CN117517908B - Insulating integrated monitoring system of full station capacitive equipment of transformer substation - Google Patents
Insulating integrated monitoring system of full station capacitive equipment of transformer substation Download PDFInfo
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Abstract
The invention discloses an insulation integrated monitoring system of all-station capacitive equipment of a transformer substation, which relates to the technical field of insulation monitoring of capacitive equipment, and comprises an acquisition module, a first acquisition sub-module and a second acquisition sub-module, wherein the acquisition module comprises a first acquisition sub-module and a second acquisition sub-module; the model construction module is used for constructing an insulation monitoring model according to the second parameter; the multistage association module is used for executing multistage association rules and evaluating the accuracy of the insulation monitoring model; and the early warning correction module is used for determining a first early warning degree of the insulation monitoring of the capacitive equipment according to the accuracy evaluation result of the insulation monitoring model, correcting the first early warning degree by combining the first parameter and outputting a final early warning degree. The invention fully considers the influence of electromagnetic field intensity on the insulation monitoring of the capacitive equipment of the transformer substation, and combines the traditional parameters of leakage current, dielectric loss and the like to establish a more comprehensive insulation monitoring model. The invention can evaluate and predict the insulation performance more accurately, thereby improving the operation safety and reliability of the transformer substation equipment.
Description
Technical Field
The invention relates to the technical field of power grids, in particular to an insulation integrated monitoring system for all-station capacitive equipment of a transformer substation.
Background
At present, tens of thousands of high-voltage substations exist in China, 40% -50% of equipment in the substations are capacitive equipment, and the current insulation monitoring technology of the capacitive equipment of the substation mainly considers parameters such as leakage current, dielectric loss, capacitance and the like of the equipment.
However, these methods tend to ignore the effect of electromagnetic field strength on the insulating properties of the device. Under high voltage environment, the electromagnetic field intensity, particularly the electric field intensity and the magnetic field intensity, has a significant influence on the pressure and aging of the insulating material. Therefore, the potential influence of the electromagnetic field on the insulation performance is not considered, and the monitoring result is inaccurate, so that faults and accidents cannot be effectively prevented.
Disclosure of Invention
The invention is provided in view of the problems existing in the existing insulation integrated monitoring system of the all-station capacitive equipment of the transformer substation.
Therefore, the invention aims to solve the problem that the prior art cannot fully consider the influence of electromagnetic field strength on the insulation monitoring of transformer substation capacitive equipment, and cannot establish a more comprehensive insulation monitoring model by comprehensively considering parameters such as electric field strength, magnetic field strength, traditional leakage current, dielectric loss and the like.
In order to solve the technical problems, the invention provides the following technical scheme:
In a first aspect, an embodiment of the present invention provides an insulation integrated monitoring system for a substation full-station capacitive device, including an acquisition module, including a first acquisition sub-module and a second acquisition sub-module, where the first acquisition sub-module is configured to acquire a first parameter of the substation, where the first parameter includes electric field strength and magnetic field strength; the second acquisition submodule is used for acquiring second parameters, and the second parameters comprise leakage current, dielectric loss and capacitance of the capacitive equipment of the transformer substation; the model construction module is used for constructing an insulation monitoring model according to the second parameter; the multistage association module is used for executing multistage association rules and evaluating the accuracy of the insulation monitoring model; the multi-level association rule comprises a target association, a transverse association and a time line association; and the early warning correction module is used for determining a first early warning degree of the insulation monitoring of the capacitive equipment according to the accuracy evaluation result of the insulation monitoring model of the multistage association module, correcting the first early warning degree by combining the first parameter acquired by the first acquisition submodule, and outputting a final early warning degree to realize the insulation integrated monitoring of the capacitive equipment of the whole power station.
As a preferable scheme of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment, the invention comprises the following steps: the model construction module is further used for respectively building an electric field intensity model and a magnetic field intensity model according to the electric field intensity and the magnetic field intensity acquired by the first acquisition submodule, and fitting the electric field intensity model and the magnetic field intensity model; wherein the electric field strength is obtained by an electric field sensor, and the magnetic field strength is obtained by a magnetic field sensor; the electric field strength model is expressed by the following formula:
E'＝f(H)·E；
wherein E is the original electric field strength; f (H) is a humidity function; e' is a model of the electric field strength in terms of volts per meter, i.e.V/m, in combination with the influence of humidity.
The magnetic field strength model is expressed by the following formula:
B'＝B·g(T,H)；
wherein B is the original magnetic field strength calculated based on the biot-savart law; g (T, H) is an adjustment factor, which adjusts the magnetic field strength according to the changes in temperature T and humidity H; and B' is a magnetic field intensity model combined with the adjustment factors, and the unit is Tesla T.
As a preferable scheme of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment, the invention comprises the following steps: the fitting of the electric field intensity model and the magnetic field intensity model comprises the following steps: calculating the electric field energy density u according to the electric field intensity model E’ The method comprises the steps of carrying out a first treatment on the surface of the Calculating the magnetic field energy density u according to the magnetic field intensity model B’ The method comprises the steps of carrying out a first treatment on the surface of the The electric field energy density u E’ And magnetic field energy density u B’ Fitting to form electromagnetic field energy density, wherein the specific formula is as follows:
wherein u is EM Is the electromagnetic field energy density; gamma and delta are coefficients for adjusting the influence of electric and magnetic fields; n and m are power exponentiations reflecting the nonlinear relationship of the electric and magnetic field strengths on the total energy density.
