Online monitoring system and method for detecting acetylene gas dissolved in transformer oil

Abstract

The invention provides an on-line monitoring system and method for detecting acetylene gas dissolved in transformer oil, and belongs to the technical field of transformer monitoring. A TDLAS monitoring module monitors the change of an acetylene gas absorption spectrum in an oil tank of a transformer by using a laser transmitter, calculates the concentration of the acetylene gas, and transmits the acetylene gas concentration to an oil tank of the transformer; the electrochemical monitoring module detects the concentration of acetylene gas by measuring the change of electrochemical signals in an oil tank of a transformer, and the data processing and analyzing module receives data from the TDLAS monitoring module and the electrochemical monitoring module for processing, analysis and prediction. The problems that an existing optical detection technology for dissolved acetylene in transformer oil is large in gas chamber size, complex in structure, sensitive to cross of various characteristic gases, insufficient in sensitivity, prone to being affected by vibration or noise and excessively dependent on oil-gas separation, transformer oil needs to be sampled and then subjected to laboratory analysis, time consumption is long, and detection efficiency is high are solved. And real-time monitoring cannot be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of transformer monitoring. Specifically, it relates to an on-line monitoring system and method for detecting dissolved acetylene gas in transformer oil. Background Art

[0002] Power transformers are the most core equipment in power grid operation. Accidents caused by transformer failures occur frequently. Therefore, monitoring the operating status of power transformers is the key to ensuring the safe operation of the power grid. At present, oil-immersed transformers are commonly used for high-voltage and large-capacity power transformers at home and abroad. And dissolved gas analysis in oil is the primary method for detecting faults in oil-immersed transformers. Among the fault characteristic gases, acetylene is one of the important characteristic gases reflecting low-energy spark discharge and high-energy arc discharge. Therefore, on-line monitoring of dissolved acetylene in transformer oil is an important means to achieve early fault warning and has important engineering significance.

[0003] Acetylene is one of the key characteristic gases most commonly used for evaluating the operating status of transformers among numerous characteristic gases. Therefore, the detection of dissolved acetylene in oil is an important method for the condition assessment of oil-immersed power transformers. Optical sensing has gradually become a research hotspot due to its advantages such as good insulation performance, strong anti-electromagnetic interference ability, and excellent detection performance. However, the existing optical detection technology for dissolved acetylene in transformer oil still has problems such as a relatively large gas chamber volume and complex structure, cross-sensitivity to multiple characteristic gases, insufficient sensitivity, susceptibility to vibration or noise, and over-reliance on oil-gas separation. Therefore, it is of great practical significance to develop an on-line monitoring system and method for quickly detecting dissolved acetylene gas in transformer oil. Summary of the Invention

[0004] The embodiments of the present invention provide an on-line monitoring system and method for detecting dissolved acetylene gas in transformer oil, which solves the problems existing in the existing optical detection technology for dissolved acetylene in transformer oil, such as a relatively large gas chamber volume and complex structure, cross-sensitivity to multiple characteristic gases, insufficient sensitivity, susceptibility to vibration or noise, and over-reliance on oil-gas separation.

[0005] In view of the above problems, the technical solution proposed by the present invention is as follows:

[0006] The present invention provides an on-line monitoring system for detecting dissolved acetylene gas in transformer oil, including a TDLAS monitoring module. The TDLAS monitoring module is based on Beer-Lambert's law and uses a laser emitter to monitor the change in the absorption spectrum of acetylene gas in the transformer's oil tank and calculate the concentration of acetylene gas.

[0007] An electrochemical monitoring module, which detects the concentration of acetylene gas by measuring the change in the electrochemical signal in the transformer's oil tank.

[0008] A data processing and analysis module, which receives data from the TDLAS monitoring module and the electrochemical monitoring module for processing, analysis and prediction, and is wirelessly communicatively connected to the TDLAS monitoring module and the electrochemical monitoring module;

[0009] An early warning module, which is used to trigger an alarm when the detected acetylene gas concentration exceeds a preset threshold.

