A Method for DC Bias Vibration Analysis and Identification of SLCC-HVDC Converter Transformers
By constructing a field-circuit coupling platform for the simulation circuit model and finite element model of the SLCC-HVDC system, and combining it with the Hilbert-Huang transform, the problem of accurate analysis and quantitative determination of DC bias vibration in the SLCC-HVDC system was solved, and the safe and stable operation of the system was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CONSTR BRANCH OF STATE GRID JIANGSU ELECTRIC POWER CO LTD
- Filing Date
- 2026-02-09
- Publication Date
- 2026-06-30
AI Technical Summary
Existing technologies lack a generalized model for the dual bias mechanism of SLCC-HVDC systems, and cannot fully reconstruct the entire process from bias to vibration intensification. Traditional vibration monitoring methods are difficult to adapt to the non-stationary characteristics of bias vibration, are insensitive to early bias identification, and cannot quantitatively characterize the degree of bias.
A simulation circuit model of the SLCC-HVDC system was constructed. Combined with the finite element model, the vibration response signal was decomposed using the Hilbert-Huang transform through a field-circuit coupling simulation platform. The energy ratio of the secondary state to the secondary state and the energy distribution entropy were calculated. A judgment threshold was set to achieve accurate analysis and quantitative judgment of DC bias.
It enables precise analysis and quantitative determination of DC bias vibration of converter transformer in SLCC-HVDC system, applicable to various systems, providing theoretical basis for vibration control optimization, and ensuring safe and stable operation of the system.
Smart Images
Figure CN122309951A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method for analyzing and identifying DC bias vibration of an SLCC-HVDC converter transformer, belonging to the field of high-voltage DC transmission technology. Background Technology
[0002] To achieve the "dual carbon" goals and meet the requirements of new power system construction, high-voltage direct current (HVDC) transmission technology, with its advantages of low line loss, large transmission capacity, and long transmission distance, has become a key technology for the large-scale consumption of cross-regional renewable energy. Among them, SLCC-HVDC (Multi-Source Adaptive Commutation Converter System), as a new generation of DC transmission topology, achieves reactive power compensation, harmonic control, and fault voltage support through static var compensator and filter (SVF) devices, significantly improving the reliability and flexibility of system operation.
[0003] However, the introduction of the SVF module exposes the SLCC-HVDC system to the risk of "dual bias": on the one hand, there are traditional bias mechanisms such as uneven firing angles on the LCC side, AC line induction, and monopolar field return operation; on the other hand, there is asymmetric bias on the SVF side due to the dispersion of device parameters and the adaptability of control strategies. The two superimposed form a composite bias excitation source. DC bias can lead to deep saturation of the converter transformer core magnetic circuit, causing excitation current distortion, which in turn induces intensified core vibration. This vibration exhibits strong non-stationary, frequency-modulated, and amplitude-modulated characteristics, accelerating equipment insulation aging and structural fatigue, and seriously threatening the safe operation of the system.
[0004] Existing technologies have the following shortcomings: 1) They lack a generalized model for the dual biasing mechanism of SLCC systems, and are mostly limited to specific biasing causes, thus restricting their applicability; 2) They sever the field-circuit interaction between the system side and the equipment side, failing to fully reconstruct the entire process from biasing to vibration intensification; 3) Traditional vibration monitoring methods are difficult to adapt to the non-stationary characteristics of biasing vibration, are insensitive to early biasing identification, and cannot quantitatively characterize the degree of biasing. Therefore, a generalized and accurate DC biasing vibration analysis and identification scheme is urgently needed. Summary of the Invention
[0005] The technical problem to be solved by the present invention is to provide a method for analyzing and identifying DC bias vibration of SLCC-HVDC converter transformers, so as to realize accurate analysis and quantitative determination of DC bias vibration of converter transformers.