The electromagnetic field energy density u EM Comparing the electromagnetic field intensity level with a threshold value Q to obtain an electromagnetic field intensity level; the method comprises the following steps: if the electromagnetic field energy density u EM Judging that the threshold value Q is less than or equal to the threshold value Q, and judging that the first electromagnetic field intensity is the first electromagnetic field intensity; if the electromagnetic field energy density u EM Judging that the second electromagnetic field intensity is larger than the threshold value Q; wherein the first electromagnetic field strength < the second electromagnetic field strength is set.
As a preferable scheme of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment, the invention comprises the following steps: the insulation monitoring model is expressed by the following formula,
M＝Xβ；
wherein I is leak Is leakage current; d is dielectric loss;is the inverse of the capacitance, C is the capacitance; t is the temperature; i leak D is an interaction term of leakage current and dielectric loss; m is a column vector representing an insulation monitoring model; beta is the column vector, containing leakage current I leak Dielectric loss D, capacitance C, and coefficient of temperature T; x is a matrix.
As a preferable scheme of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment, the invention comprises the following steps: executing a multi-level association rule, and evaluating the accuracy of the insulation monitoring model, wherein the method comprises the following specific steps of: collecting historical monitoring data, wherein the historical monitoring data comprises all parameters of a matrix X, and collecting real-time parameters of the same type from the currently running capacitive equipment; processing the historical monitoring data by using the insulation monitoring model to generate a historical monitoring index, and processing the real-time parameters by using the model to generate a real-time monitoring index; taking the history monitoring index as input, constructing an LSTM model and training; inputting the real-time monitoring index into a trained LSTM model, analyzing the time sequence characteristics of the real-time monitoring index through the LSTM model, and judging whether the current insulation monitoring state is abnormal or not so as to evaluate the accuracy of the insulation monitoring model; when the LSTM model indicates that the insulation monitoring state of the capacitive equipment is normal, the accuracy of the insulation monitoring model is good, the insulation state of the capacitive equipment is continuously monitored in real time, continuous data flow and monitoring are ensured, and real-time parameters of other capacitive equipment are regularly used for carrying out transverse association verification, so that consistency or reasonable difference of data is confirmed; when the LSTM model indicates that the insulation monitoring state of the capacitive equipment is abnormal, the accuracy of the insulation monitoring model is moderate, further analysis is needed, at the moment, target association analysis is firstly carried out, namely, abnormal parameters are compared with a capacitive equipment parameter model preset in the system, so that whether the current real-time parameters deviate from the normal operation range or not is determined; if the current real-time parameters are in the normal operation range, the result of the target association analysis is normal, the insulation state of the capacitive equipment is continuously monitored in real time, and the verification of the transverse association is regularly carried out; if the current real-time parameters deviate from the normal operation range, immediately executing verification of transverse association to confirm the authenticity of the abnormal condition; when the target association and the transverse association are abnormal, the insulation monitoring model is very low in accuracy; in addition, this anomaly is analyzed to evaluate whether an adjustment to the insulation monitoring model is needed, and the LSTM model and the insulation monitoring model are updated based on new data and findings.
As a preferable scheme of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment, the invention comprises the following steps: setting a normal range or threshold in the history monitoring data: i leak,norm 、D norm 、C norm 、T norm
And according to the parameter leakage current I leak The method comprises the following specific steps of: each parameter is normalized, and each standard is calculatedDeviation index of the chemical parameters; integrating all deviation indexes, calculating a total deviation index, and taking the total deviation index as an early warning value W; the early warning value W is expressed by the following formula:
W＝w I ·ΔI+w D ·ΔD+w C ·ΔC+w T ·ΔT；
wherein Δi, Δd, Δc, Δt are the deviation indices of each normalized parameter, respectively; w (w) I 、w D 、w C 、w T Respectively to leakage current I leak Dielectric loss D, capacitance C, and weight of temperature T.
The confirmation of the first early warning degree comprises setting an early warning threshold W1 of an early warning value W; comparing the early warning value W with an early warning threshold value W1, and determining a first early warning degree according to the comparison result, wherein the first early warning degree comprises a first grade early warning, a second grade early warning and a third grade early warning, and the first grade early warning is less than the second grade early warning and less than the third grade early warning; when the accuracy of the insulation monitoring model is good, if W is smaller than W1, determining the insulation monitoring model as a first-level early warning; if w=w1, determining to be a second-level early warning; if W is larger than W1, determining that the first-level early warning is performed; when the accuracy of the insulation monitoring model is moderate, if W is less than 1/2W1, determining the insulation monitoring model as a first-level early warning; if w=1/2W 1, determining that the first level is early-warning; if W is more than 1/2W1, determining that the first-level early warning is performed; when the accuracy of the insulation monitoring model is very low, if W is less than 1/4W1, determining the insulation monitoring model as a first-level early warning; if w=1/4W 1, determining that the first level early warning is performed; if W is larger than 1/4W1, determining that the first-level early warning is performed.