[0010] As a preferred technical solution of the present invention, the TDLAS monitoring module includes a laser emitter, an absorption cell and a photodetector. An adjustable laser diode is arranged inside the laser emitter. The absorption cell is a long optical path cell, and high reflection filters are installed at both ends inside the absorption cell. The photodetector is used to analyze the change of light intensity to determine the concentration of acetylene gas from the light penetrating out of the oil tank of the transformer.

[0011] As a preferred technical solution of the present invention, the electrochemical monitoring module includes a chemical sensor. The chemical sensor is directly inserted into the oil tank of the transformer. The chemical sensor is a gas-sensitive element doped with tin dioxide material, and rare earth doped oxides and precious metal hydrochloric acid catalysts are doped into the tin dioxide material. A waterproof and breathable membrane is arranged to wrap the insertion end of the chemical sensor.

[0012] As a preferred technical solution of the present invention, the data processing and analysis module includes an enhancement analysis unit, a real-time analysis unit, a data fusion unit and a result output unit;

[0013] The enhancement analysis unit is used to analyze the data from the TDLAS monitoring module and the electrochemical monitoring module and provide an interpretable analysis result;

[0014] The real-time analysis unit performs real-time data analysis based on the data analysis result of the enhancement analysis unit;

[0015] The data fusion unit is used to fuse and sort out the analysis data of the enhancement analysis unit and the real-time analysis unit;

[0016] The result output unit is used to output the integrated data in the form of a chart.

[0017] As a preferred technical solution of the present invention, the enhancement analysis unit includes an enhanced data acquisition subunit, an enhanced data analysis subunit and a prediction subunit;

[0018] The enhanced data acquisition subunit calculates the relationship between the data monitored by the TDLAS monitoring module and the electrochemical monitoring module by using correlation analysis;

[0019] The enhanced data analysis subunit uses EDA analysis to actively gain insights using statistical charts, identify anomalies in the data, and cluster the data;

[0020] The prediction subunit uses machine learning algorithms to perform predictive analysis on the data analyzed by the enhanced data analysis subunit.

[0021] As a preferred technical solution of the present invention, the real-time analysis unit includes a data stream processing subunit, a pattern recognition subunit, and a prediction model subunit;

[0022] The data stream processing subunit uses stream processing algorithms to perform real-time processing and analysis on the data of the enhanced data analysis subunit, providing convenience for subsequent processing;

[0023] The pattern recognition subunit uses statistical methods or signal processing techniques to identify specific patterns from the data processed by the data stream processing subunit;

[0024] The prediction model subunit selects a prediction model based on the patterns identified by the pattern recognition subunit, validates the model, and applies the trained model to data processing and analysis.

[0025] As a preferred technical solution of the present invention, the stream processing algorithms in the data stream processing subunit include window algorithms, moving average algorithms, and weighted moving average algorithms. Batch calculations are performed on the data within a fixed time period through the window algorithm, the arithmetic mean of the most recent fixed number of data points is calculated through the moving average algorithm, and each data point is given the same weight in the calculation. Different weights are given to different data points within the window when calculating the average value through the weighted moving average algorithm.

[0026] On the other hand, a method for an on-line monitoring system for detecting dissolved acetylene gas in transformer oil includes the following steps:

[0027] Step S1, configure the TDLAS monitoring module and the electrochemical monitoring module in the oil tank of the transformer, and synchronously start the TDLAS monitoring module and the electrochemical monitoring module to monitor the acetylene gas in the oil tank of the transformer;

[0028] Step S2, the TDLAS monitoring module emits a laser beam through a laser emitter, and the laser beam sequentially passes through the absorption cell, the oil tank, and the transformer oil, and is monitored by a photodetector;

[0029] Step S3, the electrochemical monitoring module immerses the chemical sensor in the transformer oil in the oil tank, and the acetylene gas undergoes an oxidation-reduction reaction on the surface of the working electrode to generate a current signal, and the change in the current signal is measured;

