Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

Zhang, Jingxuan; Sun, Hexu; Sun, Zexian; Dong, Weichao; Dong, Yan; Gong, Siyuan

doi:10.1109/access.2020.2992089

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…In the process of mining frequent itemsets, scan data, customer service will be a frequent itemsets, and then according to the setting of threshold platform security coefficient, will not satisfy the operation of the platform delete data sets, then get the new data set, and according to the new standard data set of frequent items, support count of descending order. In the actual application process, in order to improve the defect of scanning customer service database times, the improved FP-growth algorithm is used to adjust the arrangement of customer service data [12,13,14]. At the same time, according to the algorithm only need to traverse the characteristics of the database, separate from the original database for secondary mining, do not need to mining platform database again.…”

Section: Improve Fp-growth Algorithm To Build Rule Configuration Modelmentioning

confidence: 99%

Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Yang

Gong

Liu

et al. 2022

J. Phys.: Conf. Ser.

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Aiming at the problems of long time-consuming monitoring and poor monitoring accuracy in traditional customer service data analysis platform operation abnormality monitoring methods, a customer service data analysis platform operation abnormality monitoring method based on the improved FP-Growth algorithm is designed. Obtain customer service data sets, classify data types, filter customer behavior, identify the operating status of the data analysis platform, improve the FP-Growth algorithm to build a rule configuration model, set the platform safety factor threshold, and keep the reconstruction error of customer service data to a minimum Within the scope, optimize the abnormal monitoring mode. The experimental results show that the average recovery time of the proposed customer service data analysis platform operation anomaly monitoring method is 5.239s, and the average platform operation anomaly accuracy rate is 97.3%, indicating that the customer service data analysis platform integrated with the improved FP-Growth algorithm operates abnormally The monitoring method performs better.

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Section: Improve Fp-growth Algorithm To Build Rule Configuration Modelmentioning

confidence: 99%

Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Yang

Gong

Liu

et al. 2022

J. Phys.: Conf. Ser.

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“…Promotion (a → b) = confidence (a → b) / (support a) (9) For example, we have calculated the confidence level of milk and eggs (confidence) = 2 / 4. If the support of milk is 3 / 5, then we can calculate the support of milk and eggs: lift = 0.83.…”

Section: Chinese Word Segmentationmentioning

confidence: 99%

“…It can improve the performance of nearest neighbor classification, overcome the influence of outliers on the classification performance of small sample data set, and improve the classification accuracy of classification algorithm [8]. When the number of samples is large or tends to be infinite, the nearest neighbor classification has higher classification accuracy, but when the sample size is limited, the nearest neighbor classification may produce larger classification error [9]. The classification performance of KNN classifier is very sensitive to the neighborhood size k value.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Association Degree Algorithm Based on Complex Network Public Opinion

Luo¹,

Xu²

2020

IJFCC

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The network public opinion plays an irreplaceable role in today's society. The analysis and processing of data mining can effectively find out the sensitive network public opinion and prevent the intensification of social public opinion. In this paper, we propose an association degree algorithm between personal speech and user real name information in Chinese community. By defining the user's name, address, ID number, phone number, QQ number, e-mail, MSN and other information as keywords and setting different weights. The hot public opinion phrases in user comments are extracted, the word frequency is counted, the text semantic similarity classification model is constructed, the semantic tree of unknown text is automatically constructed, the semantic relevance based on the topic is calculated, and the relevance between the public opinion topic and the specified user information is obtained. The algorithm in this paper uses Chinese segmentation index technology and common algorithms of text de duplication technology. From the technical feasibility, economic feasibility and other aspects of the feasibility analysis; from the system response speed, scalability and security three main levels of the system function requirements analysis. The results show that the system design meets the requirements and improves the accuracy of online public opinion text collection and emotional analysis.

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“…The mining algorithms of frequent itemset can generally be divided into two types, namely accurate and heuristic algorithms. The most classical accurate algorithms include Apriori [6], FP-Growth [7], and many derived algorithms [8][9][10][11][12][13][14][15][16][17][18][19][20]. The theory of accurate algorithms seems to be perfect.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

Zhang

et al. 2021

Applied Sciences

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The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.

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Reliability Assessment of Wind Power Converter Considering SCADA Multistate Parameters Prediction Using FP-Growth, WPT, K-Means and LSTM Network

Cited by 14 publications

References 24 publications

Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Operation Anomaly Monitoring of Customer Service Data Analysis Platform Based on Improved FP-Growth Algorithm

Analysis of Association Degree Algorithm Based on Complex Network Public Opinion

Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

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