International Journal of Power and Energy Conversion

Srivastava, Kalash; Gazdhar, Abhik Jitendra; Maurya, Rakesh; Kumar, Shailendra

2024 International Journal of Power and Energy Conversion

doi: 10.1504/ijpec.2024.141872

This paper presents a modified three-phase dual-active bridge converter (3p-DAB) for the charging/discharging of electric vehicle (EV) batteries. The proposed converter has two distinct features namely: 1) small resistance can be used because the switches are only under one-third of their corresponding voltage stress. As a result, conduction losses are decreased; 2) all switches are turned ON/OFF with soft switching to minimise switching losses. In this paper, mathematical modelling, control, and design of suggested three-phase DAB converter (T2-DAB)-based EV battery charging system are carried out under various operating conditions. In order to control the power flow, a single-phase shift (SPS) modulation strategy is employed during the constant current (CC) and constant voltage (CV) charging modes. A prototype of the suggested T2-DAB converter is developed in the laboratory. The comparison of experimental findings to the theoretical analysis is found favourable.

Venkatesh, B.; Khan, Mudassir; Babu, J. Chinna; Nagaraju, C.H.; Mohan, R. Madhan

2024 International Journal of Power and Energy Conversion

doi: 10.1504/ijpec.2024.141871

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20's ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends.

Lolamo, Mathewos; Kumar, Rajan; Sharma, Veena

2024 International Journal of Power and Energy Conversion

doi: 10.1504/ijpec.2024.141873

Incorporating renewable energy sources (RES), like wind and solar energy, is crucial for combating climate change and promoting sustainability. However, it introduces challenges to power quality (PQ) in distribution systems due to RES output variability, including voltage and frequency fluctuations and harmonics. Distributed flexible alternating current transmission system (DFACTS) devices, like unified power quality conditioner and distributed static compensator, effectively mitigate these challenges when appropriately controlled. Integrating energy storage systems (ESSs) like battery ESS with these devices and controlling them efficiently using traditional, AI-based, or adaptive control algorithms further enhances the mitigation performance of PQ in RES-connected grids. This review highlights PQ issues, categorisation, and mitigation strategies. It also highlights improving PQ in RES-integrated grids by enhancing DFACTS device performance through ESS combination and effective control. This review synthesises PQ enhancement strategies in RES-integrated grids from existing literature to support future research.

Rhiat, Mohammed; Karrouchi, Mohammed; Hassari, Anas; Melhaoui, Mustapha; Atmane, Ilias; Ouariachi, Mostafa El; Schmitz, Pascal; Benhala, Bachir; Hirech, Kamal

2024 International Journal of Power and Energy Conversion

doi: 10.1504/ijpec.2024.141867

Our study introduces a cost-effective design for a solar irradiance metre that uses a photovoltaic (PV) cell, offering an affordable alternative to traditionally expensive irradiance measuring devices like pyranometers. The core idea of our approach is to establish a straightforward correlation between the solar irradiance and the short circuit current produced by the PV cell. Through detailed simulations, we have shown that it is possible to accurately assess solar irradiance by monitoring the voltage difference across a shunt resistor linked to the PV cell. This voltage fluctuation is then amplified, filtered, and converted into a digital signal by a microcontroller, with real-time results promptly displayed on a small LCD screen for immediate feedback. Designed for use in solar energy projects, our system enables effective monitoring of direct solar irradiance, thus aiding in the estimation of power generation by PV panels.

Mistry, Avani; Vyas, Kaustubh; Yadav, Ravindrakumar

2024 International Journal of Power and Energy Conversion

doi: 10.1504/ijpec.2024.141866

For ensuring the safety of persons and equipment in substation, ground potential rise (GPR) should be within the acceptable limits, IEEE Standard 80-2013 is widely accepted guide for substation grounding system design. This review papers deals with the various grounding system parameters, their mathematical equations, designs of grounding system in the substation soil, design and development of software, evaluating the results obtained and methods for improving grounding safety by suggesting alternative measures. Various case studies have been taken into account to analyse the latest trends in this field. Softwares like ESGSD, CDEGS are designed for implementing concepts given in the IEEE 80-2013 guide into analytical domains to obtain grid design and calculating various parameters of the design. Detailed analysis of soil resistivity is also done in this paper. Thus, judiciously designed grounding system is cost effective and it also increases the level of safety of person-equipments to acceptable level.

Zhang, Haigang; Wang, Zizhuo; Zhou, Haoqiang; Zeng, Song; Yin, Ming; Xu, Junpeng; Wang, Bulai; Zou, Jinbai

2024 International Journal of Power and Energy Conversion

doi: 10.1504/ijpec.2024.141868

To address the issue of imprecise fault diagnosis and forecasting in traction transformers, this study introduces a composite algorithm that integrates IGWO-SVM and QPSO-LSTM to realise the fault prediction of traction transformer. In this research, the focus is on utilising the concentration levels of dissolved gases in the oil of traction transformers as training data. A fault diagnosis model is constructed leveraging the capabilities of support vector machine (SVM) technology. To improve the performance of the model, this paper adopts an improved grey wolf optimisation algorithm (IGWO), which adjusts the parameters of SVM, enabling it to diagnose the faults of traction transformer effectively. To predict the faults of the traction transformer, this study introduces a model based on the long short-term memory (LSTM) network, which is further enhanced by the integration of the quantum particle swarm optimisation algorithm (QPSO). The QPSO algorithm is employed to fine-tune the parameters of the LSTM network, thereby enhancing the precision and efficiency of its predictive capabilities. Through experimental comparisons with alternative methodologies, the effectiveness and superiority of the proposed algorithm in fault diagnosis and prediction are verified, and the application value of the proposed algorithm in the field of traction transformer fault prediction is demonstrated.