An intelligent transformer monitoring system

Abstract

The present application relates to the technical field of transformers, and in particular to an intelligent transformer monitoring system. It includes: a central control unit, which is used to establish multiple monitoring points according to the equipment parameters of the transformer, and set the characteristic monitoring index of each monitoring point; a monitoring unit, which includes multiple monitoring submodules, which are set at each monitoring point and are used to collect the operating parameters of each monitoring point; an associated correction unit, which is used to establish an associated model and an environmental disturbance model according to all monitoring points; multiple monitoring points are established according to the equipment parameters of the transformer, and the required monitoring characteristic monitoring indexes of each monitoring point are set to achieve comprehensive monitoring of the transformer, and by constructing an associated model, the influence relationship between each monitoring point is generated, so as to dynamically adjust the safety threshold of a single monitoring point, improve the early warning efficiency of the potential operating risk of the transformer, and ensure the safe operation of the transformer.

Description

Technical Field

[0001] The present application relates to the technical field of transformers, and in particular to an intelligent transformer monitoring system. Background Art

[0002] With the development of the Internet of Things for power transmission and transformation equipment, there are many parameters for the working status of transformers, and it is necessary to monitor the operation of transformers so that the transformers can operate stably and solve faults quickly when they occur.

[0003] During the long-term operation of the transformer, it will be affected by many factors. In addition, the transformer itself is very complex, which makes the transformer very prone to failure. The existing monitoring equipment is easily affected by the surrounding environment and is not easy to accurately monitor the operating parameters of the transformer in real time. The safety level is low and the installation and maintenance costs are high, which brings inconvenience to the monitoring of the transformer. Summary of the invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides an intelligent transformer monitoring system, aiming to improve the monitoring efficiency of the transformer and ensure the safe and stable operation of the transformer.

[0005] In some embodiments of the present application, multiple monitoring points are established according to the equipment parameters of the transformer, and the monitoring characteristics and monitoring indicators required for each monitoring point are set to achieve comprehensive monitoring of the transformer. By constructing an association model, the influence relationship between the various monitoring points is generated, so that the safety threshold of a single monitoring point is dynamically adjusted, thereby improving the early warning efficiency of potential operating risks of the transformer and ensuring the safe operation of the transformer.

[0006] In some embodiments of the present application, by adding an associated correction module, multiple environmental disturbance indicators are generated, and a first-level environmental disturbance model corresponding to each monitoring point is generated, so that the real-time operating parameters collected by each monitoring point can be corrected according to the real-time environmental parameters, eliminating the interference of environmental factors, improving the recognition accuracy and maintenance efficiency of potential risks of the transformer, and reducing the risk of transformer failure.

[0007] In some embodiments of the present application, an intelligent transformer monitoring system is provided, comprising: The central control unit is used to establish multiple monitoring points according to the transformer equipment parameters and set the characteristic monitoring indicators of each monitoring point; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at each monitoring point and are used to collect operating parameters of each monitoring point; A correlation correction unit, used to establish a correlation model and an environmental disturbance model based on all monitoring points; The central control unit comprises: The first processing module is used to establish a monitoring point sequence A, A=(a1, a2…ai…an), where ai is the i-th monitoring point and n is the number of monitoring points; The second processing module is used to obtain the operating parameters of each monitoring point according to the preset monitoring time node and generate the operating risk value of each monitoring point; The third processing module is used to determine whether to generate a warning instruction according to all operation risk values.

[0008] In some embodiments of the present application, the association correction unit includes: The first association module is used to generate association evaluation values ​​between various monitoring points and set an association model; The first correction module is used to generate multiple environmental disturbance indicators according to historical environmental parameters, and generate a primary environmental disturbance model for each monitoring point; The first correction module is also used to establish a first-level environmental disturbance model series B, B=(b1, b2...bi...bn), where bi is the first-level environmental disturbance model of the i-th monitoring point.