[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A method for analyzing and identifying DC bias magnetic vibration in an SLCC-HVDC converter transformer includes the following steps: Step 1: Based on the topological characteristics of the SLCC-HVDC system, analyze the dual biasing mechanism of LCC-side biasing and SVF-side asymmetric biasing, and construct a simulation circuit model of the SLCC-HVDC system to simulate different biasing intensity conditions and obtain the DC biasing current under the corresponding conditions. Step 2: Based on the electromagnetic-vibration multi-physics coupling principle, and combined with the structural characteristics of the converter transformer, a finite element model of the converter transformer is constructed. The energy disturbance principle is used to realize the parameter feedback between the simulation circuit model and the finite element model of the system, forming a field-circuit coupling simulation platform. Step 3: Using the field-circuit coupling simulation platform, obtain the vibration response signal of the converter transformer core under different bias magnetic intensities, and use the Hilbert-Huang transform to adaptively decompose the vibration response signal into multiple intrinsic mode function components. Step 4: Based on the frequency distribution of the intrinsic mode function components, divide the system into high-frequency additional state group, mid-frequency secondary state group, and low-frequency stray state group, and calculate the additional state-secondary state energy ratio and energy distribution entropy; Step 5: Set the threshold for no bias and the critical threshold for bias. Based on the numerical change trends of the energy ratio of the secondary state to the secondary state and the energy distribution entropy, quantitatively determine the severity of DC bias of the converter transformer.
[0007] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: This invention solves the problems of existing technologies being unable to accurately characterize the dual bias vibration characteristics of SLCC-HVDC systems and being insensitive to early bias identification. It is applicable to converter transformers of various SLCC-HVDC systems, providing a theoretical basis for the formulation of DC bias suppression strategies and vibration control optimization, and ensuring the safe and stable operation of the system. Attached Figure Description
[0008] Figure 1 This is a flowchart of a method for analyzing and identifying DC bias vibration of an SLCC-HVDC converter transformer according to the present invention; Figure 2 This is a simulation model diagram of the SLCC-HVDC system; Figure 3 This is a 3D model diagram of a converter transformer; Figure 4 This is a flowchart of the finite element model calculation for a converter transformer; Figure 5 This is a waveform diagram of the SVF output voltage and current; Figure 6 These are waveforms of the DC bias current under different bias magnetic intensities; Figure 7These are vibration displacement diagrams of the iron core under different forces, where (a) is the deformation of the iron core under the action of Maxwell force, and (b) is the deformation of the iron core under the action of magnetostrictive force. Figure 8 It is a time-domain waveform diagram of vibration acceleration; Figure 9 This is the frequency spectrum of the DC biased magnetic vibration signal HHT. Figure 10 This is an IMF component diagram of the vibration signal. Detailed Implementation
[0009] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0010] This invention provides a method for analyzing and identifying DC bias vibration in SLCC-HVDC converter transformers, such as... Figure 1 As shown, this method achieves accurate analysis and quantitative determination of DC bias vibration in converter transformers through a complete process of "mechanism modeling - simulation modeling - signal processing - feature extraction - bias magnetic identification". It includes the following steps: The dual biasing mechanism of "traditional biasing on the LCC side + asymmetric biasing on the SVF side" is clarified. The traditional biasing on the LCC side includes biasing caused by unbalanced converter valve firing angle, positive sequence second harmonic voltage, AC line induced current, and monopolar field return line operation. The asymmetric biasing on the SVF side includes DC component biasing caused by device parameter dispersion and control strategy adaptability. Based on the topology characteristics of the SLCC-HVDC system, a system simulation circuit model is constructed using MATLAB / Simulink. By adjusting biasing influencing factors such as LCC side firing angle deviation and SVF side parameter dispersion, different biasing intensity conditions are simulated, thereby obtaining the DC biasing current corresponding to different biasing intensities. Based on the electromagnetic-vibration multiphysics coupling principle and combined with the structural characteristics of the converter transformer, a finite element model of the converter transformer is constructed using COMSOL Multiphysics, including a magnetic field model and a vibration model. The principle of energy perturbation is used to realize the parameter feedback between the system simulation circuit model and the magnetic field model, forming a field-circuit coupled simulation platform to simulate the vibration response under different bias magnetic conditions. The vibration response signals of the converter transformer core under different DC bias conditions were obtained through the field-circuit coupling simulation platform. The vibration signals were adaptively decomposed into multiple intrinsic mode function (IMF) components using the Hilbert-Huang transform (HHT). Based on the frequency distribution of the IMF components, the system is divided into high-frequency additional state group, mid-frequency secondary state group, and low-frequency stray state group, and the energy ratio of the additional state to the secondary state is calculated. ), and energy distribution entropy ( ) as the bias magnetic characteristic parameters; Set the general determination threshold of the characteristic parameters, and according to and the numerical change trend of, quantitatively determine the severity of DC bias of the converter transformer.
