Converter transformer iron core state characteristic analysis method, system, medium and equipment
By acquiring neutral point current signals in real time and utilizing wavelet multi-resolution analysis and machine learning algorithms, the problem of difficult assessment of the core condition of CLCC converter transformers was solved. This enabled accurate assessment of the core's residual magnetism and saturation tendency, reducing operational risks and extending equipment lifespan.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-04
- Publication Date
- 2026-04-07
AI Technical Summary
Existing technologies struggle to accurately assess the residual magnetism and saturation tendency of CLCC converter transformer cores in real time, especially the impact of DC bias magnetism caused during forced commutation on core conditions, for which there is no systematic analysis method.
By acquiring neutral point current signals in real time, wavelet multi-resolution analysis and machine learning algorithms are used to extract transient DC bias components, assess the residual magnetism and saturation tendency of the iron core, and generate early warnings in combination with operating parameters.
It enables real-time and accurate assessment of the core condition of CLCC converter transformers, preventing core saturation, reducing operational risks, and extending equipment life.
Smart Images
Figure CN121805709A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power system converter transformer, and particularly relates to a CLCC converter transformer core state feature analysis method, system, medium and equipment. BACKGROUND
[0002] The controllable line commutated converter (CLCC) adds auxiliary branches based on fully controlled IGBT devices to the traditional 6-pulse bridge structure to form a hybrid converter, which can not only inherit the economic advantages of the traditional LCC converter, but also fundamentally avoid the occurrence of commutation failure. However, the CLCC introduces IGBT auxiliary branches for forced commutation, and the commutation process is accompanied by the rapid injection of high-frequency voltage pulses. When this steep voltage wave front propagates in the winding of the converter transformer, it will excite the multi-conductor transmission line effect due to the coupling of the distributed capacitance and inductance parameters of the winding, resulting in the superposition of complex standing waves and traveling waves in the interlayer, interturn and ground insulation structure of the winding, aggravating the core vibration and noise, and threatening the long-term stable operation of the converter transformer.
[0003] The core working state of the converter transformer, as the core equipment of the DC power transmission system, directly affects the insulation life and operation reliability of the transformer. Under the CLCC working condition, due to the rapid turn-off and turn-on of the current in the forced commutation process, the neutral point current will have obvious DC bias magnetization phenomenon, resulting in core half-period saturation, excitation current distortion, and further causing core loss increase, temperature rise aggravation and short-circuit resistance decline. Traditional transformer state monitoring methods mostly rely on vibration analysis, oil chromatographic analysis, etc., but these methods are difficult to capture the dynamic characteristics of the core under the CLCC specific working condition in real time. In particular, the bias magnetization component in the neutral point current is closely related to the core residual magnetization state, but the existing technology lacks targeted analysis means.
[0004] At present, the research on the neutral point current of the converter transformer at home and abroad mainly focuses on harmonic analysis and fault diagnosis, but there is still no systematic analysis method for the DC bias magnetization caused by forced commutation under the CLCC working condition and its influence on the core state. The existing technology mostly uses Fourier transform for harmonic extraction, but this method cannot effectively capture the transient bias characteristics; while wavelet analysis is widely used in signal processing, but its combination in the evaluation of the core state of the converter transformer is still blank.
[0005] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present application, and therefore can contain information that is not prior art known to those of ordinary skill in the art. SUMMARY
[0006] The application provides a CLCC converter transformer core state feature analysis method, system, medium and equipment, which can evaluate the residual magnetism state and saturation tendency of the core in real time and accurately.