[0009] In some embodiments of the present application, the second processing module is further used to: According to the monitoring point sequence A, the i-th monitoring point is set as the target monitoring point in sequence; Obtain the operating parameters of the target monitoring point at the current monitoring time node; Establish the characteristic monitoring index sequence C of the target monitoring point at the current monitoring time node, C=(c1,c2…c i …c m ),in, ci is the reference value of the i-th characteristic monitoring indicator of the target monitoring point at the current monitoring time node; Obtain the environmental parameters of the current monitoring time node, and generate the compensation coefficients of each characteristic monitoring indicator of the target monitoring point at the current monitoring time node; Generate the operation risk value f of the target monitoring point at the current monitoring time node; Generate the operation risk value of each monitoring point at the current monitoring time node in sequence; Establish an operation risk value series F, F=(f1,f2…fi…fn), where fi is the operation risk value of the i-th monitoring point at the current monitoring time node.

[0010] In some embodiments of the present application, when generating the compensation coefficient of each characteristic monitoring indicator of the target monitoring point at the current monitoring time node, it includes: Establish the environmental disturbance index sequence D at the current time node, D=(d1,d2…di…dr), where di is the reference value of the i-th environmental disturbance index at the current monitoring time node; Generate the perturbation evaluation value k of the target monitoring point at the current monitoring time node; k = βi * (di - d'i)]; Among them, βi is the influence factor of the i-th environmental perturbation index in the target monitoring point; d'i is the standard reference value of the i-th environmental perturbation index at the target monitoring point; Judge whether to generate a first-level correction instruction according to the perturbation evaluation value k.

[0011] In some embodiments of the present application, when judging whether to generate a first-level correction instruction according to the perturbation evaluation value k, it includes: Preset the first perturbation evaluation value threshold K1; If k > K1, generate a first-level correction instruction, call the first-level environmental perturbation model of the target monitoring point according to the first-level correction instruction, and generate a compensation coefficient sequence G of the target monitoring point at the current monitoring time node according to the called first-level environmental perturbation model and the environmental perturbation index sequence D, G = (g1, g2... gi... gm), where gi is the compensation coefficient of the i-th characteristic monitoring index of the target monitoring point at the current monitoring time node; If k < K1, do not generate a first-level correction instruction, and set the compensation coefficients of each characteristic monitoring index of the target monitoring point at the current monitoring time node to 1.

[0012] In some embodiments of the present application, when generating the operation risk value f of the target monitoring point at the current monitoring time node, it includes: Generate the first-level standard values C1 of each characteristic monitoring index of the target monitoring point according to historical parameters, C1 = (c 11 , c 12 … c 1i … c 1m ), where c 1i is the first-level standard value of the i-th characteristic monitoring index of the target monitoring point Select each associated monitoring point of the target monitoring point according to the association model; Generate a sequence C2 of second-level standard values of the target monitoring point at the current monitoring time node according to the operation parameters of all associated monitoring points; C2 = (c 21 , c 22 … c 2i … c 2m ), where c 2i is the second-level standard value of the i-th characteristic monitoring index of the target monitoring point at the current monitoring time node.

[0013] In some embodiments of the present application, when generating the operation risk value f of the target monitoring point at the current monitoring time node, it further includes: f = e1 * Q1 * ηi *(c i -c 1i ) 2 ]+e2*Q2*[ η i *(c i -c 1i ) 2 ]+e3*Q3*U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; η i is the influencing factor of the i-th characteristic monitoring indicator of the target monitoring point; U is the historical evaluation value set based on the historical monitoring data of the target monitoring point.

[0014] In some embodiments of the present application, the third processing module is further used to: Get the operation risk value series F of the current monitoring time node; Preset the first operation risk value threshold F1; If fi>F1, the i-th monitoring point generates a first-level warning instruction at the current monitoring time node; If all data in the operation risk value sequence F are less than F1, the abnormal evaluation value h of the current monitoring time node is generated according to the operation risk value sequence F; Preset abnormal evaluation value threshold H1; If h>H1, the current monitoring time node generates a second-level warning instruction.

