Accurate Temperature Estimation Research Articles

Transformer hot spot temperature estimation through adaptive neuro fuzzy inference system approach

Transformer performance and efficiency can be enhanced by effectively address the properties of its insulation system. The power transformer insulation system weakens as a result of operational thermal stresses brought on by dynamic loading and shifting environmental patterns. Winding hot spot temperature is a crucial metric that must be maintained below the prescribed limit while power transformers are operating so as to maintained power system reliability. This is due to the fact that, among other variables, the time-dependent aging effect of insulation depends on transitions in hot spot temperatures. Due to the non‐linear nature of the conventional mathematical models used to determine these temperatures, and complexity of thermal phenomena, investigations still need to be exercised to fully understand the variables that associate with hot spot temperature computation with minimum error. This paper explores the possibilities of enhancing top oil and hot spot temperature estimation accuracy through the use of an adaptive neuro-fuzzy inference (ANFIS) technique. The paper presents an adaptive neuro fuzzy model to approximate the hot spot temperature of a mineral oil-filled power transformer based on loading, and established top oil temperature. Initially, a sub-ANFIS top oil temperature estimation model based on loading and ambient temperature as inputs is established. Using a hybrid optimization technique, the ANFIS membership functions were fine-tuned throughout the training process to reduce the difference between the actual and anticipated outcomes. The correctness and reliability of the created adaptive neural fuzzy model have been verified using real-world field data from a 60/90MVA, 132 kV power transformers under dynamic operating regimes. The ANFIS model results were validated against field measured values and literature-based electrical-thermal analogous models, establishing a precise input-output correlation. The developed ANFIS model achieves the highest coefficient of determination for both TOT and HST (0.98 and 0.96) and the lowest mean square error (7.8 and 10.3) among the compared thermal models. Correct determination of HST can help asset managers in thermal analysis trending of the in-service transformers, helping them to make proper loading recommendations for safeguarding the asset.

Improved Coupled Electrothermal Model of Lithium-ion Battery for Accurate Core Temperature Estimation at High Current

Improved Coupled Electrothermal Model of Lithium-ion Battery for Accurate Core Temperature Estimation at High Current

Multi-index-weighted geothermometer estimation of geothermal reservoir temperature: Applications and future directions

Geothermal reservoir temperature (GRT) is an important factor for estimation of geothermal resources and exploitative potential. Errors in the estimated temperature of a geothermal reservoir can likely result from the use of a single geothermometer or the average. Therefore, there is an urgent need for novel and more accurate methods for estimating GRT. Taking Yitong Basin, China as the study area, this study collected water samples and measured the geothermal water temperature of 26 geothermal wells. After application of the improved entropy method (IEM) to the collected data, a multi-index weighted geothermometer (MIWG) was proposed. The MIWG provided the most accurate estimation of temperature, with average errors in temperature estimation for quartz, chalcedony, Na–K, K–Mg, and MIWG of 57.58 %, 15.86 %, 29.92 %, 42.03 %, and 11.94 %, respectively. This novel approach to estimating GRT has clear advantages, including wide applicability, and can provide a new pathway for future related research.

Temperature Estimation Accuracy Improvement of Computational Fluid Dynamic Simulation by Optimizing Multiple Parameters

Temperature Estimation Accuracy Improvement of Computational Fluid Dynamic Simulation by Optimizing Multiple Parameters

Impact of intensity error on temperature estimation in laser-induced breakdown spectroscopy: a numerical study

