Main transformer partial discharge on-line monitoring system

Abstract

The invention discloses an online monitoring system for partial discharge of a main transformer. The online monitoring system specifically comprises a series of modules including sensors, detection equipment, a data acquisition module, a data processing module, a data transmission module and an online monitoring platform, wherein the sensors are reasonably arranged outside the transformer by adopting multi-band combination of ultrahigh frequency, high-frequency current, ultrasonic waves and the like; the detection equipment realizes high efficiency and flexibility through a C language underlying driver and an assembly language data interface, and has various anti-interference and fault early warning technologies; the data acquisition module is used for conditioning an original signal and improving the data quality by adopting various filters; reliable and real-time data transmission is ensured by adopting a TCP / IP protocol; the data processing module comprises a time domain and frequency domain conversion sub-module and other sub-modules for auxiliary diagnosis; the online monitoring platform is based on Web, and is rich in function and extensible; the system monitors a plurality of main transformers in a transformer substation, collects signals through sensors, processes the signals through the modules and transmits the signals to a platform, meanwhile, the system is subjected to regular inspection, software and hardware upgrading optimization and the like to guarantee stable operation of the system, and support is provided for safety of an electric power system.

Description

Technical Field

[0001] The invention relates to the technical field of power equipment monitoring, in particular to an online monitoring system for partial discharge of a main transformer. Background Art

[0002] With the rapid development of the electric power industry, large power transformers (referred to as "main transformers") are the core equipment in the power grid, and their safe and stable operation is directly related to the overall reliability of the power system. Partial discharge (PD) is the main sign and precursor of transformer insulation failure. Online monitoring of partial discharge of transformers can timely detect and prevent potential faults and ensure the stable operation of the power system; early detection and effective monitoring of partial discharge are of great significance for preventing transformer failures and extending the life of equipment. Therefore, the present invention is a set of efficient and accurate online monitoring system for partial discharge of main transformers, which realizes real-time and remote monitoring of the insulation status of transformers and provides strong guarantee for the safe and stable operation of the power system. As far as the main grid equipment is concerned, due to the influence of manufacturing materials, manufacturing processes, installation processes, power environment, operating environment, etc., partial discharge faults (defects) run through the entire life cycle of the equipment, and they affect and interfere with the normal operation of the power grid equipment. Among them, the partial discharge phenomenon caused by resonance is particularly prominent. (The source of resonance may come from users or from high-energy-consuming factories such as steel mills).

[0003] According to the experience of experts from the provincial technical training center, main grid equipment is prone to partial discharge failures when it is recently put into operation or is about to be withdrawn from operation. Accumulated partial discharges can easily cause accidents, from partial discharge generation to deflagration, which may occur at any time and may last for several years.

[0004] Due to the delayed transformation of the main grid and the large number of aging equipment in the main grid, local discharge failures and explosion accidents have frequently occurred in the system in recent years, causing unnecessary losses and large-scale power outages (large-scale power outages caused by local discharge and explosion incidents are divided into four categories according to their types: main transformers, enclosed metal switches (including GIS combination electrical appliances), PTs (voltage transformers), and cables).

[0005] In November 2019, State Grid Corporation of China issued an instruction, and a provincial company actively implemented the "Special Action to Prevent Total Power Outages and Investigate and Manage Hidden Dangers" (i.e., a special action to prevent large-scale power outages). This action covered five professions in total, sorted out more than 7,000 problems and rectified them one by one.

[0006] Based on the relevant requirements of the State Grid and provincial and municipal companies, since 2020, live detection projects have been carried out for three types of equipment in the subordinate substations, including main transformers (high frequency, temperature measurement, excluding partial discharge), enclosed metal switch cabinets, and PTs (in enclosed metal switches). Several problems were found during the detection process and reported and handled in a timely manner, effectively reducing losses.

[0007] Partial discharge occurs and develops during the operation and maintenance of equipment. Traditional preventive tests (power outage test methods) cannot effectively obtain relevant information. In particular, after the explosion of a main transformer in a certain city in 2019, the State Grid Corporation has attached great importance to the hidden dangers of partial discharge in main transformers and other main network equipment. In recent years, the State Grid Corporation has vigorously promoted the live detection scheme, which has achieved remarkable results in detecting partial discharge, but explosions still occur occasionally, posing a serious threat to the personal safety of detection personnel.

[0008] With the continuous improvement of electronic technology, information technology, and computer technology, online monitoring technology has made great progress. Compared with 40 years ago, the maturity, stability, and scalability of online monitoring have made a qualitative leap, and the computing power algorithm has also been well developed and verified. This will be conducive to changing the working mode, changing the passive status quo, and improving the lean management level of equipment operation and maintenance; as an important means of production, it provides a basis for equipment conditions and expert diagnosis; more importantly, it can be used as the main preventive measure for deflagration incidents to set up a line of defense in advance. When partial discharge and deflagration trends are detected in the main transformer, different states are evaluated according to the expert diagnosis results: pay attention to observation, continuous operation, immediate power outage inspection, power outage maintenance, and power outage replacement.

[0009] At present, traditional monitoring technology has obvious shortcomings in many aspects: in terms of sensor technology, traditional monitoring systems mostly use single-frequency sensors. For example, if only high-frequency sensors are used, it is impossible to use a combination of multi-frequency sensors such as ultra-high frequency, high-frequency current and ultrasonic waves to comprehensively monitor local discharge signals from multiple dimensions. In addition, the layout of traditional sensors on the equipment is unreasonable, and they cannot be optimized according to the structural characteristics of equipment such as transformers, resulting in monitoring blind spots and inability to fully cover areas where local discharges may occur. In terms of data acquisition and processing technology, the data acquisition unit of the traditional system lacks advanced signal conditioning technologies such as low-noise preamplifiers, nonlinear mathematical morphology filters and wavelet analysis, resulting in low quality of collected data, low signal-to-noise ratio and relatively simple data processing algorithms. It is impossible to use a combination of multiple algorithms to comprehensively analyze the characteristics of local discharge signals, and thus it is impossible to accurately extract key information for fault diagnosis. For communication technology, traditional monitoring systems may use relatively Fixed and inflexible communication protocols will lead to problems such as low system operation efficiency and unstable data transmission. On the monitoring software platform, the traditional platform functions are relatively single and unfriendly. It cannot provide multiple functional modules such as system structure diagram configuration tools, communication protocol processing modules, and data bus modules. The user interface is not conducive to operation and maintenance personnel. Functions such as real-time display, historical query, and report generation are not perfect and have poor cross-platform and scalability. They can only run under specific operating systems and hardware environments, and are difficult to adapt to changes in power system monitoring needs. In terms of fault warning and diagnosis technology, traditional technologies have weak anti-interference capabilities and do not use a combination of multiple anti-interference methods to effectively eliminate interference factors, resulting in frequent false alarms and missed alarms, affecting the accurate judgment of the insulation status of the equipment and lacking fault warning models and diagnostic rules established using machine learning or deep learning algorithms. As a result, they have limited ability to conduct in-depth analysis of partial discharge data and accurately diagnose equipment failures. Summary of the invention

