Method and system for monitoring state of main transformer of wind power substation based on digital twinning

Abstract

The invention discloses a wind power substation main transformer state monitoring method and system based on digital twinning, and the method comprises the steps: firstly obtaining a characteristic gas concentration and top oil temperature time sequence, carrying out the multi-layer wavelet packet transformation of a gas concentration sequence, and decomposing the gas concentration sequence into different frequency bands; and then, on the basis of a main transformer top oil temperature time sequence, correlation analysis is used for identifying and grouping the frequency bands, so that components related to a temperature effect and components really representing fault features are accurately decoupled, and finally, signal reconstruction is realized, and pure fault features are extracted. In this way, the accuracy and reliability of state monitoring of the main transformer of the wind power substation are remarkably improved, and therefore refined sensing and early warning of the operation state of equipment can be achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent monitoring, and more specifically, to a wind power substation main transformer state monitoring method and system based on digital twinning. BACKGROUND

[0002] Wind power, as an important part of clean energy, plays a key role in the transformation of global energy structure. Wind power substation is the core hub of wind power grid connection, and the safe and stable operation of the main transformer, as its key equipment, is directly related to the power generation efficiency of the entire wind farm and the reliability of the power grid. However, the main transformer is long-term operated in the variable environment of the wind farm, and is subjected to frequent load fluctuations and complex stresses, which is prone to insulation aging and even internal failure. Once the main transformer fails, not only will it cause huge economic losses, but also will have a serious impact on the stable operation of the power grid. Therefore, it is of great significance to build an efficient and accurate wind power substation main transformer state monitoring scheme to ensure the safety of wind power assets, prolong the service life of equipment, and improve the reliability of the power grid.

[0003] However, the current wind power substation main transformer state monitoring faces many challenges. The aging rate of transformer solid insulation is extremely sensitive to temperature, and the severe fluctuations of wind farm load cause frequent and large fluctuations of internal winding hot spot temperature, forming a "thermal cycle" that accelerates insulation aging. Traditional life assessment models, such as the method based on IEEE C57.91-2011 standard, usually assume slow temperature changes and mainly focus on equivalent thermal aging effects, but ignore the nonlinear damage caused by "thermal cycle", such as the mechanical fatigue of insulation paper in repeated thermal expansion and cold contraction, and its coupling effect with chemical aging, which cannot be effectively captured by these traditional models. In addition, existing monitoring systems mainly focus on the amplitude of temperature, and cannot effectively capture key features such as temperature change rate and fluctuation frequency, resulting in inaccurate assessment of the actual aging speed of the transformer under dynamic load when the average load and maximum temperature are similar. More importantly, in the state monitoring based on dissolved gas analysis (DGA), the thermal inertia effect inside the transformer causes a significant time delay between gas concentration changes and temperature fluctuations that drive them. Conventional instantaneous correlation analysis methods often underestimate the true correlation strength between gas concentration and oil temperature due to the failure to consider this time lag, which can easily misjudge the frequency band dominated by temperature effects as a fault feature, or drown the real fault signal in the temperature background noise, thereby introducing uncertainty and misjudgment risk for subsequent fault diagnosis, seriously affecting the sensitivity and reliability of diagnosis.

[0004] Therefore, an optimized wind power substation main transformer state monitoring method based on digital twinning is expected. SUMMARY

[0005] In order to solve the above technical problems, the present application is proposed. The present application provides a wind power substation main transformer state monitoring method and system based on digital twinning, which first acquires characteristic gas concentration and top layer oil temperature time series, then performs multi-layer wavelet packet transformation on the gas concentration series to decompose it into different frequency bands; then, based on the main transformer top layer oil temperature time series, correlation analysis is used to identify and group these frequency bands, thereby accurately decoupling the components related to temperature effect and the components truly representing fault characteristics, and finally reconstructing the signal to extract pure fault characteristics. In this way, the accuracy and reliability of the wind power substation main transformer state monitoring are significantly improved, thereby enabling fine perception and early warning of the equipment operating condition.

[0006] According to one aspect of the present application, a wind power substation main transformer state monitoring method based on digital twinning is provided, which comprises: acquiring an original characteristic gas concentration time series and a main transformer top layer oil temperature time series; performing multi-layer wavelet packet transformation on the original characteristic gas concentration time series to obtain wavelet packet coefficient series of all frequency bands; based on the main transformer top layer oil temperature time series, performing correlation-based frequency band identification and grouping on the wavelet packet coefficient series of all frequency bands to obtain a temperature effect related frequency band index set and a fault characteristic related frequency band index set; based on the temperature effect related frequency band index set and the fault characteristic related frequency band index set, performing signal reconstruction and decoupling on the wavelet packet coefficient series of all frequency bands to obtain a reconstructed pure temperature effect component and a reconstructed pure fault characteristic component; performing fault diagnosis based on the reconstructed pure fault characteristic component to obtain a fault type diagnosis result.

