Applications of fuzzy multilayer support vector machines in fault diagnosis and forecast of electric power equipment

Peng, Gang; Tang, Songping; Lin, Zhiming; Zhang, Yun

doi:10.1109/iaeac.2017.8054056

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…All of the data or subset of data from grid operations data, weather information, diagnostics data of the relay protection systems, galloping of power lines, fault tolerance current, and voltage signals have been used for the design of data-driven models for preventive maintenance in the power grids. Different machine learning models such as SVM [102], extreme learning machines [103], Long Short Term Memory (LSTM) [104], hybrid ensemble models [105], etc. are used to build datadriven models.…”

Section: B Preventive Maintenancementioning

confidence: 99%

Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

et al. 2021

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Smart grids have been gradually replacing the traditional power grids since the last decade. Such transformation is linked to adding a large number of smart meters and other sources of information extraction units. This provides various opportunities associated with the collected big data. Hence, the triumph of the smart grid energy paradigm depends on the factor of big data analytics. This includes the effective acquisition, transmission, processing, visualization, interpretation, and utilization of big data. The paper provides deep insights into various big data technologies and discusses big data analytics in the context of the smart grid. The paper also presents the challenges and opportunities brought by the advent of machine learning and big data from smart grids. INDEX TERMS Big data, data analytics, smart grid, big data management, machine learning.

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Section: B Preventive Maintenancementioning

confidence: 99%

Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

et al. 2021

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“…Through the application of algorithms in fault diagnosis using circuit breakers and transformers, explain this method can overcome abnormal data. Therefore, overcoming the sensor difficulties for unusual conditions and increasing the accuracy of equipment diagnosis (Peng et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assess the Risk Level of Power Transformer Due Short-Circuit Faults Based on Anfis

Zakri

Mustafa

Firdaus

et al. 2019

Sinergi

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A power transformer is an electrical machine that converts electrical power at different voltage levels. Faults, occur in power transformers, inhibit electrical power distribution to the consumer. Protection, therefore, of the power transformers is essential in power systems reliability. The power system can be reliable if the protection devices work well when there is a fault. A hybrid intelligent technique, which is a combination of Artificial Neural Network (ANN) and Fuzzy known as Adaptive Neuro-Fuzzy Inference Systems (ANFIS), was used in this research. The objective of this paper is the simulation of differential relays as a protection device on power transformers using Matlab/Simulink. Performance of differential relays for power transformers protection is carried out with internal and external fault scenarios. The input data were classified into three different input for ANFIS such as internal and external 1, internal and external 2, internal, external 1, and external 2, respectively. The error results of ANFIS training for the type of fault internal and external 1 is 9.46*10-7, and types of fault internal and external 2 is 1.09*10-6 internal, external 1 and external 2 are 8.59*10-7. The results obtained from the simulation were accurate and shows that the ANFIS technique is an efficient method that gives less error and a great value. Finally, the technique can minimize faults with power transformers. Finally, to prove this method can reduce faults in the power transformer, the assess of this model has been carried out through the RMSE that has been generated which is zero.

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Applications of fuzzy multilayer support vector machines in fault diagnosis and forecast of electric power equipment

Cited by 2 publications

References 17 publications

Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

Assess the Risk Level of Power Transformer Due Short-Circuit Faults Based on Anfis

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