[0010] The technical problem to be solved by the present invention is to provide an online monitoring system for partial discharge of a main transformer. The data system is based on a built-in multi-data module to realize real-time monitoring of the discharge situation of the main transformer, to provide an early warning of the discharge situation of the main transformer, and to realize rapid transmission of the discharge situation information of the main transformer and accurate positioning of the discharge position.

[0011] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0012] A main transformer partial discharge online monitoring system has the following data modules: a sensor module, a detection equipment module, a data acquisition module, a data processing module, a data transmission module and an online monitoring platform module, so as to realize comprehensive and accurate monitoring of transformer partial discharge as well as fault warning and diagnosis.

[0013] As a preferred technical solution of the present invention, the sensor module has the following submodules: A-1 ultra-high frequency sensor submodule, A-2 high frequency current sensor submodule and A-3 ultrasonic sensor submodule combination, according to the structural characteristics of the transformer combined with the structure and electric field distribution analysis of the transformer oil tank, winding, core, bushing and other parts, a plurality of monitoring points are reasonably arranged outside it to ensure that the area where partial discharge may occur is fully covered; sensors of different frequency bands are combined with the characteristic data of the monitored signals of different frequency bands to effectively distinguish interference and discharge signals, wherein:

[0014] The A-1 UHF sensor submodule is sensitive to tiny discharge events and is used to detect high-frequency electromagnetic wave signals generated by partial discharges. It can capture weak signals at the initial stage of discharge.

[0015] The A-2 high-frequency current sensor submodule is used to capture the current pulses generated by partial discharge inside the transformer insulation;

[0016] The A-3 ultrasonic sensor submodule is used to detect ultrasonic signals related to the intensity and location of the discharge event during the discharge process;

[0017] The sensor is installed at a position that has good contact with the surface of the detection object and will not be affected by adverse environments including but not limited to mechanical vibration, high temperature, and humidity. The sensor is connected to the data acquisition unit via a coaxial cable, and the coaxial cable is correctly connected and the shielding layer is well grounded.

[0018] As a preferred technical solution of the present invention, the detection equipment module has the following sub-modules: a bottom-level driver sub-module: the bottom-level driver adopts C language to develop efficient and portable bottom-level drivers; a data interface sub-module: the assembly language is adopted to realize performance optimization and fine control on a specific hardware platform to facilitate the connection of sensors and other system components.

[0019] Furthermore, the efficiency and portability of the underlying program driver submodule are specifically as follows: based on the hardware device information, the registers of the hardware devices are regulated according to the required parameter values ​​to realize the initialization and basic control of the hardware devices, and on this basis, the system clock is configured to control the running rhythm of the device; a set of programs is developed based on the C language, and mechanisms that match hardware devices at different levels are constructed, which are specifically implemented as follows: key nodes in the program are defined, so that the regulation of different hardware devices is transformed from modifying the entire program structure to modifying the content of key nodes. Therefore, without changing the interface with the upper-level program and the overall program architecture, the regulation of different hardware devices is completed, and the portability of the entire program is realized.

[0020] As a preferred technical solution of the present invention, the detection equipment module also includes: a combination of multiple anti-interference methods to realize fault warning technology, including but not limited to hardware anti-interference, background noise filtering, polarity discrimination, narrow-band interference elimination, passband filtering technology, perfect grounding point, balanced loop method, bandpass filtering and frequency selection filtering; establishing a spectrum library, using typical discharge spectra and typical interference spectra to compare and analyze in real time to identify local discharge signals, based on sensor modules, and high-frequency, ultrasonic, and ultra-high frequency monitoring principles to simultaneously monitor and confirm internal discharge signals, and later conduct in-depth analysis of the processed local discharge data, combined with a special model structure, this model structure is specifically: having different data processing layers, selecting different matrices and moving steps for each processing layer, and establishing a compression layer at the same time, simplifying the data by setting parameters and selecting types, and establishing a fault warning model and diagnostic rules based on this.

[0021] As a preferred technical solution of the present invention, the data acquisition module specifically receives the original partial discharge signal from the sensor and performs preliminary signal conditioning, including filtering, amplification and analog-to-digital conversion, wherein a low-noise preamplifier is used to preliminarily amplify the weak signal, and the preliminarily amplified signal is then passed through a digital filter to suppress interference and purify the effective signal. The digital filter effectively selects to pass or suppress signals with certain frequency components according to the set cutoff frequency parameters, and uses nonlinear mathematical morphology filters and wavelet analysis technology to process nonlinear interference and improve time-frequency resolution. Different types of interference are processed by reasonably selecting structural elements, and wavelet analysis technology performs multi-resolution analysis on the signal by selecting different wavelet functions and scale parameters.

[0022] Further, the digital filter built into the B-1 data acquisition module is specifically: able to effectively select signals that pass or suppress certain frequency components according to the set parameters such as the cutoff frequency, including but not limited to: removing high-frequency noise by setting a low-pass filter to ensure the quality of the collected data; based on the digital signal processing theory, a finite impulse response (FIR) filter or an infinite impulse response (IIR) filter can be used. The FIR filter has a linear phase characteristic and can ensure that the signal will not produce phase distortion during the filtering process. The IIR filter can achieve higher filtering performance with fewer coefficients, but there may be phase distortion problems;

[0023] The B-2 data acquisition module has a built-in nonlinear mathematical morphology filter. Specifically, it is based on the basic operations of mathematical morphology, using corrosion, expansion, opening and closing operations, and processing different types of interference by reasonably selecting structural elements. For pulse-shaped interference, smaller circular or square structural elements can be selected for corrosion and expansion operations. It can process nonlinear interference in signals, change the shape and structure of signals, improve signal quality, and work synergistically with other filtering technologies to improve the adaptability of the data acquisition module to complex interference environments.

