Fault diagnosis in grid‐connected PV NPC inverters by a model‐based and data processing combined approach

2019 ◽  
Vol 12 (12) ◽  
pp. 3254-3264 ◽  
Author(s):  
José Aagel Pecina Sánchez ◽  
Daniel U. Campos‐Delgado ◽  
Diego R. Espinoza‐Trejo ◽  
Andres A. Valdez‐Fernández ◽  
Cristian H. De Angelo
Keyword(s):  
Fault Diagnosis ◽  
Data Processing ◽  
Combined Approach ◽  
Model Based

scholarly journals A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1261
Author(s):  
Christopher Gradwohl ◽  
Vesna Dimitrievska ◽  
Federico Pittino ◽  
Wolfgang Muehleisen ◽  
András Montvay ◽  
...  
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Failure Detection ◽  
Energy Yield ◽  
Combined Approach ◽  
Levelized Cost Of Electricity ◽  
Term Operation ◽  
Model Based

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

On-Board Model Based Fault Diagnosis for CubeSat Attitude Control Subsystem: Flight Data Results

2021 ◽  
Author(s):  
Ryan Mackey ◽  
Allen Nikora ◽  
Cornelia Altenbuchner ◽  
Robert Bocchino ◽  
Michael Sievers ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Attitude Control ◽  
Flight Data ◽  
Control Subsystem ◽  
Model Based

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

The Application of Local Mean Decomposition and Variable Predictive Model-Based Class Discriminate in Gear Fault Diagnosis

2014 ◽  
Vol 1014 ◽  
pp. 510-515 ◽  
Author(s):  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Shu Guo ◽  
Min Gou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Predictive Model ◽  
Feature Vector ◽  
Local Mean Decomposition ◽  
Outer Race ◽  
Model Based ◽  
Gear Fault ◽  
Local Mean ◽  
Gear Fault Diagnosis ◽  
Non Stationary Signal

The time-domain energy message conveyed by vibration signals of different gear fault are different, so a method based on local mean decomposition (LMD) and variable predictive model-based class discriminate (VPMCD) is proposed to diagnose gear fault model. The vibration signal of gear which is the research object in this paper is decomposed into a series of product functions (PF) by LMD method. Then a further analysis is to select the PF components which contain main fault information of gear, the energy feature parameters of the selected PF components are used to form a fault feature vector. The variable predictive model-based class discriminate is a new multivariate classification approach for pattern recognition, through taking fully advantages of the fault feature vector. Finally, gear fault diagnosis is distinguished into normal state, inner race fault and outer race fault. The results show that LMD method can decompose a complex non-stationary signal into a number of PF components whose frequency is from high to low. And the method based on LMD and VPMCD has a high fault recognition function by analyzing the fault feature vector of PF.

scholarly journals Optimization of a Dynamic Fault Diagnosis Model Based on Machine Learning

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 65065-65077 ◽  
Author(s):  
Shigang Zhang ◽  
Xu Luo ◽  
Yongmin Yang ◽  
Long Wang ◽  
Xiaofei Zhang
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Model Based ◽  
Diagnosis Model ◽  
Dynamic Fault Diagnosis
Sign in / Sign up

Export Citation Format

Share Document