[0030] Step S4: Analyze the collected real-time data by using the enhancement analysis unit and the real-time analysis unit in the data processing and analysis module, integrate the analyzed data, and display it in the form of charts for the staff to view;

[0031] Step S5: While the data processing and analysis module is analyzing, synchronously compare the thresholds in the warning module, and give an alarm prompt when the data exceeds the thresholds.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] (1) The present invention conducts on-line monitoring of acetylene gas in transformer oil through TDLAS technology and electrochemical sensors, without directly contacting the transformer oil, thus avoiding the sampling process. By real-time transmitting data with the on-line monitoring system, it realizes real-time monitoring of acetylene gas in transformer oil and can be adjusted according to different monitored gases;

[0034] (2) The present invention can analyze a large amount of multi-dimensional data by using enhancement analysis and real-time analysis, automatically discover the correlation and rules between data, and can quickly process and analyze the data after it is generated, thus providing real-time feedback and warning for the on-line monitoring of acetylene gas.

[0035] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other objects, features and advantages of the present invention more obvious and understandable, the following specifically gives the specific embodiments of the present invention. Brief Description of the Drawings

[0036] Figure 1 is a structural schematic diagram of an on-line monitoring system for detecting dissolved acetylene gas in transformer oil disclosed by the present invention;

[0037] Figure 2 is a block diagram of an enhancement analysis unit of an on-line monitoring system for detecting dissolved acetylene gas in transformer oil disclosed by the present invention;

[0038] Figure 3 is a block diagram of a real-time analysis unit of an on-line monitoring system for detecting dissolved acetylene gas in transformer oil disclosed by the present invention;

[0039] Figure 4 is a usage schematic diagram of an on-line monitoring system for detecting dissolved acetylene gas in transformer oil disclosed by the present invention;

[0040] Figure 5 is a schematic diagram of the method flow of an on-line monitoring system for detecting dissolved acetylene gas in transformer oil disclosed by the present invention;

[0041] Description of reference numerals in the drawings: 101, TDLAS monitoring module; 102, electrochemical monitoring module; 103, data processing and analysis module; 1031, enhanced analysis unit; 10311, enhanced data acquisition subunit; 10312, enhanced data analysis subunit; 10313, prediction subunit; 1032, real-time analysis unit; 10321, data stream processing subunit; 10322, pattern recognition subunit; 10323, prediction model subunit; 1033, data fusion unit; 13034, data output unit; 104, result output unit; 105, laser emitter; 106, absorption cell; 107, photodetector; 108, chemical sensor; 200, fuel tank. Detailed implementation manners

[0042] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Therefore, the detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] It should be noted that like reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0045] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. are based on the orientation or positional relationships shown in the drawings, and 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 to the present invention.

[0046] In addition, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality of" means two or more unless specifically defined otherwise.

[0047] Embodiment 1

[0048] Referring to the attached Figures 1-4 As shown, the present invention provides a technical solution: an on-line monitoring system for detecting dissolved acetylene gas in transformer oil, including a TDLAS monitoring module 101. The TDLAS monitoring module 101 monitors the change in the absorption spectrum of acetylene gas in the transformer oil tank 200 based on the Beer-Lambert law, and calculates the concentration of acetylene gas by using a laser emitter 105.

[0049] An electrochemical monitoring module 102, which detects the concentration of acetylene gas by measuring the change in the electrochemical signal in the transformer oil tank 200.

[0050] A data processing and analysis module 103, which receives data from the TDLAS monitoring module 101 and the electrochemical monitoring module 102 for processing, analysis and prediction. The data processing and analysis module 103 is wirelessly communicatively connected to the TDLAS monitoring module 101 and the electrochemical monitoring module 102.

[0051] An early warning module, which is used to trigger an alarm when the detected concentration of acetylene gas exceeds a preset threshold.

[0052] The embodiment of the present invention is also implemented by the following technical solutions.

