A method and system for optimizing the dynamic energy efficiency of transformers under multiple working conditions

Abstract

The present invention relates to the technical field of transformer energy efficiency optimization, and discloses a method and system for dynamic energy efficiency optimization of transformers under multiple working conditions, including obtaining historical vibration signals of the transformer, and performing feature extraction and nonlinear analysis to obtain spectral characteristics and nonlinear characteristics; establishing a finite element model based on the change in the gap between core laminations according to the spectral characteristics and nonlinear characteristics; obtaining the real-time vibration signal of the transformer, and inputting the real-time vibration signal into the finite element model, calculating the gap data of the core laminations, and calculating the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data; and using an adaptive compensation mechanism to adjust the operating parameters of the transformer based on the energy efficiency impact coefficient. The present invention deeply combines vibration signal processing, nonlinear dynamics and energy efficiency optimization, effectively improving the evaluation accuracy and optimization effect of the transformer energy efficiency, providing an efficient solution for transformer operation and maintenance, and improving the operating efficiency and reliability of the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer energy efficiency optimization, and in particular to a method and system for dynamic energy efficiency optimization of a transformer under multiple working conditions. Background Art

[0002] As power systems develop towards higher voltages and larger capacities, transformers, as core equipment in the power grid, face significant operational stability and energy efficiency challenges, directly impacting power supply reliability and economic viability. However, traditional transformer monitoring and control technologies have limitations. These methods rely on fixed thresholds to generate alerts for vibration amplitude or frequency, but are unable to effectively identify nonlinear vibration characteristics such as harmonic distortion and intermodulation distortion. Furthermore, existing control operations for transformer dynamic compensation operate independently, lacking a multi-objective collaborative optimization mechanism. Furthermore, transformer energy efficiency evaluations are based solely on electrical parameters, such as input and output power, without considering mechanical and electromagnetic coupling losses caused by changes in core gaps, resulting in inaccurate evaluations.

[0003] Current transformer monitoring and control technologies suffer from core issues such as weak nonlinear feature recognition, a lack of multi-physics coupling modeling, and isolated compensation strategies. These issues lead to limited energy efficiency optimization, high mechanical failure rates, and escalating maintenance costs. To address these issues, an intelligent technology solution is urgently needed that integrates vibration signal analysis, nonlinear dynamics modeling, and multi-objective collaborative compensation to achieve full lifecycle management of transformers, enabling state perception, fault prediction, and energy efficiency optimization. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a dynamic energy efficiency optimization method and system for transformers under multiple working conditions, so as to solve the problems of weak nonlinear feature recognition ability, lack of multi-physical field coupling modeling, and isolated compensation strategy in current transformer monitoring and control technology, and achieve the technical effect of accurate and effective transformer energy efficiency evaluation and optimization.

[0005] In a first aspect, an embodiment of the present invention provides a method for optimizing the dynamic energy efficiency of a transformer under multiple operating conditions, comprising:

[0006] Obtain historical vibration signals of the transformer, perform feature extraction and nonlinear analysis on the historical vibration signals, and obtain spectrum features and nonlinear features;

[0007] According to the spectrum characteristics and nonlinear characteristics, a finite element model based on the change of the core lamination gap is established;

[0008] Obtain the real-time vibration signal of the transformer and input the real-time vibration signal into the finite element model to calculate the gap data of the core laminations. Based on the gap data, calculate the energy efficiency coefficient of the gap change on the transformer energy efficiency;

[0009] According to the energy efficiency impact coefficient, an adaptive compensation mechanism is used to adjust the operating parameters of the transformer until the energy efficiency index of the transformer meets the preset requirements.

[0010] Furthermore, the step of performing feature extraction and nonlinear analysis on the historical vibration signal to obtain spectral features and nonlinear features includes:

[0011] Perform noise filtering and signal reconstruction on historical vibration signals to obtain a time domain vibration signal sequence;

[0012] Performing spectrum decomposition and feature extraction on the time-domain vibration signal sequence according to Hanning window weighting and fast Fourier transform to obtain spectrum features, wherein the spectrum features include fundamental component, harmonic component, intermodulation frequency, amplitude and phase;

[0013] Vibration coupling analysis and nonlinear dynamic feature extraction are performed on a time-domain vibration signal sequence to obtain nonlinear features, which include harmonic distortion, intermodulation distortion, and jump phenomena.

[0014] Furthermore, the step of performing noise filtering and signal reconstruction on the historical vibration signal to obtain a time domain vibration signal sequence includes:

[0015] The high-frequency noise and power frequency interference of the historical vibration signal are filtered out through a Butterworth bandpass filter to obtain the noise-reduced historical vibration signal;

[0016] The Kalman filter is used to filter out the cross-interference of the noise-reduced historical vibration signal and extract the independent vibration components in each direction;

[0017] The instantaneous phase and frequency information of each vibration component is extracted through Hilbert transform, and the time domain vibration signal sequence is reconstructed.

[0018] Furthermore, the step of performing vibration coupling analysis and nonlinear dynamic feature extraction on the time domain vibration signal sequence to obtain nonlinear features includes:

[0019] Calculate the total harmonic distortion rate based on the harmonic components and fundamental components of the spectrum characteristics;

[0020] The time-domain vibration signal sequence is converted to the frequency domain, and the coherence coefficient between the vibration directions is calculated by the cross-spectral density function. The vibration principal component is extracted and the matrix is ​​reconstructed based on the coherence coefficient of the converted frequency-domain feature matrix.

[0021] The slope mutation point of the frequency-amplitude curve of the reconstructed matrix is ​​calculated, and the vibration response of the slope mutation point is analyzed using the phase plane trajectory method. The amplitude of the boundary point of the trajectory overlap area is extracted as the critical amplitude.

[0022] According to the comparison result of the coherence coefficient and the coefficient threshold, the coupling frequency band is determined. With the coupling frequency band as the center, a band-pass filter is used to extract the time domain feature sequence of the vibration signal, and the sample matrix is ​​obtained through sliding window processing.

