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Research Article
A Design of ACDC Conversion Circuit Suitable for Trackside Safety-Oriented Digital Controllability
Meili Xing*
,
Binbin Huang,
Xiguo Ren
Issue:
Volume 14, Issue 1, February 2026
Pages:
1-8
Received:
27 November 2025
Accepted:
25 December 2025
Published:
9 January 2026
Abstract: As a critical component of railway systems, existing trackside equipment relies on centralized indoor power supply screens for power. However, this configuration suffers from inherent drawbacks such as excessive cable lengths, high deployment costs, and significant voltage fluctuations. To address these issues and adapt to the distributed control scenarios of urban rail transit, this paper proposes a safety-oriented, digitally controllable AC/DC conversion circuit design tailored for trackside installation and miniaturization. Adhering to the "fault-safety" principle and "two-out-of-two" redundancy architecture, the circuit converts mains AC220V to adjustable DC output ranging from 24V to 200V. The module integrates a dual-processor control unit, power conversion circuit, voltage/current acquisition circuit, and weak current voltage conversion circuit. Key design features include electrical isolation via a high-frequency transformer, enhanced power conversion efficiency through phase-shifted full-bridge control, real-time monitoring of input/output voltage, output current, and board temperature, and bidirectional real-time communication with external devices. Notably, the circuit is designed to fail safely: in the event of abnormal acquisition signals or hardware malfunctions, the system automatically switches to a safe state with no power conversion output. To validate the design feasibility, a 1kW experimental prototype was fabricated and tested, with results confirming the effectiveness of the proposed solution.
Abstract: As a critical component of railway systems, existing trackside equipment relies on centralized indoor power supply screens for power. However, this configuration suffers from inherent drawbacks such as excessive cable lengths, high deployment costs, and significant voltage fluctuations. To address these issues and adapt to the distributed control s...
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Research Article
Toward a Greener Grid: Enabling Low-Carbon Electricity in Transmission Systems
Issue:
Volume 14, Issue 1, February 2026
Pages:
9-20
Received:
7 December 2025
Accepted:
22 December 2025
Published:
19 January 2026
Abstract: This paper investigates the transition of traditional electricity transmission systems into modern, low-carbon network essential for mitigating climate change and ensuring energy sustainability. The electricity sector remains a major contributor to global greenhouse gas emissions, making transmission modernization critical for large-scale integration of renewable energy sources such as solar, wind, and hydro. This study proposes a comprehensive carbon-aware control framework that integrates smart grid technologies, energy storage systems, and dynamic optimization models to enhance grid efficiency, reliability, and emissions performance. Using Ghana's power system as a case study, the research develops a MATLAB-based simulation of a 10-bus transmission network incorporating real-world generation data, load forecasting, and geographical analysis of renewable potential. Results indicate that integrating renewable energy with energy storage can reduce CO2 emissions by up to 50%, from 238,000 kg to 119,000 kg, though economic viability remains challenging without policy support, subsidies, or carbon credits. The simulation also highlights the role of energy storage in smoothing intermittent generation and maintaining system stability. Financial analysis and load growth projections reinforce the need for scalable investment models and regulatory reforms to support long-term de-carbonization. The proposed framework bridges the gap between emissions metrics and grid operations, offering a robust tool for policy makers, utilities, and researchers. The findings demonstrate that a low-carbon grid is both technically feasible and environmentally necessary for a sustainable energy future.
Abstract: This paper investigates the transition of traditional electricity transmission systems into modern, low-carbon network essential for mitigating climate change and ensuring energy sustainability. The electricity sector remains a major contributor to global greenhouse gas emissions, making transmission modernization critical for large-scale integrati...
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Research Article
Hunter-Prey Optimization Framework for UPQC Allocation to Improve Efficiency and Voltage Reliability of Power Distribution Network
Akash Kumar Bansal*,
Ram Narayan Singh
Issue:
Volume 14, Issue 1, February 2026
Pages:
21-33
Received:
27 December 2025
Accepted:
12 January 2026
Published:
29 January 2026
DOI:
10.11648/j.jeee.20261401.13
Downloads:
Views:
Abstract: Maintaining power quality in the power distribution network is a major concern due to the increasing penetration of nonlinear and complex equipment. Unified Power Quality Conditioners (UPQCs) have been widely used as effective compensating devices to mitigate voltage instability and current distortions. However, the major challenge lies in selecting the optimal location and rating of the UPQC in the distribution network. Proper placement of the UPQC significantly improves overall system efficiency by enhancing the voltage profile, reducing active power losses, and improving cost effectiveness. In this study, the Hunter-Prey Optimization (HPO) algorithm is employed to determine the optimal location and rating of the UPQC in the distribution network. The objective function combines active power loss, voltage deviation, and UPQC installation cost while satisfying network and control constraints. The proposed framework is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate that the HPO algorithm efficiently identifies the optimal UPQC placement and rating, resulting in a significant reduction in active power losses of 60% for the IEEE 33-bus system and 93.5% for the IEEE 69-bus system, along with a notable improvement in voltage profiles compared to the system without UPQC.
Abstract: Maintaining power quality in the power distribution network is a major concern due to the increasing penetration of nonlinear and complex equipment. Unified Power Quality Conditioners (UPQCs) have been widely used as effective compensating devices to mitigate voltage instability and current distortions. However, the major challenge lies in selectin...
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Research Article
Algorithmic Approach to an Embedded Electronic Medical Device for EMG-to-Joint Angle Regression
Issue:
Volume 14, Issue 1, February 2026
Pages:
34-45
Received:
5 January 2026
Accepted:
16 January 2026
Published:
30 January 2026
DOI:
10.11648/j.jeee.20261401.14
Downloads:
Views:
Abstract: This study proposes an algorithmic approach for the development of a bio-inspired prosthetic hand system controlled by surface electromyographic (EMG) signals, aiming to achieve natural, adaptive, and continuous motion in upper-limb prostheses. The proposed framework integrates biomedical signal processing, machine learning–based motor intention decoding, and embedded mechatronic control within a unified system. Multi-channel surface EMG signals were acquired from the forearm and processed through a dedicated pipeline including amplification, physiologically relevant filtering, feature extraction, and normalization. To infer motor intention, two learning paradigms were investigated and compared: a classical Support Vector Machine (SVM) using handcrafted EMG features, and a Long Short-Term Memory (LSTM) neural network designed to perform continuous regression of finger joint angles corresponding to the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints. While the SVM provided a baseline for gesture-related decoding, the LSTM demonstrated a clear advantage by explicitly modeling temporal dependencies and non-linear relationships in sequential EMG data, resulting in more accurate and temporally coherent kinematic predictions. Experimental validation was carried out on a custom bio-inspired prosthetic prototype equipped with potentiometric joint feedback, showing that the LSTM-based controller achieved higher prediction accuracy and smoother real-time control during representative gestures such as flexion, extension, and grasping. Furthermore, deployment using TensorFlow Lite confirmed the feasibility of embedding deep sequential models on low-power hardware platforms. Overall, this work highlights the importance of temporal modeling for EMG-driven control and establishes a robust foundation for neural-controlled prosthetic systems that combine signal intelligence, physiological relevance, and embedded optimization, contributing to the advancement of human–machine interfaces aimed at restoring dexterity and autonomy in amputee patients.
Abstract: This study proposes an algorithmic approach for the development of a bio-inspired prosthetic hand system controlled by surface electromyographic (EMG) signals, aiming to achieve natural, adaptive, and continuous motion in upper-limb prostheses. The proposed framework integrates biomedical signal processing, machine learning–based motor intention de...
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