Comprehensive intelligent monitoring system for operation state of transformer

Abstract

The invention discloses a transformer operation state comprehensive intelligent monitoring system, which comprises an information acquisition unit, a preprocessing unit, a core analysis unit, an optimization design unit and an early warning prompt unit, and is characterized in that operation data and environment data of a transformer are acquired through the information acquisition unit, and are preliminarily analyzed through the preprocessing unit; a transformer monitoring model is constructed through the core analysis unit, an influence function between environment data and operation data is constructed by monitoring the working environment of the transformer, the sensitivity of the transformer to environment change is improved, the accuracy of the transformer monitoring model is analyzed through the optimization design unit, fault indexes are processed in a targeted mode, and the fault detection accuracy is improved. The method improves the accuracy of fault recovery and risk response, deeply analyzes and evaluates the predicted risk degree of the transformer through the early warning prompt unit, and generates an early warning prompt signal to achieve the early warning processing of the fault, so as to deal with the sudden influence of the abnormal climate environment change on the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer monitoring, and in particular to a comprehensive intelligent monitoring system for transformer operation status. Background Art

[0002] A transformer is a device that uses the principle of electromagnetic induction to change AC voltage. Its main components are the primary coil, secondary coil and iron core (magnetic core). Its main functions include: voltage conversion, current conversion, impedance conversion, isolation, voltage stabilization (magnetic saturation transformer), etc.

[0003] The operating status of the transformer is affected by its working environment, and the existing integrated intelligent monitoring system for the operating status of the transformer has the defect of insufficient sensitivity to environmental changes, resulting in insufficient real-time monitoring capability and risk warning capability of the transformer operating status. Under the influence of abnormal climatic conditions, such as high fluctuations in the working environment of the transformer, or sudden extreme climate, the transformer may have unexpected failures, affecting the continuous and stable operation of the transformer;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the defects of insufficient sensitivity to environmental changes in the prior art, as well as the problem of insufficient real-time monitoring capability and risk warning capability of the transformer operating status, and to improve the comprehensiveness of data processing by comprehensively analyzing and evaluating the transformer operating status from multiple angles such as insulation performance, mechanical risk and power supply stability. By monitoring the working environment of the transformer, an influence function between environmental data and operating data is established, thereby extending the analysis and comprehensive intelligent evaluation of the transformer operating status, improving the sensitivity of the transformer to environmental changes, and deeply analyzing and evaluating the predicted risk level of the transformer through an early warning prompt unit to cope with the sudden impact of abnormal climate and environmental changes on the transformer.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] The integrated intelligent monitoring system for transformer operation status includes an information collection unit, a preprocessing unit, a core analysis unit, an optimization design unit and an early warning prompt unit, wherein the information collection unit, the preprocessing unit, the core analysis unit, the optimization design unit and the early warning prompt unit are signal connected;

[0008] The information collection unit is used to collect the operation data and environmental data of the transformer: the information collection cycle Tc is set to collect the operation data and environmental data of the transformer regularly;

[0009] The pre-processing unit is used to preliminarily analyze the operation data and environmental data of the transformer, and respectively evaluate the insulation performance, mechanical risk and power supply stability of the transformer, as well as the insulation impact degree and mechanical impact degree;

[0010] The core analysis unit comprehensively analyzes the operation data and environmental data of the transformer by constructing a transformer monitoring model, wherein the transformer monitoring model includes a comprehensive operation analysis sub-model, an environmental impact analysis sub-model and an operation risk analysis sub-model;

[0011] By building a comprehensive operation analysis sub-model, the influence functions of the transformer's insulation performance and mechanical risk on power supply stability are analyzed in turn, and then the transformer's operation status index is comprehensively output;

[0012] By establishing an environmental impact analysis sub-model, the variation function between the insulation impact degree and the insulation performance, as well as the variation function between the mechanical impact degree and the mechanical risk are fitted, and then the comprehensive impact function between the environmental data of the transformer and the operating status index of the transformer is comprehensively constructed;

[0013] By establishing an operation risk analysis sub-model and monitoring the real-time environmental data of the transformer, the model operation status index of the transformer is obtained, the comprehensive risk level of the transformer is evaluated and a risk warning signal is generated;

[0014] The optimization design unit is used to analyze the accuracy of the transformer monitoring model: by monitoring the real-time operation data of the transformer, the actual operation status index of the transformer is obtained, so as to compare and evaluate the accuracy of the transformer monitoring model, and generate optimization management signals to optimize and upgrade the transformer monitoring model;

[0015] The early warning prompt unit is used to predict the operating status of the transformer: it obtains meteorological forecast data and generates forecast management instructions to send to the information collection unit, so as to evaluate the forecast risk level of the transformer and generate early warning prompt signals to provide early warning processing for transformer failures.

[0016] Furthermore, the specific operation process of the pre-processing unit is:

[0017] A1: Operation data includes insulation parameters, mechanical parameters and power supply parameters. Insulation parameters include winding temperature, transformer oil temperature and liquid level; mechanical parameters include gas pollutant content, vibration amplitude and vibration frequency; power supply parameters include input frequency, input voltage, output frequency and output voltage of the power supply;

[0018] By constructing the No. 1 parameter analysis model, the insulation parameters, mechanical parameters and power supply parameters are preliminarily analyzed to evaluate the insulation performance, mechanical risk and power supply stability of the transformer respectively;

[0019] A2: Environmental data include insulation influencing parameters and mechanical influencing parameters; insulation influencing parameters include ambient temperature and ambient humidity; mechanical influencing parameters include instantaneous wind speed and average wind speed;

[0020] By constructing the No. 2 parameter analysis model, the insulation influence parameters and mechanical influence parameters are preliminarily analyzed to evaluate the insulation influence degree and mechanical influence degree.

[0021] Furthermore, the specific process of constructing the No. 1 parameter analysis model is as follows:

[0022] A1-1: Input the index set P into the parameter analysis model No. 1, the index set P includes n0 index elements;

[0023] Mark any index element as i, and mark the value of index element i as Zi;

[0024] Set the standard interval of index element i to [Bp1, Bp2];

[0025] By combining the numerical value Zi of the index element i and its standard interval [Bp1, Bp2], the reference value Ci of the index element i is obtained;

[0026] By combining the reference values ​​of n0 indicator elements of the indicator set P, the evaluation coefficient Xp of the indicator set P is obtained and output;

[0027] A1-2: Preliminary analysis of insulation parameters, mechanical parameters and power supply parameters, thereby outputting insulation performance assessment coefficient, mechanical risk assessment coefficient and power supply stability assessment coefficient respectively;

[0028] Among them, the insulation parameters are taken as the index set P and input into the first parameter analysis model, the evaluation coefficient of the index set P is comprehensively obtained and output, and it is marked as the insulation performance evaluation coefficient Xjy, and then the evaluation interval of the insulation performance evaluation coefficient Xjy is set, and the insulation performance of the transformer is evaluated by interval comparison;

[0029] The mechanical parameters are taken as the index set P and input into the No. 1 parameter analysis model to comprehensively obtain the evaluation coefficient of the index set P and mark it as the mechanical risk evaluation coefficient Xjx. Then, the evaluation interval of the mechanical risk evaluation coefficient Xjx is set, and the mechanical risk of the transformer is evaluated by interval comparison.