[0023] Performing phase space reconstruction on the sample matrix to obtain a reconstructed phase space matrix, and calculating the nonlinear dynamic parameters of the reconstructed phase space matrix to obtain the approximate entropy, sample entropy and maximum Lyapunov exponent;

[0024] Determine whether there is harmonic distortion based on the total harmonic distortion rate and approximate entropy;

[0025] Determine whether there is intermodulation distortion based on the intermodulation frequency and maximum Lyapunov exponent of the spectrum characteristics;

[0026] The existence of jump phenomenon is determined based on the critical amplitude, sample entropy and maximum Lyapunov exponent.

[0027] Furthermore, the step of establishing a finite element model based on the change in the gap between the core laminations according to the frequency spectrum characteristics and the nonlinear characteristics includes:

[0028] Generate the geometric structure according to the preset dimensions of the core, and use hexahedral elements to segment the core structure according to the initial lamination gap value to generate the initial finite element mesh;

[0029] According to the spectral characteristics and nonlinear characteristics, the material properties, contact modeling, boundary conditions and load application of the initial finite element mesh are determined to establish a finite element model.

[0030] Furthermore, the step of inputting the real-time vibration signal into the finite element model to calculate the gap data of the core laminations includes:

[0031] According to the real-time vibration signal, dynamic load is applied to the finite element model, and the displacement field distribution of the core lamination is calculated by the dynamic solver;

[0032] The grid deformation tracking algorithm is used to analyze the displacement field distribution of the core laminations, obtain the real-time change of the gap between the core laminations, and generate a displacement deformation cloud map;

[0033] According to the displacement deformation cloud map, the gap data of the core laminations is obtained.

[0034] Furthermore, the step of calculating the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data includes:

[0035] Perform time series analysis on the gap data and predict the gap changes to obtain the predicted gap value;

[0036] Input the predicted gap value and the first operating parameter of the transformer into a pre-built gap energy efficiency correlation model to obtain the energy efficiency impact coefficient;

[0037] The gap energy efficiency correlation model is constructed based on a mapping relationship between an energy efficiency impact coefficient and a characteristic matrix, and the characteristic matrix is ​​composed of a predicted gap value and a first operating parameter.

[0038] Furthermore, the step of adjusting the operating parameters of the transformer using an adaptive compensation mechanism according to the energy efficiency impact coefficient includes:

[0039] Acquire second operating parameters of the transformer, the second operating parameters including voltage deviation, load factor, winding temperature, and vibration amplitude;

[0040] Performing fuzzy reasoning on the energy efficiency impact coefficient and the second operating parameter according to a preset rule base to obtain weights for various compensation strategies, including voltage regulation compensation, vibration suppression compensation, and load optimization;

[0041] According to the weight of each compensation strategy, the corresponding compensation strategy is executed to adjust the operating parameters of the transformer.

[0042] Furthermore, the step of executing the corresponding compensation strategy according to the weight of each compensation strategy includes:

[0043] When performing voltage regulation compensation, the voltage deviation is mapped to the voltage regulation gear, and the objective function is to minimize the number of gear switching and voltage fluctuation, build a timing optimization model, and generate a voltage regulation sequence;

[0044] When performing vibration suppression compensation, the damper gain is increased, the cutoff frequency is adjusted according to the vibration spectrum, and the filter is adaptively updated;

[0045] When performing load optimization, a linear programming model is constructed with the minimization of core loss increment and load balance as the objective function to generate the optimal load distribution, and the reactive power compensation capacity is calculated based on the current reactive power shortage and the rated capacity of the transformer.

[0046] In a second aspect, an embodiment of the present invention provides a dynamic energy efficiency optimization system for a transformer under multiple working conditions, comprising:

[0047] The gap variation model building module is used to obtain the historical vibration signal of the transformer, and perform feature extraction and nonlinear analysis on the historical vibration signal to obtain spectrum characteristics and nonlinear characteristics;

[0048] According to the spectrum characteristics and nonlinear characteristics, a finite element model based on the change of the core lamination gap is established;

[0049] Energy efficiency impact analysis module, used to obtain the real-time vibration signal of the transformer, input the real-time vibration signal into the finite element model, calculate the gap data of the core laminations, and calculate the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data;

[0050] The adaptive compensation control module is used to adjust the operating parameters of the transformer using an adaptive compensation mechanism according to the energy efficiency impact coefficient until the energy efficiency index of the transformer meets the preset requirements.

[0051] The present invention provides a method and system for dynamic energy efficiency optimization of transformers under multiple working conditions. The present invention solves the problem of vibration energy accumulation and unpredictable jumps caused by changes in the core gap through vibration spectrum feature extraction and nonlinear dynamic modeling, realizes accurate identification of distortion types, and accurately quantifies the impact of energy efficiency through the gap energy efficiency correlation model. It also realizes dynamic optimization of transformer energy efficiency through an adaptive compensation mechanism, effectively improving the optimization effect of transformer energy efficiency. The present invention deeply combines vibration signal processing, nonlinear dynamics and energy efficiency optimization, breaking through the limitations of traditional single-dimensional regulation, effectively improving the evaluation accuracy and optimization effect of transformer energy efficiency, providing an efficient solution for transformer operation and maintenance, and effectively improving the operating efficiency and reliability of the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 1 is a flow chart of a method for optimizing the dynamic energy efficiency of a transformer under multiple working conditions according to an embodiment of the present invention;

[0053] Figure 2 3 is a structural diagram of a dynamic energy efficiency optimization system for transformers under multiple working conditions in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] See also Figure 1 The first embodiment of the present invention provides a method for optimizing the dynamic energy efficiency of a transformer under multiple working conditions, comprising steps S10 to S40:

[0056] Step S10, obtaining a historical vibration signal of the transformer, and performing feature extraction and nonlinear analysis on the historical vibration signal to obtain a spectrum feature and a nonlinear feature;

[0057] Step S20, establishing a finite element model based on the change of the gap between the core laminations according to the frequency spectrum characteristics and the nonlinear characteristics;

[0058] Step S30, obtaining a real-time vibration signal of the transformer, inputting the real-time vibration signal into a finite element model, calculating gap data of the core laminations, and calculating an energy efficiency impact coefficient of gap change on transformer energy efficiency based on the gap data;

[0059] Step S40 : According to the energy efficiency impact coefficient, an adaptive compensation mechanism is used to adjust the operating parameters of the transformer until the energy efficiency index of the transformer meets the preset requirements.

