynamic
pricing environments.

Machine Learning Model Implementation for Predicting Essential Transmission Line Outage via Reliability Index

2025-06-30T19:19:38+07:00

Currently, electric power transmission systems are operating at maximum loading capacities, frequently operating near their stability thresholds with minimal security margins. In such scenarios, monitoring of important lines for a particular loading level has become a crucial factor in ensuring the efficient operation of contemporary power systems. Thus, precisely assessing reliability for different line outage conditions is an important task for a power engineer. This paper concentrates on presenting the most recent machine learning (ML) techniques, like gradient boosting (GB), K- Nearest Neighbour (KNN), and linear regression (LR), utilized to determine the reliability index for different
outage conditions. Out of the 3 ML techniques, GB demonstrated the best performance with an R_2 score of 0.9309, a mean absolute error (MAE) of 0.2503, a mean squared error (MSE) of 0.1497, and a root mean squared error (RMSE) of 0.3869.

Conceptual Design of the Cooling System for a 3D Oil-Immersed Distribution Transformer

2025-09-30T00:05:13+07:00

This paper presents a conceptual design for the cooling system of a 3D oil-immersed distribution transformer operating at 100 kVA/22 kV. The cooling system consists of oil cooling and winding cooling. The heat generated inside the coil and the heat transfer area that connects the coil to the cooling fins of the transformer tank effectively carry heat away from inside the transformer to ensure that its internal temperature does not exceed the designed insulation value. The design boundary conditions also depend on the viscosity of the fluid inside the 3D transformer. The disparities in the average HV winding temperature-rise of 7%, the average LV winding temperature-rise of 4%, and the top oil temperature rise of 16% are all substantial. The results of the calculations align well with the test outcomes, indicating that the methods for building systems to vent oil-intermittent distribution transformers, as outlined in this work, are applicable.

Simulation-Based Optimal Speed Control of PMSM Drives Using Discrete LQR with Integral Action: Design, Analysis, and Robustness Validation

2025-10-02T11:13:55+07:00

This paper presents a discrete-time Linear Quadratic Regulator (LQR) augmented with integral action for high-performance speed control of Permanent Magnet Synchronous Motor (PMSM) drives. Integral augmentation is embedded directly into the discrete-time LQR framework to eliminate steady-state error in both reference speed tracking and load disturbance rejection. A discrete-time Lyapunov function is derived, with real-time evaluation under parameter uncertainty, to guarantee asymptotic stability of the closed-loop system. A MATLAB m-file implementation enables fine-grained tuning of sampling rates and seamless translation to embedded architectures. Robustness is assessed via a comprehensive simulation suite, comprising step changes in speed reference, load torque disturbances, ±10 % variations in stator resistance and inductance, and ±15 % variations in rotor inertia and viscous friction. Head-to-head benchmarking against a cascade PI controller and a standard discrete-time LQR (without integral action) under identical scenarios quantifies improvements in convergence speed, overshoot reduction, and disturbance rejection performance. Simulation results demonstrate rapid convergence, minimal overshoot, and zero steady-state error, confirming the proposed method as a reliable, implementation-ready alternative for robust PMSM speed control.

A Novel Direction for Non-Invasive Diabetes Prediction: Integrating Unani Wisdom with Sensor Technology

2025-05-19T10:04:12+07:00

This study aims to modernize disease diagnosis in Unani medicine by utilizing signal processing techniques. Various features such as systolic and diastolic peaks, peak-to-peak interval, augmentation, and stiffness index were extracted from the pulse signals. These features were then used to train predictive models, including KNN, J48, and Random Forest. Predictive models achieved high precision rates: 100% for KNN, 99.2% for J48, and 94.3% for Random Forest, as evaluated through cross-validation. Integration of signal processing with traditional medicine holds potential to enhance diagnostic precision and alleviate healthcare burdens, offering a pathway for synergistic advancements in healthcare systems. Implementation of signal processing techniques in Unani medicine could streamline disease diagnosis, leading to more effective patient management and healthcare resource allocation. Integrating modern diagnostics with traditional medicine practices may foster greater acceptance and accessibility of alternative healthcare approaches, promoting holistic well-being within communities. This study pioneers the application of signal processing methods to pulse signals within Unani medicine, offering a novel approach to enhance disease diagnosis and advance traditional medical systems.

An Asynchronous C-ternary Combinational logic for Low-Voltage Applications

2025-06-17T12:39:54+07:00

An asynchronous C-ternary combinational logic circuit employs based on a tripartite logic system, where the intermediate value ($V_{\mathrm{DD}}/2$) represents a spacer state. Spacer detector (SD) circuits are essential for identifying this spacer value. However, ultra-low supply voltages impose challenges on the reliable representation of the intermediate logic level. To overcome this limitation, this work proposes a low-voltage C-ternary combinational logic circuit that leverages the resistive-like characteristics of MOSFETs to generate the logical middle value. In addition, an optimized SD circuit is presented, which reduces transistor count and improves circuit efficiency. The proposed circuits were implemented and simulated using the 65 nm process at supply voltages of 0.5 V and 0.9 V in the Cadence Virtuoso analog design environment. The simulation results demonstrate the impact of temperature variations and analyze the propagation delay under both operating conditions.

Smart Safety cum Fitness Band for Women

2025-09-29T23:50:37+07:00

In this paper, an innovative wearable device is designed for women to enhance personal safety and health monitoring. An ESP32 microcontroller integrates a SpO2 sensor, heart rate monitor, and temperature sensor to track vital health parameters in real-time. The band has two buttons, namely an emergency button and a safe button, respectively. When the emergency button is pressed, it sends an alert with health data and live location to pre-defined contacts via Telegram. If a Safe Button is pressed, it will notify contacts that the user is safe. Using ESP32’s Wi-Fi, an optional GPS module for accurate location tracking, the device ensures timely assistance. With a rechargeable battery and compact design, the band offers portability, comfort, and continuous use, combining health tracking with emergency response for enhanced safety and peace of mind.

Adaptive Path Tracking Control for a Four-Mecanum-Wheel Mobile Robot with Unknown Center-of-Gravity Offset and Slope Inclination

2025-07-18T12:17:07+07:00

This paper proposes a trajectory tracking control framework for a Four-Mecanum-Wheel mobile robot operating on inclined terrain under conditions of dynamic uncertainty. The primary objective is to address the challenge of unknown center-of-gravity offset and unknown slope inclination, which can significantly impact the stability and accuracy of robot motion. To achieve this, a Model Reference Adaptive Control strategy is developed based on a full dynamic model that incorporates the effects of gravity, inertial forces, and wheel friction. The proposed controller employs Lyapunov-based adaptation laws to estimate and compensate for uncertain parameters in real time while ensuring asymptotic tracking of a desired trajectory. The simulation results under flat and inclined surface conditions demonstrate the effectiveness of the approach in maintaining tracking performance, regulating control effort, and converging parameter estimates, even when the robot experiences significant changes in center of gravity position and slope terrain.