[0007] According to another aspect of the present application, a wind power substation main transformer state monitoring system based on digital twinning is provided, which comprises: a data acquisition module for acquiring an original characteristic gas concentration time series and a main transformer top layer oil temperature time series; a multi-layer wavelet packet transformation module for performing multi-layer wavelet packet transformation on the original characteristic gas concentration time series to obtain wavelet packet coefficient series of all frequency bands; a frequency band identification and grouping module for, based on the main transformer top layer oil temperature time series, performing correlation-based frequency band identification and grouping on the wavelet packet coefficient series of all frequency bands to obtain a temperature effect related frequency band index set and a fault characteristic related frequency band index set; a signal reconstruction and decoupling module for, based on the temperature effect related frequency band index set and the fault characteristic related frequency band index set, performing signal reconstruction and decoupling on the wavelet packet coefficient series of all frequency bands to obtain a reconstructed pure temperature effect component and a reconstructed pure fault characteristic component; The fault diagnosis module is configured to perform fault diagnosis based on the reconstructed pure fault feature component to obtain a fault type diagnosis result.

[0008] Compared with the prior art, the wind power substation main transformer state monitoring method and system based on digital twinning provided by the application first acquires feature gas concentration and top layer oil temperature time series, then performs multi-layer wavelet packet transformation on the gas concentration series to decompose it into different frequency bands; then, based on the main transformer top layer oil temperature time series, correlation analysis is used to identify and group these frequency bands, so as to accurately decouple the components related to temperature effect and the components that truly represent fault features, and finally realize signal reconstruction and extraction of pure fault features. In this way, the accuracy and reliability of the wind power substation main transformer state monitoring are significantly improved, so that fine perception and early warning of the equipment operating condition can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0009] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application taken in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of embodiments of the present application and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and serve to explain the present application, but do not limit the present application. In the drawings, like reference numerals refer to like elements or steps throughout.

[0010] Figure 1 A flowchart of the wind power substation main transformer state monitoring method based on digital twinning according to the embodiments of the present application; Figure 2 A data flow schematic diagram of the wind power substation main transformer state monitoring method based on digital twinning according to the embodiments of the present application; Figure 3 A block diagram of the wind power substation main transformer state monitoring system based on digital twinning according to the embodiments of the present application. DETAILED DESCRIPTION

[0011] Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0012] As shown in the present application and claims, unless the context clearly indicates otherwise, the words "one", "an", "a", and / or "the" do not mean to specify a single number, but also include a plurality. Generally, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0013] Although the present application makes various references to certain modules in the system according to embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server. The modules are merely illustrative, and different aspects of the system and method can use different modules.

[0014] Flowcharts are used in the present application to illustrate the operations performed by the system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. Meanwhile, other operations can also be added to these processes, or one or more steps of operation can be removed from these processes.

[0015] In the technical solutions of the present application, a wind power substation main transformer state monitoring method based on digital twinning is proposed. Figure 1 A flowchart of the wind power substation main transformer state monitoring method based on digital twinning according to embodiments of the present application. Figure 2 A data flow schematic diagram of the wind power substation main transformer state monitoring method based on digital twinning according to embodiments of the present application. As shown in Figure 1 and Figure 2 The wind power substation main transformer state monitoring method based on digital twinning according to embodiments of the present application includes the steps of: S1, obtaining an original characteristic gas concentration time series and a main transformer top layer oil temperature time series; S2, performing multi-layer wavelet packet transform on the original characteristic gas concentration time series to obtain a wavelet packet coefficient sequence of all frequency bands; S3, based on the main transformer top layer oil temperature time series, performing correlation-based frequency band identification and grouping on the wavelet packet coefficient sequence of all frequency bands to obtain a temperature effect related frequency band index set and a fault feature related frequency band index set; S4, based on the temperature effect related frequency band index set and the fault feature related frequency band index set, performing signal reconstruction and decoupling on the wavelet packet coefficient sequence of all frequency bands to obtain a reconstructed pure temperature effect component and a reconstructed pure fault feature component; S5, based on the reconstructed pure fault feature component, performing fault diagnosis to obtain a fault type diagnosis result.

[0016] In particular, the S1 acquires the original characteristic gas concentration time series and the main transformer top layer oil temperature time series. It should be understood that the main transformer of the wind power substation is a key node of the power grid, and its operating state directly affects the power generation efficiency of the entire wind farm and the stability of the power grid. The aging and potential failure of the internal insulation material of the transformer are often accompanied by changes in the concentration of specific gases in the insulating oil, i.e., dissolved gas analysis (DGA) is the core means of diagnosing transformer faults. At the same time, the temperature inside the transformer, especially the top layer oil temperature, is a key physical parameter that affects gas generation, dissolution, and migration. And in the variable operating environment of the wind farm, the "thermal cycle" effect caused by load fluctuations is significant, so there is a complex dynamic correlation and time lag effect between oil temperature and gas concentration. Therefore, accurate acquisition of these two time series data not only directly reflects the health status of the transformer, but also lays a data foundation for subsequent decoupling of temperature effects and extraction of pure fault features, thereby ensuring the accuracy and reliability of the diagnosis.

