scholarly journals The Bi-Level Optimal Configuration Model of the CCHP System Based on the Improved FCM Clustering Algorithm

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 907
Author(s):  
Kaoshe Zhang ◽  
Peiji Feng ◽  
Gang Zhang ◽  
Tuo Xie ◽  
Jinwang Hou ◽  
...  
Keyword(s):  
Carbon Dioxide Emissions ◽  
Clustering Algorithm ◽  
Meteorological Data ◽  
Operating Cost ◽  
Optimal Configuration ◽  
Configuration Model ◽  
Configuration Optimization ◽  
Nsga Ii ◽  
Fcm Algorithm ◽  
Fcm Clustering

To improve the comprehensive benefits of the CCHP system, this paper proposes a bi-level optimal configuration model of the CCHP system based on the improved FCM clustering algorithm. Firstly, based on the traditional FCM clustering algorithm, the entropy method is used to introduce the PFS index and the Vp index in a weighted form to achieve a comprehensive evaluation of the clustering effect. The effectiveness of the improved FCM algorithm is verified by analyzing the clustering process of the load and meteorological data using the improved FCM algorithm. Then the best cluster number and fuzzy coefficient is found using the traversal method. Secondly, a bi-level configuration optimization model is constructed. The outer layer is the configuration optimization layer, and the inner layer is the operation optimization layer. The model is solved by combining the NSGA-II and PSO algorithms. Finally, a bi-level optimal configuration model is constructed for actual cases, and the clustering results of the improved FCM algorithm are brought into the model. The example calculation analyses show that, compared with existing methods, the proposed method significantly reduces the operating cost and carbon dioxide emissions of the CCHP microgrid.

An Improved Fuzzy C-Means Clustering Algorithm and Application in Meteorological Data

2011 ◽  
Vol 181-182 ◽  
pp. 545-550
Author(s):  
Hong Fei Li ◽  
Fu Ling Wang ◽  
Shi Jue Zheng ◽  
Li Gao
Keyword(s):  
Clustering Algorithm ◽  
Meteorological Data ◽  
Observation Data ◽  
Fuzzy Clustering Algorithm ◽  
Temperature Observation ◽  
Degree Of Membership ◽  
Traditional Algorithm ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Clustering Effect

The fuzzy clustering algorithm is sensitive to the m value and the degree of membership. Because of the deficiencies of traditional FCM clustering algorithm, we made specific improvement. Through the calculation of the value of m, the amendments of degree of membership to the discussion of issues, effectively compensate for the deficiencies of the traditional algorithm and achieve a relatively good clustering effect. Finally, through the analysis of temperature observation data of the three northeastern province of china in 2000, the reasonableness of the method is verified.

scholarly journals Optimal Configuration of the Integrated Charging Station for PV and Hydrogen Storage

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7087
Author(s):  
Min Wang ◽  
Xiaobin Dong ◽  
Youchun Zhai
Keyword(s):  
Energy Storage ◽  
Hydrogen Storage ◽  
Storage System ◽  
Fuzzy Comprehensive Evaluation ◽  
Optimal Configuration ◽  
Configuration Model ◽  
Hydrogen Energy ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Charging Station

This paper designs the integrated charging station of PV and hydrogen storage based on the charging station. The energy storage system includes hydrogen energy storage for hydrogen production, and the charging station can provide services for electric vehicles and hydrogen vehicles at the same time. To improve the independent energy supply capacity of the hybrid charging station and reduce the cost, the components are reasonably configured. To minimize the configuration cost of the integrated charging station and the proportion of power purchase to the demand of the charging station, the energy flow strategy of the integrated charging station is designed, and the optimal configuration model of optical storage capacity is constructed. The NSGA-II algorithm optimizes the non-inferior Pareto solution set, and a fuzzy comprehensive evaluation evaluates the optimal configuration.

A Novel Fuzzy Clustering Method with No Outliers Influence

2013 ◽  
Vol 300-301 ◽  
pp. 735-739 ◽  
Author(s):  
Li Jen Kao ◽  
Yo Ping Huang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Human Robot Interaction ◽  
Hand Gesture ◽  
Clustering Method ◽  
Robot Interaction ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Fcm Clustering

Fuzzy C-Means (FCM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FCM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FCM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FCM algorithm can get more accurate centers than the traditional FCM algorithm.

scholarly journals Two-Echelon Multidepot Logistics Network Design with Resource Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-28
Author(s):  
Siyu Luo ◽  
Yong Wang ◽  
Jinjun Tang ◽  
Xiangyang Guan ◽  
Maozeng Xu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Method ◽  
Resource Sharing ◽  
Clustering Algorithm ◽  
Operating Cost ◽  
Pickup And Delivery ◽  
Nsga Ii ◽  
Logistics Network ◽  
Logistics Networks

Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.

Improvement on a Fuzzy C-Means Algorithm Based on Genetic Algorithm

2014 ◽  
Vol 614 ◽  
pp. 385-388
Author(s):  
Guo Chen Jiang ◽  
Zhi Jian Sun
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Optimal Choice ◽  
Experimental Results ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
Fuzzy C Means Algorithm

Weighting exponent m is an important parameter in fuzzy c-means(FCM) algorithm. In this paper, an approach based on genetic algorithm is proposed to improve the FCM clustering algorithm through the optimal choice of the parameter m. Experimental results show that the better clustering results are obtained through the new algorithm.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

2016 ◽  
Vol 231 (1) ◽  
pp. 98-108 ◽  
Author(s):  
Ke Li ◽  
Yalei Wu ◽  
Shimin Song ◽  
Yi sun ◽  
Jun Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Electrical Characteristics ◽  
Complex Signals ◽  
Fcm Clustering

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

scholarly journals Optimal configuration of energy storage system in multi-station fusion mode

2021 ◽  
Vol 256 ◽  
pp. 02002
Author(s):  
Zhendong Du ◽  
Guosheng Jin ◽  
Yu Fang ◽  
Chenyin Yu ◽  
Yupeng Hu ◽  
...  
Keyword(s):  
Energy Storage ◽  
Power Distribution ◽  
Storage System ◽  
Energy Storage System ◽  
Optimal Configuration ◽  
Configuration Model ◽  
Utilization Rate ◽  
Electric Vehicle Charging ◽  
Fusion Mode ◽  
Msf Model

Multi-station fusion mode (MSF) generally includes energy storage system, data center and electric vehicle charging station. It can improve the utilization rate of land and power distribution resources of urban substations. This paper studies configuration of ESS in the MSF model, aiming at reducing total cost. Firstly, an AC-DC system of MSF model is established. Then, aiming at economy, the optimal configuration model of ESS is established. Finally, the effectiveness of the model is verified by a practical example, and the MATLAB toolbox YALMIP with the CPLEX solver is used to conduct the ESS planning.

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