A DC converter transformer insulation leakage detection system and detection method

Abstract

The present invention discloses a DC converter transformer insulation leakage detection system and a detection method, which relate to the technical field of leakage detection and include a circuit signal receiving module, a data extraction module, a data processing module, a data status monitoring module, a circuit fault location module, an emergency plan generation module, and an exception reporting module. The DC converter transformer insulation leakage detection system and the detection method can accurately identify the abnormal data type based on the generated data association model through the settings of the abnormal data receiving module, the abnormal type analysis module, and the abnormal type determination module. At the same time, through the abnormal type determination module, it can accurately identify the abnormal data of the current abnormal data type based on the data association model and accurately locate the abnormal occurrence position, avoiding the inability to accurately locate the abnormal position during traditional circuit detection and investing a large amount of human resources and material resources during abnormal position location.

Description

Technical Field

[0001] The present invention relates to the technical field of leakage detection, and particularly relates to a DC converter transformer insulation leakage detection system and a detection method. Background Art

[0002] In order to ensure the safety of DC converter transformers during operation, higher requirements are put forward for partial discharge tests in the factory test specifications of DC converter transformers. This test has also become the most effective means for evaluating the design and manufacturing processes of transformers. Currently, the IEC61378-2 standard is generally adopted internationally to evaluate the insulation structure of high-voltage DC equipment. The regulations regarding the DC high-voltage withstand test in the IEC61378 standard are as follows: The DC high-voltage withstand test time is 2 hours, the charging time is required to be no more than 1 minute. After the test, the voltage should drop to zero volts within 1 minute. During the DC high-voltage withstand test, partial discharge tests are carried out, and the number of pulses with an apparent discharge amount greater than 2000 pC is recorded. It is required that the number of pulses within the last 30 minutes should not exceed 30, and the number of pulses within the last 10 minutes should not exceed 10. If the recorded number of partial discharge pulses exceeds this requirement, the test should be extended by 30 minutes. If the number of partial discharge pulses meets the above requirements within the extended 30 minutes, the equipment is still qualified. Although the current standards for evaluating the insulation status of DC equipment are already very strict, they still cannot prevent insulation damage accidents from occurring in high-voltage DC equipment, especially DC converter transformers during factory tests and operation. When detecting according to the DC partial discharge test standard, the discharge pulses greater than 100 pC and less than 2000 pC are not recorded and analyzed, and only the peak values of the pulses are recorded. The information obtained is less, and it is not possible to comprehensively distinguish the types of insulation damage and their development stages.

[0003] Currently, when a DC converter transformer conducts insulation leakage detection, it is unable to accurately locate the position where the problem occurs. A large amount of manpower and material resources need to be invested in subsequent accurate positioning of the problem location. At the same time, when positioning the problem location, it is impossible to accurately identify the type of circuit problem. Personnel need a large amount of knowledge reserves during problem detection, and when identifying the type of circuit problem, it is impossible to comprehensively monitor the problem types. Summary of the Invention

[0004] The purpose of the present invention is to provide a DC converter transformer insulation leakage detection system and a detection method to solve the above deficiencies in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A DC converter transformer insulation leakage detection system includes a transformer current signal acquisition module, a transformer voltage signal acquisition module, a circuit signal receiving module, a circuit fault location module, an emergency plan generation module, and an abnormal reporting module:

[0006] The transformer current signal acquisition module is used to receive the current signal at the output end of the DC converter transformer;

[0007] The transformer voltage signal acquisition module is used to receive the voltage signal at the output end of the DC converter transformer;

[0008] The circuit signal receiving module is used to receive the internal circuit signals of the DC converter transformer, and the circuit signals include voltage signals, current signals, polarity signals, and waveform signals;

[0009] The circuit fault location module is used to receive the internal circuit signals of the DC converter transformer through the circuit signal receiving module, and analyze the abnormal types of the internal circuit of the DC converter transformer based on the data association model;