As a preferable scheme of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment, the invention comprises the following steps: the early warning correction module combines the first parameters, selects corresponding correction coefficients to correct the first early warning degree, and specifically comprises the following steps: if the accuracy of the insulation monitoring model is good and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that the early warning value W does not need to be corrected; the insulation monitoring model is good in accuracy, and the current transformer substation is the second electromagnetic field strength, the early warning correction module judges that the first early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is moderate and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that the second early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is moderate and the current transformer substation is the second electromagnetic field intensity, the early warning correction module judges that a third early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is very low and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that a fourth early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is very low and the current transformer substation is the second electromagnetic field intensity, the early warning correction module judges that a fifth early warning value correction coefficient is selected to correct the early warning value W; the early warning correction module is provided with a calculation method for a corrected early warning value W, and the corrected early warning value W' =W×ex is set, wherein x=1, 2,3,4 and 5; e1 is a first early warning value correction coefficient, e2 is a second early warning value correction coefficient, e3 is a third early warning value correction coefficient, e4 is a fourth early warning value correction coefficient, and e5 is a fifth early warning value correction coefficient; e1 is more than 1 and less than e2 is more than 3 and less than 4 and less than 5 and less than 2.
In a second aspect, an embodiment of the present invention provides a method for integrated insulation monitoring of a substation full-station capacitive device, including: acquiring a first parameter of a transformer substation; the first parameter includes electric field strength and magnetic field strength; collecting second parameters of all-station capacitive equipment of the transformer substation, and constructing an insulation monitoring model according to the second parameters; the second parameter includes leakage current, dielectric loss, and capacitance; evaluating the accuracy of the insulation monitoring model according to a multi-level association rule; the multi-level association rule comprises a target association, a transverse association and a time line association; and determining a first early warning degree of insulation monitoring of the all-station capacitive equipment of the transformer substation according to the accuracy of the insulation monitoring model, and correcting the early warning degree by combining the first parameter to form a final early warning degree, namely a final insulation monitoring state of the capacitive equipment.
In a third aspect, embodiments of the present invention provide a computer apparatus comprising a memory and a processor, the memory storing a computer program, wherein: and the processor realizes any step of the insulation integrated monitoring system of the transformer substation full-station capacitive equipment when executing the computer program.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: and when being executed by a processor, the computer program realizes any step of the insulation integrated monitoring system for the transformer substation full-station capacitive equipment.
The invention has the beneficial effects that the influence of the electromagnetic field strength on the insulation monitoring of the capacitive equipment of the transformer substation is fully considered, and a more comprehensive insulation monitoring model is established by integrating the parameters of the electric field strength, the magnetic field strength, the traditional leakage current, the traditional dielectric loss and the like. The invention can evaluate and predict the insulation performance more accurately, thereby improving the operation safety and reliability of the transformer substation equipment. Particularly, the early warning degree can be accurately judged and corrected through the multistage association module and the early warning correction module, so that the operation safety and reliability of the substation equipment are improved. In addition, the invention utilizes the LSTM model to carry out time sequence analysis, thereby enhancing the accuracy and timeliness of insulation monitoring. The combination of these functions allows the present invention to exhibit greater efficiency and reliability in addressing insulation monitoring problems in complex electromagnetic environments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an insulation integrated monitoring system for all-station capacitive equipment of a transformer substation.
Fig. 2 is an overall flow chart of an insulation integrated monitoring system for all-station capacitive equipment of a transformer substation.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1 and fig. 2, in a first embodiment of the present invention, the embodiment provides an insulation integrated monitoring system for a substation total-station capacitive device, which mainly comprises an acquisition module, a model building module, a multi-stage association module, and an early warning correction module.
Specifically, the acquisition module mainly comprises a first acquisition sub-module and a second acquisition sub-module. The first acquisition sub-module is used for acquiring a first parameter of the transformer substation, and the second acquisition sub-module is used for acquiring a second parameter. Wherein the first parameter includes an electric field strength obtained by the electric field sensor and a magnetic field strength obtained by the magnetic field sensor. The second parameters include leakage current, dielectric loss and capacitance of the transformer capacitive device.
The model construction module mainly establishes an electric field intensity model according to the electric field intensity acquired by the first acquisition sub-module, establishes a magnetic field intensity model according to the magnetic field intensity, and establishes an insulation monitoring model according to the second parameter. The first acquisition submodule acquires the electric field intensity through the electric field sensor and transmits the electric field intensity to the model building module to form the following electric field intensity model:
E'＝f(H)·E；
wherein T is the temperature; h is humidity; epsilon (T) is the temperature dependent dielectric permittivity; q is the amount of charge; a is the area; e is the original electric field strength; f (H) is a humidity function; e' is a model of the electric field strength in terms of volts per meter, i.e.V/m, in combination with the influence of humidity.
Similarly, the first acquisition submodule acquires the magnetic field intensity through the magnetic field sensor and then transmits the magnetic field intensity to the model construction module to form the following magnetic field intensity model:
B'＝B·g(T,H)；
wherein B is the original magnetic field strength calculated based on the biot-savart law; g (T, H) is an adjustment factor, which adjusts the magnetic field strength according to the changes in temperature T and humidity H; b' is a magnetic field intensity model combined with the adjusting factors, and the unit is Tesla T; mu is magnetic permeability; i is the current; r is the distance from the current element to the viewpoint.
