Oil chromatography online monitoring system and method based on centralized control main station

Abstract

The invention discloses an oil chromatography on-line monitoring system and method based on a centralized control master station, the system comprises a plurality of oil chromatography on-line monitoring terminals, a data transmission module and the centralized control master station, and the oil chromatography on-line monitoring terminals are connected to the centralized control master station through the data transmission module. The oil chromatography online monitoring terminal equipment is organically combined with the centralized control main station, so that centralized acquisition, transmission, analysis and early warning of the chromatographic data of the insulating oil of the power equipment are realized, the monitoring efficiency and accuracy are improved, the operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of oil chromatography monitoring technology, specifically to an online oil chromatography monitoring system and method based on a centralized control master station. Background Technology

[0002] In power systems, the condition of the insulating oil in electrical equipment such as transformers is a crucial factor affecting the safe operation of the equipment. Insulating oil refers to mineral-based insulating oil used in power transmission and transformation equipment such as transformers, reactors, instrument transformers, bushings, and oil circuit breakers. It mainly consists of cycloalkanes, alkanes, and aromatic hydrocarbons. During normal operation, the insulating oil in a transformer may contain nitrogen, oxygen, carbon dioxide, and carbon monoxide gases. When an abnormal condition occurs in the transformer, gases such as methane, acetylene, ethane, and hydrogen will be produced in the transformer oil. Although these gases may not necessarily damage the transformer, their presence reflects abnormalities in the transformer oil or insulating materials, and these fault gases can be used for further diagnosis of the transformer.

[0003] Oil chromatography is an effective method for detecting the composition and content of dissolved gases in insulating oil. Monitoring these gases can help detect potential internal equipment faults, such as overheating and discharge. Traditional oil chromatography monitoring methods are mostly offline or decentralized on-site online monitoring, which have the following shortcomings: 1. Offline testing requires periodic sampling and delivery to the laboratory for analysis, resulting in a long testing cycle, inability to reflect the equipment status in real time, and susceptibility to external interference during sampling and transportation, which affects the accuracy of test results. 2. Although on-site distributed online monitoring equipment can monitor in real time, each device operates independently, resulting in scattered data that is difficult to centrally manage and analyze. Furthermore, when multiple devices are monitoring, the costs of data transmission, storage, and processing are high, which is not conducive to large-scale promotion and application.

[0004] With the intelligent development of power systems, the centralized control station, as the core platform for centralized monitoring and management of power equipment, has powerful data processing, communication and control capabilities, making it possible to realize the centralization and intelligentization of online oil chromatography monitoring. Summary of the Invention

[0005] Therefore, embodiments of the present invention provide an online oil chromatography monitoring system and method based on a centralized control master station to solve the problem that traditional offline detection or on-site decentralized online monitoring methods cannot achieve centralized and intelligent management.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: According to a first aspect of the present invention, the present invention provides an online oil chromatography monitoring system based on a centralized control station. The system includes multiple online oil chromatography monitoring terminals, a data transmission module, and a centralized control station. The online oil chromatography monitoring terminals are connected to the centralized control station through the data transmission module. The oil chromatography online monitoring terminal is used to perform on-site testing of electrical equipment to obtain oil chromatography data, and transmits the detected oil chromatography data to the central control station through the data transmission module; The data transmission module is specifically used to: upload oil chromatography data to the central control station through a scheduling data network, using a standard industrial communication protocol; during data transmission, encryption technology is used to encrypt the data to prevent data leakage or tampering; The centralized control station includes a data receiving and storage module, a data analysis and diagnosis module, an early warning and alarm module, and a data display and interaction module; The data receiving and storage module is used to receive oil chromatography data from each online oil chromatography monitoring terminal, store it according to a preset format, and establish an equipment oil chromatography data archive to facilitate historical data query and comparison. The data analysis and diagnosis module is used to preprocess the collected oil chromatographic data, analyze the preprocessed oil chromatographic data using oil chromatographic analysis algorithms, diagnose the fault type, determine whether there is a fault in the electrical equipment, and generate a diagnostic report. Simultaneously, it combines equipment operating parameters and historical data to correct and verify the diagnostic results. The oil chromatographic analysis algorithms include the three-ratio method, the David's triangle algorithm, and the four-level threshold alarm analysis method. The four-level threshold alarm analysis method divides the status information into four levels of threshold alarm standards based on the magnitude and trend of the monitored state variables: reaching attention value 1, reaching attention value 2, reaching alarm value, and reaching shutdown value. This enables accurate judgment of the transformer's operating status and fault severity. The early warning and alarm module is used to set early warning and alarm thresholds based on the changing trends and diagnostic results of oil chromatography data; if the detected gas content exceeds the preset early warning threshold, an early warning signal is issued to remind maintenance personnel to pay attention to changes in equipment status; if the detected gas content exceeds the preset alarm threshold or a serious fault is diagnosed, an alarm signal is immediately issued to notify maintenance personnel to take timely measures to deal with the fault. The data display and interaction module is used to visualize oil chromatography data, diagnostic results, and equipment status information. This includes displaying the changing trends of each gas content over time using various graphs such as pie charts, line charts, and bar charts, and displaying various data information including oil chromatography data, analysis results, and equipment status information in tabular form. It also supports users to customize and query historical data and generate query reports.