[0030] The power supply parameters are taken as the indicator set P and input into the first parameter analysis model. The evaluation coefficient of the indicator set P is comprehensively obtained and output, and it is marked as the power supply stability evaluation coefficient Xdw. The evaluation interval of the power supply stability evaluation coefficient Xdw is set, and the power supply stability of the transformer is evaluated through interval comparison.

[0031] Furthermore, the specific process of constructing the second parameter analysis model is as follows:

[0032] A2-1: Input the index set Q into the second parameter analysis model, the index set Q includes n1 index elements;

[0033] Mark any indicator element as j, collect and extract the value of the indicator element j in N0 information collection cycles, and mark the value of the indicator element j in any information collection cycle as Yj;

[0034] Then, the mean value JZj of the index element j is obtained, and the fluctuation coefficient BDj of the index element j is obtained, and the standard interval of the index element j is set to [Bq1, Bq2];

[0035] The reference value Cj of the indicator element j is obtained by combining the mean value JZj of the indicator element j and its standard interval [Bq1, Bq2];

[0036] By combining the reference values ​​and fluctuation coefficients of n1 indicator elements of the indicator set Q, the influence coefficient Hq of the indicator set Q is obtained and output;

[0037] A2-2: Preliminary analysis of insulation influence parameters and mechanical influence parameters, so as to output insulation influence coefficient and mechanical influence coefficient respectively;

[0038] Among them, the insulation influence parameter is taken as the index set Q and input into the second parameter analysis model, the influence coefficient of the index set Q is comprehensively obtained and output, and it is marked as the insulation influence coefficient Hjy; then the evaluation interval of the insulation influence coefficient Hjy is set, and the insulation influence degree is evaluated by interval comparison;

[0039] The mechanical influence parameters are taken as the index set Q and input into the second parameter analysis model. The influence coefficient of the index set Q is comprehensively obtained and output, and marked as the mechanical influence coefficient Hjx. Then the evaluation interval of the mechanical influence coefficient Hjx is set, and the degree of mechanical influence is evaluated by interval comparison.

[0040] Furthermore, the specific process of constructing the comprehensive operation analysis sub-model is as follows:

[0041] B1: Collect the transformer insulation performance assessment coefficient Xjy, mechanical risk assessment coefficient Xjx and power supply stability assessment coefficient Xdw corresponding to N0 information collection cycles;

[0042] B1-1: By establishing a change curve Sa1 between the transformer insulation performance evaluation coefficient Xjy and the power supply stability evaluation coefficient Xdw, and fitting to generate the influence function F1;

[0043] B1-2: By establishing a change curve Sa2 between the mechanical risk assessment coefficient Xjx of the transformer and the power supply stability assessment coefficient Xdw, and fitting to generate the influence function F2;

[0044] B1-3: Then, the transformer operating status index Zyx is comprehensively output through the transformer insulation performance evaluation coefficient Xjy, mechanical risk evaluation coefficient Xjx and power supply stability evaluation coefficient Xdw;

[0045] B1-4: The power supply stability evaluation coefficient Xdw is then converted into the influence function F1 and the influence function F2, and the transformer operation status index Zyx is obtained.

[0046] Furthermore, the specific process of establishing the environmental impact analysis sub-model is as follows:

[0047] B2-1: Fitting and constructing the variation function between insulation influence degree and insulation performance;

[0048] Collect the insulation influence coefficient Hjy and the insulation performance evaluation coefficient Xjy of the transformer corresponding to N0 information collection cycles, establish a change curve Sb1 between the insulation influence coefficient Hjy and the insulation performance evaluation coefficient Xjy of the transformer, and fit and generate a change function G1;

[0049] B2-2: Fitting and constructing the variation function between the degree of mechanical influence and mechanical risk;

[0050] Collect the mechanical influence coefficient Hjx and the mechanical risk assessment coefficient Xjx of the transformer corresponding to N0 information collection cycles, establish a change curve Sb2 between the mechanical influence coefficient Hjx and the mechanical risk assessment coefficient Xjx of the transformer, and fit and generate a change function G2;

[0051] B2-3: Comprehensively construct a comprehensive influence function GZ between the environmental data of the transformer and the operating status index of the transformer, substitute the insulation influence coefficient Hjy and the mechanical influence coefficient Hjx into the comprehensive influence function GZ, and obtain the operating status index Zyx of the transformer.

[0052] Furthermore, the specific process of establishing the operation risk analysis sub-model is as follows:

[0053] B3-1: Obtain the model operation status index of the transformer by monitoring the real-time environmental data of the transformer;

[0054] Collect the environmental data of the transformer in real time and substitute it into the No. 2 parameter analysis model. Preliminary analysis is performed to obtain the actual insulation influence coefficient Hjy0 and the actual mechanical influence coefficient Hjx0 of the transformer at the current time node. Then substitute them into the comprehensive influence function GZ to obtain the model operation status index ZyxM of the transformer at the current time node.

[0055] B3-2: Set the evaluation interval of the model operation status index ZyxM and evaluate the comprehensive risk level of the transformer through interval comparison;

[0056] B3-3: Visualize the corresponding risks according to the signal level, so as to prompt the background technicians to handle the transformer faults accordingly;

[0057] B3-301: When a Level 1 risk warning signal is generated, no action is taken;

[0058] B3-302: When a secondary risk warning signal is generated, feedback analysis and comparison are performed to determine the fault parameters, so as to carry out targeted repair processing on the fault parameters;

[0059] B3-303: When a level 3 risk warning signal is generated, synchronous fault repair processing is performed on the insulation parameters, mechanical parameters, and power supply parameters.

[0060] Furthermore, the specific operation process of the optimization design unit is as follows:

[0061] Set the model comparison period Tm, monitor the transformer operation data in real time, and substitute it into the No. 1 parameter analysis model to obtain the actual insulation performance evaluation coefficient Xjy1, actual mechanical risk evaluation coefficient Xjx1 and actual power supply stability evaluation coefficient Xdw1 of the transformer at the current time node through preliminary analysis;

[0062] Then substitute it into the comprehensive operation analysis sub-model for calculation, and then comprehensively obtain and output the actual operation status index ZyxU of the transformer;

[0063] Compare and analyze the actual operating state index ZyxU of the transformer with the model operating state index ZyxM of the transformer to obtain the model deviation index ΔZ;

[0064] Set the evaluation interval of the model deviation index ΔZ, compare and evaluate the accuracy of the transformer monitoring model, and generate corresponding optimization management signals;

[0065] The optimization management signal is then fed back to the core processing unit, thereby performing corresponding optimization and upgrade operations on the transformer monitoring model.

