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Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the shading effects between arrays and reduce the electricity production loss due to terrain changes. Based on a uniaxial tracker on the sloping terrain of a PV farm located in Ningxia, this study established a uniaxial solar-tracking strategy for sloping terrain by integrating a spatial projection model with a dynamic shadow assessment method. In the proposed strategy, the optimal tilt angle of the PV array and related desirable adjustment are identified taking into consideration major parameters such as the shadow area ratio S and the average solar irradiance intensity G. A tool underpinned by Matlab Simulink has also been developed to realize the proposed solar-tracking strategy. With the input of a simulated ramp signal β and the dynamically changed time parameters, the tracking angle of PV arrays over the simulated duration is accurately predicted, followed by a series of experimental validations conducted on the winter solstice and a typical sunny day (15 September). Moreover, the study also explored the terrain impacts on solar tracking by comparing the sloping terrain and flat terrain applications. The analytic and experimental results indicate that (a) the maximum value of the G(β) function could serve as the input to identify the optimal tracking angle; (b) the application of the flat terrain tracking (FTT) strategy in sloping terrain would result in a reduction of average solar irradiance intensity harvested by the PV arrays with varying degrees; (c) in the context of an east–west −7° sloping terrain, compared with the FTT strategy, the sloping terrain tracking (STT) strategy enabled anti-shading tracking, and then increased the daily PV electricity yield by 0.094 kWh/kWp, which is around 1.48% of the daily energy production; (d) given a measurement with annual scale, the STT strategy could cause a 1.26% increase in the energy harvesting with a flat uniaxial PV array on a −7° slope terrain, achieving an annual increase of 25.16 kWh/kWp. The experimental comparative analysis validated the precision of the proposed solar-tracking model, which has far-reaching significance for achieving automatic solar-tracking of PV modules, as well as improving the capacity and efficiency of PV systems.

With the increasingly stringent requirements for carbon emissions, countries have increased the scale of clean energy use in recent years. As an important new clean energy source, the ratio of wind power in energy utilization has been increasing. The horizontal axis wind turbine is the main form of wind power generation, which is subject to random wind loads during operation and is prone to various failures after a long period of operation, resulting in reduced power generation efficiency or even shutdown. In order to ensure stable external power transmission, it is necessary to perform fault diagnosis for wind turbines. However, the traditional time-frequency analysis method is defective. This paper proposes a new LOD-ICA method to realize the resolution of the vibration signals mode mixing problem incorporated the merits of both methods. The LOD-ICA method and the LOD method based on noise-assisted analysis decompose the same signal to produce different signal components. The feasibility of the LOD-ICA method was verified by comparing the correlation coefficients between each of the signal components generated by the two methods and the original signal. In the field of wind turbine fault diagnosis, the LOD-ICA method is employed to the fault characteristics of gearboxes to extract the fault signs of vibration signals, further demonstrated the superiority of the LOD-ICA method in processing vibration signals of rotating machinery Refereed/Peer-reviewed

Decarbonisation of the built environment is high on the agenda for sustainable urbanisation and is increasingly recognized as a critical path to climate neutrality. As a microcosm of modern cities, precincts are at the forefront for urban planning, development and assessment, as well as for exploring the carbon–neutral potential of the built environment. Despite many innovative solutions have been proposed for decarbonising precincts, carbon assessment and carbon–neutral potential exploration in the dynamic environment is remain challenging, due to the integration of complex morphological variations, demographical changes, on premises renewable energy production and electric vehicle uptakes. This research presents a preliminary study on the life cycle carbon assessment of residential precincts, as well as their carbon neutrality opportunities and options with dynamic settings. A case study with 100 scenarios is employed to explore critical factors in precinct decarbonisation. This is followed by comparative analysis of critical factors and their impacts on precinct carbon signature and carbon–neutral potential. The article concludes that changes in energy-efficiency of building types and in demographical characteristics are the two major factors influencing precinct carbon performance. Where, redevelopment of precinct with buildings of higher-rated energy efficiency and increase of on-site renewable energy uptake can have a long-term positive impact on precinct carbon performance. Moreover, improving energy efficiency through existing building refurbishment might of the potential for a fast-track of precinct carbon. Meanwhile, demographical changes with precinct evolution, especially in family types and energy consumption behaviors, can significantly affect the carbon performance and neutrality of urban precincts in the dynamic built environment. Refereed/Peer-reviewed