Laser-induced breakdown spectroscopy (LIBS) is a cutting-edge technique for the compositional analysis of multi-element materials. Under standard circumstances for laser-induced plasma (T e = 1 eV and N e = 1016 cm−3), we simulated the emission spectrum of a binary alloy (with 70 wt.% Cu–30 wt.% Al). We used the Saha ionization equilibrium formulas to calculate the population of neutral and ionized species of each constituent element, and the Boltzmann distribution to estimate the intensities of emission lines with radiative transition probabilities. The Stark broadening equation is then used to determine the line broadening, yielding a Lorentzian profile for each line. The sum of line emissions of all constituent species will approximate the alloy’s LIBS spectra in an assumption of ideal analytical plasma. Then, we generated random errors in the intensities of spectral lines ranging from 5% to 35%. To investigate temperature estimation accuracy, we utilized three well-established approaches: the Boltzmann plot (BP) method, the Saha–Boltzmann plot (SBP) method, and the Multi-elemental SBP (MESBP) method. As intensity error increases from 5% to 35%, the estimated temperature in the BP method deviates from 0.25% to 18.3%. Whereas the intensity error is almost unaffected using the SBP method and the MESBP method. The temperature deviation is less than 2% in both situations. This study is relevant to calibration-free LIBS, in which the exact temperature determination is crucial for the abundance estimation of trace, major, and minor elements.

Comprehensive analysis of dust impact on photovoltaic module temperature: Experimental insights and mathematical modeling

Dust accumulation substantially impacts the efficiency and thermal behavior of photovoltaic (PV) modules. Addressing a current knowledge gap, this article presents a comprehensive assessment of the impact of dust on PV module temperature. This endeavor includes a combination of systematic experiments with a novel developed mathematical model that uniquely incorporates key dust parameters such as emissivity, absorbance, and transmittance. The model enhances accuracy in estimating PV module temperature compared to existing mathematical models, which often overlook heat absorption due to dust and specific dust characteristics. These parameters are measured using specialized devices to ensure realistic values. Outdoor experiments are conducted to validate the model's predictions under real weather conditions, further highlighting the importance of accurate PV temperature estimation in dusty environments. Additionally, a comparative analysis is performed with existing mathematical models for PV temperature prediction, demonstrating the superior performance of the proposed model, which achieves the lowest average prediction error (mean absolute error of 1.4). These findings provide valuable insights into the estimation of PV temperature in dusty conditions, bridging the gap between theoretical modeling and practical application and underscoring the novelty and innovation introduced in this research.

Thermo-sensitive electrical parameters of high-power IGBTs based on gate-emitter voltage measurement during switching delay intervals

High-power insulated gate bipolar transistors (IGBTs) are widely used for wind power conversion and electric traction. Accurate estimation of IGBT junction temperature and active thermal control improve the reliability of power converters in such applications. Among the various available methods for junction temperature estimation, thermo-sensitive electrical parameter (TSEP) based estimation is simple, fast and amenable to real-time implementation. Evaluation of many TSEPs (e.g. forward voltage, threshold voltage, peak dv/dt, peak di/dt etc.) require measurement of multiple electrical quantities in the power circuit and/or in the gate-drive circuit. The measurement systems need to have high precision and/or high bandwidth as well. Moreover, the quantities in the power circuit require isolated measurement systems. This paper explores possible new TSEPs, that could be obtained through a single measurement of low-voltage quantity, namely, the gate-emitter voltage. Since the gate-emitter voltage varies primarily during turn-on and turn-off delay intervals, these switching intervals and their parameters are studied over a wide range of operating conditions. Since many practical converters are expected to operate at an ambient temperature as low as −35 °C, the experimental study is carried out over a wide temperature range from −35 °C to 125 °C. Further, to ensure wider applicability of the findings of the study, four IGBTs of different makes are considered for the experimental investigations. The experimental results bring out four different parameters pertaining to turn-on delay and another four parameters pertaining to turn-off delay, which are potential TSEPs. Of these eight TSEPs, five of them can be evaluated based on measurement of gate-emitter voltage only, and four of these have not been reported in the literature previously. In addition, the experimental data are useful for development of temperature-sensitive device models for simulation, performance assessment and design of gate-drive circuit for different temperatures, and for temperature-sensitive compensation of inverter dead-time effect.