[0007] Furthermore, the data analysis and diagnosis module is specifically used for: The collected oil chromatographic data were preprocessed, including noise removal and missing value filling.

[0008] Furthermore, the early warning and alarm module is specifically used for: Warning and alarm information is pushed to relevant personnel through various means, including: displaying it in a pop-up window on the monitoring interface of the central control station to ensure timely delivery of information; and linking alarm signals with the audible and visual alarm devices of the central control station to enhance the alarm effect.

[0009] Furthermore, the data display and interaction module is specifically used for: Users can query historical data and analysis reports by setting various query conditions, including device number, time range, and gas type; and can export the query results into a standard report format, which is convenient for maintenance personnel to perform data analysis and archiving.

[0010] According to a second aspect of the present invention, an embodiment of the present invention provides a monitoring method for an online oil chromatography monitoring system based on a centralized control master station, the method comprising: Oil chromatography data is obtained by conducting on-site testing of electrical equipment through an online oil chromatography monitoring terminal. The detected oil chromatography data is then uploaded to the centralized control master station through a dispatch data network. The communication protocol adopts a standard industrial communication protocol. During data transmission, encryption technology is used to encrypt the data to prevent data leakage or tampering. The centralized control station includes a data receiving and storage module, a data analysis and diagnosis module, an early warning and alarm module, and a data display and interaction module; Based on the data receiving and storage module, oil chromatography data from various online oil chromatography monitoring terminals are received, stored in a preset format, and an equipment oil chromatography data archive is established to facilitate historical data query and comparison. Based on the data analysis and diagnosis module, the collected oil chromatographic data is preprocessed, and the preprocessed oil chromatographic data is analyzed and the fault type is diagnosed using oil chromatographic analysis algorithms to determine whether there is a fault in the electrical equipment and generate a diagnostic report. At the same time, the diagnostic results are corrected and verified by combining equipment operating parameters and historical data. The oil chromatographic analysis algorithms include the three ratio method, the David's triangle algorithm, and the four-level threshold alarm analysis method. Based on the early warning and alarm module, early warning and alarm thresholds are set according to the changing trends and diagnostic results of oil chromatography data. If the detected gas content exceeds the preset early warning threshold, an early warning signal is issued to remind maintenance personnel to pay attention to changes in equipment status. If the detected gas content exceeds the preset alarm threshold or a serious fault is diagnosed, an alarm signal is issued immediately to notify maintenance personnel to take timely measures to deal with the fault. Based on the data display and interaction module, oil chromatography data, diagnostic results, and equipment status information are visualized. This includes displaying the changing trends of each gas content over time using various graphs such as pie charts, line charts, and bar charts, and displaying various data information including oil chromatography data, analysis results, and equipment status information in tabular form. It also supports users to customize and query historical data and generate query reports.

[0011] Compared with existing technologies, the present invention provides an online oil chromatography monitoring system and method based on a centralized control station. By organically combining the online oil chromatography monitoring terminal with the centralized control station, it realizes centralized acquisition, transmission, analysis, and early warning of insulating oil chromatography data of power equipment, thereby improving monitoring efficiency and accuracy, reducing operation and maintenance costs, and ensuring the safe and stable operation of the power system. Specifically, it has the following beneficial effects: 1. Centralized Management: Through the centralized control master station, oil chromatography data from multiple substations can be centrally collected, stored, and analyzed, breaking down the data silos of traditional decentralized monitoring, facilitating unified management and monitoring, and improving operation and maintenance efficiency.