[0060] The present invention provides a method for dynamically optimizing the energy efficiency of transformers under multiple operating conditions. During actual operation, transformers are susceptible to a combination of horizontal, vertical, and torsional vibrations under abnormal operating conditions such as varying load levels, varying ambient temperatures, varying vibration intensities, and sudden short circuits. Consequently, the gap between the core laminations undergoes complex dynamic changes. For example, when the load increases, the winding current increases, the core magnetic flux density rises, and the lateral electromagnetic attraction increases. This intensifies the magnetostrictive effect between the laminations, inducing periodic vibrations and dynamic gap fluctuations. The temperature rise caused by the load current causes the silicon steel laminations to expand longitudinally, temporarily reducing the vertical gap. However, material fatigue under long-term thermal cycling may cause the gap to permanently expand. The gap also fluctuates when there is a superposition of multiple directional vibrations or frequency resonance. Furthermore, during a sudden short circuit, the short circuit current induces extremely strong Lorentz forces, subjecting the core to dynamic stresses in the order of megapascals, leading to lamination misalignment and sudden gap changes. Furthermore, after the short circuit, the electromagnetic force decreases sharply, and the laminations do not fully rebound, causing an increase in the residual gap. The change in the gap between the core laminations directly affects the energy efficiency performance of the transformer. For example, an increase in the gap will cause local overheating and noise. The equivalent conductivity model is used to simulate the impact of different numbers of lamination layers on the magnetic field, which shows that the gap change will significantly change the magnetic flux density distribution and eddy current loss of the core. Therefore, the present invention analyzes the vibration signal of the transformer to establish a finite element model based on the change in the gap between the core laminations, thereby realizing the impact analysis between the gap change and energy efficiency.

[0061] In this embodiment, data preprocessing and feature extraction are first performed on the historical vibration information of the transformer. The specific steps include:

[0062] Perform noise filtering and signal reconstruction on historical vibration signals to obtain a time domain vibration signal sequence;

[0063] Performing spectrum decomposition and feature extraction on the time-domain vibration signal sequence according to Hanning window weighting and fast Fourier transform to obtain spectrum features, wherein the spectrum features include fundamental component, harmonic component, intermodulation frequency, amplitude and phase;

[0064] Vibration coupling analysis and nonlinear dynamic feature extraction are performed on a time-domain vibration signal sequence to obtain nonlinear features, which include harmonic distortion, intermodulation distortion, and jump phenomena.

[0065] In this embodiment, an inertial measurement unit composed of a multi-axis acceleration sensor and a gyroscope is used to collect vibration signals of the transformer under multi-directional vibration. The preprocessing of the historical vibration signal includes noise filtering and signal reconstruction. A conventional filter can be used for noise filtering, and the denoised historical vibration signal is reconstructed into time domain data. In order to achieve a better noise filtering effect, in a preferred embodiment, the steps of noise filtering and signal reconstruction include:

[0066] The high-frequency noise and power frequency interference of the historical vibration signal are filtered out through a Butterworth bandpass filter to obtain the noise-reduced historical vibration signal;

[0067] The Kalman filter is used to filter out the cross-interference of the noise-reduced historical vibration signal and extract the independent vibration components in each direction;

[0068] The instantaneous phase and frequency information of each vibration component is extracted through Hilbert transform, and the time domain vibration signal sequence is reconstructed.

[0069] In this embodiment, instead of using a single filter, multiple filters are used to remove different noise interferences. First, a Butterworth bandpass filter is used to filter out high-frequency noise and power frequency interference from the historical vibration signal. Then, a Kalman filter is used to remove cross-signal interference between multiple channels. At the same time, an orthogonal decomposition method is used to identify multi-directional vibration components of the vibration signal after Kalman filtering to generate a vibration direction feature matrix. The vibration components are sorted according to the size of the eigenvalues, and the eigenvectors of the main vibration directions are extracted. The instantaneous phase and frequency information of each vibration component are extracted by Hilbert transform, and the time domain vibration signal sequence is reconstructed by inverse Fourier transform.

[0070] Then, feature extraction and nonlinear analysis are performed on the time domain vibration signal sequence to obtain spectral features and nonlinear features. When extracting spectral features, the time domain vibration signal is segmented according to the sampling frequency and sampling window length, and the data segment is weighted by the Hanning window function to reduce the impact of spectral leakage. The Hanning window function has a bell-shaped distribution in the time domain, the weight of the edge data points is small, and the weight of the middle data points is close to 1. After weighted processing, the spectral analysis results will be more accurate. Finally, the weighted data segment is converted to the frequency domain through fast Fourier transform, and the three-dimensional features of frequency, amplitude, and phase are extracted to generate a frequency domain feature matrix.

[0071] The fundamental component and harmonic component are extracted from the frequency domain feature matrix, and two frequency points f1 and f2 are set as dual-frequency excitation sources to obtain the combined frequency component, namely the intermodulation frequency mf1±nf2, where m and n are integers. The amplitude and phase characteristics at the frequency points mf1±nf2 are then detected to obtain the intermodulation characteristic parameters, which include intermodulation frequency, amplitude and phase.

[0072] In this embodiment, the nonlinear characteristics are obtained by performing vibration coupling analysis and nonlinear dynamic characteristics extraction on the time domain vibration signal sequence. The specific steps include:

[0073] Calculate the total harmonic distortion rate based on the harmonic components and fundamental components of the spectrum characteristics;

[0074] The time-domain vibration signal sequence is converted to the frequency domain, and the coherence coefficient between the vibration directions is calculated by the cross-spectral density function. The vibration principal component is extracted and the matrix is ​​reconstructed based on the coherence coefficient of the converted frequency-domain feature matrix.

[0075] The slope mutation point of the frequency-amplitude curve of the reconstructed matrix is ​​calculated, and the vibration response of the slope mutation point is analyzed using the phase plane trajectory method. The amplitude of the boundary point of the trajectory overlap area is extracted as the critical amplitude.

[0076] According to the comparison result of the coherence coefficient and the coefficient threshold, the coupling frequency band is determined. With the coupling frequency band as the center, a band-pass filter is used to extract the time domain feature sequence of the vibration signal, and the sample matrix is ​​obtained through sliding window processing.