1 km monthly precipitation and temperatures dataset for China from 1952 to 2019 based on new baseline climatology surfaces

Long-term climate data and high-quality baseline climatology surface with high resolution are essential to investigate climate change and its effect on hydrological processes and ecosystem functioning. However, large uncertainties remain in the climate products in China owing to lacking of high-density distribution network of weather stations. Here, the thin plate spline (TPS) algorithm was used to produce new baseline climatology surfaces (ChinaClim_baseline) using >2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1 km monthly precipitation and temperatures dataset for China during 1952–2019 (ChinaClim_time-series) via superimposing ChinaClim_baseline and monthly anomaly surface. Our finding showed that ChinaClim_baseline performed exceptionally well in four climatic regions, with RMSEs for precipitation and temperature element estimation of 1.276–28.439 mm and 0.310–2.040 °C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but there were clearly spatial differences. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502– 52.307 mm, and 0.461–0.939 °C for all months, respectively. In comparison to Peng's climatic surface and CHELSAcruts, R2 increased by ~7 %, RMSE and MAE dropped by ~17 % for precipitation; R2 hardly increased, while RMSE and MAE decreased by ~50 % for temperature elements. Our findings indicated that ChinaClim_baseline improved the accuracy of time-series climatic elements estimation obviously, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperature estimation. Overall, ChinaClim_baseline as an excellent baseline climatology surface can be used for obtaining high-quality and long-term climate datasets from past to future. Meantime, ChinaClim_time-series of 1 km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.

Parametric sensitivity analysis of a one-dimensional thermal-electrical model of a photovoltaic solar module

The efficiency of a photovoltaic module decreases with the increase in the temperature of the photovoltaic cell, which makes the determination of this temperature one of the determining factors in the modeling of a photovoltaic module. The objective of this work is to make a detailed study about the input parameters of a developed thermal-electrical model of a photovoltaic module, based on a sensitivity analysis. The model includes a thermal part, in which the temperature varies along the five layers of the module, and an electrical part, which is based on a single diode five parameters model. The coupling of these models into a thermal-electrical model is described. Through differential sensitivity analysis, the parameters that have a significant impact on estimating the photovoltaic cell temperature were determined. The variations of the sensitivity coefficients throughout a simulated day are presented. Parameters with the highest absolute sensitivity coefficients, such as the transmittance-absorptance product and open circuit voltage, are crucial for accurate temperature estimation and were further researched and determined with greater precision, in order to improve the model reliability. Conversely, parameters with lower absolute sensitivity coefficients, such as albedo and parallel resistance, were considered less important and could be simplified to enhance computational efficiency.

Data-driven online energy management framework for HVAC systems: An experimental study

The optimization of the operation of heating, ventilation, and air conditioning (HVAC) systems has gained considerable attention due to its energy conservation potential. Collecting various building operation data presents an opportunity to improve the efficiency of energy management and reduce consumption. In this paper, a data-driven online energy management framework is proposed for enhancing the effectiveness of HVAC systems and minimizing energy consumption. To streamline the modeling process and improve the accuracy of temperature estimation, our proposed framework adopts a symbolic regression approach to estimate building thermodynamics based on the collected data. It also formulates a model predictive control (MPC)-based HVAC scheduling strategy to optimize HVAC operations while maximizing thermal comfort and reducing energy consumption and peak power demand. We conducted on-site experiments to evaluate the proposed framework and demonstrated a notable reduction in total energy consumption in both HVAC cooling and heating operations: average reductions of 4.9% and 30.2%, and 49.3% and 73.9% for best cases in the on-site experiment under certain conditions. In the heating operation, the proposed data-driven HVAC management framework efficiently utilized indoor/outdoor heat generation to maintain comfortable temperature levels without activating the HVAC. Such effective utilization of heat sources shortened the HVAC runtimes and further reduced the total energy consumption. With the proposed framework, the average HVAC peak power demand fell by 25.8% and 35.1% during cooling and heating operations in the on-site experiment with certain scenarios. On-site experiment results demonstrated the proposed framework can efficiently suppress peak power demand, reduce energy consumption in both cooling and heating operation under certain conditions. The framework’s data-driven approach provides an effective means of optimizing HVAC operations, reducing energy consumption, and improving the thermal comfort of the building’s occupants.