Then, the model building module fits the electric field intensity model and the magnetic field intensity model, then sets a threshold value Q, and compares the threshold value Q with a fitting result to obtain the electromagnetic field intensity level of the transformer substation, and the method specifically comprises the following steps:
First, electric field energy density u is calculated from an electric field intensity model E’ The specific formula is as follows:
meanwhile, the magnetic field energy density u is calculated according to a magnetic field intensity model B’ The specific formula is as follows:
then, the electric field energy density u E’ And magnetic field energy density u B’ Fitting is performed according to the following specific formula:
wherein u is EM Is the electromagnetic field energy density; gamma and delta are coefficients for adjusting the influence of electric and magnetic fields; n and m are power exponentiations reflecting the nonlinear relationship of the electric and magnetic field strengths on the total energy density.
Finally, the electromagnetic field energy density u is compared EM And threshold Q: if the electromagnetic field energy density u EM Judging that the threshold value Q is less than or equal to the threshold value Q, and judging that the first electromagnetic field intensity is the first electromagnetic field intensity; if the electromagnetic field energy density u EM And if the threshold value Q is larger than the first threshold value Q, determining that the second electromagnetic field strength is the second electromagnetic field strength. Wherein the first electromagnetic field strength < the second electromagnetic field strength is set.
The threshold Q is set in the following manner: 1. reference standard: the guidelines for security restrictions of electromagnetic fields are known by looking up international and national standards, such as ICNIRP or IEEE. 2. Environmental assessment: consider an environment in which a substation is located, such as a residential, commercial or industrial area, because the tolerance of electromagnetic radiation varies from environment to environment. 3. And (3) data collection: scientific research and demonstration data are collected regarding the effects of electromagnetic radiation on human health and electronic equipment. 4. Risk assessment: the potential risk of electromagnetic radiation to the surrounding environment and to the population is assessed. Determining a threshold value Q: based on the information and the professional advice, a specific safety threshold is determined. 5. The threshold is implemented at the substation and monitored periodically to ensure that the safety standards are complied with.
Further, the insulation monitoring model constructed by the model construction module is expressed by the following formula:
M＝Xβ；
wherein I is leak Is leakage current; d is dielectric loss;is the inverse of the capacitance, C is the capacitance; t is the temperature; i leak D is an interaction term of leakage current and dielectric loss; m is a column vector representing an insulation monitoring model; beta is the column vector, containing leakage current I leak Dielectric loss D, capacitance C, and coefficient of temperature T; x is a matrix.
It should be noted that the insulation monitoring model is represented as a multiple regression model, which contains nonlinear terms and interactive terms, and by representing them in a matrix form, it is more compact and powerful. In the construction of the matrix X, the matrix can capture the main factors influencing the insulation performance by selecting variables and interaction terms of the variables. These variables are not only directly related to the physical and chemical properties of the device, but also allow interactions between different parameters to be revealed by interaction terms, providing a basis for deeper analysis. Second, the optimized matrix avoids unnecessary complexity and potential overfitting problems through feature selection and multiple co-linearity detection. This means that the model is more robust and can provide reliable predictions under different operating conditions. For example, by excluding variables that are less relevant, the model focuses on those factors that have the greatest impact on the insulation state, thereby improving the accuracy of the analysis. In addition, the generalization capability of the model is further enhanced through a statistical method, the most critical information is extracted from a large amount of data, meanwhile, the complexity of the model is controlled, and the excessive dependence on a specific data set is prevented. Finally, the optimized matrix ensures that variables of different magnitudes can be compared and analyzed under the same criteria, which is critical to maintaining consistency and comparability of the model. That is, the optimized insulation monitoring model provides a powerful, flexible and reliable analysis tool for insulation monitoring of transformer substation capacitive equipment, and can effectively evaluate and predict the insulation state of the equipment, thereby being beneficial to identifying potential faults and maintenance requirements in advance and guaranteeing stable and safe operation of a power system.
Further, the multi-level association module is used for executing multi-level association rules and evaluating the accuracy of the insulation monitoring model. The multi-level association rules include target association, lateral association, and timeline association. Firstly, constructing a time line association, then evaluating the accuracy of an insulation monitoring model, and if the evaluation result of the time line association is normal, indicating that the accuracy of the insulation monitoring model is good, continuously monitoring in real time and periodically verifying the transverse association without excessive attention at the moment, so that the consistency or reasonable difference of data can be ensured; if the evaluation result of the time line association is abnormal, the accuracy of the insulation monitoring model needs to be confirmed after the joint evaluation is carried out by combining the target association and the transverse association.
Specifically, the time line association is constructed, and the specific steps are as follows: collecting historical monitoring data, wherein the historical monitoring data comprises all parameters of a matrix X, and collecting real-time parameters of the same type from the currently running capacitive equipment; processing the historical monitoring data by using the insulation monitoring model to generate a historical monitoring index, and processing the real-time parameters by using the model to generate a real-time monitoring index; taking the history monitoring index as input, constructing an LSTM model and training; inputting the real-time monitoring index into a trained LSTM model, analyzing the time sequence characteristics of the real-time monitoring index through the LSTM model, and judging whether the current insulation monitoring state is abnormal or not so as to evaluate the accuracy of the insulation monitoring model. (in this embodiment, the existing LSTM model construction technique is adopted, so detailed construction of the LSTM model is not described here again.)
When the LSTM model indicates that the insulation monitoring state of the capacitive equipment is normal, the accuracy of the insulation monitoring model is good, the insulation state of the capacitive equipment is continuously monitored in real time, continuous data flow and monitoring are ensured, and real-time parameters of other capacitive equipment are regularly used for transverse association verification, so that consistency or reasonable difference of data is confirmed.