[0077] Performing phase space reconstruction on the sample matrix to obtain a reconstructed phase space matrix, and calculating the nonlinear dynamic parameters of the reconstructed phase space matrix to obtain the approximate entropy, sample entropy and maximum Lyapunov exponent;

[0078] Determine whether there is harmonic distortion based on the total harmonic distortion rate and approximate entropy;

[0079] Determine whether there is intermodulation distortion based on the intermodulation frequency and maximum Lyapunov exponent of the spectrum characteristics;

[0080] The existence of jump phenomenon is determined based on the critical amplitude, sample entropy and maximum Lyapunov exponent.

[0081] In this embodiment, the nonlinear characteristics include harmonic distortion, intermodulation distortion and jumping phenomenon, and each nonlinear characteristic can be characterized by different parameters. First, for harmonic distortion analysis, the most direct characterization parameter is the total harmonic distortion rate THD, which can be calculated by the ratio of the square root of the sum of the square root of all harmonic effective values ​​to the effective value of the fundamental wave. Preferably, when the total harmonic distortion rate is greater than 30%, it is believed that the core hysteresis nonlinearity causes harmonic accumulation and thus harmonic distortion. For intermodulation distortion analysis, the most direct characterization parameter is the combined frequency amplitude, that is, the intermodulation frequency, which can be obtained through the spectrum characteristics. If the intermodulation frequency is significant, it is believed that multi-directional vibration coupling has caused frequency modulation and intermodulation distortion has occurred.

[0082] The jumping phenomenon is mostly caused by a sudden change in stiffness or contact separation, which leads to a sudden change in dynamic response, thereby causing the jumping phenomenon. In order to amplify the characteristics of nonlinear dynamics and improve the detection sensitivity, in this embodiment, the vibration signal is purified and feature enhanced. Specifically, the time domain signal is first Fourier transformed to convert it into a frequency domain signal, and the frequency domain signal is subjected to frequency domain analysis through the cross-spectral density function to measure the correlation of multi-directional vibrations at specific frequencies, thereby obtaining the coherence coefficient between the vibration directions. The strong coupling frequency band and weak noise coupling are separated from the frequency domain signal, that is, the frequency domain feature matrix, through the coherence coefficient. Then, the frequency domain feature matrix is ​​orthogonally decomposed based on the coherence coefficient to extract the vibration mode with the largest variance, that is, the main components of vibration in each direction are obtained, and the matrix is ​​reconstructed based on the vibration main components, so that the reconstructed frequency domain feature matrix only retains the main components, eliminating the interference of non-correlated noise on subsequent analysis, while also reducing the data dimension and improving the calculation efficiency. Then, the frequency amplitude curve of the reconstructed frequency domain feature matrix is ​​analyzed to obtain the slope mutation point of the curve. For example, the slope changes from positive to negative at 180Hz. Since the slope mutation of the frequency amplitude curve is only significant in the principal component, this feature can be amplified by reconstructing the matrix.

[0083] The slope mutation point is used as the jump frequency feature point, and the phase plane trajectory method is used to analyze the vibration response at the jump frequency feature point. The boundary point of the trajectory overlap area is extracted and regarded as the critical point of the jump phenomenon. The amplitude of this boundary point is regarded as the critical amplitude. When the critical amplitude is greater than the threshold, for example, the vibration response jump is considered to have occurred.

[0084] Although direct characterization of nonlinear phenomena is achieved through the above steps, certain limitations still exist. Among them, the total harmonic distortion rate only reflects the proportion of harmonic energy, but cannot describe the system complexity caused by harmonics, such as vibration mode mutations; the significant combined frequency amplitude only indicates the existence of intermodulation, but cannot predict its long-term impact on system stability; and the slope mutation and trajectory overlap are phenomenon descriptions, but cannot quantify the dynamic sensitivity of the critical state.

[0085] In order to quantify the complexity of nonlinear behavior and system states, such as chaos and unpredictability, this embodiment also adds the characterization of dynamic parameters, including approximate entropy, sample entropy, and maximum Lyapunov exponent. Among them, the approximate entropy is added to the harmonic distortion, and the total harmonic distortion rate reflects the proportion of harmonic energy to total energy, indicating that the hysteresis nonlinearity of the core leads to harmonic accumulation. The approximate entropy is used to further quantify the complexity of the harmonic distribution. For example, when the total harmonic distortion rate is higher than the distortion threshold, if the approximate entropy is low complexity, it indicates that the harmonic components are highly regular, which may be caused by fixed hysteresis loss and does not require emergency treatment. High complexity indicates that the harmonic distribution is highly random, such as local insulation degradation causing random discharge, which requires priority maintenance.

[0086] The maximum Lyapunov exponent is added to the intermodulation distortion, and the frequency modulation effect caused by multi-directional vibration coupling is significantly reflected by the combined frequency amplitude. The stability of the system is characterized by the maximum Lyapunov exponent. When the maximum Lyapunov exponent is greater than zero, it indicates that the intermodulation distortion may cause chaotic vibration. In the subsequent compensation mechanism, it is necessary to suppress the vibration coupling in advance. When the maximum Lyapunov exponent is approximately equal to zero, it indicates that the system is highly stable and only the changes in the combined frequency amplitude need to be monitored.

[0087] For the jumping phenomenon, the slope mutation and dynamic parameters are jointly analyzed. The slope mutation point, also known as the jumping critical point, reflects the stiffness mutation or contact separation, and the critical amplitude of the Poincare section trajectory overlap is used to determine whether the jumping phenomenon occurs. At the same time, the sample entropy is used to quantify the irregularity of the signal near the jumping critical point. The high entropy value indicates that the response mode is diverse, and the chaotic characteristics of the maximum Lyapunov exponent indicate the chain reaction that may be caused by the jumping.