[0007] A CLCC converter transformer core state feature analysis method comprises the following steps:
[0008] In the operation process of the CLCC converter valve, a neutral point current signal of the converter transformer is collected in real time;
[0009] The neutral point current signal is subjected to wavelet multi-resolution analysis, decomposed into approximate coefficients and detail coefficients, and the high-frequency detail component is reconstructed to extract a transient DC bias component;
[0010] The bias amplitude, bias duration and bias direction are calculated based on the transient DC bias component;
[0011] The residual magnetism state and saturation tendency of the core are evaluated according to the bias amplitude, bias duration and bias direction, the core is determined to have a positive saturation tendency when the bias direction is positive, and the core is determined to have a negative saturation tendency when the bias direction is negative, and the residual magnetism level of the core is quantified based on the bias amplitude and bias duration;
[0012] In combination with the operation parameters of the converter transformer, a core state evaluation report is generated, and a loss increase, short-circuit resistance decrease or insulation deterioration risk warning is output.
[0013] In the CLCC converter transformer core state feature analysis method, the wavelet multi-resolution analysis adopts a Mallat discrete wavelet transform algorithm, a db4 wavelet base function is selected to decompose the neutral point current signal for 4-6 layers, and the transient DC bias component caused by the CLCC forced commutation is extracted by reconstructing the first to third layer detail coefficients.
[0014] In the CLCC converter transformer core state feature analysis method, the bias duration is determined by detecting the time interval between the zero-crossing points of the transient DC bias component, and the bias amplitude is obtained by calculating the average value or integral value of the transient DC bias component in one commutation period.
[0015] In the CLCC converter transformer core state feature analysis method, the historical bias amplitude, bias duration and bias direction constitute a historical bias parameter data set;
[0016] Based on the historical bias parameter data set, a core state evaluation model is constructed;
[0017] The machine learning algorithm is used for pattern recognition of the bias amplitude bias, duration frequency change trend, prediction of the residual magnetism accumulation trend of the iron core, the saturation risk level and the potential fault occurrence probability.
[0018] The machine learning algorithm includes one or more of a support vector machine, a random forest or a long short-term memory network, the training input is a bias feature vector in a plurality of continuous operation cycles, and the output is an iron core health state score or a maintenance suggestion level.
[0019] In the CLCC converter transformer iron core state feature analysis method, when the bias amplitude exceeds the preset threshold and the duration is greater than 5 ms, it is determined that the iron core is in a critical saturation state and a first-level warning is triggered; when the bias direction remains consistent in a plurality of continuous cycles, it is determined that the residual magnetism accumulation is significant, and a second-level warning is triggered.
[0020] A system of a CLCC converter transformer iron core state feature analysis method comprises,
[0021] A neutral point current signal acquisition system acquires real-time neutral point current signals of the converter transformer, and the neutral point current signal acquisition system comprises,
[0022] Two AC / DC current probes are sleeved on the neutral point grounding wire of the converter transformer,
[0023] A high-speed oscilloscope is connected to one of the AC / DC current probes for waveform recording,
[0024] A signal analyzer is connected to the other AC / DC current probe for real-time frequency spectrum and amplitude analysis,
[0025] A data processing unit is connected to the high-speed oscilloscope and the signal analyzer to perform a wavelet analysis algorithm to obtain a transient DC bias component;
[0026] A bias parameter calculation module is connected to the neutral point current signal acquisition system to calculate the bias amplitude, bias duration and bias direction based on the transient DC bias component;
[0027] An evaluation module evaluates the residual magnetism state and saturation tendency of the iron core according to the bias amplitude, bias duration and bias direction;
[0028] A warning module generates an iron core state evaluation report and outputs a loss increase, short-circuit resistance decrease or insulation deterioration risk warning in combination with the converter transformer operation parameters.
[0029] In the system, the evaluation module includes one or more of a support vector machine, a random forest or a long short-term memory network unit.
[0030] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.
[0031] An electronic device, the electronic device comprising:
[0032] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,
[0033] The processor implements the method when executing the program.
[0034] Compared with existing technologies, this invention has the following advantages: Based on the bias magnetic characteristics of the neutral point current during the operation of the CLCC converter valve, this invention extracts the neutral point current signal using a high-precision AC / DC probe. Combined with wavelet analysis technology, it extracts the periodic features and bias components from the current signal. Based on the bias amplitude and time parameters, it assesses the residual magnetism and saturation tendency of the core, achieving real-time monitoring and risk analysis of the converter transformer core's operating status. It can assess the residual magnetism and saturation tendency of the core in real time and accurately. Attached Figure Description
[0035] Various other advantages and benefits of the present invention will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.
