A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

Sheng, Gehao; Hou, Honghao; Jiang, Xiuchen; Chen, Yufeng

doi:10.1109/tsg.2016.2562123

Cited by 70 publications

(32 citation statements)

References 15 publications

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“…Different failure modes will have different effects on the change trend of the fault characteristic parameters, which are quantified by association rule mining algorithms, such as Apriori and FP-Growth algorithms [19,20]. The Apriori algorithm scans the whole database in each loop to calculate the support and confidence coefficient of the candidate item sets.…”

Section: The Association Rules Mining For Failure Modes and The Faultmentioning

confidence: 99%

A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters

Hang

Wang

Yang³

et al. 2017

Energies

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Abstract:Although traditional fault diagnosis methods can qualitatively identify the failure modes for power equipment, it is difficult to evaluate the failure probability quantitatively. In this paper, a failure probability calculation method for power equipment based on multi-characteristic parameters is proposed. After collecting the historical data of different fault characteristic parameters, the distribution functions and the cumulative distribution functions of each parameter, which are applied to dispersing the parameters and calculating the differential warning values, are calculated by using the two-parameter Weibull model. To calculate the membership functions of parameters for each failure mode, the Apriori algorithm is chosen to mine the association rules between parameters and failure modes. After that, the failure probability of each failure mode is obtained by integrating the membership functions of different parameters by a weighted method, and the important weight of each parameter is calculated by the differential warning values. According to the failure probability calculation result, the series model is established to estimate the failure probability of the equipment. Finally, an application example for two 220 kV transformers is presented to show the detailed process of the method. Compared with traditional fault diagnosis methods, the calculation results not only identify the failure modes correctly, but also reflect the failure probability changing trend of the equipment accurately.

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Section: The Association Rules Mining For Failure Modes and The Faultmentioning

confidence: 99%

A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters

Hang

Wang

Yang³

et al. 2017

Energies

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“…In addition, some scholars have paid special attention to the remaining life [12][13][14][15] of the transformer. The state prediction models proposed in these studies include the neural network [4], support vector machine regression [2,3], fuzzy logic [14], nonparametric regression [10], and probabilistic graph [16]. These methods have demonstrated their effectiveness in a number of circumstances, and some research results have been obtained.…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Operating State Prediction Method Based on an LSTM Network

et al. 2018

Self Cite

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Abstract:The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

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“…(1) In data mining, there is an important principle (Apriori Principle) about the association rule, which states that if an itemset is frequent, then all of its subsets must also be frequent [20]. Suppose that both Alice and Bob are suppliers of a supermarket .…”

Section: Introductionmentioning

confidence: 99%

Efficient Secure Multiparty Subset Computation

Zhou

Dou

et al. 2017

Security and Communication Networks

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Secure subset problem is important in secure multiparty computation, which is a vital field in cryptography. Most of the existing protocols for this problem can only keep the elements of one set private, while leaking the elements of the other set. In other words, they cannot solve the secure subset problem perfectly. While a few studies have addressed actual secure subsets, these protocols were mainly based on the oblivious polynomial evaluations with inefficient computation. In this study, we first design an efficient secure subset protocol for sets whose elements are drawn from a known set based on a new encoding method and homomorphic encryption scheme. If the elements of the sets are taken from a large domain, the existing protocol is inefficient. Using the Bloom filter and homomorphic encryption scheme, we further present an efficient protocol with linear computational complexity in the cardinality of the large set, and this is considered to be practical for inputs consisting of a large number of data. However, the second protocol that we design may yield a false positive. This probability can be rapidly decreased by reexecuting the protocol with different hash functions. Furthermore, we present the experimental performance analyses of these protocols.

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A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

Cited by 70 publications

References 15 publications

A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters

A Failure Probability Calculation Method for Power Equipment Based on Multi-Characteristic Parameters

Power Transformer Operating State Prediction Method Based on an LSTM Network

Efficient Secure Multiparty Subset Computation

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