[0024] As a preferred technical solution of the present invention, the data transmission module is specifically as follows: the data transmission at the process layer adopts the TCP / IP protocol for remote communication to ensure the reliability and real-time performance of data transmission. The TCP / IP protocol supports the transmission of multiple data formats to meet the needs of remote monitoring and data management. The TCP protocol ensures the integrity and sequence of data packets by establishing a reliable connection, and the IP protocol is responsible for the routing and addressing of data packets.

[0025] The data processing module further includes the following submodules:

[0026] C-1 Time-domain to frequency-domain conversion submodule, develop a conversion method, regard the time-domain signal as a combination of many simple fluctuations of different frequencies, develop a calculation method, analyze the calculation results to know which frequency fluctuations are contained in the original complex signal and the intensity of each frequency fluctuation, so as to obtain the representation of the signal in the frequency domain. The calculation method is to decompose a type of complex data into many small data groups, and then analyze the characteristics of each small data group and then analyze the frequency distribution characteristics of the discharge signal. After the analysis, the small data groups are reorganized into the original time-domain signal data, specifically:

[0027] C-1-1 Select core data points according to the data situation, define the data range of the group with the core data points as the center, and determine the importance of the core data points according to the amount of data in the group. Cancel the data grouping of unimportant core data points and reclassify the data in the group into other data groups. Repeat this process until all data are grouped and processed;

[0028] C-1-2 processes the data in each group, and performs data transformation again within the group according to the maximum value, minimum value and mean value within the group, specifically: fit the maximum value and minimum value within the group to obtain the mean value within the group, and then use the mean value within the group to compare with each data within the group to obtain new data, and judge the validity of the new data according to the set parameters, if it is valid, extract it, otherwise use the invalid data as new data and return it to the data group, repeat the above process until there is no new data to be extracted in the extracted data group, and use the extracted data as a new data group to replace the original data group, use the mathematical characteristics of the data including but not limited to periodicity and symmetry to divide the data in the new data group according to different data positions, and perform weighted summation to obtain the frequency domain signal;

[0029] C-2 spectral density function submodule, analyzes the converted frequency domain signal, reveals the frequency distribution, amplitude and phase information of the signal, so as to identify different types of discharge phenomena, including but not limited to the pulse characteristics of partial discharge and the frequency characteristics of the discharge source. By analyzing these frequency domain characteristics, we can determine which type of partial discharge is occurring, and then accurately convert the frequency domain signal into the time domain signal according to the inverse transformation described in C-1. The conversion process should ensure that the data is not distorted and accurately reflect the waveform characteristics of the original signal.

[0030] The C-3 data analysis technology processing submodule includes multi-resolution analysis, identification of discharge phenomena, detection and discrimination of abnormal frequency components in discharge signals, identification of potential insulation defects and equipment failures, and the auxiliary diagnosis of specific fault types and locations inside the transformer based on changes in local discharge characteristic parameters in combination with fault warning models and diagnostic rules.

[0031] As a preferred technical solution of the present invention, the online monitoring platform module is designed to cross the Windows operating system platform and includes a system structure diagram configuration tool submodule, a communication protocol processing submodule, a data bus submodule, a system function redundancy control submodule, and a statistical calculation submodule, specifically:

[0032] A web-based online monitoring software platform that supports multi-user access and combines remote access, real-time display, historical query, and report generation functions;

[0033] The main platform module is designed to cross the Windows operating system platform, based on 64-bit Windows system programming, and runs in a multi-process and multi-threaded manner. It has the characteristics of being able to cross multiple software and hardware platforms and having good scalability;

[0034] Possess dynamically scalable communication protocol management and security integration technology, including but not limited to the use of unified graphical configuration tools and databases to achieve configuration integration, and implement fixed value calling and other functions on the monitoring host to improve system management efficiency and data consistency;

[0035] The system structure diagram configuration tool can configure the submodules according to the layout of the actual monitoring equipment to meet the data transmission requirements in different application scenarios;

[0036] Its core module is programmed using C / C++ standard libraries, and its interface is developed based on QT and Java technologies;

[0037] Flexible networking mode, select the master / slave server mode, single / dual network mode as needed, and the number of network nodes can be flexibly increased or decreased as needed to adapt to different network environments and equipment layouts;

[0038] It has powerful script calculation function, which can realize various calculation signal expressions and complex operation control rule logic configuration through calculation, and can calculate analog quantity, state quantity and electrical quantity;

[0039] This system has complete simulation debugging tools, including but not limited to special communication debugging tools, simulation data generation tools, and built-in diagnostic maintenance modules to ensure the reliable operation of the monitoring system. It also includes complete communication message monitoring tools to facilitate the troubleshooting and location of communication problems during the debugging process;

[0040] The communication protocol processing submodule is used to ensure the accuracy of data transmission, the data bus submodule is used to achieve efficient data transmission and sharing, the system function redundancy control submodule is used to improve the reliability of the system, and the statistical calculation submodule is used to perform statistical analysis on the collected data.

[0041] The beneficial effects of adopting the above technical solution are: the present invention constructs a main transformer partial discharge online monitoring system integrating data collection, processing, analysis and early warning; realizes automatic detection, positioning and quantitative analysis of partial discharge signals; provides an intuitive monitoring interface and report to assist operation and maintenance personnel to quickly judge the insulation status of the transformer; discovers potential faults in advance through fault warning and diagnosis algorithms, and reduces the transformer failure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 : Operation flow chart of the main transformer partial discharge online monitoring system;

[0043] Figure 2 : Schematic diagram of the integrated online monitoring platform architecture;

[0044] Figure 3 : Power transformer experimental report diagram;

[0045] Figure 4 :System platform software installation program diagram (1);

[0046] Figure 5 : System platform software installation program diagram (2);

[0047] Figure 6 :System platform software installation program diagram (3);

[0048] Figure 7 : System platform software installation program diagram (4);

[0049] Figure 8 : Real-time partial discharge data curve graph.

[0050] Fig. 9 : Real-time partial discharge PRPS / PRPD three-dimensional map;

[0051] Fig.10 : Real-time partial discharge data graph;

[0052] Fig.11 : Manager interface diagram;

[0053] Fig.12 : Historical monitoring data chart;

[0054] Fig.13 : Query and export graphs of partial discharge historical data;

[0055] Fig.14 : Historical monitoring data chart;

[0056] Fig.15 : Frequency diagram of the relationship between measurement point events. DETAILED DESCRIPTION

[0057] The present invention is described in detail in the following examples. All the equipment used in the present invention are conventional commercial products and can be directly purchased from the market.