[0036] In the attached diagram:
[0037] Figure 1 This is a schematic diagram of the process of the present invention;
[0038] Figure 2 This is a schematic diagram of the CLCC converter valve topology of the present invention;
[0039] Figure 3 This is a schematic diagram of the neutral point current signal acquisition system of the present invention;
[0040] Figure 4 This is a flowchart of the model training process of the present invention;
[0041] Figure 5 This is a wavelet analysis coefficient decomposition diagram of the present invention;
[0042] Figures 6(a) to 6(b) are schematic diagrams of the neutral point current bias characteristics of the present invention. Figure 6(a) is a schematic diagram of the neutral point current bias characteristics of pole 1, and Figure 6(b) is a schematic diagram of the neutral point current bias characteristics of pole 2.
[0043] Figure 7 This is a flowchart of the risk analysis for this invention.
[0044] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation
[0045] The following will refer to Figures 1 to 7 Specific embodiments of the invention will be described in more detail below. While specific embodiments of the invention are shown in the accompanying drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.
[0046] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions are preferred embodiments for carrying out the invention; however, these descriptions are for the purpose of understanding the general principles of the specification and are not intended to limit the scope of the invention. The scope of protection of this invention is determined by the appended claims.
[0047] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. The accompanying drawings do not constitute a limitation on the embodiments of the present invention.
[0048] like Figures 1 to 7 As shown, the core condition characteristic analysis method for CLCC converter transformers includes the following steps:
[0049] During the operation of the CLCC converter valve, the neutral point current signal of the converter transformer is collected in real time.
[0050] Wavelet multi-resolution analysis is performed on the neutral point current signal to decompose it into approximation coefficients and detail coefficients, and high-frequency detail components are reconstructed to extract transient DC bias components. The acquired neutral point AC current signal is then passed through the filter bank. The low-pass filter is responsible for extracting the approximation part A of the signal, obtaining the main contour and low-frequency trend of the signal, corresponding to the smoothing information of the signal. The high-pass filter is responsible for extracting the detail part D of the signal, obtaining the high-frequency changes and local details of the signal, corresponding to the abrupt changes and transient information of the signal. The rapid turn-off operation of the IGBT device in the CLCC valve injects a high-frequency voltage pulse in the nanosecond to microsecond range into the system. During propagation, this pulse interacts with the distributed parameters of the transformer and excites corresponding high-frequency oscillating transient components in the neutral point current. The main energy of these components is concentrated in the frequency band corresponding to the first to third level detail coefficients (D1, D2, D3) after wavelet decomposition. Using the reconstruction filter bank corresponding to the wavelet decomposition, the detail coefficients D1, D2, and D3 are upsampled and filtered for reconstruction to obtain three time-domain signal components. Then, the three reconstructed components are superimposed and synthesized to finally obtain the "high-frequency transient component" to be extracted in this invention. In this synthesized signal, slowly changing components such as the power frequency fundamental wave and low-order harmonics have been effectively filtered out, and only the pure transient bias characteristics that can characterize the essence of the CLCC commutation process are retained.
[0051] The bias amplitude, bias duration, and bias direction are calculated based on the transient DC bias component.
[0052] Based on the bias amplitude, bias duration, and bias direction, the remanence and saturation tendency of the iron core are evaluated. When the bias direction is positive, the iron core is determined to have a positive saturation tendency, and when the bias direction is negative, the iron core is determined to have a negative saturation tendency. The remanence level of the iron core is quantified based on the bias amplitude and bias duration.
[0053] Based on the operating parameters of the converter transformer, a core condition assessment report is generated, and warnings are issued regarding risks such as increased losses, decreased short-circuit withstand capability, or insulation degradation. The operating parameters of the converter transformer mainly consist of its voltage and current, from which its power is derived. The core condition assessment report primarily addresses whether the transformer is biased (positive or negative bias), whether its losses have increased, and the strength of its short-circuit withstand capability.
