Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

NOVEL GENERALIZED GREY INCIDENCE MODEL BASED ON INTERVAL GREY NUMBERS

To extend the traditional generalized grey incidence model, a novel grey incidence model based on inter- val grey numbers is constructed. Considering the numerical information of indexes cannot be accurately obtained and can be defined as interval grey numbers, the interval grey numbers are defined as standard interval grey num- bers which are split in white part and grey part. The absolute degree of incidence and relative degree of incidence based on the interval grey numbers are constructed and their arithmetic are given. Finally, an example about commercial aircraft index selection illuminates the effectiveness of the model. The results show that the model can sort indexes better and can extend the grey incidence models significantly.

ESTIMATION METHOD FOR AIRCRAFT SIMILARITY BASED ON FUZZY THEORY AND GREY INCIDENCE ANALYSIS

An estimation method for aircraft similarity based on fuzzy theory and grey incidence analysis is presented. This estimation method is made up of the triangular fuzzy transforming model of linguistic variables and the method of grey incidence analysis. Nine feature attributes of aircraft are selected to estimate the similarity between the new aircraft and the existing aircraft. A new aircraft X and other six existing aircrafts are taken as examples. Analyses show that similarity estimation results obtained from the method are in accordance with practice.

MULTIVARIATE ABSOLUTE DEGREE OF GREY INCIDENCE BASED ON DISTRIBUTION CHARACTERISTICS OF POINTS

The analysis result of absolute degree of grey incidence for multivariate time series is often inconsistent with the qualitative analysis. To overcome this shortage, a multivariate absolute degree of grey incidence based on distribution characteristics of points is proposed. Based on the geometric description of multivariate time se- ries, the neighborhood extrema are extracted in the different regions, and a characteristic point set is constructed. Then according to the distribution of the characteristic point set, a characteristic point sequence reflecting the ge- ometric features of multivariate time series is obtained. The incidence analysis between multivariate time series is transformed into the relational analysis between characteristic point sequences, and a grey incidence model is established. The model possesses the properties of translational invariance, transpose and rank transform invari- ance, and satisfies the grey incidence analysis axioms. Finally, two cases are studied and the results prove the ef- fectiveness of the model.

Grey incidence clustering method based on multidimensional dynamic time warping distance

The traditional grey incidence degree is mainly based on the distance analysis methods, which is measured by the displacement difference between corresponding points between sequences. When some data of sequences are missing (inconsistency in the length of the sequences), the only way is to delete the longer sequences or to fill the shorter sequences. Therefore, some uncertainty is introduced. To solve this problem, by introducing three-dimensional grey incidence degree (3D-GID), a novel GID based on the multidimensional dynamic time warping distance (MDDTW distance-GID) is proposed. On the basis of it, the corresponding grey incidence clustering (MDDTW distance-GIC) method is constructed. It not only has the simpler computation process, but also can be applied to the incidence comparison between uncertain multidimensional sequences directly. The experiment shows that MDDTW distance-GIC is more accurate when dealing with the uncertain sequences. Compared with the traditional GIC method, the precision of the MDDTW distance-GIC method has increased nearly 30%.

Comprehensive multivariate grey incidence degree based on principal component analysis

To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.

A multivariate grey incidence model for different scale data based on spatial pyramid pooling

In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.

Anti-ultraviolet aging tests of asphalts adapting to environment in Tibetan Plateau of China

Ultraviolet （UV） aging is one of the main factors which cause premature damage of asphalt pavements in the Tibetan Plateau, China. According to the measured levels of UV radiation, aging tests of styrene-butadiene rubber （SBR） asphalts with different contents of three anti-UV-aging agents including nano-TiO2, CeO2 and carbon black are performed. Common indices, which include retained penetration after thin film oven tests （TFOT） and softening point, and strategic highway research program （SHRP） indices of aged asphalts are evaluated. Infrared absorption spectral analysis is performed on asphalt specimens with 0. 8% carbon black which have been aged for different aging times （500, 1000 and 1 500 h）. By grey incidence analysis, the optimal contents of anti-UV-aging agents are determined. The results show that TiO2 and CeO2 are not only good UV absorbing or shielding agents, but also strong oxidants. Carbon black is a good anti-UV-aging agent, and its optimal content is about 0. 8% of asphalt weight. UV aging of asphalt mainly occurs in the early stages of aging. The longer the aging time, the more severe the aging of asphalt.

Analysis of Agricultural Eco-Efficiency and Influencing Factors in Henan Province

In order to improve the agricultural eco-efficiency and promote the sustainable development of agriculture in Henan Province, China, based on the footprint theory, the super-efficiency SBM model is used to scientifically calculate and analyze the agricultural eco-efficiency in Henan Province. On this basis, the influencing factors of agricultural eco-efficiency in Henan Province are quantitatively analyzed by using the grey incidence analysis model. The results show that unilaterally considering one of grey water footprints and carbon footprints will overestimate or underestimate the agricultural eco-efficiency of Henan Province in different degrees in different time periods, and the agricultural eco-efficiency obtained by comprehensively considering grey water footprint and carbon footprint (GWCAEE) is more in line with the reality of agricultural development in Henan Province. In 2000-2004, GWCAEE in Henan Province was better. During 2005-2014, GWCAEE in Henan Province showed a fluctuating decline and continued to be in an inefficient state. From 2015 to 2019, GWCAEE of Henan Province gradually increased, and it became effective in 2019. In recent years, GWCAEE has developed well. Through the grey incidence analysis between 12 influencing factors including endogenous factors and exogenous factors and GWCAEE, it is found that the six leading factors of GWCAEE in Henan Province are agricultural structure, financial input for agriculture, number of agricultural employees, crop sown area, consumption of chemical pesticide, consumption of agricultural diesel oil. According to the above research conclusions, suggestions for improving agricultural eco-efficiency in Henan Province are put forward.