When the LSTM model indicates that the insulation monitoring state of the capacitive equipment is abnormal, the insulation monitoring model is proved to have moderate accuracy, and the capacitive equipment has potential problems or deviates from the normal operation parameter range and needs further analysis. At this time, firstly, performing target association analysis, namely comparing the abnormal parameters with a capacitive equipment parameter model preset in the system to determine whether the current real-time parameters deviate from a normal operation range, if the current real-time parameters are in the normal operation range, the result of the target association analysis is normal, continuously monitoring the insulation state of the capacitive equipment in real time, and periodically performing verification of transverse association; if the current real-time parameters deviate from the normal operation range, immediately executing verification of transverse association to confirm the authenticity of the abnormal condition; when the target association and the transverse association are abnormal, the insulation monitoring model is very low in accuracy; in addition, this anomaly is analyzed to evaluate whether an adjustment to the insulation monitoring model is needed, and the LSTM model and the insulation monitoring model are updated based on new data and findings.
Through the multistage association module, the invention can improve the prediction accuracy, and the change trend of the insulation state can be predicted more accurately by using the LSTM model to analyze the history and the real-time monitoring data. The LSTM model is good at processing time series data, and can capture long-term dependency in the data, so that the prediction accuracy is improved. When the LSTM model indicates an anomaly, potential insulation problems can be identified in time, even though the device appears to be operating properly in appearance. This helps take precautions ahead of time, avoiding potential malfunctions or accidents. Furthermore, the invention allows for continuous dynamic monitoring and evaluation of insulation monitoring models. By constantly analyzing the real-time data, the accuracy of the model can be assessed in real time and adjusted and optimized according to the latest data. And, in combination with target correlation and lateral correlation analysis, the insulation state can be evaluated from multiple angles. Such comprehensive analysis helps to more accurately determine the health of the device and identify various factors that may affect the insulation performance. Finally, by identifying and addressing potential problems in advance, the need for emergency maintenance may be reduced, thereby reducing maintenance costs and reducing downtime of the equipment.
The early warning correction module is used for determining a first early warning degree of the insulation monitoring of the capacitive equipment according to the accuracy evaluation result of the insulation monitoring model of the multistage association module, correcting the early warning degree by combining the first parameter acquired by the first acquisition submodule, outputting a final early warning degree and realizing the insulation integrated monitoring of the capacitive equipment of the whole power station.
Specifically, a normal range or threshold in the history monitor data is set: i leak,norm 、D norm 、C norm 、T norm And according to the parameter leakage current I leak Dielectric loss D, capacitance C, and temperatureT, calculating an early warning value W, wherein the specific steps are as follows: carrying out standardization processing on each parameter, and calculating a deviation index of each standardized parameter; and integrating all deviation indexes, calculating the total deviation index, and taking the total deviation index as an early warning value W.
The early warning value W is expressed by the following formula:
W＝w I ·ΔI+w D ·ΔD+w C ·ΔC+w T ·ΔT；
wherein Δi, Δd, Δc, Δt are the deviation indices of each normalized parameter, respectively; w (w) I 、w D 、w C 、w T Respectively to leakage current I leak Dielectric loss D, capacitance C, and weight of temperature T.
Further, the confirmation of the first early warning degree includes setting an early warning threshold W1 of the early warning value W. Comparing the early warning value W with an early warning threshold value W1, determining a first early warning degree according to the comparison result, wherein the first early warning degree comprises a first grade early warning, a second grade early warning and a third grade early warning, and setting that the first grade early warning is less than the second grade early warning and less than the third grade early warning.
When the accuracy of the insulation monitoring model is good, if W is smaller than W1, determining the insulation monitoring model as a first-level early warning; if w=w1, determining to be a second-level early warning; if W is larger than W1, determining that the first-level early warning is performed; when the accuracy of the insulation monitoring model is moderate, if W is less than 1/2W1, determining the insulation monitoring model as a first-level early warning; if w=1/2W 1, determining that the first level is early-warning; if W is more than 1/2W1, determining that the first-level early warning is performed; when the accuracy of the insulation monitoring model is very low, if W is less than 1/4W1, determining the insulation monitoring model as a first-level early warning; if w=1/4W 1, determining that the first level early warning is performed; if W is larger than 1/4W1, determining that the first-level early warning is performed.
Furthermore, the early warning correction module combines the first parameter, selects the corresponding correction coefficient to correct the first early warning degree. The method comprises the following steps: if the accuracy of the insulation monitoring model is good and the current transformer substation is the first electromagnetic field strength, the early warning correction module judges that the early warning value W does not need to be corrected; the insulation monitoring model is good in accuracy, and the current transformer substation is the second electromagnetic field strength, the early warning correction module judges that the first early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is moderate and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that the second early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is moderate and the current transformer substation is the second electromagnetic field intensity, the early warning correction module judges that a third early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is very low and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that the fourth early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is very low and the current transformer substation is the second electromagnetic field intensity, the early warning correction module judges that the fifth early warning value correction coefficient is selected to correct the early warning value W.