[0054] In a preferred embodiment of the method for analyzing the core condition characteristics of a CLCC converter transformer, the wavelet multi-resolution analysis employs the Mallat discrete wavelet transform algorithm, selects the db4 wavelet basis function to decompose the neutral point current signal into 4 to 6 levels, and extracts the transient DC bias component caused by CLCC forced commutation by reconstructing the detail coefficients of the 1st to 3rd levels.
[0055] In a preferred embodiment of the CLCC converter transformer core condition characteristic analysis method, the bias duration is determined by detecting the time interval between the zero-crossing points of the transient DC bias component, and the bias amplitude is calculated by the average or integral value of the transient DC bias component within one commutation cycle.
[0056] In a preferred embodiment of the CLCC converter transformer core condition characteristic analysis method, the historical bias amplitude, bias duration and bias direction constitute a historical bias parameter dataset.
[0057] A core condition assessment model is constructed based on historical bias parameter datasets.
[0058] Machine learning algorithms are used to perform pattern recognition on the trends of bias amplitude, bias duration, and frequency changes, and to predict the accumulation trend of residual magnetism in the iron core, the saturation risk level, and the probability of potential failures.
[0059] In a preferred embodiment of the CLCC converter transformer core condition feature analysis method, the machine learning algorithm includes one or more of support vector machine, random forest or long short-term memory network, the training input is the bias feature vector within multiple consecutive operating cycles, and the output is the core health status score or maintenance recommendation level.
[0060] In a preferred embodiment of the CLCC converter transformer core condition characteristic analysis method, when the bias amplitude exceeds a preset threshold and the duration is greater than 5 ms, the core is determined to be in a critical saturation state and a first-level warning is triggered; when the bias direction remains consistent for multiple consecutive cycles, the residual magnetism is determined to be significant and a second-level warning is triggered.
[0061] A system for analyzing the core condition characteristics of a CLCC converter transformer includes,
[0062] The neutral point current signal acquisition system acquires the neutral point current signal of the converter transformer in real time. The neutral point current signal acquisition system includes...
[0063] Two AC / DC current probes are connected to the neutral point grounding wire of the converter transformer.
[0064] A high-speed oscilloscope, connected to one of the AC / DC current probes for waveform recording.
[0065] The signal analyzer is connected to another AC / DC current probe for real-time spectrum and amplitude analysis.
[0066] The data processing unit connects to a high-speed oscilloscope and a signal analyzer to execute wavelet analysis algorithms to obtain transient DC bias components.
[0067] The bias parameter calculation module is connected to the neutral point current signal acquisition system to calculate the bias amplitude, bias duration and bias direction based on the transient DC bias component.
[0068] The evaluation module evaluates the remanence and saturation tendency of the core based on the bias amplitude, bias duration, and bias direction.
[0069] The early warning module combines the operating parameters of the converter transformer to generate a core condition assessment report and outputs early warnings of risks such as increased losses, decreased short-circuit withstand capability, or insulation deterioration.
[0070] In a preferred embodiment of the system, the evaluation module includes one or more of support vector machines, random forests, or long short-term memory network units.
[0071] A computer storage medium including computer instructions that, when run on a computer, cause the computer to perform the method.
[0072] An electronic device, the electronic device comprising:
[0073] Memory, processor, and computer programs stored in memory and executable on the processor, wherein,
[0074] The processor implements the method when executing the program.
[0075] In one embodiment, the method includes,
[0076] Based on the bias magnetization characteristics of the neutral point current during the operation of the CLCC converter valve, a neutral point current signal acquisition system was built.
[0077] Based on the neutral point current signal acquisition system, the neutral point current signal is extracted and recorded in real time.
[0078] Based on the neutral point current signal, and using wavelet analysis, the periodic features and bias components in the current signal are extracted.
