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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Liu, Yiming Li, Changyun Hou, Qingtao Yan, Hongwei Cao, Xin Xu, Minling Shandong Univ Sci & Technol Coll Elect & Automat Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781665422482

With the continuous expansion of the scale of power grids, the amount of monitoring data of power equipment is growing and the reliability demand of power equipment is increasing. In order to cope with power transformer accidents caused by damp faults in oil-immersed bushings, this paper applies big data clustering technology to construct a bushing damp fault evaluation index system, and combines the posting progress obtained from TOPSIS method to achieve a quantitative assessment of the damp state of bushings. The effectiveness of this method is also verified with examples.

关键词： clustering algorithm TOPSIS bushing fault assessment

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A Novel Hybrid SBM clustering Method Based on Fuzzy Time Series

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IEEE ACCESS 2023年 11卷 60693-60708页

作者： Zhang, Ren-Long Liu, Xiao-Hong Guizhou Univ Sch Management Guiyang 550025 Peoples R China

With the development of machine learning algorithm and fuzzy theory, the fuzzy clustering algorithm based on time series has received more and more attention. Based on the time series theory and considering the correlation of data attributes, it proposes a novel multivariate fuzzy time series clustering method based on Slacks Based Measure (MFTS-SBM). Compared with traditional fuzzy clustering that it has the ability to deal with fuzziness and uncertainty, the proposed hybrid SBM clustering method employs with input and output items and considers the clustering results and the influencing factors of nonparametric frontier. Thus, it is important for data decision making because decision makers are interested in understanding the changes required to combine input variables in order to classify them into the desired clusters. The simulation experiment results of different samples are given to explain the use and effectiveness of the proposed hybrid SBM clustering method. Therefore, the hybrid method has strong theoretical significance and practical value.

关键词： Fuzzy time series SBM nonparametric frontier clustering algorithm

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clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

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DRONES 2023年第11期7卷

作者： Xiao, Peng Li, Ni Xie, Feng Ni, Haihong Zhang, Min Wang, Ban Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Xian Aircraft Intelligent Recognit & Control Key L Xian 710072 Peoples R China Aviat Ind Corp China Chengdu Aircraft Design & Res Inst Chengdu 610091 Peoples R China

Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.

关键词： heterogeneous UAVs multi-region coverage-path planning regional allocation clustering algorithm

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Adaptive clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting

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ENERGIES 2023年第17期16卷 6230-6230页

作者： Qi, Yuanhang Luo, Haoyu Luo, Yuhui Liao, Rixu Ye, Liwei Univ Elect Sci & Technol China Zhongshan Inst Sch Comp Sci Zhongshan 528402 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Baiyun Univ Sch Accountancy Guangzhou 510550 Peoples R China

Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days;(ii) the same kind of data are used as the training data of long short-term memory network;(iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day.

关键词： power load forecasting neural network clustering algorithm long short-term memory network

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Modeling and short-term prediction of HVAC system with a clustering algorithm

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ENERGY AND BUILDINGS 2014年第0期82卷 310-321页

作者： Tang, Fan Kusiak, Andrew Wei, Xiupeng Univ Iowa Seamans Ctr 3131 Dept Mech & Ind Engn Iowa City IA 52242 USA

Energy consumption and air quality index (AQI) prediction is important for efficient heating, ventilation, and air conditioning (HVAC) system operation and management. A data-mining approach is presented in this paper for modeling and short-term prediction of the complicated non-linear system. The multilayer perceptron (MLP) ensemble performs best among the data mining algorithms discussed in this paper. A clustering-based method from preprocessing input data to construct the prediction models is proposed to decreases the prediction errors and the computational cost. The effectiveness of the proposed method is validated through a practical case study with both modeling and short-term prediction. The analytical results showed that the method was capable of reducing the prediction errors for modeling and short-term prediction by 11.05% and 12.21%, respectively, comparing with the models built without clustering method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： HVAC Energy Air quality index Modeling Prediction clustering algorithm

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A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on clustering algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2014年第6期9卷 1891-1899页

作者： Liu, Wenxia Chen, Qi Zhang, Yuying Qiu, Guobing Lin, Chenghui North China Elect Power Univ Sch Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China Guizhou Elect Power Res Inst Guiyang Peoples R China

A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be applied to the wind farm in mountainous land. The component failures in the wind farm like the cable and WT failures which contribute to the wind farm power output (WFPO) and reliability is investigated. Combing the wind speed distribution and the characteristic of wind turbine power output (WTPO), Monte Carlo simulation (MCS) is used to obtain the WFPO. Based on clustering algorithm the multi-state model of a wind farm is proposed. The accuracy of the model is analyzed and then applied to IEEE-RTS 79 for adequacy assessment.