[0010] The circuit fault location module is also used to determine the abnormal occurrence location for the abnormal types of the internal circuit of the DC converter transformer based on the problem confirmation model;

[0011] The emergency plan generation module is used to generate an emergency plan through the plan generation module and based on the abnormal types and abnormal occurrence locations of the internal circuit of the DC converter transformer determined by the circuit fault location module;

[0012] The abnormality reporting module is used to send the abnormal types and abnormal occurrence locations of the internal circuit of the DC converter transformer determined by the circuit fault location module to the maintenance personnel's handheld terminal.

[0013] Further, the circuit fault location module includes

[0014] Abnormal data receiving module, which is used to receive the internal circuit signals of the DC converter transformer received by the circuit signal receiving module, and compare the internal circuit signals A of the DC converter transformer received by the circuit signal receiving module with the historical internal circuit signals B of the DC converter transformer based on the data comparison model. Mark the internal circuit signals of the DC converter transformer with the data change amount C exceeding the preset change threshold D as abnormal data, and send the abnormal data to the abnormal type analysis model. The abnormal data includes abnormal voltage data, abnormal current data, abnormal polarity data, and abnormal waveform data. Among them, the internal circuit signals A of the DC converter transformer received by the circuit signal receiving module include voltage signal A1, current signal A2, polarity signal A3, and waveform signal A4. The historical internal circuit signals B of the DC converter transformer include historical voltage signal B1, historical current signal B2, historical polarity signal B3, and historical waveform signal B4. The data change amount C includes voltage signal change amount C1 = |A1 - B1|, current signal change amount C2 = |A2 - B2|, polarity signal change amount C3 = |A3 - B3|, and waveform signal change amount C4 = |A4 - B4|. The preset change threshold D includes voltage signal change amount D1, current signal change amount D2, polarity signal change amount D3, and waveform signal change amount D4. If C1 > D1, mark the voltage signal A1 as abnormal voltage data. If C2 > D2, mark the current signal A2 as abnormal current data. If C3 > D3, mark the polarity signal A3 as abnormal polarity data. If C4 > D4, mark the waveform signal A4 as abnormal waveform data;

[0015] Abnormal type analysis module, which is used to receive the abnormal data sent by the abnormal data receiving module and judge the type of current abnormal data occurrence based on the data association model;

[0016] Abnormal type determination module, which is used to determine the abnormal type of the internal circuit of the DC converter transformer based on the subsequent circuit signal change law received by the circuit signal receiving module;

[0017] Abnormal occurrence position simulation module, which is used to simulate the subsequent circuit signal simulation data at the abnormal occurrence position of the current circuit type based on the abnormal type of the internal circuit of the DC converter transformer obtained in the abnormal type determination module and through the circuit simulation model;

[0018] Abnormal occurrence position monitoring module, which is used to monitor the subsequent circuit signal monitoring data at the abnormal occurrence position of the current circuit type based on the abnormal type of the internal circuit of the DC converter transformer obtained in the abnormal type determination module and through the data monitoring model;

[0019] An abnormal occurrence location confirmation module, which is used to mark the abnormal occurrence location where the detection result of the abnormal occurrence location monitoring module is the same as the prediction result of the abnormal occurrence location simulation module based on the data comparison model as the confirmed abnormal occurrence location.

[0020] Further, the abnormal type analysis module includes a data reception module, a data correlation analysis module, a model output module, and a model matching module:

[0021] The data reception module is used to receive the internal circuit signal parameters when the DC converter transformer is in an open-circuit state;

[0022] The data correlation analysis module is used to receive the internal circuit signals of the DC converter transformer in the open-circuit state received by the data reception module and generate a data correlation model. The data correlation analysis module includes a data adjustment module, a data recording module, and a data deduplication module. The data adjustment module is used to randomly adjust the circuit signal parameters received by the data reception module based on the data adjustment model. The data recording module is used to record the variation rules of other circuit signal parameters during the process of the data adjustment module adjusting the data. The data deduplication module is used to deduplicate the variation rules of other circuit signal parameters recorded by the data recording module based on the deduplication model;

[0023] The model output module is used to output the data correlation model generated by the data correlation analysis module;

[0024] The model matching module is used to match the type of abnormal data occurrence corresponding to the current abnormal data in the data correlation model based on the data matching model.

