Article
Energy & Fuels
Amor Hamied, Adel Mellit, Mohamed Benghanem, Sahbi Boubaker
Summary: In this paper, a low-cost monitoring system for an off-grid photovoltaic system is designed. The system is used to supply a small-scale greenhouse farm in the Sahara region. A fault diagnosis algorithm is developed and integrated into a microcontroller for real-time validation. The system is evaluated under specific climate conditions using IoT techniques to remotely monitor data and notify users of the PV system's state.
Article
Green & Sustainable Science & Technology
Kuei-Hsiang Chao, Wen-Cheng Pu, Hsuan-Hao Chen
Summary: The study aims to develop a cost-effective fault detection system for photovoltaic module arrays (PVMA). The system utilizes fault detection modules and data visualization tools, which are connected to the Wi-Fi network and transfer data to analyze the performance of the modules. It allows for real-time monitoring and immediate notification to maintenance personnel when faults are detected.
Article
Chemistry, Analytical
Johannes Rossouw van der Merwe, David Contreras Franco, Jonathan Hansen, Tobias Brieger, Tobias Feigl, Felix Ott, Dorsaf Jdidi, Alexander Ruegamer, Wolfgang Felber
Summary: This article introduces a low-cost commercial-off-the-shelf (COTS) receiver for GNSS interference monitoring, detection, and classification, using machine learning on tailored signal pre-processing. It shows that accurate and reliable detection and classification are possible with COTS hardware, without the need for human-in-the-loop (HIL) calibration. The low-cost receivers with high performance enable significantly more receivers to be deployed, resulting in a higher probability of intercept (POI).
Article
Green & Sustainable Science & Technology
A. Mellit, M. Benghanem, S. Kalogirou, A. Massi Pavan
Summary: In this paper, a novel embedded system is introduced for remote monitoring and fault diagnosis of photovoltaic systems. The system utilizes machine learning algorithms on a low-cost edge device for real-time deployment. An artificial neural network is developed to detect faults, and an effective stacking ensemble learning algorithm is used to classify the nature of the faults. The performance of the method is evaluated using common error metrics, and additional algorithms are embedded into the edge device to remotely control the photovoltaic array parameters.
Review
Green & Sustainable Science & Technology
B. Li, C. Delpha, D. Diallo, A. Migan-Dubois
Summary: This review systematically studies the application of Artificial Neural Network (ANN) and hybridized ANN models for PV fault detection and diagnosis, extracting and analyzing the targeted PV faults, detectable faults, data types and amounts, model configurations, and FDD performance for each application. The main trends, challenges, and prospects for the application of ANN for PV FDD are identified and presented.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Electrical & Electronic
Shahamat Shahzad Khan, Huiqing Wen, Haochen Shi, Yihua Hu, Lin Jiang, Guipeng Chen
Summary: This paper presents a reconfigurable dual-active-bridge (R-DAB) converter with a fast, accurate, robust, and low-cost fault detection and fault isolation scheme for power electronic devices (PEDs). The proposed scheme utilizes the center-tap current in the primary and secondary bridge as the universal fault signature, and it can detect and isolate various open circuit faults (OCFs) of PEDs under varying input and output conditions. Experimental results validate the effectiveness of the proposed technique.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Energy & Fuels
Belqasem Aljafari, Siva Rama Krishna Madeti, Priya Ranjan Satpathy, Sudhakar Babu Thanikanti, Bamidele Victor Ayodele
Summary: This paper proposes a novel fault detection and diagnosis technique for a grid-tied photovoltaic system. The proposed method can locate and differentiate faults at the module level, and it reduces computing needs, making it easier for users to operate.
Article
Engineering, Electrical & Electronic
Anton Dianov
Summary: This article proposes a novel method for detection of open-phase faults in low-cost motor drives. The proposed algorithm analyzes current signals and operates effectively even under high noise to signal ratio. The method has been implemented in a commercial permanent magnet synchronous motor and passed standardization according to safety standards.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2022)
Article
Energy & Fuels
Sandra Gallmetzer, Sascha Lindig, Magnus Herz, David Moser
Summary: With the growing focus on sustainability, the PV sector is facing new challenges in operating and maintaining PV systems. Automation is crucial in minimizing downtime due to faults and ensuring efficient operation.
Article
Computer Science, Interdisciplinary Applications
Abdellatif Seghiour, Hamou Ait Abbas, Aissa Chouder, Abdlhamid Rabhi
Summary: This paper presents an application of deep learning for fault detection in a PV system in Algeria. It proposes a deep learning-based method for detection and classification of various PV fault types, which has been validated using real operating data.
SIMULATION MODELLING PRACTICE AND THEORY
(2023)
Article
Green & Sustainable Science & Technology
Priya Ranjan Satpathy, Belqasem Aljafari, Sudhakar Babu Thanikanti, Siva Rama Krishna Madeti
Summary: This paper investigates the reliability of array configurations in solar PV systems under various electrical faults. A powerline communication-based PV monitoring system is developed to monitor performance, detect faults, and access data. From the analysis, series-parallel configuration shows a higher average tolerance compared to honeycomb, bridge-linked, and total-cross-tied configurations.
Article
Engineering, Electrical & Electronic
Rowida Meligy, Hicham Klaina, Imanol Picallo, Peio Lopez-Iturri, Leyre Azpilicueta, Jose Javier Astrain, Mohamed Rady, Jesus Villadangos, Ana Vazquez Alejos, Francisco Falcone
Summary: The advancements in renewable solar energy have led to the development of Linear Fresnel Reflector (LFR) technology, where solar energy is focused onto a tubular receiver by longitudinal mirrors. Efficiency in the energy generation process is optimized by varying tilt angles of each mirror based on solar tracking, with encoders or inclinometers providing angular information. The proposed low-cost monitoring system for LFR plants utilizes accelerometers and wireless transceivers for real-time data collection, ensuring fault detection and efficiency maintenance.
IEEE SENSORS JOURNAL
(2021)
Article
Energy & Fuels
Vorachack Kongphet, Anne Migan-Dubois, Claude Delpha, Jean-Yves Lechenadec, Demba Diallo
Summary: The study introduces a low-cost embedded tracer for rapid measurement of PV module I-V curve and extraction of key parameters. Experimental data suggests that under varying temperatures and irradiances, using the main characteristics of the I-V curve can accurately detect degradation of series and shunt resistances.
Review
Computer Science, Artificial Intelligence
Ghada M. El-Banby, Nada M. Moawad, Belal A. Abouzalm, Wessam F. Abouzaid, E. A. Ramadan
Summary: This article discusses the significance of solar energy and photovoltaic systems in electricity generation, and emphasizes the need for accurate monitoring and periodic follow-up. The article explores the classification of PV system faults and detection techniques, with a focus on the benefits of thermography methods and their integration with artificial intelligence tools.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Marilda Ardito, Fabiana Mascolo, Martina Valentini, Francesco Dell'Olio
Summary: Posture monitoring is essential for preventing spine pathologies, with the market pushing for new technical solutions that are comfortable and cost-effective. A prototype microcontroller-based system has been developed to signal incorrect postures with great sensitivity, showing potential for integration into garments and accurate detection of back flections.
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.