Multi-component opportunistic maintenance optimization for wind turbines with consideration of seasonal factor

Aiming at wind turbines,the opportunistic maintenance optimization is carried out for multi-component system,where minimal repair,imperfect repair,replacement as well as their effects on component’s effective age are considered.At each inspection point,appropriate maintenance mode is selected according to the component’s effective age and its maintenance threshold.To utilize the maintenance opportunities for the components among the wind turbines,opportunistic maintenance approach is adopted.Meanwhile,the influence of seasonal factor on the component’s failure rate and improvement factor’s decrease with the increase of repair’s times are also taken into account.The maintenance threshold is set as the decision variable,and an opportunistic maintenance optimization model is proposed to minimize wind turbine’s life-cycle maintenance cost.Moreover,genetic algorithm is adopted to solve the model,and the effectiveness is verified with a case study.The results show that based on the component’s inherent reliability and maintainability,the proposed model can provide optimal maintenance plans accordingly.Furthermore,the higher the component’s reliability and maintainability are,the less the times of repair and replacement will be.

Seasonal variations in the phytoplankton community and the relationship between environmental factors of the sea around Xiaoheishan Island in China

Seasonal variations in the phytoplankton community and the relationship between environmental factors of the sea area around Xiaoheishan Island are investigated in the present study. Xiaoheishan Island is located at 37°58′14″N and 120°38′46″E in Shandong Province, China. A total of 65 species of phytoplankton belonging to three phyla and 27 genera were identified, with Bacillariophyta having the largest number of species. The annual average chlorophyll a concentration for this area was 3.11 μg/L, and there occurs a Skeletonema costatum bloom in winter. The Shannon-Weaver indexes(log_2) of the phytoplankton from all stations were higher than 1, and the Pielou indexes were all higher than 0.3. The results of the canonical correspondence analysis(CCA) indicated that water temperature, PO_4^(3ˉ) and Cu were the environmental factors that had the greatest influence on the distribution of the phytoplankton community throughout the entire year. Although the concentration of heavy metal is well up to the state standards of the first grade of China(GB 3097-1997), these metals still have an impact on the phytoplankton community from this area.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

Normalization of VIIRS DNB images for improved estimation of socioeconomic indicators

Monthly Visible Infrared Imaging Radiometer Suite(VIIRS)Day-Night Band(DNB)composite data are widely used in research,such as estimations of socioeconomic parameters.However,some surface conditions affect the VIIRS DNB radiance,which may create some estimation bias in certain regions.In this paper,we propose a novel normalization algorithm for VIIRS DNB monthly composite data.The aim is to normalize VIIRS radiance,collected under different surface conditions,to a reference point,so that the bias is reduced.The algorithm is based on the utilization of stable lit pixels as a reference and a nonlinear regression algorithm,to match un-normalized data to the reference data.Experimental results show that the algorithm could improve correlation(R2)between the total sum of nightlights(TOL),electric power consumption(EPC),and gross domestic product(GDP)at both a global and local scale.The algorithm could significantly diminish the seasonal component of un-normalized nightlights radiance caused by snow.The intensified nightlights radiance in sandy regions could also be reduced to a more reasonable range in comparison with other regions.Visual inspection shows that the brightness of snow-affected and sandy regions was strongly reduced after undergoing normalization.