Accurate assessment of hot-spot temperature is essential for the safe operation of power transformers.Existing dynamic thermal models cannot estimate hot-spot temperature accurately since some input parameters are rou...Accurate assessment of hot-spot temperature is essential for the safe operation of power transformers.Existing dynamic thermal models cannot estimate hot-spot temperature accurately since some input parameters are roughly determined by transformer capacity and cooling mode while ignoring the effect of winding structure,tank dimensions,and material physical properties.To improve the accuracy of temperature assessment,empirical parameters of the IEC thermal model including thermal constants,winding and oil exponents are optimised with the help of numerical simulation in this article.Based on energy conservation and heat transfer theory,a computational fluid dynamic(CFD)model of a transformer in oil natural air natural(ONAN)cooling mode is established.This CFD model simulates the entity's structure,sizes,and multi-stage heat dissipation processes realistically,so it can more precisely calculate the dynamic hot-spot temperature.According to the simulated temperature curves at different operating conditions,the thermal constants and oil exponent are estimated using non-linear regression,and the winding exponent is optimised using linear regression.A case study is conducted on an ONAN transformer.It shows the improved IEC model with optimised parameters can more accurately evaluate hot-spot temperature,and the absolute error is decreased by 2.4 K(38.7%)compared with traditional thermal models.展开更多
This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local...This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local estimates are not lost too much, the authors propose an optimal distributed fusion algorithm which is equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, based on the above global optimality result and sensor data compression, the authors propose a suboptimal distributed fusion algorithm. Numerical examples show that this suboptimal algorithm still works well and significantly better than the standard distributed Kalman filtering fusion subject to packet loss even if the process noise power is quite large.展开更多
基金National Key Research and Development Program,Grant/Award Number:2020YFB1709701。
文摘Accurate assessment of hot-spot temperature is essential for the safe operation of power transformers.Existing dynamic thermal models cannot estimate hot-spot temperature accurately since some input parameters are roughly determined by transformer capacity and cooling mode while ignoring the effect of winding structure,tank dimensions,and material physical properties.To improve the accuracy of temperature assessment,empirical parameters of the IEC thermal model including thermal constants,winding and oil exponents are optimised with the help of numerical simulation in this article.Based on energy conservation and heat transfer theory,a computational fluid dynamic(CFD)model of a transformer in oil natural air natural(ONAN)cooling mode is established.This CFD model simulates the entity's structure,sizes,and multi-stage heat dissipation processes realistically,so it can more precisely calculate the dynamic hot-spot temperature.According to the simulated temperature curves at different operating conditions,the thermal constants and oil exponent are estimated using non-linear regression,and the winding exponent is optimised using linear regression.A case study is conducted on an ONAN transformer.It shows the improved IEC model with optimised parameters can more accurately evaluate hot-spot temperature,and the absolute error is decreased by 2.4 K(38.7%)compared with traditional thermal models.
基金supported by the National Natural Science Foundation of China under Grant Nos.60934009, 60901037 and 61004138
文摘This paper considers the distributed Kalman filtering fusion with passive packet loss or initiative intermittent communications from local estimators to fusion center while the process noise does exist. When the local estimates are not lost too much, the authors propose an optimal distributed fusion algorithm which is equivalent to the corresponding centralized Kalman filtering fusion with complete communications even if the process noise does exist. When this condition is not satisfied, based on the above global optimality result and sensor data compression, the authors propose a suboptimal distributed fusion algorithm. Numerical examples show that this suboptimal algorithm still works well and significantly better than the standard distributed Kalman filtering fusion subject to packet loss even if the process noise power is quite large.