scholarly journals A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4262 ◽  
Author(s):  
Sitong Lv ◽  
Jianguo Li ◽  
Yongxin Guo ◽  
Zhong Shi
Keyword(s):  
Cluster Analysis ◽  
Distributed Generation ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Optimal Allocation ◽  
Reduction Method ◽  
Scene Analysis ◽  
Analysis Method ◽  
Scenario Reduction ◽  
Cluster Analysis Method

In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of DG access to the power grid, the multi-scenario analysis method commonly used in DG optimal allocation method is studied in this paper. In order to solve the problems that may arise from using large-scale scenes in the planning of DG considering uncertainties by using multi-scene analysis method, the cluster analysis method suitable for large-scale scene reduction in scene reduction method is introduced firstly, and then an improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the reduction method is verified. Finally, the method mentioned in this paper is compared with other commonly used methods through IEEE-33 node system.

scholarly journals Distributed Generation Planning in Active Distribution Networks Based on Multi-Scene Analysis

2019 ◽  
Author(s):  
Sitong Lv ◽  
Jianguo Li ◽  
Yongxin Guo ◽  
Zhong Shi
Keyword(s):  
Distributed Generation ◽  
Scenario Analysis ◽  
Optimal Allocation ◽  
Reduction Method ◽  
Programming Model ◽  
Distribution Network ◽  
Active Management ◽  
Scenario Reduction ◽  
Active Distribution Network ◽  
Primal Dual

In recent years, distributed generation technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of Distributed Generation (DG) and meet the challenges after power grid access, Active Distribution Network (ADN) is considered as the future development direction of traditional distribution network because of its ability of active management. Nowadays, multi-scenario analysis is widely used in the research of optimal allocation of distributed power supply in active distribution network. Aiming at the problems that may arise when using multi-scenario analysis to plan DG with uncertainties in large-scale scenarios, a scenario reduction method based on improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the method is verified. At present, there are few studies on the optimal allocation of DG in ADN under fault state. In this paper, comprehensive safety indicators are introduced. Considering the timing characteristics of DG and the influence of active management mode, a bi-level programming model is established, which aims at minimizing the investment of annual life cycle and the removal of active power. The bi-level model is a complex mixed integer non-linear programming model. A hybrid algorithm combining cuckoo search algorithm and primal dual interior point method is used to solve the model. Finally, through the simulation of the IEEE-33 node system, the superiority of the scenario reduction method and the comprehensive security index used in this paper to optimize the configuration of DG in ADN is verified.

An Earthquake Clustering Method Based on Soft Distance Calculations

2013 ◽  
Vol 411-414 ◽  
pp. 1104-1107
Author(s):  
Jun Xia Chai ◽  
Dao Hua Liu
Keyword(s):  
Cluster Analysis ◽  
Strong Earthquake ◽  
Clustering Algorithm ◽  
Cluster Center ◽  
Analysis Method ◽  
Clustering Methods ◽  
Clustering Method ◽  
Accurate Calculation ◽  
Cluster Analysis Method ◽  
Data Source

The traditional cluster analysis method based on the true distance is not conducive to the accurate calculation of earthquake different fault rupture propagation and healing rate. This paper proposed and gave a new clustering method based on soft distance calculations. The clustering process based on soft distance calculations, the calculation method for soft distances and the specific clustering algorithm based on soft distances are given. For the real sample points of strong earthquake as a data source, we use this clustering method and other traditional clustering methods to cluster and analysis the data source, and analysis results have showed that the clustering method obtained the same cluster center with the earth stress field evolution, so this method has objective truth. The cluster analysis method for the earthquake fault zones in the accurate calculation of the next strong earthquake provides a good basis for the calculations.

scholarly journals Detection of drought events in Greece using daily precipitation

2010 ◽  
Vol 41 (2) ◽  
pp. 126-133 ◽  
Author(s):  
N. Kalamaras ◽  
H. Michalopoulou ◽  
H. R. Byun
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Daily Precipitation ◽  
Hierarchical Cluster ◽  
Seasonal Distribution ◽  
Drought Severity ◽  
Severe Drought ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
Insight Into

In this study a method proposed by Byun & Wilhite, which estimates drought severity and duration using daily precipitation values, is applied to data from stations at different locations in Greece. Subsequently, a series of indices is calculated to facilitate the detection of drought events at these sites. The results provide insight into the trend of drought severity in the region. In addition, the seasonal distribution of days with moderate and severe drought is examined. Finally, the Hierarchical Cluster Analysis method is used to identify sites with similar drought features.

Cluster Analysis Using N-gram Statistics for Daihinmin Programs and Performance Evaluations

2016 ◽  
Vol 4 (2) ◽  
pp. 33-57 ◽  
Author(s):  
Seiya Okubo ◽  
Takaaki Ayabe ◽  
Tetsuro Nishino
Keyword(s):  
Cluster Analysis ◽  
Imperfect Information ◽  
Computer Game ◽  
Computer Experiments ◽  
Performance Evaluations ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
N Gram ◽  
And Performance ◽  
Feature Values

In this paper, the authors elucidate the characteristics of the computer game Daihinmin, a popular Japanese card game that uses imperfect information. They first propose a method to extract feature values using n-gram statistics and a cluster analysis method that employs feature values. By representing the program card hands as several symbols, and the order of hands as simplified symbol strings, they obtain data that is suitable for feature extraction. The authors then evaluate the effectiveness of the proposed method through computer experiments. In these experiments, they apply their method to ten programs that were used in the UEC Computer Daihinmin Convention. In addition, the authors evaluate the robustness of the proposed method and apply it to recent programs. Finally, they show that their proposed method can successfully cluster Daihinmin programs with high probability.

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