[0058] In the description of the following embodiments, specific details such as specific system structures, technologies, etc. are provided for illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0059] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, components.

[0060] Example 1: System Overview

[0061] In a large substation, a main transformer partial discharge online monitoring system is installed to monitor the partial discharge of multiple large power transformers (the substation has 5 main transformers, numbered TB1-TB5) in real time to ensure the safe and stable operation of the power system.

[0062] See attached Figure 1 The system consists of sensor module, detection equipment module, data acquisition module, data processing module, data transmission module and online monitoring platform module.

[0063] Example 2: Sensor Module

[0064] 1. Sensor selection and layout

[0065] See attached Figure 3,According to the structural characteristics of the transformer, multiple monitoring points are reasonably arranged on the outside of its oil tank, winding, core, bushing, etc., and a combination of A-1 ultra-high frequency sensor submodule, A-2 high frequency current sensor submodule and A-3 ultrasonic sensor submodule is adopted;

[0066] For the TB1 main transformer, a UHF sensor was arranged every 2 meters on the surface of the oil tank, with a total of 10 sensors arranged; 6 high-frequency current sensors were arranged at key locations near the windings; and 2 ultrasonic sensors were evenly arranged on each of the four sides of the transformer casing;

[0067] The other main transformers (TB2-TB5) were arranged according to similar rules, but the number and location of sensors were slightly adjusted according to the specific size and structural characteristics of the transformers;

[0068] The ultra-high frequency sensor submodule is sensitive to tiny discharge events and is used to detect high-frequency electromagnetic wave signals generated by local discharge. It can capture weak signals in the early stage of discharge, including its detection frequency range of 300MHz-3GHz, which can detect signals with a minimum discharge amount of 1pC; the high-frequency current sensor submodule is used to capture the current pulses generated by local discharge inside the transformer insulation, and its measurement range is 0.1mA-10A; the ultrasonic sensor submodule is used to detect ultrasonic signals related to the intensity and position of the discharge event during the discharge process, and its detection frequency range is 20kHz-200kHz.

[0069] 2. Sensor installation

[0070] The sensor is installed in a position that has good contact with the surface of the detection object. Through special fixing devices and sealing measures, it is ensured that it will not be affected by adverse environmental influences such as mechanical vibration, high temperature (the ambient temperature of the substation can reach up to 40°C in summer, and the sensor can work normally in a high temperature environment of 55°C), and humidity (the ambient humidity can reach up to 90%, and the sensor protection level is IP67, which can effectively resist humid environments);

[0071] The sensor is connected to the data acquisition unit via a coaxial cable, and the coaxial cable is correctly connected and the shielding layer is well grounded; the length of the coaxial cable is determined according to the distance from the sensor to the data acquisition unit, for example, the longest coaxial cable is 30 meters and the shortest is 5 meters.

[0072] Example 3: Detection Equipment Module

[0073] 1. Low-level driver submodule

[0074] An efficient and portable low-level driver was developed in C language. Based on the hardware device information, the registers of the hardware devices were adjusted according to the required parameter values: the sampling frequency of the UHF sensor was set to 1000Hz and the gain was set to 20dB; the current resolution of the high-frequency current sensor was set to 0.01mA; the trigger threshold of the ultrasonic sensor was set to 1mV; the hardware devices were initialized and basically controlled, and on this basis, the system clock was configured to control the device operation rhythm: the system clock frequency was set to 10MHz to meet the real-time requirements of data acquisition and processing.

[0075] 2. Data interface submodule

[0076] Assembly language is used to achieve performance optimization and fine control on specific hardware platforms to facilitate the connection of sensors and other system components: accurate data transmission and efficient collaboration of equipment are ensured through fine configuration of hardware interfaces; the baud rate of data transmission is set to 115200bps, the data bit is 8 bits, the stop bit is 1 bit, and the check bit is none.

[0077] 3. Anti-interference and fault warning technology

[0078] Fault warning technology with a combination of multiple anti-interference methods:

[0079] In terms of hardware, it adopts a fully sealed metal shell design with a thickness of 2mm. It has strong electromagnetic compatibility and anti-interference ability, and can effectively shield external electromagnetic interference, such as the power frequency electromagnetic field interference generated by nearby high-voltage equipment (intensity can reach 100V / m);

[0080] Background noise filtering function: By setting the filtering parameters, the interference propagated through space can be effectively eliminated. For example, for background noise with a frequency lower than 50Hz and higher than 1kHz, its amplitude is reduced to less than 10% of the original amplitude after filtering;

[0081] The polarity discrimination function can accurately eliminate interference intruding through the power line: when the interference signal from the power line is detected, it is distinguished from the normal discharge signal through the polarity discrimination algorithm and suppressed;

[0082] The narrow-band interference elimination technology can effectively eliminate narrow-band interference: for narrow-band interference signals between 100kHz and 200kHz, the amplitude is reduced to less than 5% of the original amplitude after interference elimination;

[0083] Use passband filtering technology and perfect grounding points to prevent interference caused by ground current: the cutoff frequency of the passband filter is set to 50Hz-1kHz, and the grounding resistance is less than 0.1Ω, which effectively reduces ground current interference.

[0084] Polarity discrimination method is used to distinguish the discharge of the detected equipment and the interference of line transmission signals, with an accuracy rate of more than 95%;

[0085] The balanced loop method is used to suppress radiation interference and power supply interference, so that the radiation interference and power supply interference are reduced to an acceptable level: the radiation interference intensity is reduced to less than 20% of the original intensity;

[0086] Bandpass filtering and frequency-selective filtering are used to identify and eliminate narrow-band signals such as radio waves and oscillating waves similar to radio waves: for radio wave interference signals with frequencies between 800MHz and 900MHz, their amplitude is reduced to less than 3% of the original amplitude after filtering;

[0087] A spectrum library is established, which contains 100 typical discharge spectra and 50 typical interference spectra. The typical discharge spectra and typical interference spectra are used for real-time comparative analysis to identify partial discharge signals, with an accuracy rate of over 90%. Based on the sensor module, as well as the high-frequency, ultrasonic and ultra-high frequency monitoring principles, the internal discharge signals are simultaneously monitored and confirmed by mutual confirmation. In the later stage, when the processed partial discharge data is deeply analyzed, a special model structure is combined. The model has 3 different data processing layers. For each processing layer, different square matrices are selected (such as 3×3 square matrices for the first layer, 5×5 square matrices for the second layer, and 7×7 square matrices for the third layer) and moving steps (1, 2, and 3 respectively). At the same time, a compression layer is established. The data is simplified by setting parameters (such as a compression ratio of 0.5) and selecting types (using wavelet compression algorithm). On this basis, a fault warning model and diagnosis rules are established, and the accuracy rate of fault warning reaches over 85%.

