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.

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.

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.