[0017] Specifically, the original characteristic gas concentration time series refers to the ordered data set of the concentrations of key characteristic gases such as hydrogen, methane, ethane, ethylene, acetylene, carbon monoxide, and carbon dioxide extracted and measured from the transformer insulating oil over time under continuous or periodic monitoring. These gases are generated in different proportions and concentrations under different types of transformer faults (such as partial discharge, overheating, and electric arc), and their time series data can reveal the evolution trend of the fault. The main transformer top layer oil temperature time series refers to the continuous measurement of the temperature of the transformer top insulating oil over time. The top layer oil temperature is an important indicator of measuring the overall thermal load and heat dissipation of the transformer, and its fluctuations directly affect the aging rate of the insulation material and the behavior of the dissolved gas in the oil. The synchronous acquisition of these two time series data is crucial for understanding the internal physicochemical mechanisms between fault gas generation and temperature, especially considering the time lag effect caused by the thermal inertia inside the transformer.

[0018] In implementation, the original feature gas concentration time series and the main transformer top layer oil temperature time series are usually obtained by relying on advanced online monitoring systems deployed on the main transformer of the wind power substation. For gas concentration data, an online dissolved gas analysis (DGA) monitoring device is used, which can extract samples from the transformer oil in real time or quasi-real time, and separate and quantitatively analyze the dissolved gas by techniques such as gas chromatography, photoacoustic spectroscopy or infrared absorption, record the obtained various feature gas concentration values together with the corresponding sampling time stamps to form continuous time series data. At the same time, in order to obtain the top layer oil temperature time series, the transformer is usually equipped with high-precision temperature sensors such as platinum resistance (RTD) or thermocouple, which are installed at the top of the transformer oil tank to continuously monitor and record oil temperature data. These temperature data also have accurate time stamps, which are synchronized in time with the gas concentration data. All collected original data, including gas concentration and oil temperature, are transmitted to the data acquisition system or digital twin platform for unified storage and management, providing reliable and comprehensive data support for subsequent signal processing and fault diagnosis.

[0019] In particular, S2, the original feature gas concentration time series is subjected to multi-layer wavelet packet transform to obtain wavelet packet coefficient sequences of all frequency bands. It should be understood that the original feature gas concentration time series is often complex and non-stationary, and may mix signals generated by multiple physical and chemical processes inside the transformer, including normal temperature effects and potential fault features. In order to effectively identify and distinguish these signals from different sources, the original signal needs to be carefully decomposed in time and frequency. As an advanced time-frequency analysis tool, multi-layer wavelet packet transform can provide more detailed frequency band division than traditional Fourier transform or single wavelet transform, thereby revealing hidden information of the signal at different frequency scales, and providing more distinguishable basic data for subsequent signal reconstruction and fault diagnosis. Therefore, in the technical solution of the present application, the original feature gas concentration time series is subjected to multi-layer wavelet packet transform to obtain wavelet packet coefficient sequences of all frequency bands. Specifically, multi-layer wavelet packet transform is a mathematical tool that decomposes signals in different frequency subbands. It not only decomposes the low-frequency (approximation) part like wavelet transform, but also further decomposes the high-frequency (detail) part, thereby achieving comprehensive and fine division of the entire frequency domain. The obtained wavelet packet coefficient sequence refers to the sequence of wavelet packet decomposition coefficients corresponding to the original signal in each specific frequency subband after multi-layer wavelet packet transform. These coefficients represent the energy distribution and local feature information of the signal in that frequency band.

[0020] In implementation, first, the wavelet base function and the target decomposition layer number are determined. Among them, the selection of the wavelet base function is crucial, and its shape and symmetry (such as Daubechies wavelet, Symlets wavelet, etc.) should match the characteristics of the signal to be analyzed to maximize the capture of the local characteristics of the signal; the target decomposition layer number defines the depth of signal decomposition and the fineness of frequency band division; the higher the decomposition layer number, the more frequency bands are obtained, the stronger the analysis ability of signal frequency details, but at the same time, the calculation complexity is also increased; Further, based on the wavelet base function and the target decomposition layer number, the original characteristic gas concentration time series is decomposed and iterated to obtain the wavelet packet coefficient sequence of all frequency bands. That is, after determining the appropriate base function and decomposition layer number, the wavelet packet transform realizes the decomposition of the signal by iteratively applying a pair of orthogonal filters (low-pass filter and high-pass filter). In each iteration, a signal or sub-band is decomposed into two new sub-bands: one represents its low-frequency component (approximation coefficient), and the other represents its high-frequency component (detail coefficient). Unlike traditional wavelet transform, which only iteratively decomposes low-frequency components, wavelet packet transform further decomposes each branch (including low-frequency and high-frequency) until the target decomposition layer number is reached. For example, for an L-layer decomposition, the original signal will finally be decomposed into non-overlapping frequency bands. Each frequency band is represented by its corresponding set of wavelet packet coefficient sequences, which accurately capture the dynamic changes of the original gas concentration signal in a specific frequency range.