[0015] In some embodiments of the present application, when generating the abnormal evaluation value h of the current monitoring time node, it includes: h=e4*Q4*[ pi*Y(i)*(fi-F2)]+e5*Q5*[ pi*fi]; Wherein, e4 is the preset fourth weight coefficient; e5 is the preset fifth weight coefficient; Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; pi is the influencing factor of the i-th monitoring point; Y(i) is the selection coefficient; if (fi-F2)>0, Y(i)=1 / (fi-F2); if (fi-F2)<0, Y(i)=0; F2 is the preset second risk assessment value threshold, and F2 <F1。

[0016] Compared with the prior art, the intelligent transformer monitoring system according to the embodiment of the present application has the following beneficial effects: Establish multiple monitoring points according to the equipment parameters of the transformer, and set the monitoring characteristics and monitoring indicators required for each monitoring point to achieve comprehensive monitoring of the transformer. By building a correlation model, the influence relationship between the various monitoring points is generated, so as to dynamically adjust the safety threshold of a single monitoring point, improve the early warning efficiency of the potential operating risks of the transformer, and ensure the safe operation of the transformer.

[0017] By adding an associated correction module, multiple environmental disturbance indicators are generated, and a first-level environmental disturbance model corresponding to each monitoring point is generated. Therefore, the real-time operating parameters collected at each monitoring point can be corrected according to the real-time environmental parameters, eliminating the interference of environmental factors, improving the recognition accuracy and maintenance efficiency of potential risks of transformers, and reducing the risk of transformer failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural diagram of an intelligent transformer monitoring system in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0019] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0020] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0022] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0023] like Figure 1 As shown, an intelligent transformer monitoring system according to a preferred embodiment of the present application includes: The central control unit is used to establish multiple monitoring points according to the transformer equipment parameters and set the characteristic monitoring indicators of each monitoring point; A monitoring unit, including a plurality of monitoring submodules, which are arranged at each monitoring point and are used to collect operating parameters of each monitoring point; A correlation correction unit, used to establish a correlation model and an environmental disturbance model based on all monitoring points; The central control unit includes: The first processing module is used to establish a monitoring point sequence A, A=(a1, a2…ai…an), where ai is the i-th monitoring point and n is the number of monitoring points; The second processing module is used to obtain the operating parameters of each monitoring point according to the preset monitoring time node and generate the operating risk value of each monitoring point; The third processing module is used to determine whether to generate a warning instruction according to all operation risk values.

[0024] Specifically, multiple monitoring points are established according to the equipment parameters of the transformer, such as oil and gas monitoring points, micro-water monitoring points, temperature monitoring points, current monitoring points, partial discharge monitoring points and other monitoring points.

[0025] Specifically, the monitoring submodule is preferably various types of sensors, including but not limited to; temperature sensors, gas sensors, micro-water sensors, temperature sensors, current transformers, humidity sensors, ultra-high frequency sensors, etc., and sensors adapted according to different settings of characteristic monitoring indicators that need to be monitored at each monitoring point.

[0026] Specifically, according to the different positions of each monitoring point, the corresponding characteristic monitoring indicators are set, and the characteristic monitoring indicators include but are not limited to temperature value, current value, sound wave value, pulse value, brightness, oil temperature, oil color spectrum, micro water in oil and other parameters. The characteristic evaluation indicators can be set by extracting characteristic parameters of each fault category based on historical parameters.

[0027] Specifically, the association correction unit includes: The first association module is used to generate association evaluation values ​​between various monitoring points and set an association model; The first correction module is used to generate multiple environmental disturbance indicators according to historical environmental parameters, and generate a primary environmental disturbance model for each monitoring point; The first correction module is also used to establish a first-level environmental disturbance model series B, B=(b1, b2...bi...bn), where bi is the first-level environmental disturbance model of the i-th monitoring point.

[0028] Specifically, the environmental disturbance indicators include but are not limited to external environmental temperature, external environmental humidity, dust particles and other parameters.