[0066] Furthermore, the specific operation process of the early warning prompt unit is:

[0067] Set the forecast time node to obtain the meteorological forecast data of the forecast time node, and generate a forecast management instruction to send to the information collection unit;

[0068] The information collection unit receives the forecast management instruction and extracts the forecast value of the environmental data through the meteorological forecast data, and then outputs it to the preprocessing unit and the core analysis unit. The forecast value of the environmental data is substituted into the No. 2 parameter analysis model, and the predicted insulation influence coefficient HjyY and the predicted mechanical influence coefficient HjxY of the transformer at the forecast time node are preliminarily analyzed and obtained, and then substituted into the comprehensive influence function GZ to obtain the predicted operation status index ZyxY of the transformer;

[0069] Set the risk interval of the transformer's predicted operating status index ZyxY, evaluate the transformer's predicted risk level, and generate corresponding early warning signals;

[0070] The early warning prompt unit receives the early warning prompt signal and performs early warning processing on the transformer fault.

[0071] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0072] The present invention collects the operation data and environmental data of the transformer through the information collection unit, performs preliminary analysis through the preprocessing unit, and then constructs a transformer monitoring model through the core analysis unit, comprehensively analyzes and evaluates the operation status of the transformer from multiple angles such as insulation performance, mechanical risk and power supply stability, improves the comprehensiveness of data processing, and builds an influence function between environmental data and operation data by monitoring the working environment of the transformer, thereby extending the analysis to comprehensively and intelligently evaluate the operation status of the transformer and generate a risk warning signal, thereby improving the sensitivity of the transformer to environmental changes;

[0073] The present invention analyzes the accuracy of the transformer monitoring model through the optimization design unit, generates an optimization management signal to optimize and upgrade the model, thereby performing targeted processing on fault indicators, improving the accuracy of fault repair and risk response, and deeply analyzing and evaluating the predicted risk level of the transformer through the early warning prompt unit, generating an early warning prompt signal to realize early warning processing of faults, so as to cope with the sudden impact of abnormal climate and environmental changes on the transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 Shows a schematic diagram of the modules of the present invention;

[0075] Figure 2 The overall process diagram of the present invention is shown;

[0076] Figure 3 A schematic flow chart of a transformer monitoring model of the present invention is shown. DETAILED DESCRIPTION

[0077] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] Embodiment 1:

[0079] like Figure 1-3As shown, the integrated intelligent monitoring system for transformer operation status includes an information collection unit, a preprocessing unit, a core analysis unit, an optimization design unit and an early warning prompt unit, wherein the information collection unit, the preprocessing unit, the core analysis unit, the optimization design unit and the early warning prompt unit are signal connected;

[0080] The working steps are as follows:

[0081] S1: The information collection unit collects the operation data and environmental data of the transformer: the information collection cycle Tc is set to collect the operation data and environmental data of the transformer regularly;

[0082] The operating data includes insulation parameters, mechanical parameters and power supply parameters. The insulation parameters include winding temperature, transformer oil temperature and liquid level; the mechanical parameters include gas pollutant content, vibration amplitude and vibration frequency; the power supply parameters include input frequency, input voltage, output frequency and output voltage of the power supply;

[0083] Among them, the winding temperature and transformer oil temperature are collected through temperature sensors, and the liquid level of the insulating oil is obtained through transformer oil tank monitoring; nw kinds of gas pollutants are preset, such as dust, smoke, etc., and the content of any pollutant is collected through a gas detector, and then the gas pollutant content is accumulated; the vibration amplitude and vibration frequency are collected through a vibration sensor; the input frequency, input voltage, output frequency and output voltage of the power supply are collected through electric meter monitoring;

[0084] S2: The pre-processing unit preliminarily analyzes the operation data and environmental data of the transformer, and evaluates the insulation performance, mechanical risk and power supply stability of the transformer, as well as the insulation impact degree and mechanical impact degree;

[0085] A1: By constructing the No. 1 parameter analysis model, the insulation parameters, mechanical parameters and power supply parameters are preliminarily analyzed to evaluate the insulation performance, mechanical risk and power supply stability of the transformer respectively;

[0086] The specific process of constructing the No. 1 parameter analysis model is as follows:

[0087] A1-1: Input the index set P into the parameter analysis model No. 1, the index set P includes n0 index elements;

[0088] Mark any index element as i, and mark the value of index element i as Zi;

[0089] Set the standard interval of index element i to [Bp1, Bp2];

[0090] By combining the numerical value Zi of the indicator element i and its standard interval [Bp1, Bp2], the reference value Ci of the indicator element i is obtained:

[0091] Wherein, φi is the adjustment coefficient of the index element i, and φi is greater than 0; the adjustment coefficient φi is a constant value that makes the reference value Ci of the index element i always remain greater than 0, and the adjustment coefficient φi is obtained by preset;

[0092] When the value Zi of the index element i is in the standard interval [Bp1, Bp2], the reference value Ci of the index element i is greater than or equal to the adjustment coefficient φi, indicating that the index element i is normal; conversely, when the value Zi of the index element i is greater than or less than the standard interval [Bp1, Bp2], the reference value Ci of the index element i is less than the adjustment coefficient φi, indicating that the index element i is abnormal, and the greater the deviation between the value Zi and the standard interval [Bp1, Bp2], the smaller the reference value Ci of the index element i, indicating that the degree of abnormality of the index element i is higher;

[0093] By combining the reference values ​​of n0 indicator elements of the indicator set P, the evaluation coefficient Xp of the indicator set P is obtained and output:

[0094] Among them, αi is the weight factor coefficient of the reference value Ci of the indicator element i, and αi is greater than 0; the weight factor coefficient αi is preset and obtained after being measured by a large amount of experimental data; when the reference value Ci of the indicator element i is higher, the evaluation coefficient Xp of the indicator set P is higher, indicating that the status evaluation of the indicator set P is better;

[0095] A1-2: Preliminary analysis of insulation parameters, mechanical parameters and power supply parameters, thereby outputting insulation performance assessment coefficient, mechanical risk assessment coefficient and power supply stability assessment coefficient respectively;