[0088] In this embodiment, the approximate entropy, sample entropy and maximum Lyapunov exponent can all be obtained based on the calculation of nonlinear dynamic parameters of the reconstructed phase space matrix. When reconstructing the phase space matrix, the frequency band with a coupling relationship is first determined by cross-spectral density analysis. The coherence coefficient is calculated in the above steps, so the coefficient can be directly applied here. The coherence coefficient is screened by a preset threshold range, such as (0.5-0.7), to determine the coupling frequency band. Then, with the coupling frequency band as the center, a bandpass filter is used to extract the time domain feature sequence of the vibration signal, and a sliding window is processed to obtain a sample matrix; the autocorrelation function is calculated according to the sample matrix, and the first delayed zero point is used as the time delay parameter. The minimum embedding dimension is obtained by solving the F nearest neighbor rule criterion to generate the reconstructed phase space matrix, and the conditional probability density distribution of the reconstructed phase space matrix is ​​calculated. The approximate entropy value is calculated by the pattern matching method, the sample entropy is calculated by sequence similarity, and the maximum Lyapunov exponent is calculated by the small data method. Since total harmonic distortion and intermodulation frequency already exist in the spectrum characteristics, in this embodiment, the dynamic parameters are combined into a vector according to the weights to characterize the nonlinear characteristics, including critical amplitude, approximate entropy, sample entropy and maximum Lyapunov exponent.

[0089] After obtaining the spectrum characteristics and nonlinear characteristics, a finite element model based on the change in the gap between the core laminations can be established. The specific steps include:

[0090] Generate the geometric structure according to the preset dimensions of the core, and use hexahedral elements to segment the core structure according to the initial lamination gap value to generate the initial finite element mesh;

[0091] According to the spectral characteristics and nonlinear characteristics, the material properties, contact modeling, boundary conditions and load application of the initial finite element mesh are determined to establish a finite element model.

[0092] In this embodiment, a geometric structure is first generated based on the preset core dimensions, an initial lamination gap is defined, and the mesh is generated using hexahedral elements to generate an initial finite element mesh. Laminate element property parameters are defined for the initial finite element mesh, and anisotropic elastic parameters are used to describe the laminate material properties. Laminate interface contact pairs are generated based on the preset friction coefficient, and the tangential force at the contact surface is calculated using the Coulomb friction law. Normal and tangential contact stiffnesses are applied to the laminate contact surfaces, and a contact stress matrix is ​​generated based on the contact surface pressure distribution. Surface-to-surface contact elements are used to describe the force transmission characteristics of the laminate interface, and a laminate interface contact response matrix is ​​established. The mapping relationship between nonlinear characteristics must also be considered when designing material anisotropy and loss distribution, dynamic coupling of the contact interface, and transient dynamic response and plastic yield. Boundary displacement constraints are then applied to the laminate contact response matrix, with a fixed bottom constraint and a free top. Elastic-plastic constitutive relations are used to describe the laminate deformation characteristics. A stiffness matrix is ​​generated based on the stress-strain relationship, and the contact state parameters are updated based on the laminate deformation. Vibration spectrum data is used to generate displacement loading boundary conditions. The laminate transient response is calculated using an explicit dynamics algorithm. The laminate gap distribution is updated using node displacements, and a laminate vibration deformation response matrix is ​​established. A convergence criterion is applied to the laminate vibration deformation response matrix, and the nonlinear equilibrium equation is solved using the Newton iteration method. The laminate force equilibrium state is determined based on the residual error, and a dynamic deformation cloud map of the laminate is generated. This dynamic deformation cloud map is used to track the gap change. It should be noted that the specific steps for constructing the finite element model can refer to the steps for constructing a conventional finite element model and will not be repeated here.

[0093] After the finite element model is constructed, the real-time change in the gap between the core laminations is calculated by inputting the real-time vibration signal of the transformer into the finite element model. The specific steps include:

[0094] According to the real-time vibration signal, dynamic load is applied to the finite element model, and the displacement field distribution of the core lamination is calculated by the dynamic solver;

[0095] The grid deformation tracking algorithm is used to analyze the displacement field distribution of the core laminations, obtain the real-time change of the gap between the core laminations, and generate a displacement deformation cloud map;

[0096] According to the displacement deformation cloud map, the gap data of the core laminations is obtained.

[0097] In this embodiment, the vibration amplitude, frequency and phase information of the real-time vibration signal are obtained and applied to the finite element model as a dynamic load. The displacement field distribution of the core laminations is calculated by a dynamic solver, and the real-time change of the lamination gap is recorded by a grid deformation tracking algorithm. A displacement deformation cloud map is generated, and the gap data of the laminations is extracted based on the displacement deformation cloud map.

[0098] After obtaining the gap data, the energy efficiency coefficient of the gap change on the transformer energy efficiency can be calculated based on the gap data. The specific steps include:

[0099] Perform time series analysis on the gap data and predict the gap changes to obtain the predicted gap value;

[0100] Input the predicted gap value and the first operating parameter of the transformer into a pre-built gap energy efficiency correlation model to obtain the energy efficiency impact coefficient;

[0101] The gap energy efficiency correlation model is constructed based on a mapping relationship between an energy efficiency impact coefficient and a characteristic matrix, and the characteristic matrix is ​​composed of a predicted gap value and a first operating parameter.

[0102] In this embodiment, a Butterworth low-pass filter is used to filter the gap data to remove high-frequency noise, and the gap data is segmented based on a sliding window to form time series data. Feature extraction is performed on the time series data, and the gap features include statistical features and frequency domain features. The statistical features include mean, variance, and trend slope obtained by linear fitting, and the frequency domain features include fundamental wave amplitude and total harmonic distortion. The gap features are then input into a pre-built gap prediction model to obtain a predicted gap value, wherein the gap prediction model is constructed based on a long short-term memory neural network. Of course, other neural network models for analyzing time series data can also be used to construct a gap prediction model, and this is not too limited here.

[0103] In a preferred embodiment, in order to reduce the amount of calculation, the present invention further provides a determination of a gap safety threshold. The gap safety threshold is a safe upper limit of the gap and is pre-set based on industry standards. In addition, in order to enable the gap safety threshold to be adaptively and dynamically adjusted based on the operating status of the transformer, in this embodiment, the gap safety threshold can also be dynamically adjusted based on the load rate of the transformer:

[0104]

[0105] Where, d sd represents the gap safety threshold after dynamic adjustment, d s represents the preset gap safety threshold, α represents the dynamic coefficient, L represents the load rate, L min and L max Respectively represent the minimum and maximum values ​​of the load rate.

