Automated classification of heat sources detected using SWIR remote sensing

Abstract The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale

Geothermal Energy: Delivering on the Global Potential

After decades of being largely the preserve of countries in volcanic regions, the use of geothermal energy—for both heat and power applications—is now expanding worldwide. This reflects its excellent low-carbon credentials and its ability to offer baseload and dispatchable output - rare amongst the mainstream renewables. Yet uptake of geothermal still lags behind that of solar and wind, principally because of (i) uncertainties over resource availability in poorly-explored reservoirs and (ii) the concentration of full-lifetime costs into early-stage capital expenditure (capex). Recent advances in reservoir characterization techniques are beginning to narrow the bounds of exploration uncertainty, both by improving estimates of reservoir geometry and properties, and by providing pre-drilling estimates of temperature at depth. Advances in drilling technologies and management have potential to significantly lower initial capex, while operating expenditure is being further reduced by more effective reservoir management — supported by robust mathematical models — and increasingly efficient energy conversion systems (flash, binary and combined-heat-and-power). Advances in characterization and modelling are also improving management of shallow low-enthalpy resources that can only be exploited using heat-pump technology. Taken together with increased public appreciation of the benefits of geothermal, the technology is finally ready to take its place as a mainstream renewable technology, This book draws together some of the latest developments in concepts and technology that are enabling the growing realisation of the global potential of geothermal energy in all its manifestations.After decades of being largely the preserve of countries in volcanic regions, the use of geothermal energy—for both heat and power applications—is now expanding worldwide. This reflects its excellent low-carbon credentials and its ability to offer baseload and dispatchable output - rare amongst the mainstream renewables. Yet uptake of geothermal still lags behind that of solar and wind, principally because of (i) uncertainties over resource availability in poorly-explored reservoirs and (ii) the concentration of full-lifetime costs into early-stage capital expenditure (capex). Recent advances in reservoir characterization techniques are beginning to narrow the bounds of exploration uncertainty, both by improving estimates of reservoir geometry and properties, and by providing pre-drilling estimates of temperature at depth. Advances in drilling technologies and management have potential to significantly lower initial capex, while operating expenditure is being further reduced by more effective reservoir management — supported by robust mathematical models — and increasingly efficient energy conversion systems (flash, binary and combined-heat-and-power). Advances in characterization and modelling are also improving management of shallow low-enthalpy resources that can only be exploited using heat-pump technology. Taken together with increased public appreciation of the benefits of geothermal, the technology is finally ready to take its place as a mainstream renewable technology

Electrical Signature Analysis of Synchronous Motors Under Some Mechanical Anomalies

Electrical Signature Analysis (ESA) has been introduced for some time to investigate the electrical anomalies of electric machines, especially for induction motors. More recently hints of using such an approach to analyze mechanical anomalies have appeared in the literature. Among them, some articles cover synchronous motors usually being employed to improve the power factor, drive green vehicles and reciprocating compressors or pumps with higher efficiency. Similarly with induction motors, the common mechanical anomalies of synchronous motor being analyzed using the ESA are air-gap eccentricity and single point bearing defects. However torsional effects, which are usually induced by torsional vibration of rotors and by generalized roughness bearing defects, have seldom been investigated using the ESA. This work presents an analytical method for ESA of rotor torsional vibration and an experimentally demonstrated approach for ESA of generalized roughness bearing defects. The torsional vibration of a shaft assembly usually induces rotor speed fluctuations resulting from the excitations in the electromagnetic (EM) or load torque. Actually, there is strong coupling within the system which is dynamically dependent on the interactions between the electromagnetic air-gap torque of the synchronous machine and the rotordynamics of the rotor shaft assembly. Typically this problem is solved as a one-way coupling by the unidirectional load transfer method, which is based on predetermined or assumed EM or load profile. It ignores the two-way interactions, especially during a start-up transient. In this work, a coupled equivalent circuit method is applied to reflect this coupling, and the simulation results show the significance of the proposed method by the practical case study of Electric Submersible Pump (ESP) system. The generalized roughness bearing anomaly is linked to load torque ripples which can cause speed oscillations, while being related to current signature by phase modulation. Considering that the induced characteristic signature is usually subtle broadband changes in the current spectrum, this signature is easily affected by input power quality variations, machine manufacturing imperfections and the interaction of both. A signal segmentation technique is introduced to isolate the influence of these disturbances and improve the effectiveness of applying the ESA for this kind of bearing defects. Furthermore, an improved experimental procedure is employed to closely resemble naturally occurring degradation of bearing, while isolating the influence of shaft currents on the signature of bearing defects during the experiments. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous motors

A fault detection system for a geothermal heat exchanger sensor based on intelligent techniques

[Abstract ]:This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.Junta de Castilla y León; LE078G18. UXXI2018/000149. U-220.Ministerio de Economía, Industria y Competitividad; DPI2016-79960-C3-2-