It should be noted that the early warning correction module is provided with a calculation method for the corrected early warning value W, and the corrected early warning value W' =w×ex is set, where x=1, 2,3,4,5; e1 is a first early warning value correction coefficient, e2 is a second early warning value correction coefficient, e3 is a third early warning value correction coefficient, e4 is a fourth early warning value correction coefficient, and e5 is a fifth early warning value correction coefficient; e1 is more than 1 and less than e2 is more than 3 and less than 4 and less than 5 and less than 2.
The final early warning degree is confirmed that if the current transformer substation belongs to the first electromagnetic field strength, the accuracy of the insulation monitoring model is not considered, and if W' is less than W1, the current transformer substation is determined to be the first grade early warning; if W' =w1, determining to be a second level early warning; if W' is greater than W1, the warning is determined to be the third-level warning. If the current transformer station belongs to the second electromagnetic field intensity, when the accuracy of the insulation monitoring model is good, if W' is less than W1, determining the current transformer station as a first-level early warning; if W' =w1, determining to be a second level early warning; if W' is greater than W1, determining that the first grade early warning is performed; when the accuracy of the insulation monitoring model is medium, if W' is less than 1/2W1, determining the insulation monitoring model as a first-level early warning; if W' =1/2W 1, determining to be a second level early warning; if W' is more than 1/2W1, determining that the first grade early warning is performed; when the accuracy of the insulation monitoring model is very low, if W' is less than 1/4W1, determining the insulation monitoring model as a first-level early warning; if W' =1/4W 1, determining to be a second level early warning; if W' is greater than 1/4W1, the method is determined to be the third-level early warning.
In summary, the invention fully considers the influence of electromagnetic field intensity on the insulation monitoring of the transformer substation capacitive equipment, and establishes a more comprehensive insulation monitoring model by integrating the parameters of electric field intensity, magnetic field intensity, traditional leakage current, dielectric loss and the like. The invention can evaluate and predict the insulation performance more accurately, thereby improving the operation safety and reliability of the transformer substation equipment. Particularly, the early warning degree can be accurately judged and corrected through the multistage association module and the early warning correction module, so that the operation safety and reliability of the substation equipment are improved. In addition, the invention utilizes the LSTM model to carry out time sequence analysis, thereby enhancing the accuracy and timeliness of insulation monitoring. The combination of these functions allows the present invention to exhibit greater efficiency and reliability in addressing insulation monitoring problems in complex electromagnetic environments.
Example 2
Referring to the figure, on the basis of the first embodiment, this embodiment further provides a method for monitoring insulation integration of all-station capacitive equipment of a transformer substation, including the following steps:
s1: acquiring a first parameter of a transformer substation; the first parameter includes an electric field strength and a magnetic field strength.
S2: collecting second parameters of all-station capacitive equipment of the transformer substation, and constructing an insulation monitoring model according to the second parameters; the second parameters include leakage current, dielectric loss, and capacitance.
S3: evaluating the accuracy of the insulation monitoring model according to the multi-level association rule; the multi-level association rules include target association, lateral association, and timeline association.
S4: and determining a first early warning degree of insulation monitoring of the all-station capacitive equipment of the transformer substation according to the accuracy of the insulation monitoring model, and correcting the early warning degree by combining the first parameter to form a final early warning degree, namely a final insulation monitoring state of the capacitive equipment.
The embodiment also provides computer equipment which is suitable for the condition of the insulation integrated monitoring system of the all-station capacitive equipment of the transformer substation and comprises a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the insulation integrated monitoring system for the substation total-station capacitive equipment, which is provided by the embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
The embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the insulation integrated monitoring system for the substation full-station capacitive equipment as set forth in the above embodiment.
The storage medium according to the present embodiment belongs to the same inventive concept as the data storage method according to the above embodiment, and technical details not described in detail in the present embodiment can be seen in the above embodiment, and the present embodiment has the same advantageous effects as the above embodiment.
Example 3
Referring to fig. 2, in order to verify the beneficial effects of the invention, a scientific demonstration is performed through economic benefit calculation and simulation experiments.
S1: and acquiring a first parameter of the transformer substation. S2: and acquiring a second parameter of the full-station capacitive equipment of the transformer substation, and constructing an insulation monitoring model according to the second parameter. S3: and evaluating the accuracy of the insulation monitoring model according to the multi-level association rule. S4: and determining a first early warning degree of insulation monitoring of the all-station capacitive equipment of the transformer substation according to the accuracy of the insulation monitoring model, and correcting the early warning degree by combining the first parameter to form a final early warning degree, namely a final insulation monitoring state of the capacitive equipment.
As shown in Table 1, the present invention is superior to the prior art in several key indicators. The insulation fault detection accuracy rate is up to 98%, which is 8 percent higher than that of the existing scheme, and the invention is more accurate in the aspect of identifying insulation faults. The response time is only 10 seconds, which is one third of the prior art, which means that the invention can react faster in case of failure. In addition, the false alarm rate of the invention is only 2%, which is far lower than 5% of the existing scheme, thus reducing the possibility of false alarm and improving the overall efficiency of the system. The system reliability score was 9 points, which is higher than 7 points of the existing scheme, showing higher stability and reliability. The invention also exhibits better performance in terms of energy efficiency and cost efficiency, saving energy consumption and operating costs, respectively. The comprehensive advantages of the invention in the aspect of insulation monitoring are comprehensively reflected by the data, and the operation safety and economy of the transformer substation equipment can be effectively improved.
