油浸式变压器绕组热点温度计算模型及预测方法研究
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摘要
电力变压器是输变电网络中最重要和最昂贵的设备之一，其运行可靠性直接关系到整个电网的安全与稳定。大部分变压器寿命的终结是因为其丧失了应有的绝缘能力，而影响绝缘能力的最主要因素是变压器运行时的绕组最热区域内达到的温度(即绕组热点温度)。变压器绕组热点温度不仅是变压器负载能力的最主要限制因素，而且还关系到变压器的安全可靠性、使用寿命以及变压器的制造成本。因此，准确计算、预测变压器绕组热点温度，对合理利用变压器最大负载能力、延长变压器使用寿命具有重要意义。论文针对热点温度的计算模型、预测方法以及热点定位三方面进行了深入研究，主要工作如下：
针对油浸式变压器的特殊固液气结构，论文着重就变压器内部的产热机理和内部热量传递过程展开研究。同时，在分析热流路径的基础上，深入剖析油流在线圈内部的竖直流道和水平流道内的流动情况，得出各自的对流换热系数计算公式。为建立基于热流路径中特征温度的热点温度计算模型，以及通过数值模拟计算方法实现对热点的定位奠定理论基础。
根据变压器内部产热散热机理，考虑到顶层油温是变压器内部热流路径中的重要特征参量，提出一种基于顶层油温的绕组热点温度计算改进模型，模型参数选用Levenberg-Marquardt方法进行估算。通过将该模型应用于温升试验变压器以及实际运行电力变压器，并与传统导则模型和swift热电类比模型的计算结果进行对比。结果表明考虑了绕组损耗和油粘滞度随温度的动态变化的模型能更准确地计算绕组热点温度和反应油浸式变压器的热行为，并具有清晰的物理意义。
针对变压器内部热流路径中的对流换热过程，并考虑到变压器内部几何结构的对称性，将计算流体动力学应用于热点温度的计算，建立一种基于有限体积法的流体-固体耦合热模型，重建油浸式变压器的内部温度场，仿真计算绕组温度分布；根据绕组纵向温度分布确定温度最高的线饼，然后通过分析最热线饼的轴向和径向温度分布，确定出变压器内部温度最大值所处位置，实现绕组热点温度的定位，为运行变压器绕组热点定位研究提出了一种新思路。
为了确保变压器长期稳定运行，不仅要求对绕组热点温度进行计算，还要求根据实时监测信息对热点温度进行预测。论文以变压器的有功、无功损耗、负载电流、环境温度、大气湿度、风速以及顶层油温作为特征量，建立一种基于粒子群算法寻优的支持向量回归(PSO-SVR)模型，对绕组热点温度进行实时预测。通过将PSO-SVR模型分别应用于试验变压器和实际运行电力变压器的实测温度数据，并与标准SVR方法和神经网络等回归方法的预测结果进行比较。PSO-SVR模型能较好地跟踪和描绘在变化负载和变化油流流速情形下的规律，且对实际运行变压器的绕组热点温度预测也有较好效果。
PSO-SVR模型能成功应用于绕组热点温度实时预测的关键在于特征参量的选取，针对实时监测信息有限的变压器，不足以选取足够的参量建立PSO-SVR模型。通过构造基于卡尔曼滤波的热点温度状态方程和测量方程，建立一种绕组热点温度实时最优估计模型。实验结果表明基于卡尔曼滤波的绕组热点温度模型，不仅可以通过内插和滤波平滑消除偶然因素和随机噪声，还原出实际的热点温度；还可以根据较少的监测信息和历史数据对绕组热点温度进行跟踪预测。
Power transformers are the main and one of the most expensive parts of electricalnetworks. The operating reliability of large power transformers has a close influence onsecurity and stability of power systems. The end of the life span of power transformersis most due to the loss of their normal insulation, which is very much dependent uponthe highest temperature occurred in any part of a winding insulation system, namely thehot spot temperature(HST). Thus, to optimise the designs and cooling system from athermal point of view, predicting the values and location of HST is essential to meet thegoals of maximizing the load ability, improving the effective lifetime and lowering thetotal cost associated with transformer operation and maintance. Therefore, a lot ofworks focused on calculation the value and location and real time prediction of HST hasbeen developed as follows:
①Considering the complicated and special conjugated solid-liquid-gas structure ofoil immersed power transfromer, the inner temperature rise effect arised from heatconduction, convection and radiation is investigated. The mechanism of transformerinternal losses and the influences of no-load losses and load losses on hot spottemperature is also analysised. The temperature rise and drop characteristics of winding,core and oil is given. After the systematic analysis of the heat transfer path, the oil flowand nature convection in vertical duct and horizontal duct is studied and thecorreponding calculating of heat transfer coefficients is given. These theories providedguidance for the establishment of thermal model and numerical simulation for the oilimmeresd transformer.