[0029] Specifically, by analyzing the location parameters and working parameters of the target monitoring point and a single monitoring point, when the operating parameters of a monitoring point change, it will cause the characteristic monitoring indicators of the target monitoring point to fluctuate, and the target monitoring point and the corresponding monitoring point are set as associated monitoring points. A data correction model between the two monitoring points is generated based on historical data, and an associated model is constructed through the association relationship between each monitoring point and the data correction model.

[0030] It can be understood that in the above embodiment, multiple monitoring points are established according to the equipment parameters of the transformer, and the monitoring characteristics and monitoring indicators required for each monitoring point are set to achieve comprehensive monitoring of the transformer. By constructing an association model, the influence relationship between the various monitoring points is generated, so that the safety threshold of a single monitoring point is dynamically adjusted, thereby improving the early warning efficiency of potential operating risks of the transformer and ensuring the safe operation of the transformer.

[0031] In a preferred embodiment of the present application, the second processing module is also used for: According to the monitoring point sequence A, the i-th monitoring point is set as the target monitoring point in sequence; Obtain the operating parameters of the target monitoring point at the current monitoring time node; Establish the characteristic monitoring index sequence C of the target monitoring point at the current monitoring time node, C=(c1,c2…c i …c m ),in, ci is the reference value of the i-th characteristic monitoring indicator of the target monitoring point at the current monitoring time node; Obtain the environmental parameters of the current monitoring time node, and generate the compensation coefficients of each characteristic monitoring indicator of the target monitoring point at the current monitoring time node; Generate the operation risk value f of the target monitoring point at the current monitoring time node; Generate the operation risk value of each monitoring point at the current monitoring time node in sequence; Establish an operation risk value series F, F=(f1,f2…fi…fn), where fi is the operation risk value of the i-th monitoring point at the current monitoring time node.

[0032] Specifically, when generating the compensation coefficient of each characteristic monitoring indicator of the target monitoring point at the current monitoring time node, it includes: Establish the environmental disturbance index sequence D at the current time node, D=(d1,d2…di…dr), where di is the reference value of the i-th environmental disturbance index at the current monitoring time node; Generate the perturbation evaluation value k of the target monitoring point at the current monitoring time node; k = βi * (di - d'i)]; where βi is the influence factor of the i-th environmental perturbation index at the target monitoring point; d'i is the standard reference value of the i-th environmental perturbation index at the target monitoring point; Judge whether to generate a first-level correction instruction according to the perturbation evaluation value k.

[0033] Specifically, the influence factor and standard reference value of its environmental perturbation index need to be set in combination with the historical parameters of the target monitoring point. When the environmental perturbation index changes, the greater the fluctuation of the characteristic monitoring index of the target monitoring point, the greater the corresponding influence factor.

[0034] Specifically, the greater the perturbation evaluation value, the greater the perturbation of the current environmental parameters to the real-time state diagnosis of the target monitoring point.

[0035] Specifically, when judging whether to generate a first-level correction instruction according to the perturbation evaluation value k, it includes: Preset the first perturbation evaluation value threshold K1; If k > K1, generate a first-level correction instruction, call the first-level environmental perturbation model of the target monitoring point according to the first-level correction instruction, and generate the compensation coefficient sequence G of the target monitoring point at the current monitoring time node according to the called first-level environmental perturbation model and the environmental perturbation index sequence D, G = (g1, g2... gi... gm), where gi is the compensation coefficient of the i-th characteristic monitoring index of the target monitoring point at the current monitoring time node; If k < K1, do not generate a first-level correction instruction, and set the compensation coefficient of each characteristic monitoring index of the target monitoring point at the current monitoring time node to 1.

[0036] Specifically, generate the corresponding perturbation evaluation value according to the real-time environmental parameters, and generate the corresponding compensation coefficient according to the real-time compensation evaluation value, so as to correct the real-time operation parameters of the target monitoring point, eliminate the influence of environmental factors on the state diagnosis of the target monitoring point, timely warn of the potential risks of the target monitoring point, and ensure the safe operation of the transformer.