[0096] Among them, the insulation parameters are taken as the index set P and input into the parameter analysis model No. 1. The insulation parameters include winding temperature, transformer oil temperature and liquid level, then n0 is equal to 3, and the values ​​of winding temperature, transformer oil temperature and liquid level are marked as Wr, Wy and Ly respectively; the winding temperature is taken as the index element i, and the reference value of the winding temperature is Cwr; the transformer oil temperature is taken as the index element i, and the reference value of the transformer oil temperature is Cwy; the liquid level is taken as the index element i, and the reference value of the liquid level is Cly; then the evaluation coefficient of the index set P is comprehensively obtained and output, and it is marked as the insulation performance evaluation coefficient Xjy, and then the evaluation interval of the insulation performance evaluation coefficient Xjy is set, and the insulation performance of the transformer is evaluated by interval comparison. When the insulation performance evaluation coefficient Xjy is higher, it means that the insulation parameter is within the standard interval, indicating that the winding temperature, transformer oil temperature and liquid level are characterized normally, and the insulation performance of the transformer is evaluated better;

[0097] The mechanical parameters are taken as the index set P and input into the parameter analysis model No. 1. The mechanical parameters include gas pollutant content, vibration amplitude and vibration frequency, then n0=3, and the values ​​of gas pollutant content, vibration amplitude and vibration frequency are marked as Vw, Zf and Zp respectively; the gas pollutant content is taken as the index element i, and the reference value of the gas pollutant content is Cvw; the vibration amplitude is taken as the index element i, and the reference value of the vibration amplitude is Czf; the vibration frequency is taken as the index element i, and the reference value of the vibration frequency is Czp; and then the evaluation coefficient of the index set P is comprehensively obtained, and it is marked as the mechanical risk assessment coefficient Xjx, and then the evaluation interval of the mechanical risk assessment coefficient Xjx is set, and the mechanical risk of the transformer is evaluated by interval comparison. When the mechanical risk assessment coefficient Xjx is higher, it means that the mechanical parameters are within the standard interval, indicating that the influence of gas pollutants and vibration on the mechanical structure is smaller, so the mechanical risk of the transformer is evaluated to be lower;

[0098] Among them, pollutants in the air, such as dust and smoke, may cause accumulation on the transformer surface, thus affecting the heat dissipation and insulation effects. Excessive vibration may cause the fastening structure to loosen, affecting electrical connections and even causing mechanical failures.

[0099] The power supply parameters are taken as the index set P and input into the No. 1 parameter analysis model. The power supply parameters include the input frequency, input voltage, output frequency and output voltage of the power supply, then n0=4, and the values ​​of the input frequency, input voltage, output frequency and output voltage of the power supply are marked as Ip, Iv, Op and Ov respectively; the input frequency of the power supply is taken as the index element i, and the reference value of the input frequency of the power supply is Cip; the input voltage of the power supply is taken as the index element i, and the reference value of the input voltage of the power supply is Civ; the output frequency of the power supply is taken as the index element i, and the reference value of the output frequency of the power supply is Cop; the output voltage of the power supply is taken as the index element i, and the reference value of the output voltage of the power supply is Cov; and then the evaluation coefficient of the index set P is comprehensively obtained and output, and marked as the power supply stability evaluation coefficient Xdw, and then the evaluation interval of the power supply stability evaluation coefficient Xdw is set, and the power supply stability of the transformer is evaluated by interval comparison. When the power supply stability evaluation coefficient Xdw is higher, it means that the power supply parameters are within the standard interval, indicating that the input frequency, input voltage, output frequency and output voltage of the power supply are characterized normally, and the power supply stability performance of the transformer is evaluated better;

[0100] A2: Environmental data include insulation influencing parameters and mechanical influencing parameters; insulation influencing parameters include ambient temperature and ambient humidity; mechanical influencing parameters include instantaneous wind speed and average wind speed;

[0101] By constructing the No. 2 parameter analysis model, the insulation influence parameters and mechanical influence parameters are preliminarily analyzed to evaluate the insulation influence degree and mechanical influence degree;

[0102] The specific process of constructing the No. 2 parameter analysis model is as follows:

[0103] A2-1: Input the index set Q into the second parameter analysis model, the index set Q includes n1 index elements;

[0104] Mark any indicator element as j, collect and extract the value of the indicator element j in N0 information collection cycles, and mark the value of the indicator element j in any information collection cycle as Yj;

[0105] Then get the mean value JZj of index element j: When the mean JZj of indicator element j is higher, it means that the overall level of indicator element j is high;

[0106] And get the volatility coefficient BDj of indicator element j: When the volatility coefficient BDj of indicator element j is higher, it means that the volatility of indicator element j is higher;

[0107] Set the standard interval of index element j to [Bq1, Bq2];

[0108] By combining the mean value JZj of indicator element j and its standard interval [Bq1, Bq2], the reference value Cj of indicator element j is obtained:

[0109] Wherein, φj is the adjustment coefficient of the index element j, and φj is greater than 0; the adjustment coefficient φj is a constant value that makes the reference value Cj of the index element j always remain greater than 0, and the adjustment coefficient φj is obtained by preset;

[0110] When the mean JZj of indicator element j is in the standard interval [Bq1, Bq2], the smaller the reference value Cj of indicator element j is, the normal the overall level of indicator element j is; conversely, when the mean JZj of indicator element j is greater than or less than the standard interval [Bq1, Bq2], the larger the reference value Cj of indicator element j is, the abnormal the overall level of indicator element j is, and the greater the deviation between the mean JZj and the standard interval [Bq1, Bq2], the larger the reference value Cj of indicator element j is, the higher the abnormality of indicator element j is;

[0111] By combining the reference values ​​and fluctuation coefficients of the n1 indicator elements of the indicator set Q, the influence coefficient Hq of the indicator set Q is obtained and output:

[0112] Among them, βj is the weight factor coefficient of the reference value Cj of the indicator element j, and βj is greater than 0; the weight factor coefficient βj is preset and obtained after a large amount of experimental data is measured; when the reference value Cj of the indicator element j is higher, the influence coefficient Hq of the indicator set Q is higher, indicating that the influence degree of the indicator set Q is higher;

[0113] A2-2: Preliminary analysis of insulation influence parameters and mechanical influence parameters, so as to output insulation influence coefficient and mechanical influence coefficient respectively;

[0114] The insulation influencing parameters are taken as the index set Q and input into the second parameter analysis model. The insulation influencing parameters include ambient temperature and ambient humidity, then n1 is equal to 2, and the values ​​of ambient temperature and ambient humidity are marked as Wh and Sh respectively; the ambient temperature is taken as the index element j to obtain the reference value and fluctuation coefficient of the ambient temperature; the ambient humidity is taken as the index element j to obtain the reference value and fluctuation coefficient of the ambient humidity; then the influence coefficient of the index set Q is comprehensively obtained and output, and it is marked as the insulation influence coefficient Hjy; then the evaluation interval of the insulation influence coefficient Hjy is set, and the insulation influence degree is evaluated by interval comparison. The higher the insulation influence coefficient Hjy is, the lower the insulation influence coefficient Hjy is. , it means that the higher the degree of deviation of the insulation impact parameter from its standard range and the greater the degree of fluctuation, the higher the abnormal influence of ambient temperature and ambient humidity on the insulation performance of the transformer, and the higher the degree of insulation impact assessment; among them, the ambient temperature range should be moderate, and too high or too low temperature will affect the performance of the transformer. Too high temperature can cause aging of the winding insulation material, reduced tolerance, and increase the risk of failure; too low temperature may cause embrittlement of the steel core and insulation material, affecting the performance of the transformer; ambient humidity has a direct impact on the insulation material and equipment status. Too high humidity will cause the insulation performance to decline; too low humidity may cause static electricity accumulation and increase the risk of tripping;