[0012] 2. Real-time performance and accuracy: The online oil chromatography monitoring terminal collects data in real time, and the centralized control station quickly analyzes and processes the data. It can promptly detect potential faults inside the equipment. By combining multiple analysis algorithms and equipment operation information, it improves the accuracy of fault diagnosis and provides strong support for the safe operation of the equipment.

[0013] 3. Intelligent early warning and alarm: Based on data analysis results, it automatically issues early warning and alarm signals and pushes them to relevant personnel through various means to realize intelligent fault early warning, help operation and maintenance personnel take measures in advance, and reduce fault losses.

[0014] 4. Convenient data visualization and interaction: Displays monitoring data and analysis results in intuitive graphical and tabular formats, supports user-defined queries and report generation, and facilitates maintenance personnel to keep abreast of equipment status in real time, conduct remote monitoring and management, and improve the convenience and efficiency of maintenance work. Attached Figure Description

[0015] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A schematic diagram of the structure of an online oil chromatography monitoring system based on a centralized control station is provided for an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the workflow of an online oil chromatography monitoring method based on a centralized control station, as provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0017] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0018] The first embodiment of this invention provides an online oil chromatography monitoring system based on a centralized control station. By organically combining the online oil chromatography monitoring equipment with the centralized control station, it achieves centralized collection, transmission, analysis, and early warning of insulating oil chromatography data from power equipment, improving monitoring efficiency and accuracy, reducing operation and maintenance costs, and ensuring the safe and stable operation of the power system. Figure 1 As shown, the system includes multiple online oil chromatography monitoring terminals, a data transmission module, and a central control station. The online oil chromatography monitoring terminals are connected to the central control station through the data transmission module.

[0019] The online oil chromatography monitoring terminal is used to obtain oil chromatography data for on-site testing of electrical equipment, and transmits the detected oil chromatography data to the central control station through the data transmission module.

[0020] In this embodiment, the data transmission module is specifically used for: uploading data to the centralized control master station via a scheduling data network, using standard industrial communication protocols (such as IEC61850, IEC104, etc.) to ensure the stability and reliability of data transmission. During data transmission, encryption technology is used to encrypt the data to prevent data leakage or tampering.

[0021] The centralized control station includes a data receiving and storage module, a data analysis and diagnosis module, an early warning and alarm module, and a data display and interaction module.

[0022] The data receiving and storage module is used to receive oil chromatography data from various online oil chromatography monitoring terminals, store it according to a preset format, and establish an equipment oil chromatography data archive to facilitate historical data query and comparison.

[0023] The data analysis and diagnosis module is used to preprocess the collected oil chromatographic data, use oil chromatographic analysis algorithms to diagnose faults in the preprocessed oil chromatographic data, determine whether there are faults in the electrical equipment, and generate a diagnostic report.

[0024] In this embodiment, the data analysis and diagnosis module is specifically used to: analyze real-time collected data using oil chromatography analysis algorithms, such as the three-ratio method, the David's triangle algorithm, and the four-level threshold alarm analysis method, to determine whether there are potential faults within the equipment. Combining information such as equipment operating conditions and historical data, a comprehensive diagnosis of the fault type and severity is performed, and a diagnostic report is generated. Specifically, as follows: The collected oil chromatography data are analyzed in real time. First, the data is preprocessed, such as removing noise and filling missing values. Then, oil chromatography analysis algorithms are used for fault diagnosis.

[0025] Taking the three-ratio method as an example, calculate the gas ratios: acetylene (C2H2) / ethylene (C2H4), methane (CH4) / hydrogen (H2), and ethylene (C2H4) / ethane (C2H6). Based on the ratio range, determine the fault type, such as overheating or discharge faults. Combine equipment operating parameters (such as temperature and load) and historical data to correct and verify the diagnostic results, improving diagnostic accuracy. For example, when the equipment is operating under high load, the gas content may slightly increase due to normal temperature rise; in this case, a comprehensive judgment based on the actual operating conditions of the equipment is needed to determine whether it is a fault.