[0088] Example 4: Data Acquisition Module

[0089] 1. Signal conditioning process

[0090] Receive the original partial discharge signal from the sensor and use a low-noise preamplifier to preliminarily amplify the weak signal: for the weak high-frequency signal collected by the UHF sensor, the preamplifier has a gain of 30dB, which amplifies it to a suitable level range; for the weak current signal collected by the high-frequency current sensor, the current amplification factor of the preamplifier is 100 times; for the weak ultrasonic signal collected by the ultrasonic sensor, the voltage amplification factor of the preamplifier is 50 times;

[0091] The initially amplified signal is then passed through a digital filter to suppress interference and purify the effective signal. The digital filter effectively selects to pass or suppress signals with certain frequency components based on the set cutoff frequency and other parameters. Nonlinear mathematical morphology filters and wavelet analysis techniques are used to process nonlinear interference and improve time-frequency resolution.

[0092] 2. Specific implementation

[0093] The built-in digital filter of the data acquisition module can effectively select signals that pass or suppress certain frequency components according to the set parameters such as the cutoff frequency: high-frequency noise is removed by setting a low-pass filter with a cutoff frequency of 1kHz to ensure the quality of the collected data; based on the digital signal processing theory, a finite impulse response (FIR) filter is used, which has a linear phase characteristic and can ensure that the signal will not produce phase distortion during the filtering process, or an IIR filter is used, which can achieve higher filtering performance with fewer coefficients, but there may be phase distortion problems: for a discharge signal containing multiple frequency components, the FIR filter can accurately retain the useful frequency components, while the IIR filter may cause a slight phase shift in some cases, but the filtering effect generally meets the requirements.

[0094] The built-in nonlinear mathematical morphology filter of the data acquisition module is based on the basic operations of mathematical morphology, and adopts operations such as corrosion, expansion, opening and closing operations. It handles different types of interference by reasonably selecting structural elements: For pulse-shaped interference, a circular structural element with a radius of 2 is selected for corrosion and expansion operations, which can handle nonlinear interference in the signal, change the shape and structure of the signal, improve the signal quality, and work together with other filtering technologies to improve the adaptability of the data acquisition module to complex interference environments, so that the signal-to-noise ratio of the signal is increased to more than 20dB.

[0095] Example 5: Data transmission module

[0096] The data transmission at the process layer uses the TCP / IP protocol for remote communication to ensure the reliability and real-time performance of data transmission. The TCP / IP protocol supports the transmission of multiple data formats to meet the needs of remote monitoring and data management. The TCP protocol ensures the integrity and order of data packets by establishing a reliable connection; the IP protocol is responsible for the routing and addressing of data packets.

[0097] When the partial discharge data of the TB1 main transformer is monitored, the data is accurately transmitted to the remote monitoring center through the TCP protocol. The timeout of the TCP connection is set to 5 seconds. If no confirmation information is received within 5 seconds, the data packet is resent. The IP protocol routes and addresses the data packet according to the IP address of the monitoring center (for example, 192.168.100.100) to ensure that the data can reach the target server along the optimal path. In order to ensure the efficiency of data transmission, the network transmission rate is set to 10Mbps, which can meet the requirements of real-time data transmission.

[0098] Example 6: Data processing module

[0099] 1. Time domain and frequency domain conversion submodule

[0100] See attached Figure 8 , invented a conversion method, which regards the time domain signal as a combination of many simple fluctuations of different frequencies. Through a specific calculation method, the calculation results are analyzed to know which frequency fluctuations are contained in the original complex signal and the intensity of each frequency fluctuation, so as to obtain the representation of the signal in the frequency domain: for the collected partial discharge signal of the TB1 main transformer, it is decomposed into multiple small data groups according to the rules, and the characteristics of the 100 sampling points contained in each small data group are analyzed, and then the frequency distribution characteristics of the discharge signal are analyzed; the analysis results show that the discharge signal is mainly concentrated in the two frequency bands of 100Hz-1kHz and 300MHz-1GHz;

[0101] After analysis, the small data groups are reorganized into the original time domain signal data. The error between the reorganized signal and the original signal is within 1%, ensuring the accuracy of the signal.

[0102] 2. Spectral density function submodule

[0103] See attached Fig. 9 , analyze the converted frequency domain signal, reveal the signal's frequency distribution, amplitude and phase information to identify different types of discharge phenomena, including but not limited to the pulse characteristics of partial discharge and the frequency characteristics of the discharge source: for the analysis of the discharge signal of the TB1 main transformer, it is found in the frequency domain that its pulse characteristics show an obvious peak in the 100Hz-200Hz frequency band, and the frequency characteristics of the discharge source show that the main frequency components are between 300MHz-500MHz;

[0104] By analyzing these frequency domain features, it is possible to determine what type of partial discharge is occurring, and then accurately convert the frequency domain signal into a time domain signal based on the inverse transform of the time domain to frequency domain conversion submodule. The conversion process ensures that the data is not distorted and accurately reflects the waveform characteristics of the original signal: the time domain signal obtained by inverse transform has a similarity of more than 95% with the original acquired time domain signal.

[0105] 3. Data analysis technology processing submodule

[0106] See attached Fig.10 , including multi-resolution analysis, identification of discharge phenomena, detection and identification of abnormal frequency components in discharge signals, identification of potential insulation defects and equipment failures, etc.: Through multi-resolution analysis, it is possible to identify the characteristic changes of discharge signals at different frequency resolutions; for the TB1 main transformer, when abnormal frequency components (amplitude 50% higher than normal) are detected in the 500MHz-600MHz frequency band, the fault warning model and diagnostic rules are combined to determine that there may be insulation defects, and further analysis determines that the location of the defect may be in the upper area of ​​the winding;

[0107] Combining the fault warning model and diagnostic rules, the specific fault type and location inside the transformer are diagnosed based on the changes in the local discharge characteristic parameters, and the accuracy of fault diagnosis reaches more than 80%.