[0106] Only when the predicted gap value exceeds the gap safety threshold will an alarm be triggered and the energy efficiency impact assessment be initiated. When the predicted gap value is less than the gap safety threshold, it is assumed that the gap change has no significant impact on the transformer's energy efficiency. Therefore, the transformer can continue to operate according to the current operating parameters without the need for compensation adjustments. This approach effectively reduces the computational complexity of energy efficiency optimization while ensuring the transformer's energy efficiency, thereby improving optimization efficiency.

[0107] When energy efficiency evaluation is required, this embodiment performs correlation analysis between the predicted gap value and the real-time operating parameters of the transformer to calculate the energy efficiency impact coefficient of the gap change on the transformer energy efficiency. In this embodiment, the correlation analysis is performed using a gap energy efficiency correlation model, which is constructed by mapping the energy efficiency impact coefficient to a feature matrix, where the feature matrix is ​​composed of the predicted gap value and the operating parameters. Specifically, the gap energy efficiency correlation model is pre-constructed using historical data. The historical data includes the measured core lamination gap value and a first operating parameter. The first operating parameter includes the load factor, winding temperature, operating time, ambient temperature, and the measured efficiency value. For the measured core lamination gap value, the gap change is calculated by calculating the difference between the measured gap value and the nominal gap. The efficiency change rate of the measured efficiency value is used as the energy efficiency impact coefficient. A multivariate linear regression equation is established with the gap change and the operating parameters as input features and the energy efficiency impact coefficient as output. The equation is solved based on a machine learning model, such as a random forest method for feature importance ranking and hyperparameter tuning, thereby constructing the gap energy efficiency correlation model.

[0108] When using the predicted gap value for energy efficiency analysis, first calculate the gap change between the predicted gap value and the nominal gap. Then, input the gap change and the real-time collected transformer operating parameters into the gap energy efficiency correlation model to obtain the energy efficiency impact coefficient. The energy efficiency impact coefficient characterizes the change in transformer efficiency. Based on the energy efficiency impact coefficient and combined with the transformer operating parameters, the transformer can be adaptively compensated and controlled. The specific steps include:

[0109] Acquire second operating parameters of the transformer, the second operating parameters including voltage deviation, load factor, winding temperature, and vibration amplitude;

[0110] Performing fuzzy reasoning on the energy efficiency impact coefficient and the second operating parameter according to a preset rule base to obtain weights for various compensation strategies, including voltage regulation compensation, vibration suppression compensation, and load optimization;

[0111] According to the weight of each compensation strategy, the corresponding compensation strategy is executed to adjust the operating parameters of the transformer.

[0112] In this embodiment, the transformer's second operating parameters, including voltage deviation, load factor, winding temperature, and vibration amplitude, are collected in real time. These operating parameters are normalized to eliminate dimensional differences. Fuzzy reasoning is performed on the normalized energy efficiency impact coefficient and the second operating parameters based on a preset rule base. The rules in the rule base are generated based on expert experience, historical data, and physical constraints. These rules include prioritizing voltage deviation under high load, forcing load reduction when vibration is excessive, and giving absolute priority to the cooling system when temperature exceeds the limit. Within the rule base, corresponding compensation actions and weights are output based on different rules. During reasoning, the Mamdani method is used to calculate the membership of the input variables to each fuzzy set, activate matching rules, and tailor the output membership functions according to the rule weights. The union of these functions is then taken as the final fuzzy output. Defuzzification is then performed using the center of gravity method to calculate the clarity value and output compensation actions and corresponding weights. Compensation actions include voltage regulation compensation, vibration suppression compensation, and load optimization.

[0113] In a preferred embodiment, the present invention adds conflict arbitration and dynamic adjustment mechanisms during inference to improve the accuracy of inference results. First, a base weight is set for the compensation strategy. When a rule conflict occurs, the weight is dynamically adjusted. When multiple rule objectives conflict, the weight of the secondary objective is reduced. For example, if output rule 1 is voltage regulation compensation with a weight of 0.33, and rule 2 is vibration suppression compensation with a weight of 0.67, but voltage regulation upshifting exacerbates vibration, that is, rule 1 with a lower weight conflicts with rule 2 with a higher weight, then the voltage regulation weight is reduced according to a preset ratio, and the vibration suppression weight is increased. At the same time, a mandatory priority is set, giving absolute priority to certain rules. For example, when the winding temperature exceeds the limit, the load optimization weight is forced to zero. Finally, based on the results of fuzzy inference, all actions are allocated resources according to the final weight, thereby achieving dynamic optimization of the transformer's energy efficiency. It should be noted that the rules and compensation strategies in the rule base can be flexibly set according to the actual operating conditions of the transformer, and are not limited in detail here. The establishment of the rule base and the fuzzy inference process can refer to the conventional library construction and inference steps and will not be detailed here.

[0114] In a preferred embodiment, the steps for executing different compensation strategies include:

[0115] When performing voltage regulation compensation, the voltage deviation is mapped to the voltage regulation gear, and the objective function is to minimize the number of gear switching and voltage fluctuation, build a timing optimization model, and generate a voltage regulation sequence;

[0116] When performing vibration suppression compensation, the damper gain is increased, the cutoff frequency is adjusted according to the vibration spectrum, and the filter is adaptively updated;

[0117] When performing load optimization, a linear programming model is constructed with the minimization of core loss increment and load balance as the objective function to generate the optimal load distribution, and the reactive power compensation capacity is calculated based on the current reactive power shortage and the rated capacity of the transformer.

[0118] In this embodiment, for voltage regulation compensation, the voltage regulation gear is determined by calculating the ratio of the voltage deviation to the gear ratio, and then dynamic programming is performed based on minimizing the number of gear switching times and voltage fluctuations to generate a smooth voltage regulation sequence. For vibration suppression compensation, if the amplitude is greater than the critical amplitude, the damper gain is increased. The gain value is determined by multiplying the reference gain by the gain coefficient. At the same time, the cutoff frequency is adjusted according to the vibration spectrum, and the filter is adaptively updated. For load optimization, a linear programming model is constructed to optimize the load distribution with the core loss increment and load balance minimization as the objective function. Its objective function is expressed as:

[0119]

[0120] Where, ΔP core It represents the core loss increment, that is, the increase in eddy current and hysteresis loss caused by the change in core gap. β represents the load balancing weight coefficient. L i Indicates the load rate of the i-th transformer, that is, the percentage of the current actual load to the rated capacity, L avg Represents the average load rate of the system, that is, the average load rate of all transformers participating in the optimization.