[0026] The early warning and alarm module is used to issue early warning or alarm signals in a timely manner when equipment failure is diagnosed, so as to remind maintenance personnel to pay attention to the equipment status and take measures in advance.

[0027] In this embodiment, the early warning and alarm module is specifically used to: set early warning and alarm thresholds based on the changing trends and diagnostic results of oil chromatography data. When the gas content exceeds the early warning threshold, the system issues an early warning signal to remind maintenance personnel to pay attention to changes in equipment status; when the gas content exceeds the alarm threshold or a serious fault is diagnosed, an alarm signal is immediately issued to notify maintenance personnel to take timely measures to handle the fault. Early warning and alarm information can be pushed to relevant personnel in various ways, such as displaying it in a pop-up window on the central control station monitoring interface, ensuring timely information delivery. The alarm signal can be linked with the audible and visual alarm device of the central control station to enhance the alarm effect.

[0028] The data display and interaction module is used to visualize oil chromatography data, diagnostic results, and equipment status information, and supports users to customize historical data queries and generate query reports.

[0029] In this embodiment, the data display and interaction module is specifically used to: present oil chromatography monitoring data and analysis results in an intuitive and easy-to-understand manner; display the changing trends of each gas content over time using graphs (such as pie charts, line charts, bar charts, etc.); and list basic equipment information, real-time data, diagnostic results, etc., in tabular form. It supports user-defined query functions, allowing users to query historical data and analysis reports based on equipment number, time range, gas type, and other conditions. Simultaneously, it provides a report generation function, which can export query results to standard report formats (such as Excel, PDF, etc.) to facilitate data analysis and archiving by maintenance personnel.

[0030] The main interface design of the centralized control master station in this embodiment mainly includes the following: 1. Device Overview: The Device Overview function integrates data from online oil chromatography monitoring devices from a global perspective, centrally displaying equipment alarm and warning information and the progress of defect handling. Through multi-dimensional data aggregation, it not only intuitively presents core indicators such as device online rate and number of alarms / warnings, but also categorizes and statistically analyzes the total number, number of alarms, and number of warnings for devices of different service years.

[0031] 2. Key Focus: When early warning or alarm information is generated, it can be manually added to the key focus information. For example, if a transformer experiences abnormal operating conditions such as single main transformer operation, heavy overload operation, or a continuous increase in the content of dissolved gas components in the oil, it should be immediately added to the key monitoring list after technical review and confirmation by the expert team. After being added, a special tracking mechanism should be activated to ensure timely handling of equipment hazards through closed-loop management.

[0032] 3. Offline / Offline Comparison: When the initial monitoring value of the online oil chromatography data falls within the range of "reaching attention value 1 but not reaching attention value 2" or "reaching attention value 2 but not reaching alarm value," manual external oil sampling for offline testing is required. After the offline test is completed, the system automatically compares and analyzes the test data with the online data to provide professional advice for further handling. After the analysis is completed, the data can be included in the key monitoring list, and the system will also provide a detailed visual display of the comparison data (such as the content of components in the offline oil chromatography, the content of components in the online oil chromatography, the absolute error, the relative error, etc.).

[0033] 4. Geographic Map Monitoring: Organized hierarchically, the system integrates and presents real-time monitoring data, historical data, and trend analysis results from online monitoring devices. Utilizing visualization techniques such as tabular statistics and curve analysis, combined with intelligent algorithms like the David's Triangle and the three-ratio method, it performs multi-dimensional analysis and intuitive display of abnormal information.

[0034] 5. Alarm / Warning Overview: The alarm query function provides convenient and efficient alarm information retrieval and analysis capabilities, supports rapid location of various alarms from online monitoring (such as equipment failure, communication interruption, etc.), and analyzes the situation of oil chromatograph gas concentration exceeding the limit through four-level threshold settings, using different color markings for different levels of intensity to distinguish them clearly.

[0035] 6. On-site Verification: The platform automatically analyzes on-site verification data of oil chromatography based on standardized specifications. It autonomously determines the equipment's qualification status based on preset rules and thresholds, improving work efficiency through fully automated processing and technically avoiding data deviations caused by human intervention (such as data falsification). Simultaneously, the system compares on-site verification data with standard models in real time, ensuring that the data more accurately reflects the actual condition of the oil chromatography system. This helps staff accurately grasp the equipment's operating status and provides reliable data support for equipment condition assessment.

