Open Circuit Fault Diagnosis of Cascaded H-Bridge MLI Using k-NN Classifier Based on PPCA

Kuraku, Nagendra Vara Prasad; Ali, Murad; He, Yigang

doi:10.1007/978-3-319-74690-6_42

Cited by 2 publications

(2 citation statements)

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“…faults in a cascaded Multilevel inverter. [26]. It tends to select features that are close neighbors and forms a cluster corresponding to the fault type.…”

Section: Ml-based Classifiermentioning

confidence: 99%

“…The availability of rich data encourages the usage of data-driven methods like machine learning (ML) approaches. Predominant of the ML techniques, namely Support vector machine (SVM) [8], Artificial Neural Network (ANN) [25], and K-nearest neighbor (KNN) [26], belong to the data-driven approach. Fault diagnosis using intelligent data-driven algorithms has turned out to be a research hotspot in recent years [27][28].…”

Section: Introductionmentioning

confidence: 99%

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Combined Optimizer for Automatic Design of Machine Learning-Based Fault Classifier for Multilevel Inverters

et al. 2022

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Fault detection and classification are fundamental requirements of multilevel inverters (MLIs) to ensure constant operation and improved reliability. Nowadays, the machine learning (ML) technique is utilized for fault diagnosis in MLIs due to its inherent features such as high accuracy, reduced computation time, and complexity. However, the rich availability of parts and classifiers in ML techniques demands a tedious investigation of every combination to design an optimal fault classifier. To overcome this problem, a combined optimiser is proposed to automate the ML-based classifier design, which involves selecting optimal features and classifier. Mean, total harmonic distortion, root mean square, and different harmonic orders (upto 12 th order) of the output voltage of MLI are considered as features and four different ML techniques like K-nearest neighborhood, decision tree, Naive Bayesian classifier, and support vector machines are considered. Ant Colony Optimization (ACO) is used to formulate a combinatorial optimization routine to maximize classification accuracy by optimal selection of features and classifier. The proposed technique is used to design a fault classifier for two different MLIs, such as Cascaded H-bridge MLI (CHBMLI) and Packed U cell inverter (PUC), during the fault conditions (open circuit and short circuit) to check the feasibility. Simulation results illustrate classification accuracy of 97.84% and 98.61% for CHBMLI and PUC, respectively. Experimental validation of the designed classifier on the inverter prototype is also carried out and illustrates 95.56% and 94.28% classification accuracy for CHBMLI and PUC, respectively.

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“…faults in a cascaded Multilevel inverter. [26]. It tends to select features that are close neighbors and forms a cluster corresponding to the fault type.…”

Section: Ml-based Classifiermentioning

confidence: 99%