[0115] The mechanical influence parameters are taken as the index set Q and input into the second parameter analysis model. The mechanical influence parameters include instantaneous wind speed and average wind speed, then n1 is equal to 2, and the values ​​of instantaneous wind speed and average wind speed are marked as Vs and Vf respectively; the instantaneous wind speed is taken as the index element j to obtain the reference value and fluctuation coefficient of the instantaneous wind speed; the average wind speed is taken as the index element j to obtain the reference value and fluctuation coefficient of the average wind speed; then the influence coefficient of the index set Q is comprehensively obtained and output, and it is marked as the mechanical influence coefficient Hjx; then the evaluation interval of the mechanical influence coefficient Hjx is set, and the degree of mechanical influence is evaluated by interval comparison. When the mechanical influence coefficient Hjx is higher, it means that the deviation degree of the mechanical influence parameter from its standard interval is higher and the degree of fluctuation is greater, indicating that the instantaneous wind speed and the average wind speed have higher abnormal influence on the mechanical structure of the transformer, and the higher the degree of mechanical influence is evaluated; among them, the transformer may be affected by external effects such as instantaneous wind speed and average wind speed, causing mechanical vibration and noise, thereby posing a potential threat to its operating stability;

[0116] S3: The core analysis unit comprehensively analyzes the operation data and environmental data of the transformer by constructing a transformer monitoring model, wherein the transformer monitoring model includes a comprehensive operation analysis sub-model, an environmental impact analysis sub-model and an operation risk analysis sub-model;

[0117] S3-1: By constructing a comprehensive operation analysis sub-model, we first analyze the impact function of the transformer's insulation performance and mechanical risk on power supply stability, and then comprehensively output the transformer's operating status index;

[0118] The specific process of constructing the comprehensive operation analysis sub-model is as follows:

[0119] B1: Collect the transformer insulation performance assessment coefficient Xjy, mechanical risk assessment coefficient Xjx and power supply stability assessment coefficient Xdw corresponding to N0 information collection cycles;

[0120] B1-1: By establishing a change curve Sa1 between the insulation performance evaluation coefficient Xjy of the transformer and the power supply stability evaluation coefficient Xdw, and fitting to generate an influence function F1: Xdw = F1 (Xjy), the insulation performance evaluation coefficient Xjy is substituted into the influence function F1 to obtain the power supply stability evaluation coefficient Xdw;

[0121] B1-2: By establishing a change curve Sa2 between the mechanical risk assessment coefficient Xjx of the transformer and the power supply stability assessment coefficient Xdw, and fitting to generate an influence function F2: Xdw = F2 (Xjx), the mechanical risk assessment coefficient Xjx is substituted into the influence function F2 to obtain the power supply stability assessment coefficient Xdw;

[0122] Therefore, the power supply stability assessment coefficient Xdw is expressed by the transformer insulation performance assessment coefficient Xjy and the mechanical risk assessment coefficient Xjx:

[0123] B1-3: Then, through the transformer insulation performance evaluation coefficient Xjy, mechanical risk evaluation coefficient Xjx and power supply stability evaluation coefficient Xdw, the transformer operation status index Zyx is comprehensively output:

[0124]

[0125] Among them, ω1, ω2 and ω3 are weight factor coefficients of the transformer's insulation performance evaluation coefficient Xjy, mechanical risk evaluation coefficient Xjx and power supply stability evaluation coefficient Xdw, respectively, and ω1, ω2 and ω3 are all greater than 0; when the transformer's insulation performance evaluation coefficient Xjy is higher, the mechanical risk evaluation coefficient Xjx is lower and the power supply stability evaluation coefficient Xdw is higher, the transformer's operating status index Zyx is higher, indicating that the transformer's operating status is better; through comprehensive analysis of insulation performance, mechanical risk and power supply stability, the comprehensiveness of data processing is improved;

[0126] B1-4: The power supply stability evaluation coefficient Xdw is then converted into the influence function F1 and the influence function F2, and the transformer operation status index Zyx is:

[0127] S3-2: By establishing an environmental impact analysis sub-model, first fit and construct the variation function between the insulation impact degree and the insulation performance, and the variation function between the mechanical impact degree and the mechanical risk, and then comprehensively construct the comprehensive impact function between the environmental data of the transformer and the transformer operation status index;

[0128] The specific process of establishing the environmental impact analysis sub-model is as follows:

[0129] B2-1: Fitting and constructing the variation function between insulation influence degree and insulation performance;

[0130] Collect the insulation influence coefficient Hjy and insulation performance evaluation coefficient Xjy of the transformer corresponding to N0 information collection cycles, establish a change curve Sb1 between the insulation influence coefficient Hjy and the insulation performance evaluation coefficient Xjy of the transformer, and fit to generate a change function G1: Xjy=G1(Hjy), substitute the insulation influence coefficient Hjy into the change function G1, and then obtain the insulation performance evaluation coefficient Xjy;

[0131] B2-2: Fitting and constructing the variation function between the degree of mechanical influence and mechanical risk;

[0132] Collect the mechanical influence coefficient Hjx and mechanical risk assessment coefficient Xjx of the transformer corresponding to N0 information collection cycles, establish a change curve Sb2 between the mechanical influence coefficient Hjx and the mechanical risk assessment coefficient Xjx of the transformer, and fit to generate a change function G2: Xjx = G2 (Hjx), substitute the mechanical influence coefficient Hjx into the change function G1, and obtain the mechanical risk assessment coefficient Xjx;

[0133] B2-3: Comprehensively construct a comprehensive influence function GZ between the environmental data of the transformer and the operating status index of the transformer: Zyx=GZ(Hjy, Hjx), substitute the insulation influence coefficient Hjy and the mechanical influence coefficient Hjx into the comprehensive influence function GZ, and then obtain the operating status index Zyx of the transformer:

[0134]

[0135] S3-3: By establishing an operation risk analysis sub-model and monitoring the real-time environmental data of the transformer, the model operation status index of the transformer is obtained, the comprehensive risk level of the transformer is evaluated and a risk warning signal is generated;

[0136] The specific process of establishing the operation risk analysis sub-model is as follows:

[0137] B3-1: Obtain the model operation status index of the transformer by monitoring the real-time environmental data of the transformer;

[0138] Collect the environmental data of the transformer in real time and substitute it into the No. 2 parameter analysis model. Preliminary analysis is performed to obtain the actual insulation influence coefficient Hjy0 and the actual mechanical influence coefficient Hjx0 of the transformer at the current time node. Then substitute them into the comprehensive influence function GZ to obtain the model operation status index ZyxM of the transformer at the current time node.