[0036] The core algorithms used in this embodiment, such as the three-ratio method, the David's triangle method, and the four-level threshold alarm analysis method, are described below: 1. Three-ratio method: The three-ratio method is a transformer fault diagnosis method based on oil chromatography analysis. Based on historical statistical patterns of the relationship between fault type and the proportion of characteristic gases in the oil, it constructs a fault diagnosis model involving "feature extraction → classification coding → probability matching." This model analyzes the three-ratio values ​​of five characteristic gases dissolved in transformer oil: hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), and acetylene (C2H2). Specifically, it calculates the ratio of acetylene (C2H2) to ethylene (C2H4). The ratio of methane (C2H4) to acetylene (C2H4) is derived from thermodynamics and practical experience and is typically used when the content of characteristic gases exceeds a certain threshold. Through data induction and logical reasoning, it transforms ambiguous gas data into clear fault diagnosis results.

[0037] The core logic of the algorithm is to "utilize the statistical differences in the 'proportion of characteristic gas components' under different fault types, and classify the gas data of the device to be diagnosed into known fault types through preset 'ratio features' and 'coding rules', so as to achieve statistical inference from 'data to conclusion'".

[0038] First, by analyzing a large number of historical failure cases, we statistically determined the correspondence between failures such as "overheating, partial discharge, and arc discharge" and the three gas ratios "CH4 / H2, C2H4 / C2H6, and C2H2 / C2H4" (forming a "sample distribution"). Then, we substituted the gas ratios of new equipment (new samples) into the pattern to determine which type of failure it is most likely to belong to.

[0039] During operation, the algorithm encodes the gas ratios, assigning different codes to each ratio based on its range. For example, when the acetylene (C2H2) / ethylene (C2H4) ratio is less than 0.1, it is coded as 0; when the ratio is between 0.1 and 1, it is coded as 1; and when the ratio is greater than 1, it is coded as 2. The coding rules for the other two ratios are the same.

[0040] The three-ratio method divides each ratio into three codes: "0, 1, and 2," and then uses "code combinations" to correspond to the fault type. Step 1: Discretization (converting continuous data into categorical data).

[0041] For example, “CH4 / H2” is divided into statistical intervals: <0.1 (code 0), 0.1-1 (code 1), and >1 (code 2). This is because historical samples show that code 0 is likely to correspond to partial discharge, and code 2 is likely to correspond to high temperature overheating. By “discretizing”, the classification logic is simplified (avoiding ambiguity in continuous data).

[0042] Step 2: Establish a coding-fault mapping table (based on sample frequency statistics).

[0043] Thousands of fault cases were collected, and the frequency of fault types corresponding to each "coding combination" (such as 001, 112) was statistically analyzed. For example, in the "001" combination, 85% of the cases were "partial discharge" and 15% were "slight overheating". Therefore, "001" was mainly associated with "partial discharge" (statistical "maximum likelihood estimation", selecting the fault type with the highest probability as the judgment result).

[0044] Based on the coding combination of the three ratios, different fault types inside the transformer can be identified. Table 1 shows some common coding combinations and their corresponding fault types.

[0045] Table 1. Common coding combinations and corresponding fault types in the three-ratio method.

[0046] 2. David's Triangle Method: The David's Triangle is a transformer fault diagnosis method based on dissolved gas analysis in transformer oil. It uses the concentration ratios of characteristic dissolved gases—methane (CH4), ethylene (C2H4), and acetylene (C2H2)—in transformer oil to determine the fault type. The fault type is identified based on the region where these ratios fall. The David's Triangle consistently provides a diagnostic result with a low error rate. Its advantage lies in combining a graphical method (triangle diagram) for qualitative fault type determination. The core principle is to use the concentration ratios of the three characteristic gases to create a triangle diagram, and then determine the fault type based on the size, shape, and position of the inner triangle.