[0108] 7. Online Monitoring Platform Module

[0109] 1. System design and functions

[0110] See attached Figure 4 -Attached Figure 7 This is a Web-based online monitoring software platform that supports multi-user access. It combines remote access, real-time display, historical query, report generation and other functions. The main platform module is based on the 64-bit Windows system and is programmed across the Windows operating system platform. It runs in a multi-process and multi-threaded manner and has the characteristics of being able to cross multiple software and hardware platforms and having good scalability. It has dynamically scalable communication protocol management and integrated security technology, and uses a unified graphical configuration tool and database to achieve configuration integration and implement fixed value calling and other functions on the monitoring host to improve system management efficiency and data consistency.

[0111] 2. Specific implementation

[0112] See attached Figure 2 ,The system structure diagram configuration tool sub-module is configured according to the layout of the actual monitoring equipment to meet the data transmission requirements in different application scenarios: for the 5 main transformers of the substation, the monitoring parameters (such as the number of sensors, sampling frequency, etc.) of each main transformer are set through the system structure diagram configuration tool, and the network topology is configured, with the monitoring center as the center, a star topology is adopted, and the monitoring data of the 5 main transformers are aggregated to the monitoring center through the network switch;

[0113] Its core module is programmed with C / C++ standard library, and its interface is developed based on QT and Java technology. The networking mode is flexible, and the active / standby server mode, single / dual network mode and the number of network nodes can be flexibly increased or decreased as needed to adapt to different network environments and equipment layouts. When the network traffic is large, the dual network mode is selected to use two network interfaces (such as Gigabit Ethernet interface) for data transmission to improve the transmission speed and reliability.

[0114] It has powerful script calculation function, which can realize various calculation signal expressions and complex operation control rule logic configuration through calculation, and can calculate analog quantity, state quantity and electric quantity: through script calculation, it can calculate the average value, maximum value and other statistical parameters of partial discharge of each main transformer in real time, and make alarm judgment according to the set threshold value (such as alarming when the average value of partial discharge exceeds 10pC);

[0115] It is equipped with complete simulation debugging tools, including special communication debugging tools, simulation data generation tools, and built-in diagnostic maintenance modules to ensure the reliable operation of the monitoring system; it also includes complete communication message monitoring tools to facilitate the troubleshooting and location of communication problems during the debugging process: in the system debugging stage, the simulation data generation tool is used to simulate different types of partial discharge signals and interference signals to test the system's acquisition, processing and transmission functions; the communication debugging tool is used to check whether the network connection is normal and whether the data transmission is accurate; the diagnostic maintenance module is used to monitor the system's operating status in real time, and when a fault occurs, an alarm is issued in a timely manner to indicate the cause of the fault;

[0116] See attached Fig.11 - Appendix 12, the communication protocol processing submodule is used to ensure the accuracy of data transmission, the data bus submodule is used to achieve efficient data transmission and sharing, the system function redundancy control submodule is used to improve the reliability of the system, and the statistical calculation submodule is used to perform statistical analysis on the collected data: the operation and maintenance personnel can remotely log in to the platform through the browser to view the partial discharge data and status of each main transformer in real time; the system can generate various reports based on the collected data: daily reports, weekly reports, monthly reports, etc., in order to conduct long-term analysis of the operation of the transformer; when the equipment layout changes, the system configuration can be quickly adjusted through the system structure diagram configuration tool submodule to ensure the accuracy and stability of data transmission; during the debugging process, the communication debugging tool and the simulation data generation tool are used to quickly locate and solve communication problems.

[0117] Example 8: System operation and maintenance

[0118] See attached Figure 1 ,After the system was put into operation, partial discharge monitoring was continuously carried out on five transformers;

[0119] 1. The sensor module collects signals of different frequency bands in real time: at a certain moment, the UHF sensor of the TB1 main transformer collects a signal with a frequency of 500MHz and an amplitude of 2mV; the high-frequency current sensor collects a signal with a current of 0.5mA; and the ultrasonic sensor collects a signal with a frequency of 80kHz and an amplitude of 10mV.

[0120] 2. The data acquisition module performs preliminary processing such as filtering, amplification and analog-to-digital conversion on the original signal: after amplification by the preamplifier, the amplitude of the UHF sensor signal becomes 60mV, the high-frequency current sensor signal becomes 50mA, and the ultrasonic sensor signal becomes 500mV; after digital filter processing, the interference signal is effectively removed and the signal-to-noise ratio is improved.

[0121] 3. The underlying driver and data interface of the detection equipment module ensure the stable operation and efficient collaboration of each module. At the same time, the anti-interference and fault warning technology effectively eliminates interference signals: at a certain moment, an interference signal with a frequency of 150kHz is detected, and its amplitude is reduced to less than 2% of the original amplitude through narrow-band interference elimination technology, ensuring the accuracy of the data.

[0122] 4. The data transmission module reliably transmits the processed data to the online monitoring platform: within a certain period of time, the monitoring data of the TB1 main transformer is transmitted to the online monitoring platform at a rate of 10Mbps, and no data loss or error occurs during the transmission process.

[0123] See attached Figure 13-Figure 15 , the staff of the online monitoring platform can intuitively observe the partial discharge of each transformer through the real-time display interface: the partial discharge amount, discharge frequency, discharge location and other information of each main transformer can be seen on the real-time display interface; the historical query function can trace past data to analyze the discharge trend: the partial discharge data changes of each main transformer in the past week can be queried, and the discharge trend curve can be drawn; the report generation function provides detailed data support for equipment maintenance and management: the generated weekly report can be used to understand the average partial discharge amount, maximum partial discharge amount, discharge times and other information of each main transformer in a week.

[0124] The statistical calculation submodule of the system performs statistical analysis on the collected data to assist the staff in discovering potential problems: by calculating the standard deviation of the partial discharge of each main transformer, the stability of the discharge can be determined; the data analysis technology processing submodule accurately identifies the discharge phenomenon and abnormal frequency components through functions such as multi-resolution analysis, and combines the fault warning model with the diagnostic rules to timely diagnose possible insulation defects and equipment failures inside the transformer: when it is found that the discharge of a main transformer in a certain frequency band increases abnormally, the fault warning model is combined to determine that there may be an insulation defect, and further determine that the defect may be located in a certain area of ​​the winding.