[0025] A method for detecting insulation leakage of a DC converter transformer includes the following working steps:

[0026] S1. Receive the internal circuit signals of the DC converter transformer through the circuit signal reception module;

[0027] S2. Obtain the circuit signal data received in step S1 through the data extraction model;

[0028] S3. Perform data processing on the circuit signal data obtained in step S2 through the data processing module;

[0029] S4. Real-time monitor the status of the circuit signal data processed by the data processing module through the status monitoring module, and send the circuit signal data in a stable state to step S5;

[0030] S5. Analyze the internal circuit abnormal type of the DC converter transformer based on step S4 and the data correlation model through the circuit fault location module;

[0031] S6. Determine the location where the abnormality occurs for the abnormal type of the internal circuit of the DC converter transformer based on the problem confirmation model;

[0032] S7. Through the solution generation module, and based on the abnormal type of the internal circuit of the DC converter transformer determined in step S5 and the location where the abnormality occurs in the internal circuit of the DC converter transformer determined in step S6, generate an emergency solution;

[0033] S8. Send the abnormal type and the location where the abnormality occurs in the internal circuit of the DC converter transformer determined by the circuit fault location module to the maintenance personnel's handheld terminal through the abnormal reporting module.

[0034] Further, the circuit fault location module analyzes the abnormal type of the internal circuit of the DC converter transformer based on the abnormal type analysis model, including the following working steps:

[0035] A1. Receive the internal circuit signal of the DC converter transformer received by the circuit signal receiving module through the abnormal data receiving module;

[0036] A2. Based on the data comparison model, compare the internal circuit signal A of the DC converter transformer received by the circuit signal receiving module with the historical internal circuit signal B of the DC converter transformer, and mark the internal circuit signal of the DC converter transformer whose data change amount C exceeds the preset change threshold D as abnormal data;

[0037] A3. Based on the abnormal data marked in step A2, and based on the data association model, judge the type of the current abnormal data occurrence;

[0038] A4. Determine the abnormal type of the internal circuit of the DC converter transformer based on the change rule of the subsequent circuit signal received by the circuit signal receiving module.

[0039] Further, the circuit fault location module determines the location where the abnormality occurs for the abnormal type of the internal circuit of the DC converter transformer based on the problem confirmation model, including the following working steps:

[0040] B1. Based on the abnormal type of the internal circuit of the DC converter transformer obtained in step A4, and through the circuit simulation model, simulate the subsequent circuit signal simulation data at the location where the current circuit abnormality occurs;

[0041] B2. Based on the abnormal type of the internal circuit of the DC converter transformer obtained in step A4, and through the data monitoring model, monitor the subsequent circuit signal monitoring data at the location where the current circuit abnormality occurs;

[0042] B3. Based on the data comparison model, mark the abnormal occurrence location where the detection result of the abnormal occurrence location monitoring module is the same as the prediction result of the abnormal occurrence location simulation module as the confirmed abnormal occurrence location.

