Formerly the Department of Industrial Electrical Power Conversion https://www.um.edu.mt/library/oar/handle/123456789/5676 2026-10-17T15:32:06Z 2026-10-17T15:32:06Z Patil, Sangita Jadhav, Seema Tran, Tien Anh Bhowmik, A. Panda, Jibitesh Kumar https://www.um.edu.mt/library/oar/handle/123456789/149849 2026-10-08T07:40:57Z 2026-01-01T00:00:00Z

Title: Smart and sustainable energy management of hybrid PV/wind systems using AI-based fuzzy control Authors: Patil, Sangita; Jadhav, Seema; Tran, Tien Anh; Bhowmik, A.; Panda, Jibitesh Kumar Abstract: The rapid growth of renewable energy penetration has intensified the need for robust and intelligent energy management strategies capable of addressing the intermittency of solar and wind resources. This study presents a simplified fuzzy logic controller (FLC)–based energy management system (EMS) for a grid-connected PV/Wind hybrid system integrated with battery energy storage. A fuzzy logic–based Maximum Power Point Tracking (MPPT) scheme is developed for the photovoltaic subsystem to ensure efficient power extraction under variable irradiance and temperature conditions. MATLAB/Simulink-based investigations reveal that the proposed FLC-MPPT achieves a tracking efficiency of 98.6%, with a settling time reduced by 32% compared to conventional perturb-and-observe methods. The hybrid EMS effectively coordinates power flow among PV, wind, battery, and grid, reducing grid dependency by 41% and improving battery state-of-charge regulation within ±5% limits under dynamic load conditions. Battery cycling stress is reduced by 27%, enhancing storage lifespan. Furthermore, the integration of a Unified Power Flow Controller (UPFC) significantly improves system resilience under grid disturbances, achieving 38% voltage sag mitigation and 45% reduction in power oscillations during simulated fault conditions. The results confirm that the proposed simplified FLC-based EMS enhances energy utilization, system stability, and power quality in grid-linked hybrid renewable systems. This work directly supports UN SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) by enabling efficient, reliable integration of renewable energy sources into smart power systems.

2026-01-01T00:00:00Z Tran, Tien Anh Bolbot, Victor Tam, Ivan CK https://www.um.edu.mt/library/oar/handle/123456789/148758 2026-08-27T11:27:03Z 2026-01-01T00:00:00Z

Title: IoT technologies for renewable energy generation : assessment, monitoring, and control of resources Authors: Tran, Tien Anh; Bolbot, Victor; Tam, Ivan CK Abstract: This book provides an understanding of the significant applications of IoT technologies in renewable energy. It explores their integration with various renewable energy sources, including solar, wind, bioenergy, piezoelectric materials, and solutions addressing water shortage challenges, highlighting advancements and practical implementations in each area. A comparative analysis of recent techniques is presented to evaluate the efficiency of IoT‑based renewable energy systems. This book also focuses on the economic feasibility of IoT-enabled renewable energy solutions.; Features:; • Addresses multiple renewable energy technologies with IoT-based monitoring and control.; • Explores solar, wind, and hybrid energy systems, including energy storage and microgrid integration.; • Investigates renewable energy microgrid systems.; • Focuses on climate goals and the Sustainable Development Goals.; • Includes discussions of marine renewable energies and offshore wind turbine generation.; This book is aimed at graduate students and researchers in renewable energy engineering, IoT and embedded systems, and electrical engineering.

2026-01-01T00:00:00Z Li, Guangdi Zhang, Yaodong Gao, Hao Zhou, Bowen Xiao, Zhaoxia Micallef, Alexander Apap, Maurice Licari, John https://www.um.edu.mt/library/oar/handle/123456789/148487 2026-08-14T08:13:52Z 2025-06-01T00:00:00Z

Title: Grid-forming converters with frequency & voltage active support and distributed cooperative control for active and reactive power sharing Authors: Li, Guangdi; Zhang, Yaodong; Gao, Hao; Zhou, Bowen; Xiao, Zhaoxia; Micallef, Alexander; Apap, Maurice; Licari, John Abstract: In islanded AC microgrids, grid-forming converters (GFCs) often employ virtual synchronous generators (VSGs) with virtual inertia and droop characteristics. However, this approach can lead to deviations in frequency and voltage from their rated values, affecting power quality and potentially compromising the security of the power supply. In addition, the power allocation is influenced by line impedance, and due to the uneven parameters among GFCs, it cannot be fully evenly shared. This paper presents a distributed control strategy for GFC that integrates frequency-voltage active support with power coordination control. This distributed strategy is implemented using multi-agent consensus theory, which ensures that the frequency and voltage of the GFCs remain at their rated values while accurately distributing active and reactive power across each GFC. It addresses the limitations of VSG control while maintaining a lower communication burden. Furthermore, several Hardware-in-the-Loop (HIL) experiments were conducted across multiple cases, validating the effectiveness of the proposed strategy and further enhancing it through cross-case analysis.

2025-06-01T00:00:00Z Xiao, Zhaoxia Liu, Wenlong Xiong, Junjie Gao, Jian Sun, Puhang Fang, Hongwei Micallef, Alexander https://www.um.edu.mt/library/oar/handle/123456789/148478 2026-08-13T07:23:32Z 2025-01-01T00:00:00Z

Title: Sampling design of energy storage converter Authors: Xiao, Zhaoxia; Liu, Wenlong; Xiong, Junjie; Gao, Jian; Sun, Puhang; Fang, Hongwei; Micallef, Alexander Abstract: Energy storage converters are widely used in power systems, new energy vehicles, wind power generation and other fields, and are of great significance in grid peak shaving, valley filling, smoothing new energy fluctuations and other aspects. The sampling part plays an important role as a bridge between the primary and secondary side control of the energy storage converter device. The accuracy of the sampling part directly affects the accuracy of the control algorithm. The sampling part of this paper includes a sampling circuit, a conditioning circuit and a calibration design. The sampling circuit is composed of a Hall voltage and current sensor. The AC voltage and current signals output by the sensor cannot be directly input to the A/D converter of the DSP. The output values need to be converted into 0 to 3V signals through the signal conditioning circuit and then input to the A/D converter. The calibration design is to calibrate and normalise the digital quantity output by the A/D converter, and then apply it to the control algorithm to improve the speed of the control algorithm. Finally, the accuracy of the sampling is verified by comparing the oscilloscope waveform with the upper computer sampling waveform through precharging experiments and energy storage discharge experiments of the energy storage device.

2025-01-01T00:00:00Z