Fuzzy Observer Stabilization for Discrete-Time Takagi–Sugeno Uncertain Systems with k-Samples Variations

Bouyahya, Ali; Manai, Yassine; Haggège, Joseph

doi:10.1007/s40313-020-00577-w

Cited by 3 publications

(1 citation statement)

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“…Therefore, ANFIS is used as the classifier in the developed method. ANFIS has excellent performance in different engineering tasks such as signal processing, classification, fault detection, control of nonlinear systems, image processing, clustering and time series prediction (Fateh and Fateh 2020;Bouyahya et al 2020;Farahani and Rahmani 2019).…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Katooli

Koochaki

2020

J Control Autom Electr Syst

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Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the subsystems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure.

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Section: Introductionmentioning

confidence: 99%