[0037] It can be understood that in the above embodiments, by generating multiple environmental perturbation indexes and generating the corresponding first-level environmental perturbation models for each monitoring point, the real-time operation parameters collected by each monitoring point can be corrected according to the real-time environmental parameters, eliminating the interference of environmental factors, improving the recognition accuracy and maintenance efficiency of the potential risks of the transformer, and reducing the failure risk of the transformer.

[0038] In a preferred embodiment of the present application, when generating the operation risk value f of the target monitoring point at the current monitoring time node, it includes: Generate the first-level standard value C1 of each characteristic monitoring index of the target monitoring point according to the historical parameters, C1=(c 11 ,c 12 …c 1i …c 1m ), where c 1i is the primary standard value of the i-th characteristic monitoring indicator of the target monitoring point Selecting each associated monitoring point of the target monitoring point according to the association model; Generate the secondary standard value sequence C2 of the target monitoring point at the current monitoring time node according to the operating parameters of all associated monitoring points; C2=(c 21 ,c 22 …c 2i …c 2m ), where c 2i It is the secondary standard value of the i-th characteristic monitoring indicator of the target monitoring point at the current monitoring time node.

[0039] Specifically, the first-level standard value is to set the optimal operating value of each characteristic monitoring indicator of the target monitoring point based on historical data; the second-level monitoring value refers to the calibration of the optimal operating value of the real-time target monitoring point after comprehensive analysis of the operating parameters of the associated monitoring points of the target monitoring point, thereby eliminating the impact of the abnormal operation of a single monitoring point on the status of the target monitoring point and improving the diagnostic accuracy of the operating status of a single monitoring point.

[0040] Specifically, when generating the operation risk value f of the target monitoring point at the current monitoring time node, it also includes: f=e1*Q1*[ η i *(c i -c 1i ) 2 ]+e2*Q2*[ η i *(c i -c 1i ) 2 ]+e3*Q3*U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; η i is the influencing factor of the i-th characteristic monitoring indicator of the target monitoring point; U is the historical evaluation value set based on the historical monitoring data of the target monitoring point.

[0041] Specifically, all parameters of the model are normalized by presetting the first fixed coefficient, the second fixed coefficient and the third fixed coefficient, so that each parameter is in the same value range.

[0042] Specifically, the greater the deviation between the historical operating parameters of the target monitoring point and the standard value, the greater the corresponding historical evaluation value. By introducing the historical evaluation value, the cumulative operating status of the target monitoring point is evaluated, thereby achieving accurate monitoring of the target monitoring point and timely warning of potential risks.

[0043] Specifically, the third processing module is also used for: Get the operation risk value series F of the current monitoring time node; Get the operation risk value series F of the current monitoring time node; Preset the first operation risk value threshold F1; If fi>F1, the i-th monitoring point generates a first-level warning instruction at the current monitoring time node; If all data in the operation risk value sequence F are less than F1, the abnormal evaluation value h of the current monitoring time node is generated according to the operation risk value sequence F; Preset abnormal evaluation value threshold H1; If h>H1, the current monitoring time node generates a second-level warning instruction.

[0044] Specifically, the first risk value threshold and the first abnormal evaluation value threshold can be set according to historical parameters.

[0045] Specifically, when generating the abnormal evaluation value h of the current monitoring time node, it includes: h=e4*Q4*[ pi*Y(i)*(fi-F2)]+e5*Q5*[ pi*fi]; Wherein, e4 is the preset fourth weight coefficient; e5 is the preset fifth weight coefficient; Q4 is the preset fourth fixed coefficient; Q5 is the preset fifth fixed coefficient; pi is the influencing factor of the i-th monitoring point; Y(i) is the selection coefficient; if (fi-F2)>0, Y(i)=1 / (fi-F2); if (fi-F2)<0, Y(i)=0; F2 is the preset second risk assessment value threshold, and F2 <F1。

[0046] Specifically, all parameters in the model are normalized by presetting the fourth fixed coefficient and the fifth fixed coefficient, so that each parameter is in the same value range.