[0047] The David Triangle method, through statistical analysis of a large amount of historical fault data, identifies variables with the strongest correlation between fault type and gas content, eliminating interfering variables unrelated to faults such as nitrogen (N2) and oxygen (O2). It selects the proportions of methane (CH4), acetylene (C2H2), and ethylene (C2H4) as "characteristic indicators" for different faults such as overheating, high-energy discharge, and low-energy discharge to determine the health status of the equipment. During overheating faults, the amount of methane (CH4) produced increases significantly with rising temperature, becoming an important indicator of equipment overheating; During high-energy discharge, acetylene (C2H2) is produced in large quantities, which is a key distinguishing variable for discharge faults; Ethylene (C2H4) is generated during slight overheating or discharge and can be used as a "baseline variable" for balance.

[0048] The core steps of the David's Triangle method used in this embodiment are as follows: 1) Variable selection and feature selection By screening for three characteristic gases—methane (CH4), acetylene (C2H2), and ethylene (C2H4)—and excluding irrelevant variables such as nitrogen (N2) and oxygen (O2), the focus is placed on the core indicators that are most strongly correlated with the fault type, thereby improving the accuracy of subsequent analysis.

[0049] 2) Data standardization The volume fraction of the three gases is calculated to offset the interference of "irrelevant variables" such as the amount of oil, volume, and years of operation of the transformer, so that the gas data are comparable.

[0050] 3) Cluster analysis + discriminant boundary construction By constructing triangular coordinates based on proportions and rationally dividing fault areas, and by using the "clustering patterns" of historical fault samples, the statistical boundaries of different fault types are determined, transforming abstract data into intuitive graphical partitions.

[0051] 4) Statistical classification and probability inference The fault type is determined based on the location of the monitoring data at this coordinate. Based on the distance between the monitoring data and the cluster center of a certain fault area, the probability of it belonging to this fault type is inferred, so as to achieve rapid diagnosis and rapid alarm.

[0052] Methane (CH4), ethylene (C2H4), and acetylene (C2H2) are typically chosen as characteristic gases. The David's triangle diagram is usually divided into multiple regions, each corresponding to a specific fault type. Table 2 shows some common fault types derived from the gas concentration ratios.

[0053] Table 2 Common coding combinations and fault types of the David's Triangle

[0054] 3. Four-level threshold alarm analysis method: The four-level threshold alarm analysis method classifies equipment status information into four levels of threshold alarm standards based on the magnitude and trend of the state variables monitored by the device: reaching attention value 1, reaching attention value 2, reaching alarm value, and reaching shutdown value. This enables accurate judgment of the transformer's operating status and fault severity. Furthermore, the threshold parameters for various devices have been specifically adapted and adjusted to suit the characteristics of equipment at different voltage levels.

[0055] In this embodiment, the analysis of the influence between gases is essentially based on statistical tools to quantify the "correlation, causality, and common driving factors of gas concentration changes," rather than relying solely on empirical judgment.

[0056] (1) Use correlation analysis to quickly identify the correlation trend between gases. Correlation analysis is an important tool for identifying whether gases change synchronously. Correlation analysis quantifies the correlation strength through the Pearson / Spearman correlation coefficient, especially in the initial screening stage of characteristic gas combinations related to faults.

[0057] The analysis process is as follows: Data preprocessing: Extract oil chromatographic data, aggregate three core data points (methane (CH4), acetylene (C2H2), and ethylene (C2H4), and remove outliers with monitoring errors exceeding 5%.

[0058] Correlation calculation: Because the concentration of related gases in oil chromatography increases non-linearly as the fault develops, the Spearman rank correlation coefficient is selected, and the result shows a strong correlation.

[0059] (2) Using covariance analysis, the net influence of interfering gases can be removed. The correlation between gases is often affected by other gases. Covariance analysis can fix the interfering factors and calculate the net correlation strength between gases. If the gas source is suspected to be the key interference, the "gas source" should be included in the analysis as a "categorical covariate".

[0060] Taking H2 as an example, the analysis of covariance is performed as follows: Dependent variable: CH4 concentration; Independent variable: H2 concentration; Covariate: Gas source (divided into two categories: "fault-generated" and "non-fault-residual," determined by oil draining tests: residue at the valve port, and potential fault inside); When covariates were not controlled, the correlation coefficient between H2 and CH4 was 0.18 (weak correlation). After controlling for covariates: In the "Fault Generation" group, the correlation coefficient between H2 and CH4 increased to 0.76 (strong correlation); in the "Non-Fault Residue" group, the correlation coefficient decreased to 0.05 (almost no correlation). Results: In the oil sample inside the main transformer (excluding residual interference), H2 and CH4 were still weakly correlated, confirming that the increase in H2 came from "non-fault residue" (H2 dropped to the qualified level after subsequent oil drainage).