[0043] Further, the data association analysis module generates a data association model, and the specific establishment steps are as follows:

[0044] C1. When the DC converter transformer is in the no-load state, measure the voltage signals V1, …, Vn, circuit current signals I1, …, In, circuit polarity signals J1, …, Jn, and circuit waveform signals X1, …, Xn of each section of the internal circuit of the DC converter transformer, where n represents the nth section of the circuit;

[0045] C2. Based on the data adjustment model, randomly adjust any number of parameters in any one of the circuit signals in step C1, and record the variation rules of the parameters of other circuit signals;

[0046] C3. Based on the data adjustment model, randomly adjust any number of parameters in any two of the circuit signals in step C1, and record the variation rules of the parameters of other circuit signals;

[0047] C4. Based on the data adjustment model, randomly adjust any number of parameters in any three of the circuit signals in step C1, and record the variation rules of other circuit signals;

[0048] C5. Based on the duplicate removal model, perform duplicate removal processing on the variation rules in steps C2, D3, and D4;

[0049] C6. Use the parameter variation rules after duplicate removal in step C5 as the data association model.

[0050] Compared with the prior art, a DC converter transformer insulation leakage detection system and detection method provided by the present invention can accurately identify the abnormal data type based on the generated data association model through the settings of the abnormal data receiving module, abnormal type analysis module, and abnormal type determination module. At the same time, through the abnormal type determination module, it can accurately identify the abnormal data of the current abnormal data type based on the data association model and accurately locate the abnormal occurrence position, avoiding the inability to accurately locate the abnormal position during traditional circuit detection and the investment of a large amount of human resources and material resources during abnormal position location. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is the system structure block diagram provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0054] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention.

[0055] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined. In addition, the terms "mounted", "connected", and "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0057] In the case of no conflict, the various embodiments of the present disclosure and the various features in the embodiments may be combined with each other.

[0058] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0059] The terms used herein are for describing particular embodiments only and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms "comprises" and / or "consists of" are used in this specification, it specifies the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0060] The embodiments described herein may be described with reference to plan views and / or cross-sectional views by means of ideal schematic diagrams of the present disclosure. Accordingly, the example illustrations may be modified according to manufacturing techniques and / or tolerances. Accordingly, the embodiments are not limited to the embodiments shown in the drawings, but include modifications of configurations formed based on manufacturing processes. Accordingly, the regions illustrated in the drawings have schematic attributes, and the shapes of the regions shown in the drawings illustrate the specific shapes of the regions of the elements, but are not intended to be limiting.

[0061] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0062] Please refer to Figure 1 , a DC converter transformer insulation leakage detection system and detection method, comprising a transformer current signal acquisition module, a transformer voltage signal acquisition module, a circuit signal receiving module, a circuit fault location module, an emergency plan generation module, and an anomaly reporting module:

[0063] The transformer current signal acquisition module is used to receive the current signal at the output end of the DC converter transformer;

[0064] The transformer voltage signal acquisition module is used to receive the voltage signal at the output end of the DC converter transformer;

[0065] The circuit signal receiving module is used to receive the internal circuit signals of the DC converter transformer, and the circuit signals include voltage signals, current signals, polarity signals, and waveform signals;

[0066] The circuit fault location module is used to receive the internal circuit signals of the DC converter transformer received by the circuit signal receiving module, and analyze the types of internal circuit anomalies of the DC converter transformer based on the data association model;

[0067] The circuit fault location module is also used to determine the location where the anomaly occurs for the types of internal circuit anomalies of the DC converter transformer based on the problem confirmation model;

[0068] The emergency plan generation module is used to generate an emergency plan through the plan generation module and based on the abnormal type and occurrence location of the internal circuit of the DC converter transformer determined by the circuit fault location module;

[0069] The abnormal report module is used to send the abnormal type and occurrence location of the internal circuit of the DC converter transformer determined by the circuit fault location module to the maintenance personnel's handheld terminal.

