Efficiency Analysis of a Novel Power Grid Fault Diagnosis Method Based on Big Data Platform

After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

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Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching

Complexity ◽

10.1155/2019/7890652 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Mingyue Tan ◽

Jiming Li ◽

Xiangqian Chen ◽

Xuezhen Cheng

Keyword(s):

Time Series ◽

Fault Diagnosis ◽

Petri Nets ◽

Power Grid ◽

New Method ◽

Uncertain Information ◽

Intuitionistic Fuzzy ◽

Fuzzy Petri Nets ◽

Diagnosis Method ◽

Diagnosis Model

To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. Then, an intuitionistic fuzzy Petri Nets fault diagnosis model, with an excellent ability to process uncertain information from intuitionistic fuzzy sets, is constructed, and the initial place value of the model is corrected by the timing confidence. Finally, an application of the fault diagnosis model for the actual grid is established to analyze and verify the diagnostic results of the new method. The results for some test cases show that the new method can improve the accuracy and fault tolerance of fault diagnosis, and, furthermore, the abnormal state of the component can be inferred.

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Research on Fault Diagnosis Method of Train Wheelset Based on Deep Learning and Big Data Analysis

Lecture Notes in Electrical Engineering - Proceedings of 2020 Chinese Intelligent Systems Conference ◽

10.1007/978-981-15-8458-9_20 ◽

2020 ◽

pp. 180-192

Author(s):

WanQing OuYang ◽

Jian Zhang ◽

Rongchao Fang ◽

HengBo Jiang ◽

Yujie Luo ◽

...

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analysis ◽

Fault Diagnosis ◽

Big Data Analysis ◽

Diagnosis Method

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Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complexity ◽

10.1155/2018/8496187 ◽

2018 ◽

Vol 2018 ◽

pp. 1-21 ◽

Cited By ~ 7

Author(s):

Yuanjun Guo ◽

Zhile Yang ◽

Shengzhong Feng ◽

Jinxing Hu

Keyword(s):

Machine Learning ◽

Big Data ◽

Smart Grid ◽

Power System ◽

Data Management ◽

Power Grid ◽

Technical Solution ◽

Data Platform ◽

Complex Power ◽

Big Data Technology

Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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