电力变压器绝缘故障诊断技术及热状态参量预测模型研究
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摘要
变压器故障智能诊断技术是正确评估变压器绝缘状态的前提,而对变压器内部热状态的准确把握很大程度上决定了变压器的负载能力和使用寿命,也是变压器绝缘故障诊断技术的延伸与扩展。只有正确地评估变压器的绝缘状态,跟踪变压器的使用寿命,才能根据变压器的实际状态来作出正确的检修决策。论文从电力变压器故障智能诊断和内部热状态预测出发,对实现变压器状态检修这两个重要环节进行了深入研究,主要研究工作如下:
①针对常见的BP神经网络的方法进行电力变压器绝缘故障诊断方法,论文着重就其网络的选定、学习样本的建立、输入方式选择、学习过程的收敛性及算法改进等问题展开研究,建立了基于带动量项和变学习率的改进BP神经网络电力变压器故障诊断方法,分析和仿真结果表明经过改进的三层BP神经网络能够较好地实现油中溶解气体含量数据到故障类型的映射。
②在推广的多输入多输出的小波神经网络形式上建立电力变压器绝缘故障诊断模型,对其诊断结论与差异进行详细的数学机理分析,结果表明小波神经网络在识别变压器故障类型时表现出优于BP神经网络的收敛速度、分类性能和判正准确率。
③研究了遗传算法进化小波神经网络应用于电力变压器故障诊断,即将遗传算法与小波神经网络两者相互结合,使小波神经网络能更好地映射电力变压器故障类型与实际油中溶解气体参数之间的非线性关系,从而对电力变压器故障进行准确的分类识别。大量实验结果表明采用遗传算法进化小波神经网络更能深入挖掘变压器数据中有关故障信息,在故障模式识别中具有最高的辨识度,且对变压器多故障的识别诊断也有很好的效果。
④系统地分析变压器内部发热过程和温度分布规律,研究变压器稳态温度计算模型和暂态计算模型。基于变压器内部热过程和温度分布,类比传热学理论和基尔霍夫理论的相似性,提出了冷却方式为强迫油循环(OF)油浸式电力变压器的一种等效顶层油温预测热模型,结果表明提出的热模型较之IEEE推荐的顶层油温预测模型能更准确预测油浸式OF变压器顶层油温。
⑤注意变压器的各种负载状况、冷却方式等具体条件,基于变压器内部热过程和温度分布而充分考虑其非线性热阻和油粘性,提出冷却方式为强迫油循环(OF)的电力变压器绕组热点温度预测热模型,实验结果表明提出的绕组热点温度热模型预测的温度能很好地反应油浸式OF变压器热动态特征且与实测值的误差明显偏小,完全在允许范围之内。
Intelligent fault diagnosis technology of power transformer is the premise of correct assessment of insulation state, and accurate grasp of internal heat state of power transformer determines the load capacity and service life in a large extent which also is the extension and expansion of insulation fault diagnosis. Meanwhile, only after correctly assessing insulation state of power transformer, tracking the service life, we can make the right maintenance decisions based on the actual state of power transformer. As a whole view of intelligent fault diagnosis and internal thermal state prediction of power transformer, this paper had an in-depth study on these two vital areas, and the main research works of this dissertation are shown as follows:
①Usually, a neural network method based on BP algorithm was employed to solve this problem, meanwhile, we mainly focused on network selecting, study samples building, input modes choosing, convergence of training process and algorithm modifying etc. Finally, this dissertation proposed a neural network based on modified BP algorithm, i.e. momentum coefficient and alter-learning coefficient, then analysis and simulation results show that three-layers neural network based on modified algorithm can build clear mapping between gases-dissolved-in-oil and fault types of power transformers.
②This dissertation presented a novel insulation faults diagnosis model of power transformers based on expanded multi-input and multi-output wavelet neural network. After analyzing the diagnostic results differences and its mathematic mechanism, we can draw some conclusions that the proposing wavelet neural networks approaches prevail the traditional BP neural network on convergence speed, classifying ability and diagnostic accuracy.
③Combining genetic algorithm and wavelet neural network, this dissertation set up an effective fault diagnostic method of power transformers, i.e. genetic algorithm evolving wavelet neural network, which can reflect the nonlinear relationship between gases-dissolved-in-oil and fault types of power transformers. What’s more, employing genetic algorithm is order to let wavelet neural network deal with all information obtained better and classify the faults types more accurately. A number of examples show that the method proposed also has good classifying capability for single-fault and multiple-fault samples of power transformers as well as the highest faults diagnostic accuracy.
④After the systematic analysis of interior heating process and temperature distribution of power transformers, this dissertation then placed the focus on the steady state and transient state temperature prediction models. Based on the fundamental heat transfer theory and interior heating process of power transformers, and drawed an analogy between the heat transmission theory and Kirchhoff’s law, this dissertation presents a thermal-electric analogous dynamic top oil temperature prediction thermal model of an oil-immersed power transformers which cooling model is OF. A lot of experimental waveforms and analysis show that the proposed model is able to yield better results than IEEE thermal model on predicting the top oil temperature of power transformers in OF cooling model.