[0061] The threshold values ​​for online monitoring devices of dissolved gases in transformer (reactor) oil in UHV substations (including ±800kV and above converter stations) are divided into four types: warning value 1, warning value 2, alarm value, and shutdown value. These values ​​include three parts: the content of characteristic gases such as acetylene, hydrogen, and total hydrocarbons, the absolute increment, and the relative growth rate. For specific requirements of the threshold values ​​of UHV transformer (reactor) devices, please refer to Table 3.

[0062] Table 3 Thresholds for Online Monitoring Devices of Dissolved Gases in Oil of UHV Transformers (Reactors)

[0063] The specific requirements for the online monitoring thresholds of dissolved gases acetylene, hydrogen, and total hydrocarbons in transformer and reactor oil of 330kV and above (excluding UHV) are shown in Table 4.

[0064] Table 4. Threshold settings for online monitoring of dissolved gases in transformer (reactor) oil of 330kV and above (excluding UHV)

[0065] Table 5 shows the requirements for the amplitude, absolute increment, and relative growth rate of online monitoring of dissolved gases acetylene, hydrogen, and total hydrocarbons in transformer and reactor oil of 220kV and below.

[0066] Table 5. Threshold settings for online monitoring of dissolved gases in transformer (reactor) oil of 220kV and below.

[0067] This invention breaks through the traditional data silo model of decentralized monitoring, achieving centralized acquisition, storage, and analysis of oil chromatographic data from multiple substations through a centralized control master station, enabling intelligent comparison of multiple devices. Simultaneously, this invention innovatively proposes a multi-level diagnostic engine for collaborative decision-making, which can automatically issue early warning and alarm signals based on data analysis results, helping maintenance personnel to take proactive measures and effectively reduce failure losses.

[0068] Corresponding to the online oil chromatography monitoring system based on a centralized control station disclosed in the above embodiments, this invention also discloses an online oil chromatography monitoring method based on a centralized control station, such as... Figure 2 As shown, the method specifically includes: S1 receives oil chromatography data from various online oil chromatography monitoring terminals, stores it according to a preset format, and establishes an equipment oil chromatography data archive to facilitate historical data query and comparison. S2, preprocess the collected oil chromatographic data, use oil chromatographic analysis algorithms to diagnose faults in the preprocessed oil chromatographic data, determine whether there are faults in the electrical equipment, and generate a diagnostic report; S3, when a device malfunction is diagnosed, promptly issues a warning or alarm signal to remind maintenance personnel to pay attention to the device status and take measures in advance; S4 provides a visual representation of oil chromatography data, diagnostic results, and equipment status information, and supports users in customizing historical data queries and generating query reports.

[0069] It should be noted that for a detailed description of the online oil chromatography monitoring system based on a centralized control station provided in the embodiments of the present invention, please refer to the relevant description of the online oil chromatography monitoring method based on a centralized control station provided in the embodiments of the present invention, which will not be repeated here.

