Large-scale Topological Environmental Model Based Particle Filters for Mobile Robot Indoor Localization

2006 ◽  
Author(s):  
Fei Yan ◽  
Yan Zhuang ◽  
Wei Wang
Keyword(s):  
Mobile Robot ◽  
Large Scale ◽  
Particle Filters ◽  
Indoor Localization ◽  
Environmental Model ◽  
Model Based

scholarly journals A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6971-6989 ◽  
Author(s):  
Hengyi Gan ◽  
Mohd Haris Bin Md Khir ◽  
Gunawan Witjaksono Bin Djaswadi ◽  
Nordin Ramli
Keyword(s):  
Hybrid Model ◽  
Large Scale ◽  
Indoor Localization ◽  
Model Based

Model-based Control Techniques for Large-Scale High-Precision Stage

2020 ◽  
Vol 140 (4) ◽  
pp. 272-280
Author(s):  
Wataru Ohnishi ◽  
Hiroshi Fujimoto ◽  
Koichi Sakata
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Model Based Control ◽  
Precision Stage ◽  
Control Techniques ◽  
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.

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