[0125] Maintenance personnel regularly inspect and maintain the system to ensure good contact and normal operation of the sensors, and check the connection of the coaxial cable and the grounding effect of the shielding layer: perform a visual inspection of the sensor once a month to check for signs of looseness, damage or corrosion; perform a quarterly tightening and conductivity test on the coaxial cable connection to ensure normal signal transmission; measure the grounding resistance of the shielding layer annually to ensure that the grounding resistance is less than 0.1Ω; at the same time, according to the actual operating conditions and equipment update needs, the system's software and hardware are upgraded and optimized to ensure that the system always has efficient and accurate monitoring capabilities and reliable fault warning and diagnosis functions.

[0126] If the data collected by a certain sensor is found to be abnormal during operation: the signal amplitude collected by a UHF sensor of a main transformer for multiple consecutive times is significantly lower than the normal range, the maintenance personnel will first check the installation of the sensor, and use professional testing equipment to test the performance of the sensor after confirming that it is firmly installed and undamaged; if the sensor itself is faulty, replace it with a new sensor in time and recalibrate and reconfigure it; for the data acquisition module, if the collected data is inaccurate or incomplete, the maintenance personnel will check whether the parameter settings of the preamplifier and digital filter are correct. If necessary, readjust the parameters and test to ensure that the quality of the collected data meets the requirements.

[0127] In terms of software, if the online monitoring platform has abnormal functions or runs slowly, the technicians will first check the resource usage of the server, including CPU usage, memory occupancy, and disk I / O; if the server resources are insufficient, consider upgrading the server hardware or optimizing the software algorithm to improve system performance and update the system software regularly. Fix possible vulnerabilities and defects to enhance the security and stability of the system: When the system is found to have data transmission security risks, update the data encryption algorithm in a timely manner to ensure the confidentiality and integrity of the monitoring data during transmission.

[0128] For the fault warning and diagnosis functions, the system is tested regularly using simulated fault data to verify the accuracy and effectiveness of the fault warning model and diagnosis rules. If inaccurate warnings or misdiagnoses are found, technicians will adjust and optimize the models and rules, and continuously improve the fault warning and diagnosis mechanisms in combination with new monitoring data and actual fault cases. When a new type of partial discharge is not accurately identified, relevant discharge data is collected to analyze its characteristics, added to the atlas library and the fault warning model is updated so that it can accurately identify this new type of discharge.

[0129] Through the above comprehensive operation and maintenance measures, it is ensured that the main transformer partial discharge online monitoring system can operate stably for a long time, providing reliable guarantee for the safety and stability of the power system.

[0130] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A main transformer partial discharge online monitoring system, characterized by: It has the following data modules: sensor module, detection equipment module, data acquisition module, data processing module, data transmission module and online monitoring platform module, which can realize comprehensive and accurate monitoring of partial discharge of transformer as well as fault warning and diagnosis.

2. The main transformer partial discharge online monitoring system according to claim 1 is characterized in that: The sensor module has the following submodules: A-1 ultra-high frequency sensor submodule, A-2 high frequency current sensor submodule and A-3 ultrasonic sensor submodule combination, according to the structural characteristics of the transformer combined with the analysis of the structure and electric field distribution of the transformer oil tank, winding, core, bushing and other parts, multiple monitoring points are arranged outside the transformer to fully cover the area where partial discharge may occur; sensors of different frequency bands are combined with the characteristic data of the monitored signals of different frequency bands to effectively distinguish interference and discharge signals, wherein: The A-1 UHF sensor submodule is sensitive to tiny discharge events and is used to detect high-frequency electromagnetic wave signals generated by partial discharge and capture weak signals at the initial stage of discharge; The A-2 high-frequency current sensor submodule is used to capture the current pulses generated by partial discharge inside the transformer insulation; The A-3 ultrasonic sensor submodule is used to detect ultrasonic signals related to the intensity and location of the discharge event during the discharge process; The sensor installation position is set to have good contact with the surface of the detection object to avoid adverse environmental influences including but not limited to mechanical vibration, high temperature, and humidity. The sensor is connected to the data acquisition unit via a coaxial cable, and the coaxial cable connection and shielding layer are well grounded.

3. A main transformer partial discharge online monitoring system according to claim 1, characterized in that: The detection equipment module has the following submodules: a bottom-level driver submodule: the bottom-level driver adopts C language to develop efficient and portable bottom-level drivers; a data interface submodule: the assembly language is used to achieve performance optimization and fine control on a specific hardware platform to facilitate the connection of sensors and other system components.

4. A main transformer partial discharge online monitoring system according to claim 3, characterized in that: The efficiency and portability of the underlying program driver submodule are specifically as follows: based on the hardware device information, the registers of the hardware devices are regulated according to the required parameter values ​​to realize the initialization and basic control of the hardware devices, and on this basis, the system clock is configured to control the running rhythm of the device; a set of programs is developed based on the C language to build mechanisms that match hardware devices at different levels, which is specifically implemented as follows: key nodes in the program are defined so that the regulation of different hardware devices is transformed from modifying the entire program structure to modifying the content of the key nodes. Therefore, the regulation of different hardware devices is completed without changing the interface with the upper-level program and the overall program architecture, thereby realizing the portability of the entire set of programs.

5. The main transformer partial discharge online monitoring system according to claim 1 is characterized by: The detection equipment module also includes: a combination of multiple anti-interference methods to achieve fault warning technology, including but not limited to hardware anti-interference, background noise filtering, polarity discrimination, narrow-band interference elimination, passband filtering technology, perfect grounding point, balanced loop method, bandpass filtering and frequency selection filtering; establishing a spectrum library, using typical discharge spectra and typical interference spectra to compare and analyze in real time to identify partial discharge signals, based on sensor modules, and high-frequency, ultrasonic, and ultra-high frequency monitoring principles to simultaneously monitor and confirm internal discharge signals, and later construct a processing model for in-depth analysis of the processed partial discharge data. The specific structure of this model is: there are different data processing layers, different matrixes and moving steps are selected for each processing layer, and a compression layer is established at the same time. The data is simplified by setting parameters and selecting types, and a fault warning model and diagnostic rules are established based on this.

6. The main transformer partial discharge online monitoring system according to claim 1 is characterized by: The data acquisition module specifically receives the original partial discharge signal from the sensor and performs preliminary signal conditioning, including filtering, amplification and analog-to-digital conversion, wherein a low-noise preamplifier is used to preliminarily amplify the weak signal, and the preliminarily amplified signal is then passed through a digital filter to suppress interference and purify the effective signal. The digital filter effectively selects and suppresses signals with certain frequency components according to the set cutoff frequency parameters, and uses nonlinear mathematical morphology filters and wavelet analysis technology to process nonlinear interference and improve time-frequency resolution. Different types of interference are processed by reasonably selecting structural elements, and wavelet analysis technology performs multi-resolution analysis on the signal by selecting different wavelet functions and scale parameters.