[0079] Based on the bias amplitude and time parameters, the residual magnetism and saturation tendency of the core are evaluated, enabling real-time monitoring and risk analysis of the operating status of the converter transformer core.
[0080] Based on the bias magnetic characteristics of the neutral point current during the operation of the CLCC converter valve, a neutral point current signal acquisition system was built. This system mainly consists of a high-precision AC / DC probe, a high-speed oscilloscope, a signal analyzer, and a data processing unit. The AC / DC probe is connected to the neutral point of the converter transformer and then to the high-speed oscilloscope and signal analyzer to achieve synchronous acquisition and processing of the current signal. The data processing unit uses wavelet analysis algorithms to perform multi-resolution analysis on the neutral point current signal, extracting the bias component and periodic characteristics.
[0081] The wavelet analysis technique employs the Mallat algorithm to perform discrete wavelet transform on the neutral point current signal, decomposing it into approximation coefficients and detail coefficients. By reconstructing the detail coefficients, high-frequency transient components are extracted, and the bias period and amplitude changes are identified.
[0082] Based on bias amplitude and time parameters, the residual magnetism state and saturation tendency of the iron core are evaluated. Specifically, this includes: calculating the DC bias value of the neutral point current, recording the bias duration and bias direction; when the bias direction is positive, the iron core is in a positive saturation tendency; when the bias direction is negative, the iron core is in a negative saturation tendency; based on the bias amplitude and duration, the residual magnetism level of the iron core is quantified, and combined with the transformer operating parameters, the risk of decreased short-circuit withstand capability and the trend of increased losses are evaluated.
[0083] The neutral point current signal acquisition system employs a dual-probe design, with one probe connected to a high-speed oscilloscope for waveform recording and the other probe connected to a signal analyzer for spectrum analysis, ensuring the synchronization and accuracy of data acquisition.
[0084] The method also includes establishing a core condition assessment model based on historical data, and using machine learning algorithms to perform pattern recognition on bias parameters to predict core aging trends and failure risks.
[0085] In one embodiment, see Figures 1 to 2 A method for analyzing the core condition characteristics of a CLCC converter transformer, comprising the following sequential steps:
[0086] (1) Based on the bias magnetic characteristics of the neutral point current during the operation of the CLCC converter valve, a neutral point current signal acquisition system was built. The system mainly consists of a high-precision AC / DC probe, a high-speed oscilloscope, a signal analyzer, and a data processing unit.
[0087] (2) Connect the AC / DC probe to the neutral point of the converter transformer, and connect the high-speed oscilloscope and signal analyzer respectively to perform signal calibration and system debugging.
[0088] (3) Start data acquisition, record the neutral point current waveform in real time, and perform preliminary filtering processing through a signal analyzer.
[0089] (4) Based on wavelet analysis technology, the current signal is subjected to discrete wavelet transform to extract approximation coefficients and detail coefficients and reconstruct high-frequency transient components.
[0090] (5) Analyze the bias components and calculate the DC bias value, bias duration and bias direction.
[0091] (6) Based on the bias parameters, evaluate the residual magnetism and saturation tendency of the iron core and generate a status report.
[0092] (7) Combine historical data and machine learning models to make risk predictions and maintenance recommendations.
[0093] See Figures 3 to 5 A method for analyzing the core condition characteristics of a CLCC converter transformer is proposed, based on wavelet analysis. The method employs the db4 wavelet basis function to perform a five-level decomposition of the neutral point current signal, obtaining detail coefficients and approximation coefficients for each level. By reconstructing the detail coefficients of levels 1-3, the high-frequency bias component is extracted, and the bias characteristics caused by the turn-off period are identified. The bias amplitude is calculated using the DC component, and the bias time is determined by zero-crossing detection.