[0139]

[0140] B3-2: Set the evaluation interval of the model operation status index ZyxM, and evaluate the comprehensive risk level of the transformer by comparing the intervals. The specific process is as follows:

[0141] The evaluation interval of the preset model operation status index ZyxM is [M1, M2]. When the model operation status index ZyxM is higher than the evaluation interval [M1, M2], it is determined that the operation status of the transformer is good, the comprehensive risk level of the transformer is low, and a first-level risk warning signal is generated; when the model operation status index ZyxM is in the evaluation interval [M1, M2], it is determined that the operation status of the transformer is general, the comprehensive risk level of the transformer is medium, and a second-level risk warning signal is generated; when the model operation status index ZyxM is lower than the evaluation interval [M1, M2], it is determined that the operation status of the transformer is poor, the comprehensive risk level of the transformer is high, and a third-level risk warning signal is generated;

[0142] B3-3: Visualize the corresponding risks according to the signal level, so as to prompt the background technicians to handle the transformer faults accordingly;

[0143] B3-301: When a Level 1 risk warning signal is generated, no action is taken;

[0144] B3-302: When a secondary risk warning signal is generated, it is fed back to the environmental impact analysis sub-model to compare and determine the fault parameters:

[0145] B3-302-1: Substitute the actual insulation influence coefficient Hjy0 of the transformer at the current time node into the variation function G1 to obtain the simulated insulation performance evaluation coefficient Xjy0 of the transformer at the current time node: Xjy0 = G1 (Hjy0);

[0146] The evaluation threshold of the simulated insulation performance evaluation coefficient Xjy0 is set to m1. When the simulated insulation performance evaluation coefficient Xjy0 is lower than the evaluation threshold m1, an insulation parameter fault is determined and the insulation parameters are processed;

[0147] B3-302-2: Substitute the actual mechanical influence coefficient Hjx0 of the transformer at the current time node into the variation function G2 to obtain the simulated mechanical risk assessment coefficient Xjx0 of the transformer at the current time node: Xjx0 = G2 (Hjx0);

[0148] The evaluation threshold of the simulated mechanical risk evaluation coefficient Xjx0 is set to m2. When the simulated mechanical risk evaluation coefficient Xjx0 is higher than the evaluation threshold m2, the mechanical parameter failure is determined and the mechanical parameters are processed;

[0149] B3-302-3: Substitute the actual insulation performance evaluation coefficient Xjy0 and the actual mechanical risk evaluation coefficient Xjx0 of the transformer at the current time node into the influence function F1 and the influence function F2 respectively to obtain the simulated power supply stability evaluation coefficient Xdw0 of the transformer at the current time node:

[0150] The evaluation threshold of the simulated power supply stability evaluation coefficient Xdw0 is set to m3. When the simulated power supply stability evaluation coefficient Xdw0 is lower than the evaluation threshold m3, it is determined that the power supply parameter is faulty, and processing is performed on the power supply parameter;

[0151] By performing targeted repairs on fault parameters, the accuracy of fault repair and risk response can be improved;

[0152] B3-303: When a third-level risk warning signal is generated, synchronous fault repair processing is performed on insulation parameters, mechanical parameters and power supply parameters;

[0153] S4: The optimization design unit is used to analyze the accuracy of the transformer monitoring model: first, by monitoring the real-time operation data of the transformer, the actual operation status index of the transformer is obtained, so as to compare and evaluate the accuracy of the transformer monitoring model, and generate an optimization management signal to optimize and upgrade the transformer monitoring model. The specific operation process is as follows:

[0154] S4-1: Set the model comparison period Tm, monitor the transformer operation data in real time, and substitute it into the No. 1 parameter analysis model to obtain the actual insulation performance evaluation coefficient Xjy1, actual mechanical risk evaluation coefficient Xjx1 and actual power supply stability evaluation coefficient Xdw1 of the transformer at the current time node through preliminary analysis;

[0155] S4-2: Then substitute it into the comprehensive operation analysis sub-model for calculation, and then comprehensively obtain and output the actual operation status index ZyxU of the transformer:

[0156] S4-3: Compare and analyze the actual operating status index ZyxU of the transformer with the model operating status index ZyxM of the transformer to obtain the model deviation index ΔZ:

[0157] When the difference between the actual operating state index ZyxU of the transformer and the model operating state index ZyxM is larger, the model deviation index ΔZ is higher, indicating that the deviation of the transformer monitoring model is higher, and thus the accuracy of the transformer monitoring model is lower;

[0158] S4-4: setting the evaluation interval of the model deviation index ΔZ, comparing and evaluating the accuracy of the transformer monitoring model, and generating corresponding optimization management signals;

[0159] There are N1 evaluation intervals of the preset model deviation index ΔZ, and any evaluation interval is marked as Ur, where r is the serial number of the evaluation interval and 1≤r≤N1. When the model deviation index ΔZ is in the evaluation interval Ur, the accuracy of the transformer monitoring model is evaluated to be level r, and an optimization management signal of level r is generated; when the model deviation index ΔZ is lower, the level r of the optimization management signal is higher, indicating that the accuracy of the transformer monitoring model is higher;

[0160] S4-5: The optimization management signal is then fed back to the core processing unit, so as to perform corresponding optimization and upgrading operations on the transformer monitoring model, and mathematical modeling is re-performed for the transformer monitoring model, so as to fit the preset value of the weight factor coefficient in the correction model, realize comprehensive intelligent monitoring of the transformer operation status through the transformer working environment, and improve the transformer's sensitivity to environmental changes;

[0161] S5: The early warning prompt unit is used to predict the operating status of the transformer: first, the meteorological forecast data is obtained through the professional meteorological platform, and a forecast management instruction is generated and sent to the information collection unit, so as to evaluate the forecast risk level of the transformer, and generate an early warning prompt signal to carry out early warning processing of the transformer fault. The specific operation process is as follows:

[0162] S5-1: Set a forecast time node, obtain the weather forecast data of the forecast time node through the professional meteorological platform, and generate a forecast management instruction and send it to the information collection unit;

[0163] S5-2: The information collection unit receives the forecast management instruction and extracts the forecast value of the environmental data through the meteorological forecast data, and then outputs it to the preprocessing unit and the core analysis unit. The forecast value of the environmental data is substituted into the No. 2 parameter analysis model, and the predicted insulation influence coefficient HjyY and the predicted mechanical influence coefficient HjxY of the transformer at the forecast time node are preliminarily analyzed and obtained, and then substituted into the comprehensive influence function GZ to obtain the predicted operating state index ZyxY of the transformer;

[0164] S5-3: Set the risk interval of the transformer's predicted operating status index ZyxY, evaluate the transformer's predicted risk level, and generate corresponding early warning signals; when the predicted operating status index ZyxY is higher, it means that the transformer's predicted operating status at the predicted time node is better, and thus the predicted risk level is lower;

[0165] S5-4: The early warning unit receives the early warning signal and performs early warning processing on the transformer fault. It optimizes and maintains the insulation parameters, mechanical parameters and power supply parameters to cope with the sudden impact of abnormal climate environment on the transformer.