[0011] In this embodiment, a 214MVA single-phase three-column converter transformer supporting the ±200kV / 3kA level SLCC-HVDC system is taken as an example to carry out specific implementation. Based on the MATLAB / Simulink software, an SLCC-HVDC system circuit model is built. As Figure 2 shown, the inverter side of this model adopts the "LCC + SVF" hybrid topology structure. Set the electrical parameters of each component according to the system design parameters, as shown in Table 1 specifically. The inverter side of this model adopts the "LCC + SVF" hybrid topology structure. The LCC is responsible for the AC-DC power conversion function, and the SVF is responsible for reactive power compensation and harmonic filtering.
[0012] Table 1 Parameters of LCC and SVF
[0013] Use the multi-physics simulation software COMSOL Multiphysics to build a three-dimensional model of the converter transformer. As Figure 3 shown. To balance the simulation accuracy and calculation efficiency, the model is reasonably simplified. The iron core column, yoke and side yoke are equivalent to a gapless whole and simplified to a regular cuboid; the grid side and valve side windings are equivalent to hollow cylindrical models, assuming that the current density inside the windings is evenly distributed; ignore the interlayer structures such as clamping parts, pads, insulating cardboard, etc. that have little impact on the electromagnetic-vibration coupling analysis, and only retain the core electromagnetic and structural components.
[0014] To improve the simulation efficiency while ensuring the calculation accuracy, the grid division adopts the partition manual division strategy: conduct refined grid division on the core areas such as the iron core column and side yoke where the magnetostrictive effect of the iron core is concentrated, the grid side and valve side windings, etc., to accurately capture the local changes of the electromagnetic field and vibration field; conduct a coarser grid division on the secondary areas such as the oil tank to reduce the calculation amount. After the grid division is completed, check the quality to ensure that the grid distortion rate is less than 5%.
[0015] At the same time, use the electromagnetic module and structural mechanics module of the multi-physics simulation software COMSOL Multiphysics to build a two-dimensional equivalent electromagnetic-vibration coupling model of the converter transformer. Set the electromagnetic and structural property parameters according to the materials of the core components of the converter transformer, as shown in Table 2. Since the iron core silicon steel sheet is a ferromagnetic material and its magnetization behavior is non-linear, it is necessary to set the B-H curve of the iron core to accurately simulate the magnetostrictive effect.
[0016] Table 2 Material Properties of Core Components of Converter Transformers
[0017] This invention employs a field-path coupling strategy to achieve dynamic simulation of bias magnetization and vibration. The model calculation process is as follows: Figure 4 As shown. The specific steps are as follows: ① Calculate the DC bias current under different bias intensities using the SLCC system simulation circuit model, and use it as the excitation signal; ② Inject the bias current into the converter transformer magnetic field model, and solve the magnetic field control equation based on the Galerkin weighted residual method to obtain the distribution of vector magnetic potential and magnetic induction intensity; ③ Calculate the instantaneous inductance based on the energy perturbation principle, and feed it back to the system simulation circuit model to update the current at the next moment; ④ Use the magnetostrictive force and Maxwell force obtained from the magnetic field model as loads, input them into the vibration model, and solve for the core vibration response; ⑤ Repeat the above steps to achieve dynamic coupling of field-circuit parameters and continuous simulation of vibration characteristics.