[0094] This example illustrates the study of the core condition of a converter transformer under CLCC (Commutation-Cooled Commutation) conditions. Synchronous measurements of the neutral point current of the transformer at the Shanghai Nanqiao Converter Station were performed. The measurement results show that a significant DC bias occurs in the neutral point current during CLCC forced commutation, with a bias amplitude of 0.456A and a duration of approximately 10ms. The bias direction alternates with the commutation cycle. Wavelet analysis was used to extract the bias parameters, revealing a higher frequency of bias occurrence and more pronounced core remanence fluctuations under CLCC conditions.
[0095] Referring to Figures 6(a) and 6(b), the core condition is evaluated based on the bias parameters. When the bias direction is positive, the core's positive saturation tendency increases, and the excitation current rises; when the bias direction is negative, the core's negative saturation tendency increases, and the winding loss rises. By establishing the bias amplitude-time relationship curve, the core's residual magnetism level is quantified, providing a basis for operation and maintenance.
[0096] The embodiment also incorporates machine learning algorithms to train on historical bias data and predict the saturation risk of the iron core.
[0097] comprehensive Figures 1 to 7 This invention provides a method for analyzing the core condition characteristics of a CLCC converter transformer, which includes extraction of neutral point current bias characteristics, wavelet analysis, core condition assessment, and risk prediction, thereby enabling comprehensive monitoring of the core's operating condition.
[0098] Furthermore, the high-frequency voltage surge generated by the CLCC converter valve during forced commutation in this invention induces complex electromagnetic transient processes in the winding, resulting in a non-stationary, short-duration, alternating DC bias component in the neutral point current. Although this bias component has a small amplitude (typically in the hundreds of milliamperes), its duration highly coincides with the half-cycle saturation range of the core hysteresis loop, directly reflecting the current remanence level and magnetization direction of the core. Traditional frequency domain methods such as Fourier transform, which assume the signal is stationary and has an infinite period, cannot effectively separate such transient biases and easily misjudge them as low-frequency harmonics or noise, thus filtering them out and losing crucial state information.
[0099] Secondly, this invention introduces discrete wavelet transform, specifically employing the db4 wavelet basis for 4-6 level decomposition, which adaptively provides high resolution in both the time and frequency domains: low-frequency approximation coefficients retain the main current waveform trend, while high-frequency detail coefficients accurately capture the transient bias pulses caused by the rapid turn-off / turn-on of the IGBT. By reconstructing the detail coefficients of levels 1-3, the fundamental frequency and conventional harmonic interference can be effectively filtered out, retaining only the bias characteristics synchronized with the commutation event, significantly improving the signal-to-noise ratio and feature extraction accuracy.
[0100] Furthermore, by quantifying the three parameters of bias direction, amplitude, and duration, this invention establishes an evaluation model directly related to the physical state of the iron core: the bias direction indicates the current magnetization polarity of the iron core (positive / negative half-cycle saturation tendency), the bias amplitude reflects the remanence, and the duration reflects the rate of change of magnetic flux and the hysteresis characteristics of the iron core response. The combination of these three parameters can accurately determine whether the iron core is in a critical saturation state, avoiding the surge in eddy current losses, localized overheating, and accumulation of mechanical stress caused by repeated half-cycle saturation.
[0101] Finally, by combining historical bias data with machine learning algorithms, the system can not only assess the current state but also predict residual magnetism accumulation trends and insulation aging risks, transforming passive monitoring into proactive early warning and providing a scientific basis for CLCC converter station operation and maintenance. In summary, this invention overcomes the technical bottleneck of real-time core state perception under CLCC-specific operating conditions through a complete technical path of "signal acquisition—wavelet analysis—parameter quantization—state mapping—risk prediction," significantly improving the safety and reliability of converter transformer operation.
[0102] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.
