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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. [ABSTRACT FROM AUTHOR]

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' 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]