7. The main transformer partial discharge online monitoring system according to claim 6 is characterized by: The digital filter built into the B-1 data acquisition module is specifically: according to the set cutoff frequency parameters, effectively select the signal that passes or suppresses certain frequency components, including but not limited to: removing high-frequency noise by setting a low-pass filter to ensure the quality of the collected data; using a finite impulse response (FIR) filter or an infinite impulse response (IIR) filter based on digital signal processing theory. The FIR filter has a linear phase characteristic to ensure that the signal avoids phase distortion during the filtering process. The IIR filter can achieve higher filtering performance with fewer coefficients, but it cannot completely avoid phase distortion; The B-2 data acquisition module has a built-in nonlinear mathematical morphology filter. Specifically, it is based on the basic operations of mathematical morphology, using corrosion, expansion, opening and closing operations. By selecting structural elements, different types of interference can be processed. For pulse-shaped interference, smaller circular or square structural elements can be selected for corrosion and expansion operations. It processes nonlinear interference in the signal, changes the shape and structure of the signal, improves signal quality, and works in synergy with other filtering technologies to improve the data acquisition module's adaptability to complex interference environments.

8. The main transformer partial discharge online monitoring system according to claim 1 is characterized by: The data transmission module is specifically as follows: the data transmission at the process layer adopts the TCP / IP protocol for remote communication to ensure the reliability and real-time performance of data transmission. The TCP / IP protocol supports the transmission of multiple data formats to meet the needs of remote monitoring and data management. The TCP protocol ensures the integrity and order of data packets by establishing a reliable connection, and the IP protocol is responsible for the routing and addressing of data packets.

9. A main transformer partial discharge online monitoring system according to claim 1 or 3, characterized in that: The data processing module includes the following submodules: C-1 Time-domain to frequency-domain conversion submodule, develop a conversion method, regard the time-domain signal as a combination of many simple fluctuations of different frequencies, develop a calculation method, analyze the calculation results to know which frequency fluctuations are contained in the original complex signal and the intensity of each frequency fluctuation, so as to obtain the representation of the signal in the frequency domain. The calculation method is to decompose a type of complex data into many small data groups, and then analyze the characteristics of each small data group and then analyze the frequency distribution characteristics of the discharge signal. After the analysis, the small data groups are reorganized into the original time-domain signal data, specifically: C-1-1 Select core data points according to the data situation, define the data range of the group with the core data points as the center, and determine the importance of the core data points according to the amount of data in the group. Cancel the data grouping of unimportant core data points and reclassify the data in the group into other data groups. Repeat this process until all data are grouped and processed; C-1-2 processes the data in each group, and performs data transformation again within the group according to the maximum value, minimum value and mean value within the group, specifically: fit the maximum value and minimum value within the group to obtain the mean value within the group, and then use the mean value within the group to compare with each data within the group to obtain new data, and judge the validity of the new data according to the set parameters, if it is valid, extract it, otherwise use the invalid data as new data and return it to the data group, repeat the above process until there is no new data to be extracted in the extracted data group, and use the extracted data as a new data group to replace the original data group, use the mathematical characteristics of the data including but not limited to periodicity and symmetry to divide the data in the new data group according to different data positions, and perform weighted summation to obtain the frequency domain signal; C-2 spectrum density function submodule, analyzes the converted frequency domain signal, reveals the frequency distribution, amplitude and phase information of the signal, so as to identify different types of discharge phenomena, including but not limited to the pulse characteristics of partial discharge and the frequency characteristics of the discharge source. By analyzing these frequency domain characteristics, it is determined what type of partial discharge is occurring, and then the frequency domain signal is accurately converted into a time domain signal according to the inverse transformation described in C-1. The conversion process should ensure that the data is not distorted and accurately reflects the waveform characteristics of the original signal. The C-3 data analysis technology processing submodule includes multi-resolution analysis, identification of discharge phenomena, detection and discrimination of abnormal frequency components in discharge signals, identification of potential insulation defects and equipment failures, and the auxiliary diagnosis of specific fault types and locations inside the transformer based on changes in local discharge characteristic parameters in combination with fault warning models and diagnostic rules.

10. The main transformer partial discharge online monitoring system according to claim 1, characterized in that: The online monitoring platform module is designed to cross the Windows operating system platform and includes a system structure diagram configuration tool submodule, a communication protocol processing submodule, a data bus submodule, a system function redundancy control submodule, and a statistical calculation submodule, specifically: A web-based online monitoring software platform that supports multi-user access and combines remote access, real-time display, historical query, and report generation functions; The main platform module is designed to cross the Windows operating system platform, based on 64-bit Windows system programming, and runs in a multi-process and multi-threaded manner. It has the characteristics of being able to cross multiple software and hardware platforms and having good scalability; Possess dynamically scalable communication protocol management and security integration technology, including but not limited to the use of unified graphic configuration tools and databases to achieve configuration integration, and implement fixed value call function on the monitoring host to improve system management efficiency and data consistency; The system structure diagram configuration tool can configure the submodules according to the layout of the actual monitoring equipment to meet the data transmission requirements in different application scenarios; Its core module is programmed using C / C++ standard libraries, and its interface is developed based on QT and Java technologies; Flexible networking mode, select the master / slave server mode, single / dual network mode as needed, and the number of network nodes can be flexibly increased or decreased as needed to adapt to different network environments and equipment layouts; It has powerful script calculation function, which can realize various calculation signal expressions and complex operation control rule logic configuration through calculation, and can calculate analog quantity, state quantity and electrical quantity; Equipped with complete simulation debugging tools, including but not limited to special communication debugging tools, simulation data generation tools, and internal diagnostic maintenance modules to ensure the reliable operation of the monitoring system. It also includes complete communication message monitoring tools to facilitate the troubleshooting and location of communication problems during debugging; The communication protocol processing submodule is used to ensure the accuracy of data transmission, the data bus submodule is used to achieve efficient data transmission and sharing, the system function redundancy control submodule is used to improve the reliability of the system, and the statistical calculation submodule is used to perform statistical analysis on the collected data.

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