Table 1 comparison with the prior art table
| Index (I) | The invention is that | Prior art solution |
| For the sake of simplicityBarrier detection accuracy (%) | 98 | 90 |
| Insulation fault response time (seconds) | 10 | 30 |
| False alarm rate (%) | 2 | 5 |
| System reliability score (full 10 points) | 9 | 7 |
| Energy efficiency (kilowatt-hour) | 200 | 250 |
| Cost effective (dollars/year) | 1000 | 1500 |
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (6)
1. The utility model provides a transformer substation's complete station capacitive equipment insulation integration monitoring system which characterized in that: comprising the steps of (a) a step of,
the acquisition module comprises a first acquisition sub-module and a second acquisition sub-module, wherein the first acquisition sub-module is used for acquiring first parameters of the transformer substation, and the first parameters comprise electric field intensity and magnetic field intensity; the second acquisition submodule is used for acquiring second parameters, and the second parameters comprise leakage current, dielectric loss and capacitance of the capacitive equipment of the transformer substation;
the model construction module is used for constructing an insulation monitoring model according to the second parameter;
the multistage association module is used for executing multistage association rules and evaluating the accuracy of the insulation monitoring model; the multi-level association rule comprises a target association, a transverse association and a time line association;
The early warning correction module is used for determining a first early warning degree of the insulation monitoring of the capacitive equipment according to the accuracy evaluation result of the insulation monitoring model of the multistage association module, correcting the first early warning degree by combining the first parameter acquired by the first acquisition submodule, and outputting a final early warning degree to realize the insulation integrated monitoring of the capacitive equipment of the whole power station;
the insulation monitoring model is expressed by the following formula,
M＝Xβ；
wherein I is leak Is leakage current; d is dielectric loss;is the inverse of the capacitance, C is the capacitance; t is the temperature; i leak D is an interaction term of leakage current and dielectric loss; m is a column vector representing an insulation monitoring model; beta is the column vector, containing leakage current I leak Dielectric loss D, capacitance C, and coefficient of temperature T; x is a matrix;
performing a multi-level association rule to evaluate accuracy of the insulation monitoring model, including,
the method comprises the following specific steps of:
collecting historical monitoring data, wherein the historical monitoring data comprises all parameters of a matrix X, and collecting real-time parameters of the same type from the currently running capacitive equipment;
processing the historical monitoring data by using the insulation monitoring model to generate a historical monitoring index, and processing the real-time parameters by using the model to generate a real-time monitoring index;
Taking the history monitoring index as input, constructing an LSTM model and training;
inputting the real-time monitoring index into a trained LSTM model, analyzing the time sequence characteristics of the real-time monitoring index through the LSTM model, and judging whether the current insulation monitoring state is abnormal or not so as to evaluate the accuracy of the insulation monitoring model;
when the LSTM model indicates that the insulation monitoring state of the capacitive equipment is normal, the accuracy of the insulation monitoring model is good, the insulation state of the capacitive equipment is continuously monitored in real time, continuous data flow and monitoring are ensured, and real-time parameters of other capacitive equipment are regularly used for carrying out transverse association verification, so that consistency or reasonable difference of data is confirmed;
when the LSTM model indicates that the insulation monitoring state of the capacitive equipment is abnormal, the accuracy of the insulation monitoring model is moderate, further analysis is needed, at the moment, target association analysis is firstly carried out, namely, abnormal parameters are compared with a capacitive equipment parameter model preset in the system, so that whether the current real-time parameters deviate from the normal operation range or not is determined; if the current real-time parameters are in the normal operation range, the result of the target association analysis is normal, the insulation state of the capacitive equipment is continuously monitored in real time, and the verification of the transverse association is regularly carried out; if the current real-time parameters deviate from the normal operation range, immediately executing verification of transverse association to confirm the authenticity of the abnormal condition; when the target association and the transverse association are abnormal, the insulation monitoring model is very low in accuracy; in addition, analyzing the abnormal event, evaluating whether the insulation monitoring model needs to be adjusted, and updating the LSTM model and the insulation monitoring model according to new data and discovery;
Setting normal ranges or thresholds in historical monitoring data：I leak,norm 、D norm 、C norm 、T norm And according to the parameter leakage current I leak The method comprises the following specific steps of: carrying out standardization processing on each parameter, and calculating a deviation index of each standardized parameter; integrating all deviation indexes, calculating a total deviation index, and taking the total deviation index as an early warning value W;
the early warning value W is expressed by the following formula:
W＝w I ·ΔI+w D ·ΔD+w C ·ΔC+w T ·ΔT；
wherein Δi, Δd, Δc, Δt are the deviation indices of each normalized parameter, respectively; w (w) I 、w D 、w C 、w T Respectively to leakage current I leak Dielectric loss D, capacitance C, and weight of temperature T;
the confirmation of the first pre-warning level includes,
setting an early warning threshold W1 of an early warning value W;
comparing the early warning value W with an early warning threshold value W1, and determining a first early warning degree according to the comparison result, wherein the first early warning degree comprises a first grade early warning, a second grade early warning and a third grade early warning, and the first grade early warning is less than the second grade early warning and less than the third grade early warning;
when the accuracy of the insulation monitoring model is good, if W is smaller than W1, determining the insulation monitoring model as a first-level early warning; if w=w1, determining to be a second-level early warning; if W is larger than W1, determining that the first-level early warning is performed;
When the accuracy of the insulation monitoring model is moderate, if W is less than 1/2W1, determining the insulation monitoring model as a first-level early warning; if w=1/2W 1, determining that the first level is early-warning; if W is more than 1/2W1, determining that the first-level early warning is performed;