[0021] In particular, the S3, based on the main transformer top oil temperature time series, performs correlation-based frequency band identification and grouping on the wavelet packet coefficient sequence of all frequency bands to obtain a temperature effect related frequency band index set and a fault feature related frequency band index set. It should be understood that in the transformer, a complex physical system with significant thermal inertia, there is a non-negligible time delay between the gas concentration signal and the temperature fluctuation driving the change. The conventional frequency band identification method often judges by calculating the instantaneous Pearson correlation coefficient, which inherently assumes that the change in gas concentration and temperature fluctuation occur synchronously. However, the physical and chemical processes such as heat conduction from the fault point to the oil body, gas precipitation and migration to the sensor all have time delay, which leads to the original instantaneous correlation analysis to underestimate the correlation strength of the two when there is a phase difference, and may misjudge the frequency band essentially dominated by temperature effect as unrelated fault feature, thereby introducing uncertainty and potential misjudgment risk for subsequent fault diagnosis. Therefore, in order to accurately distinguish the source of the signal, accurately decouple the temperature effect component and the feature component that truly reflects the potential fault, in the technical solution of the present application, the dynamic time lag relationship is explored to ensure the accuracy and reliability of subsequent fault diagnosis. Specifically, the cross-correlation function is used to quantify the similarity of two time series at different time shifts to capture the delay effect of temperature change on gas concentration. The temperature effect related frequency band index set finally obtained refers to the set of indexes (identifiers) corresponding to the frequency bands whose wavelet packet coefficient sequences are significantly correlated with the main transformer top oil temperature time series after considering the time lag. These frequency bands mainly reflect the influence of temperature change caused by normal operation or load fluctuation of the transformer on gas concentration; while the fault feature related frequency band index set refers to the set of indexes corresponding to those frequency bands that are not significantly correlated with the top oil temperature time series (or significantly correlated but unreasonable in time lag). The fluctuations of these frequency bands are more likely to come from potential faults or abnormalities inside the transformer.

[0022] In specific implementation, first, the wavelet packet coefficient sequence of the first frequency band of the first layer is extracted from the wavelet packet coefficient sequence of all frequency bands. The wavelet packet coefficient sequence of the first frequency band of the first layer is extracted from the wavelet packet coefficient sequence of all frequency bands. This means that for each specific frequency sub-band obtained by wavelet packet transform decomposition, its corresponding wavelet packet coefficient sequence will be individually investigated as a gas concentration signal component to be analyzed. Then, the wavelet packet coefficient sequence of the first frequency band of the first layer is calculated. The wavelet packet coefficient sequence of the first frequency band of the first layer is calculated. The maximum cross-correlation coefficient and optimal time delay between the wavelet packet coefficient sequence of a frequency band and the time series of the top oil temperature of the main transformer are determined. It is understandable that conventional frequency band identification methods exhibit a fundamental limitation in distinguishing signal sources. This method determines the source by calculating the instantaneous Pearson correlation coefficient between the wavelet packet coefficient sequence and the oil temperature sequence, based on the assumption that changes in gas concentration and the temperature fluctuations driving those changes occur synchronously. However, in the complex physical system of a wind power substation main transformer with significant thermal inertia, the conduction of heat from the local fault point to the oil, and the precipitation and migration of gas due to decreased solubility to the online monitoring sensors all involve non-negligible time delays. Therefore, the original instantaneous correlation analysis, failing to consider this time delay characteristic, may incorrectly underestimate the correlation strength between the gas concentration signal and the oil temperature signal when there is a phase difference. This could lead to misjudging a frequency band essentially dominated by temperature effects as an irrelevant fault feature, thus introducing uncertainty and potential misjudgment risks into subsequent fault diagnosis.

[0023] To overcome the aforementioned shortcomings, a frequency band adaptive identification mechanism based on time-delay-aware cross-correlation maximum entropy and physical constraints is proposed. This mechanism abandons instantaneous, static correlation measurements and instead delves into the dynamic time-delay relationship between signals, achieving precise decoupling of the temperature effect frequency band and the fault characteristic frequency band through the following steps.