[0047] Specifically, if the risk assessment value of a single monitoring point exceeds the second risk assessment value threshold, it means that the corresponding monitoring point is not in the optimal operating state.

[0048] Specifically, the first-level warning instruction means that the current monitoring point is operating abnormally and may fail at any time, and the corresponding monitoring point needs to be inspected and maintained immediately.

[0049] Specifically, when there are no abnormalities at all monitoring points, a global judgment is made on the transformer. If a second-level warning instruction is generated, it means that there may be operating abnormalities in the current transformer. A corresponding maintenance plan needs to be formulated to conduct a comprehensive maintenance of the transformer to eliminate potential risks.

[0050] According to the first concept of the present application, multiple monitoring points are established according to the equipment parameters of the transformer, and the monitoring characteristics and monitoring indicators required for each monitoring point are set to achieve comprehensive monitoring of the transformer. By constructing a correlation model, the influence relationship between the various monitoring points is generated, so as to dynamically adjust the safety threshold of a single monitoring point, thereby improving the early warning efficiency of the potential operating risks of the transformer and ensuring the safe operation of the transformer.

[0051] According to the second concept of the present application, by adding an associated correction module, multiple environmental disturbance indicators are generated, and a first-level environmental disturbance model corresponding to each monitoring point is generated, so that the real-time operating parameters collected by each monitoring point can be corrected according to the real-time environmental parameters, eliminating the interference of environmental factors, improving the recognition accuracy and maintenance efficiency of potential risks of transformers, and reducing the risk of transformer failure.

[0052] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.

Claims

1. An intelligent transformer monitoring system, characterized in that: include: The central control unit is used to establish multiple monitoring points according to the transformer equipment parameters and set the characteristic monitoring indicators of each monitoring point; A monitoring unit, comprising a plurality of monitoring submodules, wherein the monitoring submodules are arranged at each monitoring point and are used to collect operating parameters of each monitoring point; A correlation correction unit, used to establish a correlation model and an environmental disturbance model based on all monitoring points; The central control unit comprises: The first processing module is used to establish a monitoring point sequence A, A=(a1, a2…ai…an), where ai is the i-th monitoring point and n is the number of monitoring points; The second processing module is used to obtain the operating parameters of each monitoring point according to the preset monitoring time node and generate the operating risk value of each monitoring point; The third processing module is used to determine whether to generate a warning instruction according to all operation risk values.

2. The intelligent transformer monitoring system according to claim 1, characterized in that: The association correction unit comprises: The first association module is used to generate association evaluation values ​​between various monitoring points and set an association model; The first correction module is used to generate multiple environmental disturbance indicators according to historical environmental parameters, and generate a primary environmental disturbance model for each monitoring point; The first correction module is also used to establish a first-level environmental disturbance model series B, B=(b1, b2...bi...bn), where bi is the first-level environmental disturbance model of the i-th monitoring point.

3. The intelligent transformer monitoring system according to claim 2, characterized in that: The second processing module is also used for: According to the monitoring point sequence A, the i-th monitoring point is set as the target monitoring point in sequence; Obtain the operating parameters of the target monitoring point at the current monitoring time node; Establish the characteristic monitoring index sequence C of the target monitoring point at the current monitoring time node, C=(c1,c2…c i …c m ),in, ci is the reference value of the i-th characteristic monitoring indicator of the target monitoring point at the current monitoring time node; Obtain the environmental parameters of the current monitoring time node, and generate the compensation coefficients of each characteristic monitoring indicator of the target monitoring point at the current monitoring time node; Generate the operation risk value f of the target monitoring point at the current monitoring time node; Generate the operation risk value of each monitoring point at the current monitoring time node in sequence; Establish an operation risk value series F, F=(f1,f2…fi…fn), where fi is the operation risk value of the i-th monitoring point at the current monitoring time node.