[0166] In summary, the present invention collects the operation data and environmental data of the transformer through the information collection unit, performs preliminary analysis through the preprocessing unit, and then constructs a transformer monitoring model through the core analysis unit, comprehensively analyzes and evaluates the transformer operation status from multiple angles such as insulation performance, mechanical risk, and power supply stability, improves the comprehensiveness of data processing, comprehensively and intelligently evaluates the transformer operation status by monitoring the working environment of the transformer, and generates a risk warning signal, thereby improving the transformer's sensitivity to environmental changes;

[0167] The present invention analyzes the accuracy of the transformer monitoring model through the optimization design unit, generates an optimization management signal to optimize and upgrade the model, performs targeted processing on fault indicators, improves the accuracy of fault repair and risk response, and evaluates the predicted risk level of the transformer through the early warning prompt unit, generates an early warning prompt signal to realize early warning processing of faults, so as to cope with the sudden impact of abnormal climate environment on the transformer.

[0168] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0169] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0170] 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. The integrated intelligent monitoring system for transformer operation status is characterized by: It includes an information collection unit, a preprocessing unit, a core analysis unit, an optimization design unit and an early warning prompt unit, wherein the information collection unit, the preprocessing unit, the core analysis unit, the optimization design unit and the early warning prompt unit are signal connected; The information collection unit is used to collect the operation data and environmental data of the transformer: the information collection cycle Tc is set to collect the operation data and environmental data of the transformer regularly; The pre-processing unit is used to preliminarily analyze the operation data and environmental data of the transformer, and respectively evaluate the insulation performance, mechanical risk and power supply stability of the transformer, as well as the insulation impact degree and mechanical impact degree; The core analysis unit comprehensively analyzes the operation data and environmental data of the transformer by constructing a transformer monitoring model, wherein the transformer monitoring model includes a comprehensive operation analysis sub-model, an environmental impact analysis sub-model and an operation risk analysis sub-model; By building a comprehensive operation analysis sub-model, the influence functions of the transformer's insulation performance and mechanical risk on power supply stability are analyzed in turn, and then the transformer's operation status index is comprehensively output; By establishing an environmental impact analysis sub-model, the variation function between the insulation impact degree and the insulation performance, as well as the variation function between the mechanical impact degree and the mechanical risk are fitted, and then the comprehensive impact function between the environmental data of the transformer and the operating status index of the transformer is comprehensively constructed; By establishing an operation risk analysis sub-model and monitoring the real-time environmental data of the transformer, the model operation status index of the transformer is obtained, the comprehensive risk level of the transformer is evaluated and a risk warning signal is generated; The optimization design unit is used to analyze the accuracy of the transformer monitoring model: by monitoring the real-time operation data of the transformer, the actual operation status index of the transformer is obtained, so as to compare and evaluate the accuracy of the transformer monitoring model, and generate optimization management signals to optimize and upgrade the transformer monitoring model; The early warning prompt unit is used to predict the operating status of the transformer: it obtains meteorological forecast data and generates forecast management instructions to send to the information collection unit, so as to evaluate the forecast risk level of the transformer and generate early warning prompt signals to provide early warning processing for transformer failures.

2. The integrated intelligent monitoring system for transformer operation status according to claim 1 is characterized in that: The specific operation process of the preprocessing unit is: A1: Operation data includes insulation parameters, mechanical parameters and power supply parameters. Insulation parameters include winding temperature, transformer oil temperature and liquid level; mechanical parameters include gas pollutant content, vibration amplitude and vibration frequency; power supply parameters include input frequency, input voltage, output frequency and output voltage of the power supply; By constructing the No. 1 parameter analysis model, the insulation parameters, mechanical parameters and power supply parameters are preliminarily analyzed to evaluate the insulation performance, mechanical risk and power supply stability of the transformer respectively; A2: Environmental data include insulation influencing parameters and mechanical influencing parameters; insulation influencing parameters include ambient temperature and ambient humidity; mechanical influencing parameters include instantaneous wind speed and average wind speed; By constructing the No. 2 parameter analysis model, the insulation influence parameters and mechanical influence parameters are preliminarily analyzed to evaluate the insulation influence degree and mechanical influence degree.

3. The integrated intelligent monitoring system for transformer operation status according to claim 2 is characterized in that: The specific process of constructing the No. 1 parameter analysis model is as follows: A1-1: Input the index set P into the parameter analysis model No. 1, the index set P includes n0 index elements; Mark any index element as i, and mark the value of index element i as Zi; Set the standard interval of index element i to [Bp1, Bp2]; By combining the numerical value Zi of the index element i and its standard interval [Bp1, Bp2], the reference value Ci of the index element i is obtained; By combining the reference values ​​of n0 indicator elements of the indicator set P, the evaluation coefficient Xp of the indicator set P is obtained and output; A1-2: Preliminary analysis of insulation parameters, mechanical parameters and power supply parameters, thereby outputting insulation performance assessment coefficient, mechanical risk assessment coefficient and power supply stability assessment coefficient respectively; The insulation parameters are taken as the index set P and input into the first parameter analysis model, the evaluation coefficient of the index set P is comprehensively obtained and output, and it is marked as the insulation performance evaluation coefficient Xjy, and then the evaluation interval of the insulation performance evaluation coefficient Xjy is set, and the insulation performance of the transformer is evaluated by interval comparison; The mechanical parameters are taken as the index set P and input into the No. 1 parameter analysis model, the evaluation coefficient of the index set P is comprehensively obtained, and it is marked as the mechanical risk evaluation coefficient Xjx, and then the evaluation interval of the mechanical risk evaluation coefficient Xjx is set, and the mechanical risk of the transformer is evaluated by interval comparison; The power supply parameters are taken as the index set P and input into the first parameter analysis model. The evaluation coefficient of the index set P is comprehensively obtained and output, and it is marked as the power supply stability evaluation coefficient Xdw. The evaluation interval of the power supply stability evaluation coefficient Xdw is then set, and the power supply stability of the transformer is evaluated through interval comparison.