Investigation on thermal performance analysis of a pouch Li-ion battery under various drive cycle profiles

Knowledge of accurate battery temperature is essential for enabling safe operation of batteries in electrified vehicles application under aggressive drive profiles. Thermal modelling of batteries based on mere joule heating term is usually unable to compensate for resulting inaccuracy due to neglecting entropic heat in batteries. Inclusion of entropic effect into the thermal model derived from simple experiments can help accurate estimate of battery temperature in early design stage. This paper presents a lumped thermal model incorporating entropic effect to predict transient thermal behaviors of pouch cell under various aggressive loads. Compared with experimental results simulation model can predict results with acceptable accuracy and minimal computing cost. The simulation results with these tested conditions are found in good agreement with experimental results with a maximum RMSE error of 0.15, 0.07, 0.29, and 0.26 respectively. This shows the inclusion of reversible heat in the enhancing thermal prediction performance of the proposed electro-thermal model is significant and is a fairly good candidate for a rapid electro-thermal simulation of Li-ion pouch type batteries.

Temperature Estimation in Lithium-Ion Cells Assembled in Series-Parallel Circuits Using an Artificial Neural Network Based on Impedance Data

Lithium-ion cells are widely used in various applications. For optimal performance and safety, it is crucial to have accurate knowledge of the temperature of each cell. However, determining the temperature for individual cells is challenging as the core temperature may significantly differ from the surface temperature, leading to the need for further research in this field. This study presents the first sensorless temperature estimation method for determining the core temperature of each cell within a battery module. The accuracy of temperature estimation is in the range of ΔT≈1 K. The cell temperature is determined using an artificial neural network (ANN) based on electrochemical impedance spectroscopy (EIS) data. Additionally, by optimizing the frequency range, the number of measurement points, input neurons, measurement time, and computational effort are significantly reduced, while maintaining or even improving the accuracy of temperature estimation. The required time for the EIS measurement can be reduced to 0.5 s, and the temperature calculation takes place within a few milliseconds. The setup consists of cylindrical 18,650 lithium-ion cells assembled into modules with a 3s2p configuration. The core temperature of the cells was measured using sensors placed inside each cell. For the EIS measurement, alternating current excitation was applied across the entire module, and voltage was measured individually for each cell. Various State of Charge (SoC), ambient temperatures, and DC loads were investigated. Compared to other methods for temperature determination, the advantages of the presented study lie in the simplicity of the approach. Only one impedance chip per module is required as additional hardware to apply the AC current. The ANN consists of a simple feedforward network with only one layer in the hidden layer, resulting in minimal computational effort, making this approach attractive for real-world applications.

Performance comparison of PTCs with nanoparticles in water and nanoparticle in thermal oil

The use of nanoparticles to improve the thermal efficiency of PTCs has become a popular area of research. In this study, we present a comparative analysis of the effects of nanoparticles on the thermal performance of PTCs using water and thermal oil as heat transfer fluids. To obtain accurate temperature estimates, we developed a mathematical model that considers heat transfer in both axial and radial directions. The model includes a two-phase flow homogenous model for the liquid phase, as well as nanoparticle concentration, to achieve the best temperature estimation. We validated our model using experimental data and found that the maximum relative error was 2.3% for water and a lower error of 1.5% for thermal oil. Our analysis of thermal efficiency and exergetic efficiency showed that the addition of Al2O3 nanoparticles led to greater improvements in the thermal efficiency for water, even at the same range of nanoparticle concentration. Furthermore, our results demonstrated that exergetic efficiency was generally higher for water than for thermal oil. Overall, this study highlights the potential benefits of using nanoparticles to enhance the thermal efficiency of PTCs. Our findings could help guide the design and optimization of PTC systems in various applications.