[0024] In this process, firstly, calculate the... Layer The cross-correlation function between the wavelet packet coefficient sequence of each frequency band and the time series of the top oil temperature of the main transformer is used. In other words, by calculating the cross-correlation function, a tool is constructed to quantify the similarity between the two time series at different time shifts, in order to capture the delayed effect of temperature changes on gas concentration. Specifically, for each gas concentration frequency band coefficient sequence, a cross-correlation operation is performed with the synchronized oil temperature time series to generate a cross-correlation function regarding the time delay; this process is expressed by the formula: , in, It is a cross-correlation function. For the first Layer Wavelet packet coefficient sequence of each frequency band The time series of top oil temperature of the main transformer. Due to time lag, The sequence length is given; this function fully depicts the correlation between a specific gas concentration band and oil temperature fluctuations throughout the entire time-delay scan range, providing a comprehensive data foundation for revealing the true intrinsic relationship between the two that may be obscured by time delay. Secondly, the maximum cross-correlation coefficient and the optimal time lag are extracted from the cross-correlation function. That is, the key parameter that best represents the nature of the correlation between signals is extracted from the generated cross-correlation function, that is, the complex function relationship is simplified into a scalar index that can be used for decision-making. Specifically, the normalized cross-correlation function is searched at all possible time lags to determine the supremum of its absolute value, and the time lag corresponding to the supremum is recorded. In this process, first, based on the standard deviation of the wavelet packet coefficient sequence of the layer and the th frequency band and the standard deviation of the time sequence of the top oil temperature of the main transformer, the cross-correlation function is transformed to obtain the cross-correlation coefficient function; this process is represented by the formula: , wherein is the cross-correlation function, and are the standard deviations of the wavelet packet coefficient sequence of the layer and the th frequency band and the time sequence of the top oil temperature of the main transformer, respectively; then, the maximum correlation coefficient and the corresponding time lag are found from the cross-correlation coefficient function to obtain the maximum cross-correlation coefficient and the optimal time lag. That is, the supremum of the absolute value of the normalized cross-correlation coefficient function is searched at all possible time lags, and the time lag corresponding to the supremum is recorded. This process is represented by the formula: , , wherein is the cross-correlation coefficient function, represents finding the maximum value of the function over the entire domain, represents returning the value of the independent variable when the function reaches the maximum value, is the maximum cross-correlation coefficient, is the optimal time lag.

[0025] In this way, the maximum linear correlation strength between the two signals that is not affected by the phase difference is accurately quantified , and the optimal time delay required to achieve this maximum correlation strength is achieved. The execution effect is that two parameters with clear physical meaning are output: the degree represents the true strength of the correlation, and reveals the dynamic response time of the system to temperature disturbance, which together form a solid basis for subsequent intelligent grouping; further, based on the maximum cross-correlation coefficient and the optimal time lag, the the wavelet packet coefficient sequence of the i-th frequency band is adaptively grouped to determine whether it belongs to the temperature effect related frequency band index set or the fault feature related frequency band index set. That is, a set of strict decision logic needs to be established to avoid misjudging the higher correlation or the correlation at a physically unreasonable time lag as an effective association. Specifically, a double-verification discriminant function is constructed, which performs two tests in parallel: first, the maximum cross-correlation coefficient is transformed into a variable that approximately obeys a normal distribution using Fisher's z-transformation , and it is tested whether it is significant at a given statistical significance level ; second, the extracted optimal time lag is tested whether it falls within a physically reasonable time lag window preset according to the knowledge of transformer heat transfer and fluid dynamics.

[0026] In this process, first, the maximum cross-correlation coefficient is transformed into a variable that approximately obeys a normal distribution; second, the variable that approximately obeys a normal distribution and the optimal time lag are input into the discriminant function to adaptively group the wavelet packet coefficient sequence of the i-th frequency band in the first layer and the j-th frequency band in the second layer . The discriminant function constructs a set of strict double-verification logic to avoid misjudging the higher correlation or the correlation at a physically unreasonable time lag as an effective association. Specifically, the discriminant function is expressed as: , wherein is the variable that approximately obeys a normal distribution, is the value of the standard normal distribution at the quantile, is the statistical significance level, is the optimal time lag, is the physically reasonable time lag window. It is worth mentioning that when the discriminant function output is 1, it indicates that there is a significant and physically reasonable association between the frequency band and the temperature effect, so it is classified into the temperature effect related frequency band index set; on the contrary, when the output is 0, it is considered that the frequency band is more likely to represent a fault signal or random noise, and it is classified into the fault feature related frequency band index set. Through this double constraint, the accuracy and robustness of the frequency band grouping are ensured.

[0027] ​Thus, the basis for ensuring that a frequency band is classified as a temperature effect must satisfy both statistical significance and physical process rationality. Therefore, through this double constraint, the accuracy and robustness of the frequency band grouping are greatly improved, effectively identifying and classifying those frequency bands that have a real physical connection with temperature fluctuations into the temperature effect related frequency band index set, and classifying the remaining frequency bands representing fault signals or random noise into the fault feature related frequency band index set.

[0028] It should be understood that the above preferred mechanism realizes high-fidelity decoupling of online monitoring DGA signals. By introducing time delay awareness and double verification logic, it can penetrate the time delay fog caused by the inertia of the transformer system, accurately separate the temperature effect component mixed in the original measurement data caused by load fluctuations from the feature signal component that truly reflects the evolution trend of latent faults. In this way, an unprecedented "clean" input is provided for subsequent fault diagnosis models. By using the pure fault feature component obtained after decoupling for analysis, the problem of DGA diagnosis misjudgment caused by the dramatic changes in wind farm operating conditions can be fundamentally solved, effectively avoiding the situation where gas concentration fluctuations caused by temperature effects are misjudged as faults, and preventing real fault signals from being masked by temperature background noise, thereby significantly improving the sensitivity of wind power substation main transformer state monitoring and the reliability of diagnosis results, providing a solid technical support for realizing precise predictive maintenance and ensuring the safe and stable operation of power grid assets.