4. The integrated intelligent monitoring system for transformer operation status according to claim 3 is characterized in that: The specific process of constructing the No. 2 parameter analysis model is as follows: A2-1: Input the index set Q into the second parameter analysis model, the index set Q includes n1 index elements; Mark any indicator element as j, collect and extract the value of the indicator element j in N0 information collection cycles, and mark the value of the indicator element j in any information collection cycle as Yj; Then, the mean value JZj of the index element j is obtained, and the fluctuation coefficient BDj of the index element j is obtained, and the standard interval of the index element j is set to [Bq1, Bq2]; The reference value Cj of the indicator element j is obtained by combining the mean value JZj of the indicator element j and its standard interval [Bq1, Bq2]; By combining the reference values ​​and fluctuation coefficients of n1 indicator elements of the indicator set Q, the influence coefficient Hq of the indicator set Q is obtained and output; A2-2: Preliminary analysis of insulation influence parameters and mechanical influence parameters, so as to output insulation influence coefficient and mechanical influence coefficient respectively; The insulation influence parameters are taken as the index set Q and input into the second parameter analysis model, the influence coefficient of the index set Q is comprehensively obtained and output, and it is marked as the insulation influence coefficient Hjy; then the evaluation interval of the insulation influence coefficient Hjy is set, and the insulation influence degree is evaluated by interval comparison; The mechanical influence parameters are taken as the index set Q and input into the second parameter analysis model. The influence coefficient of the index set Q is comprehensively obtained and output, and it is marked as the mechanical influence coefficient Hjx. Then the evaluation interval of the mechanical influence coefficient Hjx is set, and the degree of mechanical influence is evaluated by interval comparison.

5. The integrated intelligent monitoring system for transformer operation status according to claim 4 is characterized in that: The specific process of constructing the comprehensive operation analysis sub-model is as follows: B1: Collect the transformer insulation performance assessment coefficient Xjy, mechanical risk assessment coefficient Xjx and power supply stability assessment coefficient Xdw corresponding to N0 information collection cycles; B1-1: By establishing a change curve Sa1 between the transformer insulation performance evaluation coefficient Xjy and the power supply stability evaluation coefficient Xdw, and fitting to generate the influence function F1; B1-2: By establishing a change curve Sa2 between the mechanical risk assessment coefficient Xjx of the transformer and the power supply stability assessment coefficient Xdw, and fitting to generate the influence function F2; B1-3: Then, the transformer operating status index Zyx is comprehensively output through the transformer insulation performance evaluation coefficient Xjy, mechanical risk evaluation coefficient Xjx and power supply stability evaluation coefficient Xdw; B1-4: The power supply stability evaluation coefficient Xdw is then converted into influence function F1 and influence function F2 to obtain the transformer operation status index Zyx.

6. The integrated intelligent monitoring system for transformer operation status according to claim 5 is characterized in that: The specific process of establishing the environmental impact analysis sub-model is as follows: B2-1: Fitting and constructing the variation function between insulation influence degree and insulation performance; Collect the insulation influence coefficient Hjy and the insulation performance evaluation coefficient Xjy of the transformer corresponding to N0 information collection cycles, establish a change curve Sb1 between the insulation influence coefficient Hjy and the insulation performance evaluation coefficient Xjy of the transformer, and fit and generate a change function G1; B2-2: Fitting and constructing the variation function between the degree of mechanical influence and mechanical risk; Collect the mechanical influence coefficient Hjx and the mechanical risk assessment coefficient Xjx of the transformer corresponding to N0 information collection cycles, establish a change curve Sb2 between the mechanical influence coefficient Hjx and the mechanical risk assessment coefficient Xjx of the transformer, and fit and generate a change function G2; B2-3: Comprehensively construct a comprehensive influence function GZ between the environmental data of the transformer and the operating status index of the transformer, substitute the insulation influence coefficient Hjy and the mechanical influence coefficient Hjx into the comprehensive influence function GZ to obtain the operating status index Zyx of the transformer.

7. The integrated intelligent monitoring system for transformer operation status according to claim 6 is characterized in that: The specific process of establishing the operation risk analysis sub-model is as follows: B3-1: Obtain the model operation status index of the transformer by monitoring the real-time environmental data of the transformer; Collect the environmental data of the transformer in real time and substitute it into the No. 2 parameter analysis model. Preliminary analysis is performed to obtain the actual insulation influence coefficient Hjy0 and the actual mechanical influence coefficient Hjx0 of the transformer at the current time node. Then substitute them into the comprehensive influence function GZ to obtain the model operation status index ZyxM of the transformer at the current time node. B3-2: Set the evaluation interval of the model operation status index ZyxM and evaluate the comprehensive risk level of the transformer through interval comparison; B3-3: Visualize the corresponding risks according to the signal level, so as to prompt the background technicians to handle the transformer faults accordingly; B3-301: When a Level 1 risk warning signal is generated, no action is taken; B3-302: When a secondary risk warning signal is generated, feedback analysis and comparison are performed to determine the fault parameters, so as to carry out targeted repair processing on the fault parameters; B3-303: When a level 3 risk warning signal is generated, synchronous fault repair processing is performed on the insulation parameters, mechanical parameters, and power supply parameters.

8. The integrated intelligent monitoring system for transformer operation status according to claim 7 is characterized in that: The specific operation process of the optimization design unit is as follows: Set the model comparison period Tm, monitor the transformer operation data in real time, and substitute it into the No. 1 parameter analysis model to obtain the actual insulation performance evaluation coefficient Xjy1, actual mechanical risk evaluation coefficient Xjx1 and actual power supply stability evaluation coefficient Xdw1 of the transformer at the current time node through preliminary analysis, and then substitute it into the comprehensive operation analysis sub-model for measurement, and then comprehensively obtain and output the actual operation status index ZyxU of the transformer; Compare and analyze the actual operating state index ZyxU of the transformer with the model operating state index ZyxM of the transformer to obtain the model deviation index ΔZ; Set the evaluation interval of the model deviation index ΔZ, compare and evaluate the accuracy of the transformer monitoring model, and generate corresponding optimization management signals; The optimization management signal is fed back to the core processing unit, so as to perform corresponding optimization and upgrade operations on the transformer monitoring model.

9. The integrated intelligent monitoring system for transformer operation status according to claim 8 is characterized in that: The specific operation process of the early warning prompt unit is as follows: Set the forecast time node to obtain the meteorological forecast data of the forecast time node, and generate a forecast management instruction to send to the information collection unit; The information collection unit receives the forecast management instruction and extracts the forecast value of the environmental data through the meteorological forecast data, and then outputs it to the preprocessing unit and the core analysis unit. The forecast value of the environmental data is substituted into the No. 2 parameter analysis model, and the predicted insulation influence coefficient HjyY and the predicted mechanical influence coefficient HjxY of the transformer at the forecast time node are preliminarily analyzed and obtained, and then substituted into the comprehensive influence function GZ to obtain the predicted operation status index ZyxY of the transformer; Set the risk interval of the transformer's predicted operating status index ZyxY, evaluate the transformer's predicted risk level, and generate corresponding early warning signals; The early warning prompt unit receives the early warning prompt signal and performs early warning processing on the transformer fault.

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