Data-driven thermal state estimation for in-orbit systems via physics-informed machine learning

Thermal analysis of spacecraft systems is a critical process for mission operations. However, knowing the temperature distribution of entire systems is not easy due to the uncertainty of the thermal mathematical model (TMM) and limited temperature sensors. This paper proposes a temperature estimation method using physics-informed machine learning (PIML). The PIML-based thermal analysis allows us to estimate the actual temperature distribution by seamlessly bridging the limited observations and the TMM. To evaluate the estimation accuracy of the proposed method, we conducted a numerical experiment using a pseudo small satellite model consisting of 100 nodes. The proposed method was applied to three different model error cases and was found to improve temperature estimation accuracy in all cases. In addition, the impact of the number of temperature sensors and their placement on estimation accuracy was investigated.

Fast multilayer temperature distribution estimation for lithium-ion battery pack

Fast and accurate temperature estimation is crucial for ensuring battery packs' safety and operation performance. However, the full-scale online temperature estimation is still challenging. In this work, a novel reduced-order multi-physics model for a lithium-ion battery pack is proposed for real-time temperature distribution estimation, containing the distributed equivalent circuit model, the three-heat-source thermal model, and the flow resistance network model. The proposed model is conducted on a direct contact liquid-cooled battery pack composed of three modules connected in series. An online parameterization methodology with a closed loop observer is designed, and the key parameters can be automatically identified and corrected. The validation results suggest that the multilayer temperature distribution of cell, module, and pack levels can be commendably described under both steady and transient conditions, where the maximum error can be controlled within 2.8 °C. Besides, the temperature variation of the coolant can be estimated during the operation. The proposed model shows excellent potential in onboard temperature estimation with tens of milliseconds for each temperature update.

Real-time temperature prediction of electric machines using machine learning with physically informed features

Accurate estimation of the internal temperatures of electric machines is critical to increasing their power density and reliability since key temperatures, such as magnet temperature, are often difficult to measure. This work presents a new machine learning based modelling approach, incorporating novel physically informed feature engineering, which achieves best-in-class accuracy and reduced training time. The different features introduced are proportional to sources of machine losses and require no prior knowledge of the machine, hence the models are completely data driven. Evaluation using a standard experimental dataset shows that modelling errors can be reduced by up to 82.5%, resulting in the lowest mean squared error recorded in the literature of 2.40 K2. Additionally, models can be trained with less training data and have lower sensitivity to data quality. Specifically, it was possible to train a loss enhanced multilayer perceptron model to a mean squared error <5 K2 with 90 h of training data, and an enhanced ordinary least squares model with just 60 h to the same criteria. The inference time of the model can be 1–2 orders of magnitude faster than competing models and requires no time to optimise hyperparameters, compared to weeks or months for other state-of-the-art prediction methods. These results are highly important for enabling low-cost real-time temperature monitoring of electric machines to improve operational efficiency, safety, reliability, and power density.

Superpave Pavement Design Temperatures in Estonia

This paper introduces the maximum and minimum pavement temperatures of Estonian asphalt pavements in accordance with calculation models developed for North America and Norway. Historical meteorological data from 1992 to 2021 obtained from 25 different weather stations in Estonia were used as an input for the respective models. Comparison between the calculation models demonstrated high variability of the pavement design temperatures, thus significantly impacting the bitumen grade selection. Based on the road weather stations data, the Norwegian and Canadian models provide the most accurate pavement temperature estimations for Estonian conditions. Calculated upper and lower-bound pavement design temperatures varied between +52 °C to +58 °C and −22 °C to −34 °C, respectively. All models showed milder pavement temperatures and lower seasonal temperature amplitudes for coastal and offshore areas. The results also indicated the importance of validating model suitability as well as correlation with actual pavement temperatures in the Baltic region.