[0029] In particular, the S4 reconstructs and decouples the wavelet packet coefficient sequence of all frequency bands based on the temperature effect related frequency band index set and the fault feature related frequency band index set to obtain a reconstructed pure temperature effect component and a reconstructed pure fault feature component. That is, through signal reconstruction and decoupling, the temperature effect component mixed in the original measurement data caused by load fluctuations is accurately separated from the feature signal component that truly reflects the evolution trend of latent faults. It is worth mentioning that signal reconstruction and decoupling refer to the process of synthesizing the corresponding wavelet packet coefficient sequence back into the original time domain signal using these grouped frequency band indexes, thereby separating the original mixed signal into independent and pure components. The reconstructed pure temperature effect component obtained finally is the time domain signal obtained by inverse wavelet packet transform of all signal components belonging to the temperature effect related frequency band, which represents the dissolved gas concentration change caused by temperature fluctuations; the reconstructed pure fault feature component is the time domain signal obtained by inverse wavelet packet transform of all signal components belonging to the fault feature related frequency band, which removes the interference of temperature effects and more directly reflects the real evolution of potential faults inside the transformer.

[0030] In implementation, first, from the wavelet packet coefficient sequence of all frequency bands, the wavelet packet coefficient sequence of all frequency bands identified as related to temperature effect is screened out. For the frequency bands not belonging to the set, the corresponding wavelet packet coefficient sequence will be zeroed or directly excluded; then, the constructed pure temperature effect wavelet packet coefficient set is taken as input to perform inverse wavelet packet transform. The inverse wavelet packet transform is the inverse process of the wavelet packet transform, which iteratively applies a pair of synthesis filters (low-pass synthesis filter and high-pass synthesis filter) to gradually combine the decomposed frequency band coefficient sequences, and finally reconstructs the original time domain signal. Here, by inversely transforming the pure temperature effect wavelet packet coefficient set, the reconstructed pure temperature effect component can be obtained. This component is a time domain signal that accurately represents the characteristic gas concentration fluctuation caused by transformer temperature change; Similarly, from the wavelet packet coefficient sequence of all frequency bands, the wavelet packet coefficient sequence of all frequency bands identified as related to fault features is screened out. For the frequency bands not belonging to the set, the corresponding wavelet packet coefficient sequence will also be zeroed or excluded; Finally, the constructed pure fault feature wavelet packet coefficient set is taken as input to perform inverse wavelet packet transform. By inversely transforming the pure fault feature wavelet packet coefficient set, the reconstructed pure fault feature component can be obtained. This component is also a time domain signal that maximally eliminates the interference of temperature effect, allowing the characteristic gas concentration change caused by fault to be clearly presented.

[0031] Through the above fine signal reconstruction and decoupling process, the temperature effect component mixed in the original measurement data caused by load fluctuation can be accurately separated from the characteristic signal component that truly reflects the evolution trend of latent fault, thereby providing a clean input for the subsequent fault diagnosis model and significantly improving the sensitivity of the main transformer state monitoring of the wind power substation and the reliability of the diagnosis result.

[0032] In particular, the S5 performs fault diagnosis based on the reconstructed pure fault feature component to obtain a fault type diagnosis result. It should be understood that the reconstructed pure fault feature component has maximally eliminated the non-fault gas concentration fluctuation caused by transformer load fluctuation and environmental temperature change, more directly and clearly reflects the gas change pattern produced by the abnormal condition inside the transformer, thereby fundamentally solving the problems of misjudgment of non-fault temperature effect fluctuation as fault and missed report of real fault signal due to noise masking, significantly improving the sensitivity of the main transformer state monitoring of the wind power substation and the reliability of the diagnosis result, and providing solid technical support for realizing precise predictive maintenance and ensuring the safe and stable operation of power grid assets. Therefore, in the technical solution of the present application, the fault diagnosis based on the reconstructed pure fault feature component ensures the authenticity and effectiveness of the diagnosis result, avoids misjudgment and missed judgment, and has decisive significance for ensuring the safety of power grid assets and optimizing operation and maintenance strategy.

[0033] wherein the fault diagnosis refers to analyzing and discriminating the reconstructed pure fault feature components to determine whether there is a fault in the transformer and the specific type of the fault (such as partial discharge, overheat fault, arc discharge, etc.). The final fault type diagnosis result refers to the specific category judgment about the current or potential fault of the transformer after diagnosis.

[0034] In specific implementation, the time sequence features of the reconstructed pure fault feature components or the extracted key parameters (such as gas production rate, ratio, etc.) can be taken as inputs and sent to a rule-based expert system, a fuzzy logic system, an artificial neural network (ANN), a support vector machine (SVM), or a deep learning model (such as a long short-term memory network LSTM, a convolutional neural network CNN, etc.). These models can establish a complex nonlinear mapping relationship by learning a large amount of historical fault data and corresponding feature gas patterns, thereby intelligently identifying the current input components and outputting accurate fault type diagnosis results.