②Based on the heat transfer theory and heat mechanism within transformer, thisdissertation concentrated on establish thermal-electrical analogy model to calculated theHST of transformers. Considering the non-linear thermal resistance, open-circuitimpedance, and the oil viscosity and winding losses with temperature changes are alsotaken into account, proposed an improved model added on the transformer top oiltemperature to calculate HST by using the viscosity and loss correction factors. Modelparameters are estimated by Levenberg-Marquardt method. In the end, by Comparingwith the measured data tested under different conditions, the model shows a goodconsistency, and can describes the temperature variation more accurately in the dynamicloading profiles than IEEE method and ‘swfit’ model.
③For the sake of optimizing the cooling design and predicting the location of HSTfor transformers, numerous study about power transformer thermal actions has beenconducted on a oil-immersed transformer prototype which has axial symmetricgeometry namely, the physical properties of the fluid (i.e. transformer oil) are supposedto temperature function, as well as, in this paper an alternative approach based on FiniteVolume Method (FVM) had been employed to resolve the control equations of flow andheat transfer, which in turn to simulate the overall temperature field and fluiddistribution. The highest temperature disc could be findout based on the lonitudinaldistribution of winding, thus the precise location of HST could be determined by theradial temperature distribution and axial temperature distribution of highest temperaturedisc. It proves this numerical method can not only simulate the fluid distribution withinpower transformer and also predict the value and location of HST effectively.
④This dissertation adopted support vector regression (SVR) to establish a modelfor the prediction of HSTs in power transformers. Among which, an improved particleswarm optimization (PSO) with passive congregation algorithm is utilized to determinethe parameters of SVR. The PSO-SVR model has been applied to predict HSTs of apower transformer. Several experimental tests have been carried out involving a reallarge power transformer, to verify the practicality and effectiveness of the proposedPSO-SVR model. In addition, PSO-SVR modeling results are compared with that ofstandard SVR and artificial neural network (ANN) by applying identical training andtest samples. In conclusion, the PSO-SVR model has better prediction accuracy andgeneralization ability than both the standard SVR model and the ANN in the HSTprediction of power transformers.
⑤For transformer which has little monitoring information, it is difficult to chooseenough characteristic parameters to establish the SVR model to predict HSTs.So theframework of Kalman filter algorithm was proposed to establish a real-time estimationmodel for HSTs. Results show that the Kalman filter-based model can not only smoothcausal factors and eliminate random noise through the interpolation and filtering torestore the real hot temperature,but also exhibited potential applicability and generalityin real-time prediction for HST,which demonstrates that the proposed model tracingHSTs according to fewer monitoring information and historical data.
针对油浸式变压器的特殊固液气结构，论文着重就变压器内部的产热机理和内部热量传递过程展开研究。同时，在分析热流路径的基础上，深入剖析油流在线圈内部的竖直流道和水平流道内的流动情况，得出各自的对流换热系数计算公式。为建立基于热流路径中特征温度的热点温度计算模型，以及通过数值模拟计算方法实现对热点的定位奠定理论基础。
根据变压器内部产热散热机理，考虑到顶层油温是变压器内部热流路径中的重要特征参量，提出一种基于顶层油温的绕组热点温度计算改进模型，模型参数选用Levenberg-Marquardt方法进行估算。通过将该模型应用于温升试验变压器以及实际运行电力变压器，并与传统导则模型和swift热电类比模型的计算结果进行对比。结果表明考虑了绕组损耗和油粘滞度随温度的动态变化的模型能更准确地计算绕组热点温度和反应油浸式变压器的热行为，并具有清晰的物理意义。
针对变压器内部热流路径中的对流换热过程，并考虑到变压器内部几何结构的对称性，将计算流体动力学应用于热点温度的计算，建立一种基于有限体积法的流体-固体耦合热模型，重建油浸式变压器的内部温度场，仿真计算绕组温度分布；根据绕组纵向温度分布确定温度最高的线饼，然后通过分析最热线饼的轴向和径向温度分布，确定出变压器内部温度最大值所处位置，实现绕组热点温度的定位，为运行变压器绕组热点定位研究提出了一种新思路。
为了确保变压器长期稳定运行，不仅要求对绕组热点温度进行计算，还要求根据实时监测信息对热点温度进行预测。论文以变压器的有功、无功损耗、负载电流、环境温度、大气湿度、风速以及顶层油温作为特征量，建立一种基于粒子群算法寻优的支持向量回归(PSO-SVR)模型，对绕组热点温度进行实时预测。通过将PSO-SVR模型分别应用于试验变压器和实际运行电力变压器的实测温度数据，并与标准SVR方法和神经网络等回归方法的预测结果进行比较。PSO-SVR模型能较好地跟踪和描绘在变化负载和变化油流流速情形下的规律，且对实际运行变压器的绕组热点温度预测也有较好效果。
PSO-SVR模型能成功应用于绕组热点温度实时预测的关键在于特征参量的选取，针对实时监测信息有限的变压器，不足以选取足够的参量建立PSO-SVR模型。通过构造基于卡尔曼滤波的热点温度状态方程和测量方程，建立一种绕组热点温度实时最优估计模型。实验结果表明基于卡尔曼滤波的绕组热点温度模型，不仅可以通过内插和滤波平滑消除偶然因素和随机噪声，还原出实际的热点温度；还可以根据较少的监测信息和历史数据对绕组热点温度进行跟踪预测。