A performance evaluation of various physics schemes on the predictions of precipitation and temperature over the Tibet Autonomous Region of China

The WRF model has become an important tool for numerical weather prediction. However, the mechanism of different physics schemes in WRF for air temperature and precipitation predictions over the Tibet Autonomous Region of China is still uncertain. In this study, WRF experiments with distinct physics schemes are conducted on a rainfall event that occurred during the last two days of June 2021 in Tibet. The predictions of air temperature and precipitation from WRF are compared against observational data and the ECMWF forecast, and explained by the land-atmosphere interaction. Results confirm that WRF produces more accurate temperature and precipitation predictions than the widely used ECMWF forecast product. These analyses also reveal large differences in WRF performance in predicting the intensity and spatial patterns of rainfall and diurnal variations of temperature due to the use of different physics schemes. Among the selected physics schemes, the RRTMG scheme yields the highest accuracy forecasts for light rain to rainstorms. The other schemes give an ambiguous performance in relation to predicting various rainfall intensities. In addition, WRF applying the Noah-MP, Mellor-Yamada-Janjic, and RRTMG schemes contributes to alleviating the predicted cold air bias from the ECMWF and WRF control experiment, which is closely related to the surface net radiation overprediction, near-surface energy accumulation and the relatively accurate estimates of ground temperature. The RRTMG scheme shows the strongest capacity for predictions of diurnal variation of temperature, with the lowest RMSE of 3.18 °C and mean error of 2.05 °C. These values are 1.69 °C and 2.03 °C lower, respectively, compared to the ECMWF. The capacity of different schemes to relieve the mode's cold bias is ranked from high to low as RRTMG > Mellor-Yamada-Janjic > Grell 3D/Noah-MP.

3-D-Lumped Thermal Network Models for the Reliability Analysis of Fan-Cooled Plate-Fin Heatsink

Conventional lumped thermal networks are most commonly used to model the thermal dissipation in the heatsink. However, they lack accuracy in addressing the temperature information when the thermal dissipation performance of the heatsink declines due to the impurities (dust) blocking the air channels. Especially in the reliability field, they cannot provide accurate temperatures to realize the reliability analysis of the cooling system. This paper proposes two novel 3-D RC-lumped thermal network models for the reliability analysis of the fan-cooled plate-fin heatsink. In the established thermal circuits, the effects of impurities on the heatsink’s thermal dissipation performance are modeled as a few adjustable thermal resistances whose value can be adjusted in accordance with the distribution of the impurities. Moreover, the proposed thermal network models are performed in SIMSCAPE and computational fluid dynamic (CFD)–based simulations and experimental results are carried out to verify the proposed 3-D thermal networks. It is demonstrated that the proposed 3-D thermal networks enable accurate and fast temperature estimation of the heatsink under different reliability conditions (different impurities profiles on the heatsink). Therefore, it can be used for the reliability analysis of the heatsink under long-term load profiles when the thermal dissipation performance decreases.

Towards low-cost soot pyrometry in laminar flames using broadband emission measurements and Artificial Neural Networks

This paper presents a low-cost approach based on Artificial Neural Networks (ANNs) for retrieving fields of soot temperature in laminar flames from broadband soot emission signals captured with a color camera. Using a framework to generate numerical simulations of soot temperature fields in laminar flames and their corresponding projections to the camera plane, we generated large datasets for designing and training different ANN models to infer the relationships between the reference temperature fields and the emission measurements captured with the camera.Experiments over simulated datasets show that properly trained ANNs outperform traditional onion-peeling deconvolution techniques used for retrieving soot temperature from emission signals, delivering accurate temperature estimations that are close to the ones obtained with the more sophisticated modulated absorption/emission techniques that require a much more complex experimental setup. We also show that ANNs trained with simulated data can provide consistent and accurate temperature fields from emission measurements taken in real experimental campaigns using both commercial and industrial-grade color cameras.