[0070] The circuit fault location module includes

[0071] The abnormal data receiving module, which is used to receive the internal circuit signals of the DC converter transformer received by the circuit signal receiving module, compare the internal circuit signals A of the DC converter transformer received by the circuit signal receiving module with the historical internal circuit signals B of the DC converter transformer based on the data comparison model, mark the internal circuit signals of the DC converter transformer with the data change amount C exceeding the preset change threshold D as abnormal data, and send the abnormal data to the abnormal type analysis model. The abnormal data includes abnormal voltage data, abnormal current data, abnormal polarity data, and abnormal waveform data. Among them, the internal circuit signals A of the DC converter transformer received by the circuit signal receiving module include voltage signal A1, current signal A2, polarity signal A3, and waveform signal A4, and the historical internal circuit signals B of the DC converter transformer include historical voltage signal B1, historical current signal B2, historical polarity signal B3, and historical waveform signal B4. The data change amount C includes voltage signal change amount C1 = | A1 - B1 | 、current signal change amount C2 = | A2 - B2 | 、polarity signal change amount C3 = | A3 - B3 | and waveform signal change amount C4 = | A4 - B4 | , and the preset change threshold D includes voltage signal change amount D1, current signal change amount D2, polarity signal change amount D3, and waveform signal change amount D4. If C1 > D1, the voltage signal A1 is marked as abnormal voltage data. If C2 > D2, the current signal A2 is marked as abnormal current data. If C3 > D3, the polarity signal A3 is marked as abnormal polarity data. If C4 > D4, the waveform signal A4 is marked as abnormal waveform data;

[0072] The abnormal type analysis module, which is used to receive the abnormal data sent by the abnormal data receiving module and judge the type of current abnormal data occurrence based on the data association model;

[0073] An abnormal type determination module, which is used to determine the abnormal type of the internal circuit of the DC converter transformer based on the variation law of the subsequent circuit signals received by the circuit signal receiving module;

[0074] An abnormal occurrence location simulation module, which is used to determine the abnormal type of the internal circuit of the DC converter transformer obtained in the abnormal type determination module, and simulate the subsequent circuit signal simulation data of the current circuit abnormal type occurrence location through a circuit simulation model;

[0075] An abnormal occurrence location monitoring module, which is used to determine the abnormal type of the internal circuit of the DC converter transformer obtained in the abnormal type determination module, and monitor the subsequent circuit signal monitoring data of the current circuit abnormal type occurrence location through a data monitoring model;

[0076] An abnormal occurrence location confirmation module, which is used to mark the abnormal occurrence location where the detection result of the abnormal occurrence location monitoring module is the same as the prediction result of the abnormal occurrence location simulation module as the confirmed abnormal occurrence location based on a data comparison model.

[0077] The abnormal type analysis module includes a data receiving module, a data correlation analysis module, a model output module, and a model matching module:

[0078] The data receiving module is used to receive the internal circuit signal parameters of the DC converter transformer when it is in an open-circuit state;

[0079] The data correlation analysis module is used to receive the internal circuit signals of the DC converter transformer received by the data receiving module when it is in an open-circuit state, and generate a data correlation model. The data correlation analysis module includes a data adjustment module, a data recording module, and a data deduplication module. The data adjustment module is used to randomly adjust the circuit signal parameters received by the data receiving module based on a data adjustment model. The data recording module is used to record the variation law of other circuit signal parameters during the process of the data adjustment module adjusting the data. The data deduplication module is used to deduplicate the variation law of other circuit signal parameters recorded by the data recording module based on a deduplication model;

[0080] The model output module is used to output the data correlation model generated by the data correlation analysis module;

[0081] The model matching module is used to match the type of abnormal data occurrence corresponding to the current abnormal data in the data correlation model based on a data matching model.

