Thermal Imaging of Hydroelectric Generator Stator Using a DTS System

Bazzo, João Paulo; Mezzadri, Felipe; Silva, Erlon Vagner da; Pipa, Daniel Rodrigues; Martelli, Cícero; Silva, Jean Carlos Cardozo da

doi:10.1109/jsen.2015.2465862

Cited by 29 publications

(15 citation statements)

References 10 publications

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“…In a previous work [ 6 ], Bazzo et al presented a thermal imaging system for stator using DTS that was tested in a 200 MW hydroelectric generator. The main details of the structure and DTS installation on the stator surface are shown in Figure 2 .…”

Section: Overview Of Thermal Imaging Systemmentioning

confidence: 99%

“…Usually, stator temperature measurements are performed through conventional localized sensors such as PT100 (Platinum Thermo-resistance) or RTD (Resistance Temperature Detector). These sensors are suitable for monitoring temperature changes during standard operation (no failure), given that the temperature distribution is considered to be rather uniform [ 6 , 7 ]. On the other hand, they are not able to identify hotspots that may arise in the stator coils as a result of insulation breakdown.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, the stator is instrumented with a few tens of localized sensors, which are not sufficient to monitor the entire structure containing hundreds of bars. Furthermore, the use of conventional electronic transducers in generators presents additional drawbacks because of their sensitivity to electromagnetic interference [ 6 , 7 ]. These factors motivate the use of other sensing technologies, aiming at monitoring the temperature distribution over the stator structure [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has shown that the distributed optical sensor technology (DTS) has great potential for applications related to monitoring of generator stator temperature [ 6 , 7 , 8 , 9 , 10 ]. DTS systems measure temperatures by means of optical fibers.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

Bazzo

Pipa

Silva³

et al. 2016

Sensors

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This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

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Section: Overview Of Thermal Imaging Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

Bazzo

Pipa

Silva³

et al. 2016

Sensors

Self Cite

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show abstract

“…Distributed temperature sensors (DTS) are important in monitoring large structures such as hydroelectric generators [1], dams [2,3], oil and gas platforms [4], subway tunnels [5], mines, and warehouses [6]. Temperature interrogation provides fundamental information about the current operating conditions of these structures, ensuring safety for employees in such locations [7].…”

Section: Introductionmentioning

confidence: 99%

NARX neural network model for strong resolution improvement in a distributed temperature sensor

et al. 2018

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This paper proposes an approach to process the response of a distributed temperature sensor using a nonlinear autoregressive with external input neural network. The developed model is composed of three steps: extraction of characteristics, regression, and reconstruction of the signal. Such an approach is robust because it does not require knowledge of the characteristics of the signal; it has a reduction of data to be processed, resulting in a low processing time, besides the simultaneous improvement of spatial resolution and temperature. We obtain total correction of the temperature resolution and spatial resolution of 5 cm of the sensor.

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Diagnosis of the three-phase induction motor using thermal imaging

Głowacz

2017

Infrared Physics & Technology

205

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Thermal Imaging of Hydroelectric Generator Stator Using a DTS System

Cited by 29 publications

References 10 publications

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring

NARX neural network model for strong resolution improvement in a distributed temperature sensor

Diagnosis of the three-phase induction motor using thermal imaging

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