As essential load-bearing equipment to support the nacelle and blades, the tower is subjected to the whole wind turbine loading. This study proposes a new method of combining acoustic emission and normalized accumulation parameters to characterize wind turbine towers Q345 steel damage. First of all, tendency analysis of the acoustic emission signal parameter was conducted to determine damage degree during the damage stage. Secondly, we normalized the accumulation of amplitude and other parameters to compare the proportion of each accumulation parameter at different stages, while studying the spectra of common acoustic emission signals. Finally, comparing the differences and similarities of the normalized accumulation parameters between three different rates, we analyze the effect of rate on the normalized accumulation parameters. These results indicate that the normalized cumulative duration parameter is suitable for characterizing the yield damage occurrence, the normalized cumulative energy parameter is very sensitive to the fracture stage, the normalized cumulative energy parameter is least influenced by the loading rate, and the energy parameter is a sensitivity factor for normalized expression, which to realizes the stage of damage judgment. Refereed/Peer-reviewed

Decarbonising the urban built environment for reaching carbon neutrality is high on the agenda for many cities undergoing rapid expansion and densification. As an important urban form, precincts have been increasingly focused on as the context for urban redevelopment planning and at the forefront for trialling carbon reduction measures. However, due to interplays between the built forms and the occupancy, the carbon performance of a precinct is significantly affected by morphological variations, demographical changes, and renewable energy system deployment. Despite much research on the development of low-carbon precincts, there is limited analysis on aggregated effects of population growth, building energy efficiency, renewable energy penetration, and carbon reduction targets in relation to precinct carbon signature and carbon neutral potential for precinct redevelopment and decarbonisation planning. In this paper, an integrated carbon assessment model, including overall precinct carbon emissions and carbon offset contributed by precinct-scale renewable energy harvesting, is developed and applied to examine the lifecycle carbon signature of urban precincts. Using a case study on a residential precinct redevelopment, scenario analysis is employed to explore opportunities for decarbonising densification development and the carbon neutral potential. Results from scenario analysis indicate that redevelopment of buildings with higher-rated energy efficiency and increase of renewable energy penetration can have a long term positive impact on the carbon performance of urban precincts. Meanwhile, demographical factors in precinct evolution also have a strong influence on a precinct&rsquo, s carbon neutral potential. Whilst population size exerts upward pressure on total carbon emissions, changes in family types and associated consumption behaviour, such as travelling, can make positive contributions to carbon reduction. The analysis also highlights the significance of embodied carbon to the total carbon signature and the carbon reduction potential of a precinct during densification, reinforcing the notion that &ldquo, develop with less&rdquo, is as important as carbon offsetting measures for decarbonising the precinct toward carbon neutrality.

Modern technological advances and rapid cost reduction have led to the widespread deployment of solar photovoltaic (PV) units, which is a cost-effective solution to improve the energy resilience and carbon offsetting for a sustainable built environment. However, PV electricity is not a pure clean energy considering the costs and carbon emissions associated with its manufacturing and operations. Moreover, the efficiency of a PV system is significantly affected by the local climate and built environment as well as energy management and storage methods. Therefore, it is essential to assess PV systems using integrated metrics for a comprehensive understanding of their net benefits. Hence, this study developed and applied life-cycle models to support PV system assessment in local contexts by identifying the morphological factors that affect PV system efficiency, by estimating energy, carbon and investment payback periods; and discussing the carbon offsetting in different energy storage contexts. Case study indicates that using PV-battery systems is an economical way to improve energy resilience and carbon offsetting. Further, communal storage is more favourable than household-level storage. Methodology and findings of this study are expected to facilitate the development of economical low-carbon energy supply as well as to support the optimal planning of low-carbon precincts. Refereed/Peer-reviewed