[0053] In an embodiment of the present invention, the TDLAS monitoring module 101 includes a laser emitter 105, an absorption cell 106, and a photodetector 107. An adjustable laser diode is provided inside the laser emitter 105. According to different monitoring requirements and based on the changes in the reaction between different gases and light, the laser wavelength of the diode is adjusted. The absorption cell 106 is a long optical path cell, and high-reflection filters are installed at both ends inside the absorption cell 106. After the laser emits from the laser emitter 105 and enters the absorption cell 106, it is reflected multiple times between the high-emissivity lenses at both ends. Each time the light beam passes through the measured gas after reflection, the laser energy will decay. Through this process, the lengthening of the measurement optical path is achieved, and at the same time, the gas absorption effect is enhanced. The photodetector 107 is used for the light penetrating from the oil tank 200 of the transformer. The oil tank is made of a material that is convenient for laser penetration, such as a transparent or semi-transparent material, or a separate laser penetration window is provided for the laser to penetrate. The detected change in light intensity is related to the concentration of acetylene gas, and the concentration of acetylene gas is determined by analyzing the change in light intensity. The installation position of the photodetector 107 in the TDLAS monitoring module 101 can be installed separately or integrated with the oil tank 200.

[0054] In an embodiment of the present invention, the electrochemical monitoring module 102 includes a chemical sensor 108. The chemical sensor 108 is directly inserted into the oil tank 200 of the transformer. The chemical sensor 108 is a gas-sensitive element doped with tin dioxide material, and rare earth doped oxides and noble metal hydrochloric acid catalysts are doped into the tin dioxide material. Among them, the rare earth doped oxide is samarium, and the noble metal hydrochloric acid catalysts include palladium, platinum, and gold. By doping the aforementioned catalysts into the chemical sensor 108, the acetylene gas can be activated, and the sensitivity can be improved. Moreover, the insertion end of the chemical sensor 108 is wrapped with a waterproof and breathable membrane to prevent the chemical sensor 108 from getting water.

[0055] In an embodiment of the present invention, the data processing and analysis module 103 includes an enhanced analysis unit 1031, a real-time analysis unit 1032, a data fusion unit 1033, and a result output unit 104;

[0056] The enhanced analysis unit 1031 is used to analyze the data from the TDLAS monitoring module 101 and the electrochemical monitoring module 102, and provide an interpretable analysis result, so that non-professionals can also understand the analysis result;

[0057] The real-time analysis unit 1032 performs real-time data analysis based on the data analysis result of the enhanced analysis unit 1031;

[0058] The data fusion unit 1033 is used to fuse and organize the analysis data of the enhanced analysis unit 1031 and the real-time analysis unit 1032;

[0059] The result output unit 104 is used to output the integrated data in the form of a chart.

[0060] In an embodiment of the present invention, the enhanced analysis unit 1031 includes an enhanced data acquisition subunit 10311, an enhanced data analysis subunit 10312, and a prediction subunit 10313;

[0061] The enhanced data acquisition subunit 10311 calculates the relationship between the data monitored by the TDLAS monitoring module 101 and the electrochemical monitoring module 102 by using correlation analysis. The Pearson correlation coefficient is used, and its value ranges from -1 to 1, where 1 represents a perfect positive correlation, -1 represents a perfect negative correlation, and 0 represents no linear relationship;

[0062] The enhanced data analysis subunit 10312 uses EDA analysis to actively gain insights through statistical charts, identify data anomalies, and cluster the data;

[0063] The details of active insight are as follows:

[0064] Define the insight objective and clarify the objective achieved through data analysis;

[0065] Establish a data framework and construct a data framework that can capture key data;

[0066] Automated analysis, automate the data analysis process through analysis tools, and regularly generate insight reports;

[0067] The steps of data clustering: select a suitable clustering algorithm, determine the clustering parameters, cluster the data, analyze the clustering results, and evaluate the clustering quality. Metrics such as the silhouette coefficient and the Davies - Bouldin index can be used;

[0068] The prediction subunit 10313 uses machine learning algorithms to perform predictive analysis on the data analyzed by the enhanced data analysis subunit 10312.