[0035] Specifically, first, in order to effectively utilize these information for fault classification, a series of key features that can represent the fault type are extracted from the reconstructed pure fault feature components. These features can include but are not limited to: the average concentration, the maximum concentration, the gas production rate (i.e. the rate of change of concentration over time), and the characteristic ratio between different gases, etc. These ratios are widely used in fault discrimination in Duval triangle method or Rogers ratio method.

[0036] Subsequently, these key features extracted from the reconstructed pure fault feature components are combined into a feature vector as the input of the trained artificial intelligence-based fault diagnosis model. For example, a multi-layer feedforward artificial neural network (ANN) can be used. The ANN has been trained through a large amount of historical fault data (including known fault types and corresponding feature gas patterns) in the offline stage. During the training process, the model learns and establishes a complex nonlinear mapping relationship from these gas features to specific fault types (such as partial discharge, low-temperature overheat, high-temperature overheat, arc discharge, etc.). When a new feature vector (from the reconstructed pure fault feature components of the current transformer) is input into this trained ANN, the ANN will infer according to its learned knowledge and output a probability distribution or a direct classification result about different fault types.

[0037] In summary, the digital twin-based condition monitoring method for main transformers in wind power substations according to embodiments of this application is explained. First, it acquires the time series of characteristic gas concentrations and top-layer oil temperature. Then, it performs multi-level wavelet packet transform on the gas concentration series, decomposing it into different frequency bands. Next, based on the top-layer oil temperature time series of the main transformer, it uses correlation analysis to identify and group these frequency bands, thereby accurately decoupling the components related to temperature effects from the components truly representing fault characteristics. Finally, it reconstructs the signal and extracts pure fault characteristics. This significantly improves the accuracy and reliability of condition monitoring of main transformers in wind power substations, enabling refined perception and early warning of equipment operating conditions.

[0038] Furthermore, a condition monitoring system for the main transformer of a wind power substation based on digital twins is also provided.

[0039] Figure 3 This is a block diagram of a digital twin-based condition monitoring system for the main transformer of a wind power substation, according to an embodiment of this application. Figure 3 As shown in the embodiment of this application, the digital twin-based wind power substation main transformer condition monitoring system 300 includes: a data acquisition module 310, used to acquire the original characteristic gas concentration time series and the main transformer top oil temperature time series; a multi-layer wavelet packet transform module 320, used to perform multi-layer wavelet packet transform on the original characteristic gas concentration time series to obtain wavelet packet coefficient sequences of all frequency bands; a frequency band identification and grouping module 330, used to perform correlation-based frequency band identification and grouping on the wavelet packet coefficient sequences of all frequency bands based on the main transformer top oil temperature time series to obtain a temperature effect-related frequency band index set and a fault feature-related frequency band index set; a signal reconstruction and decoupling module 340, used to perform signal reconstruction and decoupling on the wavelet packet coefficient sequences of all frequency bands based on the temperature effect-related frequency band index set and the fault feature-related frequency band index set to obtain reconstructed pure temperature effect components and reconstructed pure fault feature components; and a fault diagnosis module 350, used to perform fault diagnosis based on the reconstructed pure fault feature components to obtain fault type diagnosis results.

[0040] As described above, the wind power substation main transformer state monitoring system 300 based on digital twinning according to the embodiments of the present application can be implemented in various wireless terminals, such as a server with a wind power substation main transformer state monitoring algorithm based on digital twinning, etc. In a possible implementation manner, the wind power substation main transformer state monitoring system 300 based on digital twinning according to the embodiments of the present application can be integrated into a wireless terminal as a software module and / or a hardware module. For example, the wind power substation main transformer state monitoring system 300 based on digital twinning can be a software module in the operating system of the wireless terminal, or can be an application program developed for the wireless terminal; of course, the wind power substation main transformer state monitoring system 300 based on digital twinning can also be one of the many hardware modules of the wireless terminal.

[0041] Alternatively, in another example, the wind power substation main transformer state monitoring system 300 based on digital twinning and the wireless terminal can also be separate devices, and the wind power substation main transformer state monitoring system 300 based on digital twinning can be connected to the wireless terminal through a wired and / or wireless network, and transmit interactive information in an agreed data format.

[0042] The above has described the embodiments of the present disclosure, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical application, or improvement to the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for condition monitoring of the main transformer in a wind power substation based on digital twin, characterized in that, include: Obtain the original characteristic gas concentration time series and the top oil temperature time series of the main transformer; Multi-level wavelet packet transform is performed on the original characteristic gas concentration time series to obtain wavelet packet coefficient sequences for all frequency bands; Based on the time series of the top oil temperature of the main transformer, the wavelet packet coefficient sequences of all frequency bands are identified and grouped based on correlation to obtain the temperature effect related frequency band index set and the fault feature related frequency band index set. Based on the temperature effect-related frequency band index set and the fault feature-related frequency band index set, the wavelet packet coefficient sequences of all frequency bands are reconstructed and decoupled to obtain the reconstructed pure temperature effect component and the reconstructed pure fault feature component. Fault diagnosis is performed based on reconstructed pure fault feature components to obtain fault type diagnosis results.

2. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 1, characterized in that, Multi-level wavelet packet transform is performed on the original characteristic gas concentration time series to obtain wavelet packet coefficient sequences for all frequency bands, including: Determine the wavelet basis functions and the target decomposition level; Based on wavelet basis functions and the target decomposition level, the original characteristic gas concentration time series is decomposed and iterated to obtain the wavelet packet coefficient sequence of all frequency bands.

3. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 1, characterized in that, Based on the time series of the top-layer oil temperature of the main transformer, correlation-based frequency band identification and grouping are performed on the wavelet packet coefficient sequences of all frequency bands to obtain a set of frequency band indices related to temperature effects and a set of frequency band indices related to fault characteristics, including: Extract the first wavelet packet coefficient sequence from all frequency bands. Layer A sequence of wavelet packet coefficients for each frequency band; Calculate the first Layer The maximum cross-correlation coefficient and optimal time delay between the wavelet packet coefficient sequence of each frequency band and the time sequence of the top oil temperature of the main transformer; Based on the maximum cross-correlation coefficient and the optimal time delay, for the th Layer The wavelet packet coefficient sequences of each frequency band are adaptively grouped to determine whether they belong to the temperature effect-related frequency band index set or the fault feature-related frequency band index set.

4. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 3, characterized in that, Calculate the first Layer The maximum cross-correlation coefficient and optimal time delay between the wavelet packet coefficient sequence of each frequency band and the time series of the top oil temperature of the main transformer include: Calculate the first Layer The cross-correlation function between the wavelet packet coefficient sequence of each frequency band and the time series of top oil temperature of the main transformer; Extract the maximum cross-correlation coefficient and the optimal time delay from the cross-correlation function.

5. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 4, characterized in that, Extracting the maximum cross-correlation coefficient and optimal time delay from the cross-correlation function includes: Based on the Layer The standard deviations of the wavelet packet coefficient sequences of each frequency band and the top oil temperature time series of the main transformer are used to transform the cross-correlation function to obtain the cross-correlation coefficient function; The maximum correlation coefficient and its corresponding time delay are found from the cross-correlation function to obtain the maximum cross-correlation coefficient and the optimal time delay.

6. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 5, characterized in that, Based on the Layer The standard deviations of the wavelet packet coefficient sequences of each frequency band and the top oil temperature time series of the main transformer are used to transform the cross-correlation function to obtain the cross-correlation coefficient function, including: based on the first frequency band... Layer The standard deviations of the wavelet packet coefficient sequences of each frequency band and the time series of the top oil temperature of the main transformer are used to transform the cross-correlation function to obtain the cross-correlation coefficient function using the following formula: ， in, It is a cross-correlation function. and The first Layer The standard deviation of the wavelet packet coefficient sequence of each frequency band and the time series of the top oil temperature of the main transformer.

7. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 3, characterized in that, Based on the maximum cross-correlation coefficient and the optimal time delay, for the th Layer The wavelet packet coefficient sequences of each frequency band are adaptively grouped, including: Transform the variables with the largest cross-correlation coefficients into variables that approximately follow a normal distribution; The variables that approximately follow a normal distribution and the optimal time-delay input discriminant function are used to determine the first... Layer The wavelet packet coefficient sequences of each frequency band are adaptively grouped.

8. The method for monitoring the condition of the main transformer in a wind power substation based on digital twins according to claim 7, characterized in that, The discriminant function is expressed as: ， in, For variables that approximately follow a normal distribution, For the standard normal distribution quantile values, For statistical significance level, For optimal time delay, This is a physically reasonable time delay window.

9. A condition monitoring system for the main transformer of a wind power substation based on digital twin, characterized in that, include: The data acquisition module is used to acquire the original characteristic gas concentration time series and the main transformer top oil temperature time series; The multi-level wavelet packet transform module is used to perform multi-level wavelet packet transform on the original characteristic gas concentration time series to obtain the wavelet packet coefficient sequence of all frequency bands. The frequency band identification and grouping module is used to perform correlation-based frequency band identification and grouping on the wavelet packet coefficient sequences of all frequency bands based on the time series of the top oil temperature of the main transformer, so as to obtain the temperature effect related frequency band index set and the fault feature related frequency band index set. The signal reconstruction and decoupling module is used to reconstruct and decouple the wavelet packet coefficient sequences of all frequency bands based on the temperature effect-related frequency band index set and the fault feature-related frequency band index set to obtain the reconstructed pure temperature effect component and the reconstructed pure fault feature component. The fault diagnosis module is used to perform fault diagnosis based on reconstructed pure fault feature components to obtain fault type diagnosis results.

Images