[0082] Please refer to Figure 2 , a method for detecting insulation leakage of a DC converter transformer, including the following working steps:

[0083] S1, receiving the internal circuit signals of the DC converter transformer through the circuit signal receiving module;

[0084] S2. Obtain the circuit signal data received in step S1 through a data extraction model;

[0085] S3. Perform data processing on the circuit signal data obtained in step S2 through a data processing module;

[0086] S4. Real-time monitor the status of the circuit signal data processed by the data processing module through a status monitoring module, and send the circuit signal data in a stable state to step S5;

[0087] S5. Analyze the internal circuit anomaly type of the DC converter transformer based on the data association model in step S4 through a circuit fault location module;

[0088] S6. Determine the anomaly occurrence location for the internal circuit anomaly type of the DC converter transformer based on a problem confirmation model;

[0089] S7. Generate an emergency plan through a solution generation module based on the internal circuit anomaly type of the DC converter transformer determined in step S5 and the anomaly occurrence location of the internal circuit of the DC converter transformer determined in step S6;

[0090] S8. Send the internal circuit anomaly type and anomaly occurrence location of the DC converter transformer determined by the circuit fault location module to the maintenance personnel's handheld terminal through an anomaly reporting module.

[0091] The circuit fault location module analyzes the internal circuit anomaly type of the DC converter transformer based on an anomaly type analysis model, including the following working steps:

[0092] A1. Receive the internal circuit signal of the DC converter transformer received by the circuit signal receiving module through an anomaly data receiving module;

[0093] A2. Compare the internal circuit signal A of the DC converter transformer received by the circuit signal receiving module with the historical internal circuit signal B of the DC converter transformer based on a data comparison model, and mark the internal circuit signal of the DC converter transformer with a data change amount C exceeding a preset change threshold D as abnormal data;

[0094] A3. Based on the abnormal data marked in step A2, judge the type of current abnormal data occurrence based on a data association model;

[0095] A4. Determine the internal circuit anomaly type of the DC converter transformer based on the subsequent circuit signal change rule received by the circuit signal receiving module.

[0096] The circuit fault location module determines the anomaly occurrence location for the internal circuit anomaly type of the DC converter transformer based on a problem confirmation model, including the following working steps:

[0097] B1. Based on the abnormal types of the internal circuit of the DC converter transformer obtained in step A4, and simulate the subsequent circuit signal simulation data at the location where the current circuit abnormal type occurs through the circuit simulation model;

[0098] B2. Based on the abnormal types of the internal circuit of the DC converter transformer obtained in step A4, and monitor the subsequent circuit signal monitoring data at the location where the current circuit abnormal type occurs through the data monitoring model;

[0099] B3. Based on the data comparison model, mark the abnormal occurrence locations where the detection results of the abnormal occurrence location monitoring module are the same as the prediction results of the abnormal occurrence location simulation module as the confirmed abnormal occurrence locations.

[0100] The data association analysis module generates a data association model, which specifically includes the following establishment steps:

[0101] C1. When the DC converter transformer is in the no-load state, measure the voltage signals V1,..., Vn, circuit current signals I1,..., In, circuit polarity signals J1,..., Jn, and circuit waveform signals X1,..., Xn of each section of the internal circuit of the DC converter transformer, where n represents the nth section of the circuit;

[0102] C2. Based on the data adjustment model, control any number of parameters in any one of the circuit signals in step C1 to be randomly adjusted, and record the parameter change rules of other circuit signals;

[0103] C3. Based on the data adjustment model, control any number of parameters in any two of the circuit signals in step C1 to be randomly adjusted, and record the parameter change rules of other circuit signals;

[0104] C4. Based on the data adjustment model, control any number of parameters in any three of the circuit signals in step C1 to be randomly adjusted, and record the change rules of other circuit signals;

[0105] C5. Based on the de-duplication model, perform de-duplication processing on the change rules in steps C2, D3, and D4;

[0106] C6. Use the parameter change rules after de-duplication in step C5 as the data association model.