[0069] In an embodiment of the present invention, the real - time analysis unit 1032 includes a data stream processing subunit 10321, a pattern recognition subunit 10322, and a prediction model subunit 10323;

[0070] The data stream processing subunit 10321 uses stream processing algorithms to perform real - time processing and analysis on the data of the enhanced data analysis subunit 10312, providing convenience for subsequent processing;

[0071] The pattern recognition subunit 10322 uses statistical methods or signal processing techniques to identify specific patterns based on the data processed by the data flow processing subunit 10321. Statistical methods are used to identify patterns and trends in the data, such as mean, variance, standard deviation, etc., and signal processing techniques are used to analyze the patterns and features in the signals.

[0072] Based on the patterns identified by the pattern recognition subunit 10322, the prediction model subunit 10323 selects a prediction model, such as linear regression, time series model, neural network, etc., and validates the model, and applies the trained model to data processing and analysis.

[0073] In addition, a prediction model for real-time analysis refers to a model that can process data immediately when it arrives, that is, real-time data flow analysis, which is used to make predictions quickly. Enhanced predictive analysis refers to the process of using machine learning or other advanced technologies to enhance data exploration and insight generation, and to expand the capabilities of traditional analysis through automated and intelligent means. Based on enhanced analysis for real-time analysis, it is possible to pre-train through the prediction model in enhanced analysis and apply it to real-time analysis, so that real-time analysis can not only describe the current state, but also predict future trends and potential problems.

[0074] In the embodiment of the present invention, the flow processing algorithms in the data flow processing subunit 10321 include window algorithms, moving average algorithms, and weighted moving average algorithms. Batch calculations are performed on the data within a fixed time period through the window algorithm, including summation, averaging, maximum value, etc. The arithmetic mean of the most recent fixed number of data points is calculated through the moving average algorithm, and each data point is given the same weight in the calculation. Different weights are given to different data points within the window through the weighted moving average algorithm, and usually the most recent data points will have higher weights.

[0075] Embodiment Two

[0076] Refer to the appendix Figure 5 As shown in the figure, another method for an on-line monitoring system for detecting dissolved acetylene gas in transformer oil provided by the embodiment of the present invention includes the following steps:

[0077] Step S1, configure the TDLAS monitoring module 101 and the electrochemical monitoring module 102 in the oil tank 200 of the transformer, and synchronously start the TDLAS monitoring module 101 and the electrochemical monitoring module 102 to monitor the acetylene gas in the oil tank 200 of the transformer;

[0078] Step S2: The TDLAS monitoring module 101 emits a laser beam through the laser emitter 105. The laser beam successively passes through the absorption cell 106, the fuel tank 200, and the transformer oil, is monitored by the photodetector 107, and is transmitted to the absorption cell 106 through the anti-resonant hollow fiber. The absorption cell 106 strengthens the laser and transmits the laser beam into the fuel tank 200. The beam passes through the fuel tank 200 and interacts with the dissolved acetylene gas. After passing through the transformer oil in the fuel tank 200, the laser beam is received by the photodetector 107. The photodetector 107 detects the intensity change of the beam after passing through the oil sample. The degree of light intensity reduction is proportional to the concentration of the acetylene gas. The calculation of the concentration of the dissolved acetylene gas in the transformer oil using the light intensity is as follows:

[0079]

[0080] where, I A and I 1 represent the transmitted light intensity and the incident light intensity respectively, K is the absorption coefficient of the gas, C is the concentration of the gas, and L is the effective absorption optical path of the gas;

[0081] Step S3: The electrochemical monitoring module 102 immerses the chemical sensor 108 into the transformer oil in the fuel tank 200. The acetylene gas undergoes an oxidation-reduction reaction on the surface of the working electrode, generating a current signal. The change in the current signal is measured, and the calculation steps for calculating the concentration of the acetylene gas are as follows:

[0082] ΔI = I ― I 0

[0083] where, I is the real-time current signal, I 0 is the baseline current, and ΔI is the current change related to the concentration of the acetylene gas;

[0084] The slope k of the calibration curve is used to calculate the concentration C of the acetylene gas:

[0085]