4. The intelligent transformer monitoring system according to claim 3, characterized in that: When generating the compensation coefficients of each characteristic monitoring indicator of the target monitoring point at the current monitoring time node, it includes: Establish the environmental disturbance index sequence D at the current time node, D=(d1,d2…di…dr), where di is the reference value of the i-th environmental disturbance index at the current monitoring time node; Generate the disturbance evaluation value k of the target monitoring point at the current monitoring time node; k= βi*(di-d'i)]; Among them, βi is the influencing factor of the i-th environmental disturbance index in the target monitoring point; d'i is the standard reference value of the i-th environmental disturbance index at the target monitoring point; Whether to generate a first-level correction instruction is determined according to the disturbance evaluation value k.

5. The intelligent transformer monitoring system according to claim 4, characterized in that: When judging whether to generate a first-level correction instruction according to the disturbance evaluation value k, it includes: Preset the first disturbance evaluation value threshold K1; If k>K1, a first-level correction instruction is generated, and the first-level environmental disturbance model of the target monitoring point is called according to the first-level correction instruction, and the compensation coefficient sequence G of the target monitoring point at the current monitoring time node is generated according to the called first-level environmental disturbance model and the environmental disturbance index sequence D, G=(g1, g2…gi…gm), where gi is the compensation coefficient of the i-th characteristic monitoring index of the target monitoring point at the current monitoring time node; If k < K1, no first-level correction instructions are generated, and the compensation coefficients of each feature monitoring index of the target monitoring point at the current monitoring time node are all set to 1.

6. The intelligent transformer monitoring system according to claim 5, characterized in that: When generating the operation risk value f of the target monitoring point at the current monitoring time node, it includes: Generate the first-level standard value C1 of each characteristic monitoring index of the target monitoring point according to the historical parameters, C1=(c 11 ,c 12 …c 1i …c 1m ), where c 1i is the primary standard value of the i-th characteristic monitoring indicator of the target monitoring point Selecting each associated monitoring point of the target monitoring point according to the association model; Generating a second-level standard value sequence C2 of the target monitoring point at the current monitoring time node according to the operation parameters of all associated monitoring points; C2=(c 21 ,c 22 …c 2i …c 2m ), where c 2i It is the secondary standard value of the i-th characteristic monitoring indicator of the target monitoring point at the current monitoring time node.

7. The intelligent transformer monitoring system according to claim 6, characterized in that: When generating the operation risk value f of the target monitoring point at the current monitoring time node, it also includes: f=e1*Q1*[ η i *(c i -c 1i ) 2 ]+e2*Q2*[ η i *(c i -c 1i ) 2 ]+e3*Q3*U; Wherein, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; e3 is the preset third weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; Q3 is the preset third fixed coefficient; η i is the influencing factor of the i-th characteristic monitoring indicator of the target monitoring point; U is the historical evaluation value set based on the historical monitoring data of the target monitoring point.

8. The intelligent transformer monitoring system according to claim 7, characterized in that: The third processing module is further configured to: Obtaining the operation risk value sequence F of the current monitoring time node; Presetting a first operation risk value threshold F1; If fi > F1, the i-th monitoring point generates a first-level warning instruction at the current monitoring time node; If all data in the operation risk value sequence F are less than F1, an abnormal evaluation value h of the current monitoring time node is generated according to the operation risk value sequence F; Presetting an abnormal evaluation value threshold H1; If h > H1, a second-level warning instruction is generated at the current monitoring time node.

9. The intelligent transformer monitoring system according to claim 8, characterized in that: When generating the abnormal evaluation value h of the current monitoring time node, it includes: <h2 style=";text-align:left;direction:ltr">h=e4*Q4*[<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> pi*Y(i)*(fi-F2)]+e5*Q5*[<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> pi*fi]; Wherein, e4 is a preset fourth weight coefficient; e5 is a preset fifth weight coefficient; Q4 is a preset fourth fixed coefficient; Q5 is a preset fifth fixed coefficient; pi is the influence factor of the i-th monitoring point; Y(i) is a selection coefficient; if (fi - F2) > 0, Y(i) = 1 / (fi - F2); if (fi - F2) < 0, Y(i) = 0; F2 is a preset second risk evaluation value threshold, and F2 < F1.

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