Fault diagnosis and forecasting contribute significantly to the reduction of operating and maintenance associated costs, as well as to improve the resilience of wind turbine systems. Different from the existing fault diagnosis approaches using monitored vibration and acoustic data from the auxiliary equipment, this research presents a novel fault diagnosis and forecasting approach underpinned by a support vector regression model using data obtained by the supervisory control and data acquisition system (SCADA) of wind turbines (WT). To operate, the extraction of fault diagnosis features is conducted by measuring SCADA parameters. After that, confidence intervals are set up to guide the fault diagnosis implemented by the support vector regression (SVR) model. With the employment of confidence intervals as the performance indicators, an SVR-based fault detecting approach is then developed. Based on the WT SCADA data and the SVR model, a fault diagnosis strategy for large-scale doubly-fed wind turbine systems is investigated. A case study including a one-year monitoring SCADA data collected from a wind farm in Southern China is employed to validate the proposed methodology and demonstrate how it works. Results indicate that the proposed strategy can support the troubleshooting of wind turbine systems with high precision and effective response. Refereed/Peer-reviewed

Many efforts have recently been dedicated to developing smart sockets that seek to provide insights into the reduction of standby energy waste coupled to electric appliances. However, not all technical solutions consider the techno-economic benefits in the development. This research presents a hardware solution based on STM32F103 (STM32F103 devices use the Cortex-M3 core, with a maximum CPU speed of 72 MHz) for the development of an energy-efficient smart socket to address the standby energy waste of household electric appliances and associated economic losses. Input-output analysis on monitored voltage and current was employed to assess the performance and examine the precision of the developed system. As it was targeted at facilitating easier operation, the smart socket was developed to be compatible with other remote controllers of household electric appliances. Experimental results indicated that the developed system could measure voltage values accurately to avoid overvoltage for security protection. The measuring unit could monitor current values with high precision to support the energy-saving control. A functional testing was conducted on the prototypes with a lifecycle assessment employed to validate the economic attractiveness of the developed system. Results indicated that the system is user friendly and cost-effective as no extra wiring required and network environment independent. Indeed, indirect fruits, such as lifespan extension and safety enhancement, could also be achieved for appliances Refereed/Peer-reviewed

As a renewable energy source, wind energy harvesting provides a desirable solution to address the environmental concerns associated with energy production to satisfy the increasingly global demand. Over the years, the penetration of wind turbines has experienced a rapid growth, however, the impacts of turbine noise correspondingly become a major concern in wind energy harvesting. Recent studies indicate that the noise emitted by turbine operating could increase the risk of nuisance, which might further affect the well-being of local residents. However, the main factors affecting turbine noise assessment and to what extent they contribute to the assessment are still unclear. In this study, a survey-based approach is developed to identify these major factors and to explore the interactions between the factors and assessment results. Principal component analysis method was adapted to extract key factors, followed by reliability assessment, validity analysis, descriptive assessment, and correlation analysis were conducted to test the robust of the proposed methodology, as well as to examine the interactions between variables. Regression analysis was finally employed to measure the impacts on results contributed by the key factors. Findings of this study indicate that key factors including physical conditions, control capacity, and subjective opinions are of significant impact on residents&rsquo, response to wind turbine noise, while the factor of subjective opinions contributes predominately to the assessment results. Further validations also indicate that the proposed approach is robust and can be extensively applied in survey-based assessments for other fields.

Given the prejudicial environmental effects of fossil-fuel based energy production, renewable energy sources can contribute significantly to the sustainability of human society. As a clean, cost effective and inexhaustible renewable energy source, wind energy harvesting has found a wide application to replace conventional energy productions. However, concerns have been raised over the noise generated by turbine operating, which is helpful in fault diagnose but primarily identified for its adverse effects on the local ecosystems. Therefore, noise monitoring and separation is essential in wind turbine deployment. Recent developments in condition monitoring provide a solution for turbine noise and vibration analysis. However, the major component, aerodynamic noise is often distorted in modulation, which consequently affects the condition monitoring. This study is conducted to explore a novel approach to extract low-frequency elements from the aerodynamic noise background, and to improve the efficiency of online monitoring. A framework built on the spline envelope method and improved local mean decomposition has been developed for low-frequency noise extraction, and a case study with real near-field noises generated by a mountain-located wind turbine was employed to validate the proposed approach. Results indicate successful extractions with high resolution and efficiency. Findings of this research are also expected to further support the fault diagnosis and the improvement in condition monitoring of turbine systems. Refereed/Peer-reviewed