[0070] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0072] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An online oil chromatography monitoring system based on a centralized control station, characterized in that, The system includes multiple online oil chromatography monitoring terminals, a data transmission module, and a centralized control station. The online oil chromatography monitoring terminals are connected to the centralized control station through the data transmission module. The oil chromatography online monitoring terminal is used to perform on-site testing of electrical equipment to obtain oil chromatography data, and transmits the detected oil chromatography data to the central control station through the data transmission module; The data transmission module is specifically used to: upload oil chromatography data to the central control station through a scheduling data network, using a standard industrial communication protocol; during data transmission, encryption technology is used to encrypt the data to prevent data leakage or tampering; The centralized control station includes a data receiving and storage module, a data analysis and diagnosis module, an early warning and alarm module, and a data display and interaction module; The data receiving and storage module is used to receive oil chromatography data from each online oil chromatography monitoring terminal, store it according to a preset format, and establish an equipment oil chromatography data archive to facilitate historical data query and comparison. The data analysis and diagnosis module is used to preprocess the collected oil chromatographic data, analyze the preprocessed oil chromatographic data using oil chromatographic analysis algorithms, diagnose the fault type, determine whether there is a fault in the electrical equipment, and generate a diagnostic report. Simultaneously, it combines equipment operating parameters and historical data to correct and verify the diagnostic results. The oil chromatographic analysis algorithms include the three-ratio method, the David's triangle algorithm, and the four-level threshold alarm analysis method. The four-level threshold alarm analysis method divides the status information into four levels of threshold alarm standards based on the magnitude and trend of the monitored state variables: reaching attention value 1, reaching attention value 2, reaching alarm value, and reaching shutdown value. This enables accurate judgment of the transformer's operating status and fault severity. The early warning and alarm module is used to set early warning and alarm thresholds based on the changing trends and diagnostic results of oil chromatography data; if the detected gas content exceeds the preset early warning threshold, an early warning signal is issued to remind maintenance personnel to pay attention to changes in equipment status; if the detected gas content exceeds the preset alarm threshold or a serious fault is diagnosed, an alarm signal is immediately issued to notify maintenance personnel to take timely measures to deal with the fault. The data display and interaction module is used to visualize oil chromatography data, diagnostic results, and equipment status information. This includes displaying the changing trends of each gas content over time using various graphs such as pie charts, line charts, and bar charts, and displaying various data information including oil chromatography data, analysis results, and equipment status information in tabular form. It also supports users to customize and query historical data and generate query reports.

2. The online oil chromatography monitoring system based on a centralized control station according to claim 1, characterized in that, The data analysis and diagnosis module is specifically used for: The collected oil chromatographic data were preprocessed, including noise removal and missing value filling.

3. The online oil chromatography monitoring system based on a centralized control station according to claim 1, characterized in that, The early warning and alarm module is specifically used for: Warning and alarm information is pushed to relevant personnel through various means, including: displaying it in a pop-up window on the monitoring interface of the central control station to ensure timely delivery of information; and linking alarm signals with the audible and visual alarm devices of the central control station to enhance the alarm effect.

4. The online oil chromatography monitoring system based on a centralized control station according to claim 1, characterized in that, The data display and interaction module is specifically used for: Users can query historical data and analysis reports by setting various query conditions, including device number, time range, and gas type; and can export the query results into a standard report format, which is convenient for maintenance personnel to perform data analysis and archiving.

5. A monitoring method for an online oil chromatography monitoring system based on a centralized control station according to any one of claims 1-4, characterized in that, The method includes: Oil chromatography data is obtained by conducting on-site testing of electrical equipment through an online oil chromatography monitoring terminal. The detected oil chromatography data is then uploaded to the centralized control master station through a dispatch data network. The communication protocol adopts a standard industrial communication protocol. During data transmission, encryption technology is used to encrypt the data to prevent data leakage or tampering. The centralized control station includes a data receiving and storage module, a data analysis and diagnosis module, an early warning and alarm module, and a data display and interaction module; Based on the data receiving and storage module, oil chromatography data from various online oil chromatography monitoring terminals are received, stored in a preset format, and an equipment oil chromatography data archive is established to facilitate historical data query and comparison. Based on the data analysis and diagnosis module, the collected oil chromatographic data is preprocessed, and the preprocessed oil chromatographic data is analyzed and the fault type is diagnosed using oil chromatographic analysis algorithms to determine whether there is a fault in the electrical equipment and generate a diagnostic report. At the same time, the diagnostic results are corrected and verified by combining equipment operating parameters and historical data. The oil chromatographic analysis algorithms include the three ratio method, the David's triangle algorithm, and the four-level threshold alarm analysis method. Based on the early warning and alarm module, early warning and alarm thresholds are set according to the changing trends and diagnostic results of oil chromatography data. If the detected gas content exceeds the preset early warning threshold, an early warning signal is issued to remind maintenance personnel to pay attention to changes in equipment status. If the detected gas content exceeds the preset alarm threshold or a serious fault is diagnosed, an alarm signal is issued immediately to notify maintenance personnel to take timely measures to deal with the fault. Based on the data display and interaction module, oil chromatography data, diagnostic results, and equipment status information are visualized. This includes displaying the changing trends of each gas content over time using various graphs such as pie charts, line charts, and bar charts, and displaying various data information including oil chromatography data, analysis results, and equipment status information in tabular form. It also supports users to customize and query historical data and generate query reports.

Images