Antibacterial Mechanism of Copper-bearing Antibacterial Stainless Steel against E.Coli

A preliminary study was made on the antibacterial mechanism of copper-bearing antibacterial stainless steels against E.coli through experiments of microbiology such as EDTA （ethylenediaminetetraacetic acid） complexing, DNA smearing and AFM （atomic force microscope） observation. It was measured that the antibacterial stainless steels showed excellent antibacterial functions with antibacterial rate to E.coli over 99.99%. The antibacterial rate was weak if the bacteria solution was complexed by EDTA, indicating that the copper ions play a dominant role in the antibacterial effect of the antibacterial stainless steels. The electrophoresis experiment did not show the phenomenon of DNA smearing for E.coli after contacting antibacterial stainless steels, which meant that DNA of E.coli was not obviously damaged. It was observed by AFM that the morphology of E.coli changed a lot after contacting antibacterial stainless steels, such as cell walls being seriously changed and lots of contents in the cells being leaked.

A comprehensive review for wind,solar,and electrical load forecasting methods

Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales

With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.

Statistical downscaling of numerical weather prediction based on convolutional neural networks

Numerical Weather Prediction(NWP)is a necessary input for short-term wind power forecasting.Existing NWP models are all based on purely physical models.This requires mainframe computers to perform large-scale numerical calculations and the technical threshold of the assimilation process is high.There is a need to further improve the timeliness and accuracy of the assimilation process.In order to solve the above problems,NWP method based on artificial intelligence is proposed in this paper.It uses a convolutional neural network algorithm and a downscaling model from the global background field to establish a given wind turbine hub height position.We considered the actual data of a wind farm in north China as an example to analyze the calculation example.The results show that the prediction accuracy of the proposed method is equivalent to that of the traditional purely physical model.The prediction accuracy in some months is better than that of the purely physical model,and the calculation efficiency is considerably improved.The validity and advantages of the proposed method are verified from the results,and the traditional NWP method is replaced to a certain extent.

Bootstrapped Multi-Model Neural-Network Super-Ensembles for Wind Speed and Power Forecasting

The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.

Post Evaluation of Wind Resource Assessment and Micro-Siting

The design energy productions deviate from the actual situation, which are affected by the accuracy of two significant factors - the wind resource assessment and wind farm micro-siting. A running wind farm in northern China was taken as the object in this investigation. The measured data obtained in operation phase and the relevant information in design phase were integrated and a post evaluation of wind resource assessment, micro-siting and generating capacity reduction factors of the wind farm in design phase was provided. The results indicate that the representative year wind regimes of the wind farm in design phase can basically reflect the wind conditions in the wind farm without the consideration of the trends of long-term changes in wind speed;micro-siting project in design phase is superior to that in practical;generating capacity reduction factors, overall on the high side, should be further optimized considering 20-year operation period.

Assessment Method of Offshore Wind Resource Based on Multi-dimenssiional Indexes System

Traditional assessment indexes could not fully describe offshore wind resources,for the meteorological properties of offshore are more complex than onshore.As a result,the uncertainty of offshore wind power projects would be increased and final economic benefits would be affected.Therefore,a study on offshore wind resource assessment is carried out,including three processes of“studying data sources,conducting multidimensional indexes system and proposing an offshore wind resource assessment method based on analytic hierarchy process(AHP).First,measured wind data and two kinds of reanalysis data are used to analyze the characteristics and reliability of data sources.Second,indexes such as effective wind speed occurrence,affluent level occurrence,coefficient of variation,neutral state occurrence have been proposed to depict availability,richness,and stability of offshore wind resources,respectively.Combined with existing parameters(wind power density,dominant wind direction occurrence,water depth,distance to coast),a multidimensional indexes system has been built and on this basis,an offshore wind energy potential assessment method has been proposed.Furthermore,the proposed method is verified by the annual energy production of five offshore wind turbines and practical operating data of four offshore wind farms in China.This study also compares the ranking results of the AHP model to two multi-criteria decision making(MCDM)models including weighted aggregated sum product assessment(WASPAS)and multi-attribute ideal real comparative analysis(MAIRCA).Results show the proposed method gains well in practical engineering applications,where the economic score values have been considered based on the offshore reasonable utilization hours of the whole life cycle in China.

An optimized short-term wind power interval prediction method considering NWP accuracy

In recent years, the accuracy of the wind power prediction has been urgently studied and improved to satisfy the requirements of power system operation. In this paper, the relevance vector machine(RVM)-based models are established to predict the wind power and its interval for a given confidence level. An NWP improvement module is presented considering the characteristic of NWP error. Moreover, two parameter optimization algorithms are applied to further improve the prediction model and to compare each performance. To take three wind farms in China as examples, the performance of two RVM-based models optimized, respectively, by genetic algorithm(GA)and particle swarm optimization(PSO) are compared with predictions based on a genetic algorithm–artificial neural network(GA–ANN) and support vector machine. Results show that the proposed models have better prediction accuracy with GA–RVM model and more efficient calculation with PSO–RVM.