Power Data Preprocessing Method of Mountain Wind Farm Based on POT-DBSCAN

Due to the frequent changes of wind speed and wind direction,the accuracy of wind turbine(WT)power prediction using traditional data preprocessing method is low.This paper proposes a data preprocessing method which combines POT with DBSCAN(POT-DBSCAN)to improve the prediction efficiency of wind power prediction model.Firstly,according to the data of WT in the normal operation condition,the power prediction model ofWT is established based on the Particle Swarm Optimization(PSO)Arithmetic which is combined with the BP Neural Network(PSO-BP).Secondly,the wind-power data obtained from the supervisory control and data acquisition(SCADA)system is preprocessed by the POT-DBSCAN method.Then,the power prediction of the preprocessed data is carried out by PSO-BP model.Finally,the necessity of preprocessing is verified by the indexes.This case analysis shows that the prediction result of POT-DBSCAN preprocessing is better than that of the Quartile method.Therefore,the accuracy of data and prediction model can be improved by using this method.

Preprocessing method of night vision image application in apple harvesting robot

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for commercialization.The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation mode.Due to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting robot.Three different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be captured.In addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise characteristics.The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise.The preprocessing method can provide a theoretical and technical reference for subsequent image processing.

An improved adaptive preprocessing method for TDI CCD images

In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enhancement. It is a weighted average filter integrating the average filter and the improved range filter. The weighted factors are deduced in terms of a cost function, which are adjustable to different images. To validate the proposed method, extensive tests are carried out on a developed TDI CCD imaging system. The experimental results confirm that this preprocessing method can fulfill the noise removal and edge sharpening simultaneously, which can play an important role in remote sensing field.

Classification and Summarization of Solar Irradiance and Power Forecasting Methods:A Thorough Review

Solar forecasting is of great importance for ensuring safe and stable operations of the power system with increased solar power integration,thus numerous models have been presented and reviewed to predict solar irradiance and power forecasting in the past decade.Nevertheless,few studies take into account the temporal and spatial resolutions along with specific characteristics of the models.Therefore,this paper aims to demonstrate a comprehensive and systematic review to further solve these problems.First,five classifications and seven pre-processing methods of solar forecasting data are systematically reviewed,which are significant in improving forecasting accuracy.Then,various methods utilized in solar irradiance and power forecasting are thoroughly summarized and discussed,in which 128 algorithms are elaborated in tables in the light of input variables,temporal resolution,spatial resolution,forecast variables,metrics,and characteristics for a more fair and comprehensive comparison.Moreover,they are categorized into four groups,namely,statistical,physical,hybrid,and others with relevant application conditions and features.Meanwhile,six categories,along with 30 evaluation criteria,are summarized to clarify the major purposes/applicability of the different methods.The prominent merit of this study is that a total of seven perspectives and trends for further research in solar forecasting are identified,which aim to help readers more effectively utilize these approaches for future in-depth research.