[0018] Start the SLCC system simulation model and obtain the voltage and current waveforms of the SVF output under steady-state operation, such as... Figure 5 As shown in the figure, the SVF output voltage is sinusoidal, while the current waveform exhibits periodic changes without significant distortion, verifying the normal operation of the system circuit model. This voltage and current signal provides the fundamental excitation source for subsequent analysis of bias current superposition and vibration response.
[0019] By adjusting the bias magnetization influencing factors such as the LCC side firing angle deviation and SVF side parameter dispersion in the SLCC system, different bias magnetization intensities were simulated to obtain the corresponding DC bias current waveforms. Figure 6 As shown. Five typical operating conditions with average bias currents of 0A (no bias), 1.53A, 3.16A, 4.93A, and 6.86A were selected for subsequent analysis. Among them, 0A is the baseline operating condition, 1.53A~6.86A covers the range of slight to severe bias, and 6.86A exceeds the allowable DC current of each phase winding of the converter transformer, which can verify the vibration characteristics under severe bias.
[0020] To clarify the contributions of magnetostrictive force and Maxwell force to the vibration of the iron core, simulations were performed by applying each force separately to obtain the vibration displacement of the iron core under different forces, such as... Figure 7 As shown in (a) and (b). Figure 7 In the figure, (a) represents the core deformation under Maxwell force, with a peak displacement of only 0.2 μm; Figure 7 (b) shows the core deformation under the action of magnetostrictive force, with a peak displacement of up to 2.1 μm, which is 10.5 times that of the Maxwell force. The experimental results indicate that the core vibration of the converter transformer is mainly dominated by magnetostrictive force, and the influence of magnetostrictive effect should be the focus of subsequent vibration characteristic analysis.
[0021] Vibration acceleration signals were collected from characteristic observation points of the converter transformer core under five typical operating conditions using a field-circuit coupling simulation platform, and the time-domain waveforms of the vibration acceleration were obtained, such as... Figure 8 As shown (taking the unbiased and 4.93A biased conditions as examples). A comparison reveals that under the unbiased condition, the vibration time-domain waveform is relatively regular, with a period of 5ms (corresponding to half of the 100Hz fundamental frequency), and a relatively small peak value. Under the DC biased condition, the vibration peak value increases significantly, and the waveforms of the two half-cycles within the same power frequency period show obvious differences, exhibiting typical asymmetric decay characteristics. This is due to the uneven saturation of the iron core's magnetic circuit caused by the bias, leading to a nonlinear increase in the magnetostrictive effect.
[0022] The Hilbert-Huang transform (HHT) was used to perform time-frequency analysis on the vibration acceleration signal under DC bias conditions, and the HHT time-frequency spectrum was obtained, as shown below. Figure 9 As shown (taking the 4.93A biased magnetization condition as an example), the time-frequency spectrum clearly shows the changes in the frequency components and energy distribution of the vibration signal over time: under the unbiased magnetization condition, the 100Hz even harmonics dominate; under the biased magnetization condition, odd harmonic components with a fundamental frequency of 50Hz are introduced (250Hz and 350Hz are particularly prominent), accompanied by multiple high-order harmonics and low-frequency harmonics, significantly complicating the spectral structure and verifying the nonlinear modulation effect of DC biased magnetization on the core vibration.
[0023] Empirical Mode Decomposition (EMD) is performed on the vibration acceleration signal to obtain multiple intrinsic mode function (IMF) components, such as... Figure 10 As shown. The decomposition results show that the vibration signal can be decomposed into 6 IMF components (IMF1~IMF6) and 1 residual component, where IMF1 is a high-frequency component (frequency > 300 Hz, defined as high-frequency additional state group), IMF2~IMF4 are mid-frequency components (50~300 Hz, defined as mid-frequency secondary state group), and IMF5~IMF6 are low-frequency components (frequency < 50 Hz, defined as low-frequency spurious state group).