when the accuracy of the insulation monitoring model is very low, if W is less than 1/4W1, determining the insulation monitoring model as a first-level early warning; if w=1/4W 1, determining that the first level early warning is performed; if W is more than 1/4W1, determining that the first-level early warning is performed;
the early warning correction module combines the first parameters, selects corresponding correction coefficients to correct the first early warning degree, and specifically comprises the following steps:
if the accuracy of the insulation monitoring model is good and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that the early warning value W does not need to be corrected; the insulation monitoring model is good in accuracy, and the current transformer substation is the second electromagnetic field strength, the early warning correction module judges that the first early warning value correction coefficient is selected to correct the early warning value W;
if the accuracy of the insulation monitoring model is moderate and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that the second early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is moderate and the current transformer substation is the second electromagnetic field intensity, the early warning correction module judges that a third early warning value correction coefficient is selected to correct the early warning value W;
If the accuracy of the insulation monitoring model is very low and the current transformer substation is the first electromagnetic field intensity, the early warning correction module judges that a fourth early warning value correction coefficient is selected to correct the early warning value W; if the accuracy of the insulation monitoring model is very low and the current transformer substation is the second electromagnetic field intensity, the early warning correction module judges that a fifth early warning value correction coefficient is selected to correct the early warning value W;
the early warning correction module is provided with a calculation method for a corrected early warning value W, and the corrected early warning value W' =W×ex is set, wherein x=1, 2,3,4 and 5; e1 is a first early warning value correction coefficient, e2 is a second early warning value correction coefficient, e3 is a third early warning value correction coefficient, e4 is a fourth early warning value correction coefficient, and e5 is a fifth early warning value correction coefficient; e1 is more than 1 and less than e2 is more than 3 and less than 4 and less than 5 and less than 2.
2. The substation total station capacitive equipment insulation integrated monitoring system of claim 1, wherein: the model construction module is further used for respectively building an electric field intensity model and a magnetic field intensity model according to the electric field intensity and the magnetic field intensity acquired by the first acquisition submodule, and fitting the electric field intensity model and the magnetic field intensity model; wherein the electric field strength is obtained by an electric field sensor, and the magnetic field strength is obtained by a magnetic field sensor;
The electric field strength model is expressed by the following formula:
E'＝f(H)·E；
wherein E is the original electric field strength; f (H) is a humidity function; e' is an electric field strength model combined with the influence of humidity, and the unit is volt per meter, namely V/m;
the magnetic field strength model is expressed by the following formula:
B'＝B·g(T,H)；
wherein B is the original magnetic field strength calculated based on the biot-savart law; g (T, H) is an adjustment factor, which adjusts the magnetic field strength according to the changes in temperature T and humidity H; and B' is a magnetic field intensity model combined with the adjustment factors, and the unit is Tesla T.
3. The substation total station capacitive equipment insulation integrated monitoring system of claim 2, wherein: the fitting of the electric field intensity model and the magnetic field intensity model comprises the following steps:
calculating the electric field energy density u according to the electric field intensity model E’ ；
Calculating the magnetic field energy density u according to the magnetic field intensity model B’ ；
The electric field energy density u E’ And magnetic field energy density u B’ Fitting to form electromagnetic field energy density, wherein the specific formula is as follows:
wherein u is EM Is the electromagnetic field energy density; gamma and delta are coefficients for adjusting the influence of electric and magnetic fields; n and m are exponentiations reflecting the nonlinear relationship of the electric and magnetic field strength effects on the total energy density;
The electromagnetic field energy density u EM Comparing the electromagnetic field intensity level with a threshold value Q to obtain an electromagnetic field intensity level; the method comprises the following steps:
if the electromagnetic field energy density u EM Judging that the threshold value Q is less than or equal to the threshold value Q, and judging that the first electromagnetic field intensity is the first electromagnetic field intensity;
if the electromagnetic field energy density u EM Judging that the second electromagnetic field intensity is larger than the threshold value Q;
wherein the first electromagnetic field strength < the second electromagnetic field strength is set.
4. The method for monitoring the insulation integration of the all-station capacitive equipment of the transformer substation is based on the all-station capacitive equipment insulation integration monitoring system of the transformer substation, which is characterized in that: the method comprises the following steps:
acquiring a first parameter of a transformer substation; the first parameter includes electric field strength and magnetic field strength;
collecting second parameters of all-station capacitive equipment of the transformer substation, and constructing an insulation monitoring model according to the second parameters; the second parameter includes leakage current, dielectric loss, and capacitance;
evaluating the accuracy of the insulation monitoring model according to a multi-level association rule; the multi-level association rule comprises a target association, a transverse association and a time line association;
and determining a first early warning degree of insulation monitoring of the all-station capacitive equipment of the transformer substation according to the accuracy of the insulation monitoring model, and correcting the early warning degree by combining the first parameter to form a final early warning degree, namely a final insulation monitoring state of the capacitive equipment.
5. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that: the steps of the insulation integrated monitoring method for the transformer substation full-station capacitive equipment according to claim 4 are realized when the processor executes the computer program.
6. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the insulation integrated monitoring method for the substation full-station capacitive equipment of claim 4.
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