⑤Considering all kinds of loading status, cooling mode etc, then fully regarding nonlinear thermal resistance and oil viscosity based on interior heating process and temperature distribution, this dissertation built a dynamic hot spot temperature model for OF cooling model power transformers. Plenty of experimental curves proved the predicting temperature can totally reflect the prediction thermal characters of oil-immersed power transformers in OF cooling model, on the other hand, the errors between the measurable values and prediction results are fully within the scope of the permit.
①针对常见的BP神经网络的方法进行电力变压器绝缘故障诊断方法,论文着重就其网络的选定、学习样本的建立、输入方式选择、学习过程的收敛性及算法改进等问题展开研究,建立了基于带动量项和变学习率的改进BP神经网络电力变压器故障诊断方法,分析和仿真结果表明经过改进的三层BP神经网络能够较好地实现油中溶解气体含量数据到故障类型的映射。
②在推广的多输入多输出的小波神经网络形式上建立电力变压器绝缘故障诊断模型,对其诊断结论与差异进行详细的数学机理分析,结果表明小波神经网络在识别变压器故障类型时表现出优于BP神经网络的收敛速度、分类性能和判正准确率。
③研究了遗传算法进化小波神经网络应用于电力变压器故障诊断,即将遗传算法与小波神经网络两者相互结合,使小波神经网络能更好地映射电力变压器故障类型与实际油中溶解气体参数之间的非线性关系,从而对电力变压器故障进行准确的分类识别。大量实验结果表明采用遗传算法进化小波神经网络更能深入挖掘变压器数据中有关故障信息,在故障模式识别中具有最高的辨识度,且对变压器多故障的识别诊断也有很好的效果。
④系统地分析变压器内部发热过程和温度分布规律,研究变压器稳态温度计算模型和暂态计算模型。基于变压器内部热过程和温度分布,类比传热学理论和基尔霍夫理论的相似性,提出了冷却方式为强迫油循环(OF)油浸式电力变压器的一种等效顶层油温预测热模型,结果表明提出的热模型较之IEEE推荐的顶层油温预测模型能更准确预测油浸式OF变压器顶层油温。
⑤注意变压器的各种负载状况、冷却方式等具体条件,基于变压器内部热过程和温度分布而充分考虑其非线性热阻和油粘性,提出冷却方式为强迫油循环(OF)的电力变压器绕组热点温度预测热模型,实验结果表明提出的绕组热点温度热模型预测的温度能很好地反应油浸式OF变压器热动态特征且与实测值的误差明显偏小,完全在允许范围之内。
Intelligent fault diagnosis technology of power transformer is the premise of correct assessment of insulation state, and accurate grasp of internal heat state of power transformer determines the load capacity and service life in a large extent which also is the extension and expansion of insulation fault diagnosis. Meanwhile, only after correctly assessing insulation state of power transformer, tracking the service life, we can make the right maintenance decisions based on the actual state of power transformer. As a whole view of intelligent fault diagnosis and internal thermal state prediction of power transformer, this paper had an in-depth study on these two vital areas, and the main research works of this dissertation are shown as follows:
①Usually, a neural network method based on BP algorithm was employed to solve this problem, meanwhile, we mainly focused on network selecting, study samples building, input modes choosing, convergence of training process and algorithm modifying etc. Finally, this dissertation proposed a neural network based on modified BP algorithm, i.e. momentum coefficient and alter-learning coefficient, then analysis and simulation results show that three-layers neural network based on modified algorithm can build clear mapping between gases-dissolved-in-oil and fault types of power transformers.
②This dissertation presented a novel insulation faults diagnosis model of power transformers based on expanded multi-input and multi-output wavelet neural network. After analyzing the diagnostic results differences and its mathematic mechanism, we can draw some conclusions that the proposing wavelet neural networks approaches prevail the traditional BP neural network on convergence speed, classifying ability and diagnostic accuracy.
③Combining genetic algorithm and wavelet neural network, this dissertation set up an effective fault diagnostic method of power transformers, i.e. genetic algorithm evolving wavelet neural network, which can reflect the nonlinear relationship between gases-dissolved-in-oil and fault types of power transformers. What’s more, employing genetic algorithm is order to let wavelet neural network deal with all information obtained better and classify the faults types more accurately. A number of examples show that the method proposed also has good classifying capability for single-fault and multiple-fault samples of power transformers as well as the highest faults diagnostic accuracy.
④After the systematic analysis of interior heating process and temperature distribution of power transformers, this dissertation then placed the focus on the steady state and transient state temperature prediction models. Based on the fundamental heat transfer theory and interior heating process of power transformers, and drawed an analogy between the heat transmission theory and Kirchhoff’s law, this dissertation presents a thermal-electric analogous dynamic top oil temperature prediction thermal model of an oil-immersed power transformers which cooling model is OF. A lot of experimental waveforms and analysis show that the proposed model is able to yield better results than IEEE thermal model on predicting the top oil temperature of power transformers in OF cooling model.
⑤Considering all kinds of loading status, cooling mode etc, then fully regarding nonlinear thermal resistance and oil viscosity based on interior heating process and temperature distribution, this dissertation built a dynamic hot spot temperature model for OF cooling model power transformers. Plenty of experimental curves proved the predicting temperature can totally reflect the prediction thermal characters of oil-immersed power transformers in OF cooling model, on the other hand, the errors between the measurable values and prediction results are fully within the scope of the permit.
引文
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