[0107] Only some exemplary embodiments of the present invention are described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A DC converter transformer insulation leakage detection system, characterized in that: It includes a circuit signal receiving module, a data extraction module, a data processing module, a data status monitoring module, a circuit fault location module, an emergency plan generation module, and an exception reporting module: The circuit signal receiving module is used to receive the internal circuit signals of the DC converter transformer, and the circuit signals include voltage signals, current signals, polarity signals, and waveform signals; The data extraction module obtains circuit signal data through a data extraction model; After obtaining the circuit signal data through the data extraction module, the data processing module processes the circuit signal data. The processing of the circuit signal data by the data processing module includes data preprocessing, data feature extraction, and data fusion; The data status monitoring module monitors the status of the circuit signal data processed by the data processing module in real time, and sends the circuit signal data in a stable state to the circuit fault location module; The circuit fault location module is used to receive the internal circuit signal data of the DC converter transformer in a stable state received by the circuit signal receiving module, and analyze the abnormal types of the internal circuit of the DC converter transformer based on a data association model. The circuit fault location module realizes the analysis of the abnormal types of the internal circuit of the DC converter transformer through a data receiving module, a data association analysis module, a model output module, and a model matching module: The data receiving module is used to receive the internal circuit signal parameters of the DC converter transformer in an idle state; The data association analysis module is used to receive the internal circuit signals of the DC converter transformer in an idle state received by the data receiving module and generate a data association model. The data association analysis module includes a data adjustment module, a data recording module, and a data deduplication module. The data adjustment module is used to randomly adjust the circuit signal parameters received by the data receiving module based on a data adjustment model. The data recording module is used to record the change rules of other circuit signal parameters during the process of the data adjustment module adjusting the data. The data deduplication module is used to deduplicate the change rules of other circuit signal parameters recorded by the data recording module based on a deduplication model; The model output module is used to output the data association model generated by the data association analysis module; The model matching module is used to match the type of abnormal data occurrence corresponding to the current abnormal data in the data association model based on a data matching model; The circuit fault location module is also used to determine the abnormal occurrence location for the abnormal type of the internal circuit of the DC converter transformer based on a problem confirmation model; The emergency plan generation module is used to generate an emergency plan through a plan generation module and based on the abnormal type and abnormal occurrence location of the internal circuit of the DC converter transformer determined by the circuit fault location module; The exception reporting module is used to send the abnormal type and abnormal occurrence location of the internal circuit of the DC converter transformer determined by the circuit fault location module to the maintenance personnel's handheld terminal; The data association analysis module generates a data association model, which specifically includes the following establishment steps: C1. When the DC converter transformer is in an idle state, measure the voltage signals V1, …, Vn, current signals I1, …, In, polarity signals J1, …, Jn, and waveform signals X1, …, Xn of each section of the internal circuit of the DC converter transformer, where n represents the nth section of the circuit; C2. Based on the data adjustment model, control any number of parameters in any one of the circuit signals in step C1 to be randomly adjusted, and record the change rules of the parameters of the other circuit signals; C3. Based on the data adjustment model, control any number of parameters in any two of the circuit signals in step C1 to be randomly adjusted, and record the change rules of the parameters of the other circuit signals; C4. Based on the data adjustment model, control any number of parameters in any three of the circuit signals in step C1 to be randomly adjusted, and record the change rules of the other circuit signals; C5. Based on the duplicate removal model, perform duplicate removal processing on the change rules in steps C2, C3, and C4; C6. Use the parameter change rules after duplicate removal in step C5 as the data association model.