关键词： Cable failure clustering algorithm Complex terrain Reliability model Wind farm

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Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and clustering-Based algorithms

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IEEE ACCESS 2023年 11卷 69227-69240页

作者： Miraftabzadeh, Seyed Mahdi Longo, Michela Brenna, Morris Politecn Milan Dept Energy Milan Italy

The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As solar power continues to become a larger part of the energy mix, managing this intermittency will be increasingly important. This paper focuses on identifying daily photovoltaic power production patterns to gain new knowledge of the generation patterns throughout the year based on unsupervised learning algorithms. The proposed data-driven model aims to extract typical daily photovoltaic power generation patterns by transforming the high dimensional temporal features of the daily PV power output into a lower latent feature space, which is learned by a deep learning autoencoder. Subsequently, the Partitioning Around Medoids (PAM) clustering algorithm is employed to identify the six distinct dominant patterns. Finally, a new algorithm is proposed to reconstruct these patterns in their original subspace. The proposed model is applied to two distinct datasets for further analysis. The results indicate that four out of the identified patterns in both datasets exhibit high correlation (over 95%) and temporal trends. These patterns correspond to distinct weather conditions, such as entirely sunny, mostly sunny, cloudy, and negligible power generation days, which were observed approximately 61% of the analyzed period. These typical patterns can be expected to be observed in other locations as well. Identified PV power generation patterns can improve forecasting models, optimize energy management systems, and aid in implementing energy storage or demand response programs and scheduling efficiently.

关键词： & nbsp clustering algorithm data mining deep learning autoencoder pattern extraction and analysis PV power generation unsupervised learning

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clustering Approach for the Efficient Solution of Multiscale Stochastic Programming Problems: Application to Energy Hub Design and Operation under Uncertainty

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PROCESSES 2023年第4期11卷 1046页

作者： Alkatheri, Mohammed Alhameli, Falah Betancourt-Torcat, Alberto Almansoori, Ali Elkamel, Ali Univ Waterloo Dept Chem Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Khalifa Univ Sci & Technol Dept Chem Engn Sas Al Nakhl CampusPOB 2533 Abu Dhabi U Arab Emirates

The management of the supply chain for enterprise-wide operations generally consists of strategic, tactical, and operational decision stages dependent on one another and affecting various time scales. Their integration usually leads to multiscale models that are computationally intractable. The design and operation of energy hubs faces similar challenges. Renewable energies are challenging to model due to the high level of intermittency and uncertainty. The multiscale (i.e., planning and scheduling) energy hub systems that incorporate renewable energy resources become more challenging to model due to an integration of the multiscale and high level of intermittency associated with renewable energy. In this work, a mixed-integer programming (MILP) superstructure is proposed for clustering shape-based time series data featuring multiple attributes using a multi-objective optimization approach. Additionally, a data-driven statistical method is used to represent the intermittent behavior of uncertain renewable energy data. According to these methods, the design and operation of an energy hub with hydrogen storage was reformulated following a two-stage stochastic modeling technique. The main outcomes of this study are formulating a stochastic energy hub optimization model which comprehensively considers the design and operation planning, energy storage system, and uncertainties of DRERs, and proposing an efficient size reduction approach for large-sized multiple attributes demand data. The case study results show that normal clustering is closer to the optimal case (full scale model) compared with sequence clustering. In addition, there is an improvement in the objective function value using the stochastic approach instead of the deterministic. The present clustering algorithm features many unique characteristics that gives it advantages over other clustering approach and the straightforward statistical approach used to represent intermittent energy, and it can be easily

关键词： clustering algorithm multiscale supply chain computational complexity energy hub

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A novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 35卷 1-13页

作者： Zhang, Ren-Long Shan, Mi-Yuan Liu, Xiao-Hong Zhang, Li-Hong Hunan Univ Coll Business Adm Changsha 410082 Hunan Peoples R China Liverpool John Moores Univ Liverpool Business Sch Liverpool L3 5UX Merseyside England

In this paper we propose a novel fuzzy hybrid quantum artificial immune clustering algorithm based on cloud model (C-FHQAI) to solve the stochastic problem. Fuzzy hybrid quantum artificial immune algorithm can be developed with some of the advantages of information processing where there is a certain amount of indeterminism with qubits, i.e. quantum bits, replacing classical neurons having deterministic states and also in place of the classical artificial immune algorithm with quantum operators. The fuzzy combinatorial fuzzy hybrid quantum artificial immune clustering algorithm (C-FHQAI) is more expressive than the other fuzzy theories and methods. Finally, numerical examples show that the clustering effectiveness of the C-FHQAI algorithm is fast convergence and improves the accuracy of the fuzzy calculation. We find that the C-FHQAI clustering algorithm has the perspective of widespread application. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： clustering algorithm C-FHQAI Cloud model Quantum computing

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Predicting Yeast Protein Localization Sites by a New clustering algorithm Based on Weighted Feature Ensemble

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2014年第6期11卷 1563-1568页

作者： Fan, Jiancong Feng, Zheng Liu, Wenhua Pang, Jinrong Liang, Yongquan Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao 266590 Peoples R China

The analysis of protein localization sites is an important task in bioinformatics. Predicting the yeast protein localization sites is a promising domain among numerous research methods based on the yeast protein measurement data which have multiple indexes/features. In order to reflect the different contributions of those features to predicting tasks, a clustering algorithm based on weighted feature ensemble (WFE) is proposed to predict yeast protein localization sites on the basis of the gathered yeast protein localization data. WFE process firstly assigns different weights to features, and then the results are computed and presented to obtain the best outcome. Experimental results on our algorithm based on WFE and other several clustering algorithms based on the ideas of weighted features have shown that our new algorithm outperformed the other feature weighting type algorithms in accuracy and stability.

关键词： clustering algorithm Yeast Protein Data clustering Feature Weighting Ensemble Bioinformatics

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