Absence Of Temperatures Research Articles

A Novel Fault Identification Method for Photovoltaic Array via Convolutional Neural Network and Residual Gated Recurrent Unit

Under the background of the large-scale construction of photovoltaic (PV) power stations, it is crucial to discover and solve module failures in time for improving the service life and maintaining the normal operation efficiency of modules. Based on analyzing the difference of I-V curves of PV arrays under different fault states, the I-V curves, temperatures and irradiances are taken as input data, and a fusion model of convolutional neural network (CNN) and residual-gated recurrent unit (Res-GRU) is proposed to identify the PV array fault. This model consists of a 1-dimensional CNN module with a 4-layer structure and a Res-GRU module. It has the advantages of end-to-end fault diagnosis, no manual feature extraction, strong anti-interference ability, and usable in the absence of irradiances and temperatures. Moreover, it can not only identify a single fault (e.g., short circuit, partial shading, abnormal aging, etc.), but also can effectively identify hybrid faults. Experimental results show that the classification accuracy of the proposed method is 98.61%, which is better than the ones of the artificial neural network (ANN), the extreme learning machine with kernel function (KELM), the fuzzy C-mean (FCM) clustering, the residual neural network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, in the absence of temperatures and irradiances, the classification accuracy still reaches 95.23%, which has a broad application prospect in the online fault diagnoses of PV arrays.

Seasonal influence on vegetative growth and flower initiation of Spathiphyllum

Vegetative growth and flower initiation of Spathiphyllum cultivars ‘Alfa’ and ‘Cervin’ were studied under various climatic conditions over a full year’s growth cycle. The four production cycles started respectively on 18 th March, 10 th June, 10 th September and 26 th November 1998. These cycles are referred to as summer, autumn, winter and spring cultures respectively since the major part of the experimental period occurred in this specific season. This study indicated that ‘Alfa’ and ‘Cervin’ reacted in different ways during the year. Spathiphyllum ‘Alfa’ initiated flowers independently of the attained vegetative biomass and flower initiation occurred when growth rate was slow or decreased considerably. In the winter and spring cycles, ‘Alfa’ was able to initiate flowers in an early vegetative stage (12.4 leaves and 7.4 leaves respectively), whereas the summer and autumn cultured plants were larger (29.5 leaves and 23.2 leaves respectively) at the start of flower initiation. For ‘Alfa’, the absence of temperatures above 26 °C seemed to be an initial (essential) condition to start flower initiation. In addition to non-elevated temperatures, decreasing or low light intensities and shortening of the photoperiod were associated with the conversion from the vegetative to the generative stage. For Spathiphyllum ‘Cervin’, however, no linkage of climatic conditions and the start of generative development was observed. ‘Cervin’ required a more extensive vegetative development than ‘Alfa’ before flower initiation could take place. In any of the four culture cycles, flower initiation did not occur before the plants had formed approximately seven shoots and 24 leaves.