[0086] Step S4: The enhancement analysis unit 1031 and the real-time analysis unit 1032 in the data processing and analysis module 103 analyze the collected real-time data. The enhanced data acquisition sub-unit 10311 collects data from multiple data sources. The enhanced data analysis sub-unit 10312 deeply analyzes the collected data, extracts features, and performs data conversion. The data stream processing sub-unit 10321 receives the data stream in real time and performs preliminary cleaning and formatting. The pattern recognition sub-unit 10322 identifies the pattern adapted to the data stream in the real-time data stream, uses the results of the enhanced data analysis to train the model of the prediction sub-unit 10313 for rapid prediction, and integrates the analyzed data and displays it in the form of a chart for the staff to view;

[0087] In step S5, while the data processing and analysis module 103 is analyzing, the thresholds in the warning module are compared synchronously, and an alarm prompt is given when the data exceeds the thresholds.

[0088] In this embodiment, the data integration content in step S4 is to merge the results of enhanced analysis with the data sources of real-time analysis to form a unified data view, combine the features extracted in enhanced analysis with the features of real-time analysis for a more complex model. If different models are used in the two analyses, algorithms such as stacking and weighted averaging are used to combine the model outputs to obtain the integrated data, and through data visualization tools, the fused results are presented in forms such as charts and dashboards to facilitate user understanding and decision-making.

[0089] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0090] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the protection scope of the present disclosure. The appended method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy.

[0091] In the above detailed description, various features are combined in a single embodiment to simplify the present disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected by the appended claims, the present invention lies in a state less than all the features of the single disclosed embodiment. Therefore, the appended claims are hereby expressly incorporated into the detailed description, where each claim stands alone as a separate preferred embodiment of the present invention.

[0092] For software implementation, the technologies described in this application can be implemented by modules (such as procedures, functions, etc.) that execute the functions of this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented inside the processor or outside the processor. In the latter case, it is coupled to the processor in a communicative manner via various means, which are well known in the art.

Claims

1. An online monitoring system for detecting dissolved acetylene gas in transformer oil, characterized in that: The invention comprises a TDLAS monitoring module (101), wherein the TDLAS monitoring module (101) is based on the Beer-Lambert law and uses a laser transmitter (105) to monitor changes in the absorption spectrum of acetylene gas in an oil tank (200) of a transformer, and calculates the concentration of acetylene gas; An electrochemical monitoring module (102), wherein the electrochemical monitoring module (102) detects the concentration of acetylene gas by measuring changes in electrochemical signals in an oil tank (200) of the transformer; A data processing and analysis module (103), wherein the data processing and analysis module (103) receives data from the TDLAS monitoring module (101) and the electrochemical monitoring module (102) for processing, analysis and prediction, and the data processing and analysis module (103) is wirelessly connected to the TDLAS monitoring module (101) and the electrochemical monitoring module (102); An early warning module is used to trigger an alarm when the detected acetylene gas concentration exceeds a preset threshold.

2. An online monitoring system for detecting dissolved acetylene gas in transformer oil according to claim 1, characterized in that: The TDLAS monitoring module (101) comprises a laser transmitter (105), an absorption cell (106) and a photodetector (107); a tunable laser diode is arranged inside the laser transmitter (105); the absorption cell (106) is a long optical path cell, and high reflection filters are installed at both ends of the absorption cell (106); the photodetector (107) is used for analyzing the change in light intensity of light penetrating from an oil tank (200) of a transformer to determine the concentration of acetylene gas.

3. An online monitoring system for detecting dissolved acetylene gas in transformer oil according to claim 2, characterized in that: The electrochemical monitoring module (102) comprises a chemical sensor (108), which is directly inserted into the oil tank (200) of the transformer. The chemical sensor (108) is a gas-sensitive element doped with a tin dioxide material, and rare earth doped oxides and noble metal hydrochloric acid catalysts are added to the tin dioxide material. The insertion end of the chemical sensor (108) is provided with a waterproof and breathable membrane.