Claims
1. A method for analyzing the core condition characteristics of a CLCC converter transformer, characterized in that, Includes the following steps: During the operation of the CLCC converter valve, the neutral point current signal of the converter transformer is collected in real time. Wavelet multi-resolution analysis is performed on the neutral point current signal to decompose it into approximation coefficients and detail coefficients, and high-frequency detail components are reconstructed to extract transient DC bias components. The bias amplitude, bias duration, and bias direction are calculated based on the transient DC bias component. Based on the bias amplitude, bias duration and bias direction, the remanence state and saturation tendency of the iron core are evaluated. When the bias direction is positive, the iron core is determined to have a positive saturation tendency. When the bias direction is negative, the iron core is determined to have a negative saturation tendency. The remanence of the iron core is quantified based on the bias amplitude and bias duration. Based on the operating parameters of the converter transformer, a core condition assessment report is generated and a warning is issued for risks such as increased losses, decreased short-circuit withstand capability, or insulation deterioration.
2. The method for analyzing the core condition characteristics of a CLCC converter transformer according to claim 1, characterized in that, Preferably, the wavelet multi-resolution analysis employs the Mallat discrete wavelet transform algorithm, selects the db4 wavelet basis function to decompose the neutral point current signal into 4 to 6 levels, and extracts the transient DC bias component caused by CLCC forced commutation by reconstructing the detail coefficients of the 1st to 3rd levels.
3. The method for analyzing the core condition characteristics of a CLCC converter transformer according to claim 1, characterized in that, The bias duration is determined by detecting the time interval between the zero-crossing points of the transient DC bias component, and the bias amplitude is calculated by the average or integral value of the transient DC bias component over one commutation cycle.
4. The method for analyzing the core condition characteristics of a CLCC converter transformer according to claim 1, characterized in that, The historical bias magnitude, bias duration, and bias direction constitute the historical bias parameter dataset; A core condition assessment model is constructed based on historical bias parameter datasets. Machine learning algorithms are used to perform pattern recognition on the trends of bias amplitude, bias duration, and frequency changes, and to predict the accumulation trend of residual magnetism in the iron core, the saturation risk level, and the probability of potential failures.
5. The method for analyzing the core condition characteristics of a CLCC converter transformer according to claim 4, characterized in that, The machine learning algorithm includes one or more of support vector machines, random forests, or long short-term memory networks. The training input is the bias feature vector over multiple consecutive operating cycles, and the output is a core health status score or maintenance recommendation level.
6. The method for analyzing the core condition characteristics of a CLCC converter transformer according to claim 1, characterized in that, When the bias amplitude exceeds the preset threshold and the duration is greater than 5 ms, the core is determined to be in a critical saturation state and a first-level warning is triggered; when the bias direction remains consistent for multiple consecutive cycles, the residual magnetism is determined to be significant and a second-level warning is triggered.
7. A system for analyzing the core condition characteristics of a CLCC converter transformer according to any one of claims 1-6, characterized in that, It includes, The neutral point current signal acquisition system acquires the neutral point current signal of the converter transformer in real time. The neutral point current signal acquisition system includes... Two AC / DC current probes are connected to the neutral point grounding wire of the converter transformer. A high-speed oscilloscope, connected to one of the AC / DC current probes for waveform recording. The signal analyzer is connected to another AC / DC current probe for real-time spectrum and amplitude analysis. The data processing unit connects to a high-speed oscilloscope and a signal analyzer to execute wavelet analysis algorithms to obtain transient DC bias components. The bias parameter calculation module is connected to the neutral point current signal acquisition system to calculate the bias amplitude, bias duration and bias direction based on the transient DC bias component. The evaluation module evaluates the remanence and saturation tendency of the core based on the bias amplitude, bias duration, and bias direction. The early warning module combines the operating parameters of the converter transformer to generate a core condition assessment report and outputs early warnings of risks such as increased losses, decreased short-circuit withstand capability, or insulation deterioration.
8. The system according to claim 7, characterized in that, The evaluation module includes one or more of the following: support vector machine, random forest, or long short-term memory network units.
9. A computer storage medium, characterized in that, The storage medium includes computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-6.
10. An electronic device, characterized in that, The electronic device includes: Memory, processor, and computer programs stored in memory and executable on the processor, wherein, When the processor executes the program, it implements the method as described in any one of claims 1-6.