Power transformers are the main and one of the most expensive parts of electricalnetworks. The operating reliability of large power transformers has a close influence onsecurity and stability of power systems. The end of the life span of power transformersis most due to the loss of their normal insulation, which is very much dependent uponthe highest temperature occurred in any part of a winding insulation system, namely thehot spot temperature(HST). Thus, to optimise the designs and cooling system from athermal point of view, predicting the values and location of HST is essential to meet thegoals of maximizing the load ability, improving the effective lifetime and lowering thetotal cost associated with transformer operation and maintance. Therefore, a lot ofworks focused on calculation the value and location and real time prediction of HST hasbeen developed as follows:
①Considering the complicated and special conjugated solid-liquid-gas structure ofoil immersed power transfromer, the inner temperature rise effect arised from heatconduction, convection and radiation is investigated. The mechanism of transformerinternal losses and the influences of no-load losses and load losses on hot spottemperature is also analysised. The temperature rise and drop characteristics of winding,core and oil is given. After the systematic analysis of the heat transfer path, the oil flowand nature convection in vertical duct and horizontal duct is studied and thecorreponding calculating of heat transfer coefficients is given. These theories providedguidance for the establishment of thermal model and numerical simulation for the oilimmeresd transformer.
②Based on the heat transfer theory and heat mechanism within transformer, thisdissertation concentrated on establish thermal-electrical analogy model to calculated theHST of transformers. Considering the non-linear thermal resistance, open-circuitimpedance, and the oil viscosity and winding losses with temperature changes are alsotaken into account, proposed an improved model added on the transformer top oiltemperature to calculate HST by using the viscosity and loss correction factors. Modelparameters are estimated by Levenberg-Marquardt method. In the end, by Comparingwith the measured data tested under different conditions, the model shows a goodconsistency, and can describes the temperature variation more accurately in the dynamicloading profiles than IEEE method and ‘swfit’ model.
③For the sake of optimizing the cooling design and predicting the location of HSTfor transformers, numerous study about power transformer thermal actions has beenconducted on a oil-immersed transformer prototype which has axial symmetricgeometry namely, the physical properties of the fluid (i.e. transformer oil) are supposedto temperature function, as well as, in this paper an alternative approach based on FiniteVolume Method (FVM) had been employed to resolve the control equations of flow andheat transfer, which in turn to simulate the overall temperature field and fluiddistribution. The highest temperature disc could be findout based on the lonitudinaldistribution of winding, thus the precise location of HST could be determined by theradial temperature distribution and axial temperature distribution of highest temperaturedisc. It proves this numerical method can not only simulate the fluid distribution withinpower transformer and also predict the value and location of HST effectively.
④This dissertation adopted support vector regression (SVR) to establish a modelfor the prediction of HSTs in power transformers. Among which, an improved particleswarm optimization (PSO) with passive congregation algorithm is utilized to determinethe parameters of SVR. The PSO-SVR model has been applied to predict HSTs of apower transformer. Several experimental tests have been carried out involving a reallarge power transformer, to verify the practicality and effectiveness of the proposedPSO-SVR model. In addition, PSO-SVR modeling results are compared with that ofstandard SVR and artificial neural network (ANN) by applying identical training andtest samples. In conclusion, the PSO-SVR model has better prediction accuracy andgeneralization ability than both the standard SVR model and the ANN in the HSTprediction of power transformers.
⑤For transformer which has little monitoring information, it is difficult to chooseenough characteristic parameters to establish the SVR model to predict HSTs.So theframework of Kalman filter algorithm was proposed to establish a real-time estimationmodel for HSTs. Results show that the Kalman filter-based model can not only smoothcausal factors and eliminate random noise through the interpolation and filtering torestore the real hot temperature,but also exhibited potential applicability and generalityin real-time prediction for HST,which demonstrates that the proposed model tracingHSTs according to fewer monitoring information and historical data.
引文
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