4. An online monitoring system for detecting dissolved acetylene gas in transformer oil according to claim 3, characterized in that: The data processing and analysis module (103) comprises an enhanced analysis unit (1031), a real-time analysis unit (1032), a data fusion unit (1033) and a result output unit (104); The enhanced analysis unit (1031) is used to analyze the data from the TDLAS monitoring module (101) and the electrochemical monitoring module (102) to provide interpretable analysis results; The real-time analysis unit (1032) performs real-time data analysis based on the data analysis result of the enhanced analysis unit (1031); The data fusion unit (1033) is used to fuse and organize the analysis data of the enhanced analysis unit (1031) and the real-time analysis unit (1032); The result output unit (104) is used to output the integrated data in the form of charts.

5. An online monitoring system for detecting dissolved acetylene gas in transformer oil according to claim 4, characterized in that: The enhanced analysis unit (1031) comprises an enhanced data collection subunit (10311), an enhanced data analysis subunit (10312) and a prediction subunit (10313); The enhanced data acquisition subunit (10311) calculates the relationship between the data monitored by the TDLAS monitoring module (101) and the electrochemical monitoring module (102) using phase relationship analysis; The enhanced data analysis subunit (10312) uses EDA analysis to proactively gain insights using statistical charts, identify data anomalies, and cluster the data; The prediction subunit (10313) uses a machine learning algorithm to perform predictive analysis on the data analyzed by the enhanced data analysis subunit (10312).

6. An online monitoring system for detecting dissolved acetylene gas in transformer oil according to claim 5, characterized in that: The real-time analysis unit (1032) includes a data stream processing subunit (10321), a pattern recognition subunit (10322) and a prediction model subunit (10323); The data stream processing subunit (10321) uses a stream processing algorithm to process and analyze the data of the enhanced data analysis subunit (10312) in real time, thereby facilitating subsequent processing; The pattern recognition subunit (10322) uses statistical methods or signal processing techniques to recognize specific patterns based on the data processed by the data stream processing subunit (10321); The prediction model subunit (10323) selects a prediction model based on the pattern identified by the pattern recognition subunit (10322), verifies the model, and applies the trained model to data processing and analysis.

7. An online monitoring system for detecting dissolved acetylene gas in transformer oil according to claim 6, characterized in that: The stream processing algorithms in the data stream processing subunit (10321) include a window algorithm, a sliding average algorithm and a weighted moving average algorithm. The window algorithm is used to perform batch calculations on data within a fixed time period. The sliding average algorithm is used to calculate the arithmetic mean of the most recent fixed number of data points. Each data point is given the same weight in the calculation. The weighted moving average algorithm is used to give different weights to different data points in the window when calculating the average value.

8. A method for detecting an online monitoring system for acetylene gas dissolved in transformer oil, applied to an online monitoring system for acetylene gas dissolved in transformer oil as claimed in any one of claims 1 to 7, characterized in that: The following steps are involved: Step S1, configuring the TDLAS monitoring module (101) and the electrochemical monitoring module (102) in the oil tank (200) of the transformer, and synchronously starting the TDLAS monitoring module (101) and the electrochemical monitoring module (102) to monitor the acetylene gas in the oil tank (200) of the transformer; Step S2, the TDLAS monitoring module (101) emits a laser beam through the laser transmitter (105), and the laser beam passes through the absorption cell (106), the oil tank (200) and the transformer oil in sequence, and is monitored by the photoelectric detector (107); Step S3, the electrochemical monitoring module (102) immerses the chemical sensor (108) into the transformer oil in the oil tank (200), causing the acetylene gas to undergo an oxidation-reduction reaction on the surface of the working electrode to generate a current signal, and measures changes in the current signal; Step S4, using the enhanced analysis unit (1031) and the real-time analysis unit (1032) in the data processing and analysis module (103) to analyze the collected real-time data, and integrating the analyzed data and displaying them in the form of charts for staff to view; Step S5, while the data processing and analysis module (103) is analyzing, the threshold value in the early warning module is synchronously compared, and an alarm is issued when the data exceeds the threshold value.

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