CN114157023B - Distribution transformer early warning information acquisition method - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
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Abstract
The application discloses a distribution transformer early warning information acquisition method, which comprises the following steps: collecting image and sound data information in the transformer, preprocessing the collected data information, and collecting historical data information; constructing a data analysis model based on a deep neural network, and training the data analysis model by utilizing historical fault data; inputting the preprocessed data information into a trained data analysis model, and judging whether the acquired data information is fault information or not; if the information is fault information, immediately generating fault early warning information by the information, and sending the fault early warning information to a terminal server for fault early warning, so as to complete acquisition of distribution transformer early warning information. The application can monitor each transformer in real time, and send monitoring information in time, thereby improving the management quality of the whole power distribution network and having high reliability.
Description
Technical Field
The application relates to the technical field of information early warning, in particular to a distribution transformer early warning information acquisition method.
Background
In the power system, power enters a common household from a high-voltage transmission system of a power grid company, and a special distribution transformer (simply called distribution transformer) is needed in the middle of the power system to realize conversion from high-voltage power to low-voltage power. Because the distribution transformer plays a role in high-low voltage bearing, the power company is particularly concerned about the running state of the distribution transformer, so that possible faults can be found out in time and repaired in time, the power supply quality is improved, and the customer satisfaction is improved. However, at the same time, because the distribution transformers are distributed very dispersedly and are mostly located in open places, no conventional wired communication channel exists, the conventional information management between the power company and each distribution transformer monitoring point is generally implemented by monitoring each transformer monitoring point respectively and then feeding information back to the management end of the distribution system center, and the mode cannot implement real-time monitoring on each transformer monitoring point and timely send monitoring point information and management command information, so that the management of the whole power distribution network is affected, the reliability is low and timely maintenance and monitoring cannot be performed.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-described problems occurring in the prior art.
Therefore, the technical problems solved by the application are as follows: the transformer monitoring points cannot be monitored in real time, and monitoring point information and management instruction information cannot be timely sent, so that the management of the whole power distribution network is affected, the reliability is low, and the monitoring and the maintenance cannot be timely carried out.
In order to solve the technical problems, the application provides the following technical scheme:
collecting image and sound data information in the transformer, preprocessing the collected data information, and collecting historical data information;
constructing a data analysis model based on a deep neural network, and training the data analysis model by utilizing historical fault data;
inputting the preprocessed data information into a trained data analysis model, and judging whether the acquired data information is fault information or not;
if the information is fault information, immediately generating fault early warning information by the information, and sending the fault early warning information to a terminal server for fault early warning to finish acquisition of distribution transformer early warning information;
the data preprocessing procedure includes that,
the image preprocessing: continuously acquiring image information in the transformer in real time by using an infrared thermal imaging sensor, generating a picture, and carrying out graying treatment on the picture by using a weighted average strategy;
the sound pretreatment: denoising the sound data by using spectral subtraction, windowing and framing the noise-containing input voice to be denoised to obtain a frame signal; the window function adopts a Hamming window; when framing, the frame shift length is half of the frame length, half of sampling points between the front frame and the back frame are overlapped, the frame signal is subjected to fast Fourier transform to obtain the amplitude and the phase of each frame of digital voice, and the noise-carrying signal is converted into the time domain: performing frequency spectrum subtraction and phase synthesis on the voice signal with noise in a frequency domain, and converting the voice signal after the frequency spectrum subtraction and phase synthesis into a time domain; performing inverse perception weighted filtering processing on the voice signals subjected to frequency spectrum subtraction and phase synthesis to obtain denoised voice signals;
converting the denoised picture and sound data into digital signals by using an A/D converter, constructing a parameter fitting model, and fitting the picture digital signals and the sound digital signals, wherein the fitting model is as follows:
digital signal D for simultaneously inputting pictures and sounds i 、D j Wherein D is i N and D j N represents the ith and jth pictures to be fitted and sound signal vectors respectively, i and j are integers, N represents signal parameter factors, the ith and jth pictures and sound signal vectors are fitted through N iterations, and when the final fitting value S is between 0 and 1, a fitting result is output:
wherein,abbreviated as X, denotes the output fitting result value, the signal () function is used to convert the fitting factor to an integer, X ij Representing a fitting factor, c representing a constant coefficient;
the analytic model takes similarity maximization as a target optimization function:
where l represents the number of iterations, θ t Representing a historical fault data signal, t representing time;
the output value y of the analytical model for the pre-determined fault information includes,
y＝x ij (θ t X+c)+D i ·D j 。
as a preferable scheme of the distribution transformer early warning information acquisition method, the application comprises the following steps:
the fault information may include information such as,
sound judgment fault of transformer: the noise during phase failure, poor contact or falling of a voltage regulating tap changer, loosening of foreign matters and a penetrating screw rod, dirt and crack of a high-voltage bushing of the transformer, broken iron core grounding wire, internal discharge, broken wire or short circuit of an external circuit, overload of the transformer, overhigh voltage and short circuit of a winding can all generate noise;
image judgment fault of transformer: the transformer oil temperature becomes high, exceeding the normal temperature.