[0024] Based on the above IMF component division, the additional state-secondary state energy ratio is calculated ( The formula is as follows: , in, This represents the total energy of the high-frequency additional state group (frequency > 300 Hz). This represents the total energy of the mid-frequency second-state group (50~300Hz). and These are the sum of squares of the Euclidean norms of each IMF component within the corresponding group; Entropy of energy distribution ( The expression for calculating ) is: , in, , The total energy of each group , This represents the total energy of the low-frequency stray state group (frequency < 50 Hz).
[0025] The calculated characteristic parameters under different bias currents are shown in Table 3: Table 3 Characteristic parameter values under different bias currents
[0026] Based on the calculation results of the characteristic parameters, a general judgment threshold is calibrated, including the no-bias judgment threshold. and the critical threshold of bias magnetization The energy ratio of the additional state to the next state and the energy distribution entropy of the converter transformer under the condition of no DC bias are respectively used as... and As the DC bias current gradually increases, the energy distribution entropy first increases and then decreases. When the energy distribution entropy reaches its maximum, the energy ratio of the additional state to the next state corresponding to the maximum energy distribution entropy is taken as... ; ①When and When, it is determined that there is no DC bias (corresponding to a bias current of 0A); ② When and When the trend is upward, it is judged as mild to moderate magnetic bias (corresponding to a magnetic bias current of 1.53A~4.93A); ③ When and When the current is decreasing, it is determined to be severe magnetization (corresponding to a magnetization current of 6.86A). At this time, the magnetization current exceeds the allowable limit of the converter transformer, and magnetization suppression measures need to be activated.
[0027] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for analyzing and identifying DC bias vibration of SLCC-HVDC converter transformers.
[0028] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for analyzing and identifying DC bias vibration of SLCC-HVDC converter transformers.
[0029] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0030] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0031] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0032] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0033] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.
Claims
1. A method for analyzing and identifying DC bias magnetic vibration in an SLCC-HVDC converter transformer, characterized in that, Includes the following steps: Step 1: Based on the topological characteristics of the SLCC-HVDC system, analyze the dual biasing mechanism of LCC-side biasing and SVF-side asymmetric biasing, and construct a simulation circuit model of the SLCC-HVDC system to simulate different biasing intensity conditions and obtain the DC biasing current under the corresponding conditions. Step 2: Based on the electromagnetic-vibration multi-physics coupling principle, and combined with the structural characteristics of the converter transformer, a finite element model of the converter transformer is constructed. The energy disturbance principle is used to realize the parameter feedback between the simulation circuit model and the finite element model of the system, forming a field-circuit coupling simulation platform. Step 3: Using the field-circuit coupling simulation platform, obtain the vibration response signal of the converter transformer core under different bias magnetic intensities, and use the Hilbert-Huang transform to adaptively decompose the vibration response signal into multiple intrinsic mode function components. Step 4: Based on the frequency distribution of the intrinsic mode function components, divide the system into high-frequency additional state group, mid-frequency secondary state group, and low-frequency stray state group, and calculate the additional state-secondary state energy ratio and energy distribution entropy; Step 5: Set the threshold for no bias and the critical threshold for bias. Based on the numerical change trends of the energy ratio of the secondary state to the secondary state and the energy distribution entropy, quantitatively determine the severity of DC bias of the converter transformer.
2. The method for analyzing and identifying DC bias magnetic vibration of SLCC-HVDC converter transformers according to claim 1, characterized in that, In step 1, the LCC-side bias includes bias caused by unbalanced converter valve firing angle, positive sequence second harmonic voltage, AC line induced current, and monopolar field return line operation; the SVF-side asymmetric bias includes DC component bias caused by SVF device parameter dispersion and SVF control strategy adaptability. A system simulation circuit model was built using MATLAB / Simulink. By adjusting the factors affecting the bias magnetization, including the firing angle deviation on the LCC side and the parameter dispersion on the SVF side, different bias magnetization conditions were simulated, and the DC bias magnetization current under the corresponding conditions was obtained.
