Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas.
Photovoltaic (PV) module working conditions lack consistency and PV array power outputs fluctuate due to the non-uniform impact that aging has on various PV modules in a PV array. No assessment has been conducted on the energy potential of a non-uniform PV array, despite the fact that the maximum power point (MPP) can be tracked by global maximum power point tracking (GMPPT). Therefore, the present work undertakes such an assessment by devising an algorithm to optimise the PV array electrical structure as the PV modules undergo aging in a non-uniform way. To enable PV arrays with non-uniform aging to produce as much power as possible and to make maintenance more cost-effective, the work puts forward a novel approach for reconfiguring PV arrays, where the PV modules are repositioned by retaining the aged PV modules. By this approach, the selection of the best reconfiguration topology necessitates the information on the electrical parameters associated with the PV modules in an array. Furthermore, the non-uniform aging of the PV modules can engender an incompatibility effect, which can be diminished in the proposed algorithm through iterative sorting of the modules in a hierarchical pattern. To determine how effective the method is for PV arrays with non-uniform aging and of different sizes, such as 3 × 4, 5 × 8 and 7 × 8 arrays, computer simulation and analysis have been conducted, with findings indicating that, irrespective of dimensions, PV arrays with non-uniform aging can have improved power yield.
Aging is known to exert various non-uniform effects on photovoltaic (PV) modules within a PV array that consequently can result in non-uniform operational parameters affecting the individual PV modules, leading to a variable power output of the overall PV array. This study presents an algorithm for optimising the configuration of a PV array within which different PV modules are subject to non-uniform aging processes. The PV array reconfiguration approach suggests maximising power generation across non-uniformly aged PV arrays by merely repositioning, rather than replacing, the PV modules, thereby keeping maintenance costs to a minimum. Such a reconfiguration strategy demands data input on the PV module electrical parameters so that optimal reconfiguration arrangements can be selected. The algorithm repetitively sorts the PV modules according to a hierarchical pattern to minimise the impact of module mismatch arising due to non-uniform aging of panels across the array. Computer modelling and analysis have been performed to assess the efficacy of the suggested approach for a variety of dimensions of randomly non-uniformly aged PV arrays (e.g., 5 × 5 and 7 × 20 PV arrays) using MATLAB. The results demonstrate that enhanced power output is possible from a non-uniformly aged PV array and that this can be applied to a PV array of any size.
For a photovoltaic (PV) power generation system, the shading effect of PV panels caused by dust deposition is extremely unfavorable. The deposition of dust results in a severe reduction of power generation output, since the efficiency of PV panels is affected by the shading irradiance and blocking the cooling. In this study, a numerical simulation method is proposed to model the dust accumulation on PV panels to detect the effects on PV power generation caused by different wind directions and wind speeds. Due to the high accuracy of numerical simulation, and the short calculation cycle, the proposed method provides a certain prediction for the soiling management of PV panels in the wind-sand environment. Through simulations and experiments, the impacts of dust accumulation on the performance of PV panels with different wind directions are studied in detail with the wind speed changing from 4.43 m/s to 6.48 m/s and the dust particle size of 10 µm to 100 µm, which are based on the environment of Liverpool, England in a year. Besides, for PV arrays, the turbulences of the dust distribution around the PV panels are also analyzed. The data collected from experiments and simulations are used to verify the effectiveness of the proposed strategy.
This paper develops a high-performance finite control set model predictive current control (FCS-MPCC) method for induction motors (IM) to ensure the system control performance over the lowswitching-frequency (LSF) range, where the switching loss is low. The new controller is based on tripartite calculations in each control period and sliding mode (SM) rotor-related inductance observer. In detail, firstly, to solve the problem that the control performance of the traditional FCS-MPCC methods is low when they are used at low frequencies, tripartite calculations are employed in each control period to improve the prediction accuracy. By dividing one control period into three equal parts and executing the prediction algorithm in each subsection, the current estimation errors become lower compared to the conventional single-step Euler-discretization controller. Then, considering that the performance of an FCS-MPCC controller highly relies on the machine parameter values, but it is hard to directly obtain the accurate rotor-related inductances (mutual inductance and rotor inductance), this paper uses an online parameter observer based on the sliding mode (SM) principle to diagnose them in real time. It needs to be mentioned that when discussing the stability of the sigmoid-function-based SM observer, a new technique called estimation-error-limitation is initially adopted in this paper to simplify the analysis process. Finally, the proposed algorithms are verified by simulation, which is conducted on a three-phase IM at LSF situations. INDEX TERMS Induction motor, finite control set model predictive control, sliding mode observer, parameter mismatch, low switching frequency.
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