As a preferable scheme of the distribution transformer early warning information acquisition method, the application comprises the following steps:
the data acquisition time interval is 3s.
As a preferable scheme of the distribution transformer early warning information acquisition method, the application comprises the following steps:
constraint condition of the target optimization function is theta t Greater than 0.8.
As a preferable scheme of the distribution transformer early warning information acquisition method, the application comprises the following steps:
the judgment standard for judging whether the collected data information is fault information is as follows:
when the output value y is larger than 2.3, judging that the fault occurs, and generating fault early warning information.
The application has the beneficial effects that: the real-time monitoring can be implemented on each transformer, monitoring information is timely sent, the management quality of the whole power distribution network is improved, and the reliability is high.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in 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 application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a basic flow diagram of a method for acquiring distribution transformer early warning information according to an embodiment of the present application.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present application can be understood in detail, a more particular description of the application, 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 application without making any inventive effort, shall fall within the scope of the present application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application 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 application. 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 application 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 application. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present application, 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 application 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 application. 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 application will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for an embodiment of the present application, a method for acquiring early warning information of a distribution transformer is provided, including:
s1: and acquiring image and sound data information in the transformer, preprocessing the acquired data information and acquiring historical data information.
The data acquisition time interval was 3s.
Further, the data preprocessing process includes:
image preprocessing: the infrared thermal imaging sensor is utilized to continuously acquire the internal image information of the transformer in real time and generate pictures, the weighted average strategy is utilized to carry out gray processing on the pictures, the gray processing has small data size of the pictures, a real-time algorithm is easier to realize, the ROF model is utilized to carry out denoising processing on the pictures, and partial program codes realized by the method are as follows:
pretreatment of sound: denoising sound data using spectral subtraction:
windowing and framing the noise-containing input voice to be denoised to obtain a frame signal;
the window function adopts a Hamming window;
when framing, the frame shift length is half of the frame length, namely half of sampling points between the front frame and the back frame are overlapped, the frame signal is subjected to fast Fourier transform to obtain the amplitude and the phase of each frame of digital voice, and the noise-carrying signal is converted into the time domain: performing frequency spectrum subtraction and phase synthesis on the voice signal with noise in a frequency domain, and converting the voice signal after the frequency spectrum subtraction and phase synthesis into a time domain;
and performing inverse perception weighted filtering processing on the voice signals subjected to frequency spectrum subtraction and phase synthesis to obtain denoised voice signals.
The denoising can enable the obtained data signals to be more accurate, the judgment of later fault information is facilitated, the image after processing can be smoother by denoising the image through the ROF model, and meanwhile, the image edge and the structural information are kept.
S2: and constructing a data analysis model based on the deep neural network, and training the data analysis model by utilizing the historical fault data.
It should be noted that, the denoised picture and sound data are converted into digital signals by using an a/D converter; constructing a parameter fitting model, and fitting a picture digital signal and a sound digital signal, wherein the fitting model is as follows:
digital signal D for simultaneously inputting pictures and sounds i 、D j Wherein D is i N and D j N represents the ith and respectivelyThe j-th picture to be fitted and sound signal vectors, i and j are integers, N represents signal parameter factors, the j-th picture to be fitted and the sound signal vectors are fitted through N times of iteration, and when the final fitting value S is between 0 and 1, a fitting result is output:
wherein,abbreviated as X, denotes the output fitting result value, the signal () function is used to convert the fitting factor to an integer, X ij Representing a fitting factor, c representing a constant coefficient;
further, a data analysis model is built based on the deep neural network, and the data analysis model is trained by utilizing historical fault data, so that a data analysis result is more accurate, wherein the data analysis model takes similarity maximization as a target optimization function:
wherein L represents the number of iterations, θ t Representing a historical fault data signal, t representing time.
Wherein, the constraint condition of the target optimization function is theta t Greater than 0.8.
On the basis of the above, an analytical model capable of predicting the fault information is obtained, and the output value y of the analytical model for predicting the fault information comprises,
y＝x ij (θ t X+c)+D i ·D j 。
s3: and inputting the preprocessed data information into a trained data analysis model, and judging whether the acquired data information is fault information or not.