2. The insulation leakage detection system for a DC converter transformer according to claim 1, wherein: The circuit fault location module includes An abnormal data receiving module, which is used to receive the internal circuit signals of the DC converter transformer received by the circuit signal receiving module, and based on the data comparison model, compare the internal circuit signals A of the DC converter transformer received by the circuit signal receiving module with the historical internal circuit signals B of the DC converter transformer, and mark the internal circuit signals of the DC converter transformer with a data change amount C exceeding the preset change threshold D as abnormal data, and send the abnormal data to the abnormal type analysis model. The abnormal data includes abnormal voltage data, abnormal current data, abnormal polarity data, and abnormal waveform data; An abnormal type analysis module, which is used to receive the abnormal data sent by the abnormal data receiving module, and based on the data association model, judge the type of the current abnormal data; An abnormal type determination module, which is used to determine the abnormal type of the internal circuit of the DC converter transformer based on the subsequent circuit signal change rules received by the circuit signal receiving module; An abnormal occurrence location simulation module, which is used to based on the abnormal type of the internal circuit of the DC converter transformer obtained in the abnormal type determination module, and simulate the simulation data of the subsequent circuit signals at the location where the current circuit abnormal type occurs through the circuit simulation model; An abnormal occurrence location monitoring module, which is used to based on the abnormal type of the internal circuit of the DC converter transformer obtained in the abnormal type determination module, and monitor the monitoring data of the subsequent circuit signals at the location where the current circuit abnormal type occurs through the data monitoring model; An abnormal occurrence location confirmation module, which is used to based on the data comparison model, mark the abnormal occurrence location where the detection result of the abnormal occurrence location monitoring module is the same as the prediction result of the abnormal occurrence location simulation module as the confirmed abnormal occurrence location.

3. A method for detecting insulation leakage of a DC converter transformer, which is applicable to the insulation leakage detection system of a DC converter transformer according to any one of claims 1-2, characterized in that: It includes the following working steps: S1. Receive the internal circuit signals of the DC converter transformer through the circuit signal receiving module; S2. Obtain the circuit signal data received in step S1 through the data extraction model; S3. The data processing module processes the circuit signal data obtained in step S2. S4. The status monitoring module monitors the status of the circuit signal data processed by the data processing module in real time, and sends the circuit signal data in a stable state to step S5. S5. The circuit fault location module analyzes the internal circuit abnormal types of the DC converter transformer based on the data in step S4 and the data association model. S6. Based on the problem confirmation model, determine the abnormal occurrence location for the internal circuit abnormal types of the DC converter transformer. S7. Through the solution generation module, and based on the internal circuit abnormal types of the DC converter transformer determined in step S5 and the internal circuit abnormal occurrence location determined in step S6, generate an emergency solution. S8. The abnormal reporting module sends the internal circuit abnormal types and abnormal occurrence locations of the DC converter transformer determined by the circuit fault location module to the maintenance personnel's handheld terminal.

4. A method for detecting insulation leakage of a DC converter transformer according to claim 3, characterized in that: The circuit fault location module analyzes the internal circuit abnormal types of the DC converter transformer based on the abnormal type analysis model, including the following working steps: A1. The abnormal data receiving module receives the internal circuit signals of the DC converter transformer received by the circuit signal receiving module. A2. Based on the data comparison model, compare the internal circuit signal A of the DC converter transformer received by the circuit signal receiving module with the historical internal circuit signal B of the DC converter transformer, and mark the internal circuit signal of the DC converter transformer whose data change amount C exceeds the preset change threshold D as abnormal data. A3. Based on the abnormal data marked in step A2, and based on the data association model, judge the type of current abnormal data occurrence. A4. Determine the internal circuit abnormal types of the DC converter transformer based on the subsequent circuit signal change rules received by the circuit signal receiving module.

5. A method for detecting insulation leakage of a DC converter transformer according to claim 4, characterized in that: The circuit fault location module determines the abnormal occurrence location for the internal circuit abnormal types of the DC converter transformer based on the problem confirmation model, including the following working steps: B1. Based on the internal circuit abnormal types of the DC converter transformer obtained in step A4, and simulate the subsequent circuit signal simulation data at the abnormal occurrence location of the current circuit abnormal type through the circuit simulation model. B2. Based on the internal circuit abnormal types of the DC converter transformer obtained in step A4, and monitor the subsequent circuit signal monitoring data at the abnormal occurrence location of the current circuit abnormal type through the data monitoring model. B3. Based on the data comparison model, mark the abnormal occurrence location where the detection result of the abnormal occurrence location monitoring module is the same as the prediction result of the abnormal occurrence location simulation module as the confirmed abnormal occurrence location.

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