3. The method for analyzing and identifying DC bias magnetic vibration of SLCC-HVDC converter transformers according to claim 1, characterized in that, In step 2, when constructing the finite element model of the converter transformer using the multiphysics simulation software COMSOL Multiphysics, the simplified settings for the converter transformer include: treating the core column, yoke, and side yoke as a gapless whole, and simplifying the gapless whole as a regular cuboid; treating the grid-side and valve-side windings as hollow cylindrical models, assuming that the current density inside the windings is uniformly distributed; and ignoring the interlayer structure, including clamps, pads, and insulating paperboard. The principle of energy perturbation is used to achieve parameter feedback between the system simulation circuit model and the finite element model, forming a field-circuit coupled simulation platform. The finite element model includes a magnetic field model and a vibration model. The working process of the field-circuit coupled simulation platform is as follows: 1) Calculate the DC bias current under different bias intensity conditions using the system simulation circuit model, and use it as the excitation signal; 2) Inject DC bias current into the magnetic field model of the converter transformer, and solve the magnetic field control equation based on the Galerkin weighted residual method to obtain the distribution of vector magnetic potential and magnetic induction intensity; 3) Calculate the instantaneous inductance based on the principle of energy perturbation and feed it back to the system simulation circuit model to update the DC bias current at the next moment; 4) The magnetostrictive force and Maxwell force obtained from the magnetic field model are used as loads and input into the vibration model to solve the vibration response of the converter transformer core under different bias magnetic intensities. 5) Repeat the above steps to achieve dynamic coupling of field-path parameters and continuous simulation of vibration characteristics.
4. The method for analyzing and identifying DC bias magnetic vibration of SLCC-HVDC converter transformers according to claim 1, characterized in that, In step 4, intrinsic mode function components with frequencies greater than 300Hz are defined as high-frequency components and assigned to the high-frequency additional state group; intrinsic mode function components with frequencies greater than 50Hz and less than or equal to 300Hz are defined as mid-frequency components and assigned to the mid-frequency secondary state group; intrinsic mode function components with frequencies less than 50Hz are defined as low-frequency components and assigned to the low-frequency spurious state group. The formula for calculating the energy ratio of the additional state to the next state is as follows: ， The formula for calculating the energy distribution entropy is as follows: ， in, The energy ratio of the additional state to the next state. This represents the total energy of the high-frequency additional state group. The first result obtained after decomposing the vibration response signal using the Hilbert-Huang transform is... Each intrinsic mode function component For the first The intrinsic mode function components at time 1 The time-domain signal values; This serves as the grouping identifier for the intrinsic mode function components after signal decomposition, corresponding to the high-frequency additional state group; The total time-domain sampling length of the vibration response signal. This represents the total energy of the intermediate frequency second-state group. Entropy is the energy distribution. , , The total energy of the low-frequency stray state group. and All are defined as the sum of the squares of the Euclidean norms of the intrinsic mode function components within the group.
5. The method for analyzing and identifying DC bias magnetic vibration of SLCC-HVDC converter transformers according to claim 4, characterized in that, In step 5, the judgment threshold is determined based on the rated capacity and voltage level of the converter transformer, including the no-bias magnetization judgment threshold. and the critical threshold of bias magnetization The energy ratio of the additional state to the next state and the energy distribution entropy of the converter transformer under the condition of no DC bias are respectively used as... and ; As the DC bias current gradually increases, the energy distribution entropy first increases and then decreases. When the energy distribution entropy reaches its maximum, the energy ratio of the additional state to the next state corresponding to the maximum energy distribution entropy is taken as... ; When calculated in step 4 and When, it is determined that the converter transformer has no DC bias magnetism; when and When the trend is upward, the converter transformer is judged to have a slight to moderate magnetic bias; when and When the current is decreasing, the converter transformer is determined to be severely biased, at which point the DC bias current exceeds the allowable limit of the converter transformer.
6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the SLCC-HVDC converter transformer DC bias vibration analysis and identification method as described in any one of claims 1 to 5.
7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the SLCC-HVDC converter transformer DC bias vibration analysis and identification method as described in any one of claims 1 to 5.