[0121] Since an increase in the core gap will lead to a significant increase in the core loss increment, and a larger load deviation will cause worse system stability, such as local overheating, the above objective function can be used to minimize the core loss while balancing the load distribution of each transformer to avoid local overload.

[0122] In addition, reactive power compensation is required during load optimization. The reactive power compensation capacity is determined by the maximum of the current reactive power deficit and the minimum reactive power compensation capacity. The minimum reactive power compensation capacity is determined by the product of the transformer's rated capacity and the rated capacity compensation ratio. When the current reactive power deficit is less than the minimum reactive power compensation capacity, reactive power compensation is performed according to the minimum reactive power compensation capacity, thereby avoiding frequent switching. When the current reactive power deficit is greater than the minimum reactive power compensation capacity, compensation is performed according to the actual deficit to prevent voltage collapse. This ensures that the reactive power compensation capacity meets current demand while providing preventative redundancy. Reactive power compensation stabilizes voltage and indirectly reduces additional losses caused by voltage fluctuations.

[0123] The embodiment of the present invention provides a method for optimizing the dynamic energy efficiency of transformers under multiple working conditions. The present invention solves the problem of vibration energy accumulation and unpredictable jumps caused by changes in the core gap through vibration spectrum feature extraction and nonlinear dynamic modeling, realizes accurate identification of distortion types, and accurately quantifies the impact of energy efficiency through the gap energy efficiency correlation model. It also realizes dynamic optimization of transformer energy efficiency through an adaptive compensation mechanism, effectively improving the optimization effect of transformer energy efficiency. The present invention deeply combines vibration signal processing, nonlinear dynamics and energy efficiency optimization, breaks through the limitations of traditional single-dimensional regulation, effectively improves the evaluation accuracy and optimization effect of transformer energy efficiency, provides an efficient solution for transformer operation and maintenance, and effectively improves the operating efficiency and reliability of the transformer.

[0124] See also Figure 2 Based on the same inventive concept, a second embodiment of the present invention provides a dynamic energy efficiency optimization system for transformers under multiple working conditions, comprising:

[0125] The gap variation model building module 10 is used to obtain the historical vibration signal of the transformer, and perform feature extraction and nonlinear analysis on the historical vibration signal to obtain spectrum characteristics and nonlinear characteristics;

[0126] According to the spectrum characteristics and nonlinear characteristics, a finite element model based on the change of the core lamination gap is established;

[0127] Energy efficiency impact analysis module 20, used to obtain real-time vibration signals of the transformer, input the real-time vibration signals into the finite element model, calculate the gap data of the core laminations, and calculate the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data;

[0128] The adaptive compensation control module 30 is used to adjust the operating parameters of the transformer using an adaptive compensation mechanism according to the energy efficiency impact coefficient until the energy efficiency index of the transformer meets the preset requirements.

[0129] The technical features and technical effects of the dynamic energy efficiency optimization system for transformers under multiple working conditions proposed in the embodiment of the present invention are the same as those of the method proposed in the embodiment of the present invention, and will not be described in detail here. Each module in the above-mentioned dynamic energy efficiency optimization system for transformers under multiple working conditions can be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0130] In summary, the embodiment of the present invention provides a method and system for optimizing the dynamic energy efficiency of a transformer under multiple working conditions. The method obtains the historical vibration signal of the transformer, performs feature extraction and nonlinear analysis on the historical vibration signal, and obtains spectral characteristics and nonlinear characteristics; establishes a finite element model based on the change in the gap between the core laminations based on the spectral characteristics and nonlinear characteristics; obtains the real-time vibration signal of the transformer, inputs the real-time vibration signal into the finite element model, calculates the gap data of the core laminations, and calculates the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data; and uses an adaptive compensation mechanism to adjust the operating parameters of the transformer based on the energy efficiency impact coefficient until the energy efficiency index of the transformer meets the preset requirements. The present invention solves the problem of vibration energy accumulation and unpredictable jump caused by the change in the core gap through vibration spectrum feature extraction and nonlinear dynamic modeling, realizes accurate identification of distortion types, accurately quantifies the energy efficiency impact through the gap energy efficiency correlation model, and realizes dynamic optimization of the transformer energy efficiency through the adaptive compensation mechanism, effectively improving the optimization effect of the transformer energy efficiency. The present invention deeply combines vibration signal processing, nonlinear dynamics and energy efficiency optimization, breaking through the limitations of traditional single-dimensional regulation, effectively improving the evaluation accuracy and optimization effect of transformer energy efficiency, providing an efficient solution for transformer operation and maintenance, and effectively improving the operating efficiency and reliability of the transformer.

[0131] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] The above-described embodiments merely represent several preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art could make several improvements and substitutions without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be based on the scope of protection of the claims.

Claims

1. A method for optimizing the dynamic energy efficiency of a transformer under multiple working conditions, characterized in that: include: Obtain historical vibration signals of the transformer, perform feature extraction and nonlinear analysis on the historical vibration signals, and obtain spectrum features and nonlinear features; According to the spectrum characteristics and nonlinear characteristics, a finite element model based on the change of the core lamination gap is established; Obtain the real-time vibration signal of the transformer and input the real-time vibration signal into the finite element model to calculate the gap data of the core laminations. Based on the gap data, calculate the energy efficiency coefficient of the gap change on the transformer energy efficiency; According to the energy efficiency impact coefficient, an adaptive compensation mechanism is used to adjust the operating parameters of the transformer until the energy efficiency index of the transformer meets the preset requirements.

2. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 1, characterized in that: The step of performing feature extraction and nonlinear analysis on the historical vibration signal to obtain spectrum features and nonlinear features includes: Perform noise filtering and signal reconstruction on historical vibration signals to obtain a time domain vibration signal sequence; Performing spectrum decomposition and feature extraction on the time-domain vibration signal sequence according to Hanning window weighting and fast Fourier transform to obtain spectrum features, wherein the spectrum features include fundamental component, harmonic component, intermodulation frequency, amplitude and phase; Vibration coupling analysis and nonlinear dynamic feature extraction are performed on a time-domain vibration signal sequence to obtain nonlinear features, which include harmonic distortion, intermodulation distortion, and jump phenomena.

3. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 2, characterized in that: The step of performing noise filtering and signal reconstruction on the historical vibration signal to obtain a time domain vibration signal sequence includes: The high-frequency noise and power frequency interference of the historical vibration signal are filtered out through a Butterworth bandpass filter to obtain the noise-reduced historical vibration signal; The Kalman filter is used to filter out the cross-interference of the noise-reduced historical vibration signal and extract the independent vibration components in each direction; The instantaneous phase and frequency information of each vibration component is extracted through Hilbert transform, and the time domain vibration signal sequence is reconstructed.

4. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 2, characterized in that: The step of performing vibration coupling analysis and nonlinear dynamic feature extraction on the time domain vibration signal sequence to obtain nonlinear features includes: Calculate the total harmonic distortion rate based on the harmonic components and fundamental components of the spectrum characteristics; The time-domain vibration signal sequence is converted to the frequency domain, and the coherence coefficient between the vibration directions is calculated by the cross-spectral density function. The vibration principal component is extracted and the matrix is ​​reconstructed based on the coherence coefficient of the converted frequency-domain feature matrix. The slope mutation point of the frequency-amplitude curve of the reconstructed matrix is ​​calculated, and the vibration response of the slope mutation point is analyzed using the phase plane trajectory method. The amplitude of the boundary point of the trajectory overlap area is extracted as the critical amplitude. According to the comparison results of the coherence coefficient and the coefficient threshold, the coupling frequency band is determined. With the coupling frequency band as the center, a band-pass filter is used to extract the time domain feature sequence of the vibration signal, and the sample matrix is ​​obtained through sliding window processing. Perform phase space reconstruction on the sample matrix to obtain a reconstructed phase space matrix, and calculate the nonlinear dynamic parameters of the reconstructed phase space matrix to obtain the approximate entropy, sample entropy and maximum Lyapunov exponent; Determine whether there is harmonic distortion based on the total harmonic distortion rate and approximate entropy; Determine whether there is intermodulation distortion based on the intermodulation frequency and maximum Lyapunov exponent of the spectrum characteristics; The existence of jump phenomenon is determined based on the critical amplitude, sample entropy and maximum Lyapunov exponent.

5. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 1, characterized in that: The step of establishing a finite element model based on the change in the gap between the core laminations according to the frequency spectrum characteristics and the nonlinear characteristics includes: Generate the geometric structure according to the preset dimensions of the core, and use hexahedral elements to segment the core structure according to the initial lamination gap value to generate the initial finite element mesh; According to the spectral characteristics and nonlinear characteristics, the material properties, contact modeling, boundary conditions and load application of the initial finite element mesh are determined to establish a finite element model.

6. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 1, characterized in that: The step of inputting the real-time vibration signal into the finite element model to calculate the gap data of the core laminations includes: According to the real-time vibration signal, dynamic load is applied to the finite element model, and the displacement field distribution of the core lamination is calculated by the dynamic solver; The grid deformation tracking algorithm is used to analyze the displacement field distribution of the core laminations, obtain the real-time change of the gap between the core laminations, and generate a displacement deformation cloud map; According to the displacement deformation cloud map, the gap data of the core laminations is obtained.

7. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 1, characterized in that: The step of calculating the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data includes: Perform time series analysis on the gap data and predict the gap changes to obtain the predicted gap value; Input the predicted gap value and the first operating parameter of the transformer into a pre-built gap energy efficiency correlation model to obtain the energy efficiency impact coefficient; The gap energy efficiency correlation model is constructed based on a mapping relationship between an energy efficiency impact coefficient and a characteristic matrix, and the characteristic matrix is ​​composed of a predicted gap value and a first operating parameter.

8. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 1, characterized in that: The step of adjusting the operating parameters of the transformer using an adaptive compensation mechanism according to the energy efficiency impact coefficient includes: Acquire second operating parameters of the transformer, the second operating parameters including voltage deviation, load factor, winding temperature, and vibration amplitude; Performing fuzzy reasoning on the energy efficiency impact coefficient and the second operating parameter according to a preset rule base to obtain weights for various compensation strategies, including voltage regulation compensation, vibration suppression compensation, and load optimization; According to the weight of each compensation strategy, the corresponding compensation strategy is executed to adjust the operating parameters of the transformer.

9. The method for dynamic energy efficiency optimization of transformers under multiple working conditions according to claim 8, characterized in that: The step of executing the corresponding compensation strategy according to the weight of each compensation strategy includes: When performing voltage regulation compensation, the voltage deviation is mapped to the voltage regulation gear, and the objective function is to minimize the number of gear switching and voltage fluctuation, build a timing optimization model, and generate a voltage regulation sequence; When performing vibration suppression compensation, the damper gain is increased, the cutoff frequency is adjusted according to the vibration spectrum, and the filter is adaptively updated; When performing load optimization, a linear programming model is constructed with the minimization of core loss increment and load balance as the objective function to generate the optimal load distribution, and the reactive power compensation capacity is calculated based on the current reactive power shortage and the rated capacity of the transformer.

10. A dynamic energy efficiency optimization system for transformers under multiple working conditions, characterized in that: include: The gap variation model building module is used to obtain the historical vibration signal of the transformer, and perform feature extraction and nonlinear analysis on the historical vibration signal to obtain spectrum characteristics and nonlinear characteristics; According to the spectrum characteristics and nonlinear characteristics, a finite element model based on the change of the core lamination gap is established; Energy efficiency impact analysis module, used to obtain the real-time vibration signal of the transformer, input the real-time vibration signal into the finite element model, calculate the gap data of the core laminations, and calculate the energy efficiency impact coefficient of the gap change on the transformer energy efficiency based on the gap data; The adaptive compensation control module is used to adjust the operating parameters of the transformer using an adaptive compensation mechanism according to the energy efficiency impact coefficient until the energy efficiency index of the transformer meets the preset requirements.

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