scholarly journals Text Independent Speaker Identification with Finite Multivariate Generalized Gaussian Mixture Model and Hierarchical Clustering Algorithm

2010 ◽  
Vol 11 (11) ◽  
pp. 25-31
Author(s):  
V Sailaja ◽  
K. Srinivasa Rao ◽  
K.V.V.S. Reddy
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Speaker Identification ◽  
Gaussian Mixture ◽  
Hierarchical Clustering Algorithm

scholarly journals Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4901
Author(s):  
Zhenyu He ◽  
Xiaochen Zhang ◽  
Chao Liu ◽  
Te Han
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Pv System ◽  
Pv Inverter ◽  
Data Clusters ◽  
The Status ◽  
Fault Prognostics

The fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop approaches that accurately predict the occurrence of inverter faults to ensure the PV system’s safety. This paper proposes a fault prognostics method which makes full use of the similarities between inverter clusters. First, a feature space was constructed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Then, the fast clustering algorithm was used to search the center inverter of each sampling time from the feature space. The status of the center inverter was adopted to establish the health baseline. Finally, the Gaussian mixture model was established with two data clusters based on the central inverter and the inverter to be predicted. The divergence of the two clusters could be used to predict the inverter’s fault. The performance of the proposed method was evaluated with real PV monitoring data. The experimental results showed that the proposed method successfully predicted the occurrence of an inverter fault 3 months in advance.

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