When the output value y is greater than 2.3, it is determined that a fault occurs, and fault early warning information is generated.
S4: if the information is the fault information, immediately sending the information generation fault early warning information to a terminal server for fault early warning, and completing the acquisition of the distribution transformer early warning information.
Note that, the configuration failure information includes:
if the information is fault information, generating fault early warning information by the information immediately, and sending the fault early warning information to a terminal server for fault early warning, so that the acquisition of the distribution transformer early warning information is completed.
Sound judgment fault of transformer: the noise during phase failure, poor contact or falling of a voltage regulating tap changer, loosening of foreign matters and a penetrating screw rod, dirt and crack of a high-voltage bushing of the transformer, broken iron core grounding wire, internal discharge, broken wire or short circuit of an external circuit, overload of the transformer, overhigh voltage and short circuit of a winding can all generate noise;
image judgment fault of transformer: the transformer oil temperature becomes high, exceeding the normal temperature.
According to the method, firstly, the collected data information is preprocessed, so that the processing of the subsequent steps is faster and more accurate, the fitting model is adopted to fit the picture and the sound information, the fault information monitoring range is enlarged, the data analysis model is built based on the deep neural network, the data analysis model is trained by utilizing the historical fault data, the analysis result is more accurate, and the acquisition accuracy of the early warning information is improved.
Example 2
The embodiment is another embodiment of the present application, and the embodiment is different from the first embodiment in that a verification test of a method for acquiring pre-warning information of a distribution transformer is provided, so as to verify and explain the technical effects adopted in the method.
The traditional technical scheme is as follows: the transformer monitoring points cannot be monitored in real time, and monitoring point information and management instruction information cannot be timely sent, so that the management of the whole power distribution network is affected, the reliability is low, and the monitoring and the maintenance cannot be timely carried out. Compared with the traditional method, the method has higher information acquisition integrity and lower time delay. In the embodiment, a method for respectively monitoring the monitoring points of each transformer and the method for respectively measuring and comparing the information acquisition precision of the simulation power grid system in real time are adopted in the prior art.
Test environment: and simulating the operation of the power grid system and the transmission of different information on the simulation platform, starting the automatic test equipment by using the traditional method and the method, and realizing the simulation test of the two methods by using MATLB software programming, and obtaining simulation data according to experimental results, wherein the results are shown in the following table.
Table 1: comparison table of experimental results.
| Test sample | Conventional method | The method of the application |
| Time delay | ＞10min | 50～100s |
| Information integrity | 87％ | 98％ |
From the above table, it can be seen that the method of the present application has stronger robustness than the conventional method.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application 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 application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered in the scope of the claims of the present application.
Claims (5)
1. The distribution transformer early warning information acquisition method is characterized by comprising the following steps of:
collecting image and sound data information in the transformer, preprocessing the collected data information, and collecting historical data information;
constructing a data analysis model based on a deep neural network, and training the data analysis model by utilizing historical fault data;
inputting the preprocessed data information into a trained data analysis model, and judging whether the acquired data information is fault information or not;
if the information is fault information, generating fault early warning information by the fault information immediately, and sending the fault early warning information to a terminal server for fault early warning to complete acquisition of distribution transformer early warning information;
the data preprocessing procedure includes that,
the image preprocessing: continuously acquiring image information in the transformer in real time by using an infrared thermal imaging sensor, generating a picture, and carrying out graying treatment on the picture by using a weighted average strategy;
the sound pretreatment: denoising the sound data by using spectral subtraction, windowing and framing the noise-containing input voice to be denoised to obtain a frame signal; the window function adopts a Hamming window; when framing, the frame shift length is half of the frame length, half of sampling points between the front frame and the back frame are overlapped, the frame signal is subjected to fast Fourier transform to obtain the amplitude and the phase of each frame of digital voice, and the noise-carrying signal is converted into the time domain: performing frequency spectrum subtraction and phase synthesis on the voice signal with noise in a frequency domain, and converting the voice signal after the frequency spectrum subtraction and phase synthesis into a time domain; performing inverse perception weighted filtering processing on the voice signals subjected to frequency spectrum subtraction and phase synthesis to obtain denoised voice signals;
converting the picture and the denoised sound data into digital signals by using an A/D converter, constructing a parameter fitting model, and fitting the picture digital signals and the sound digital signals, wherein the fitting model is as follows:
digital signal D for simultaneously inputting pictures and sounds i 、D j Wherein D is i N and D j N represents the ith and jth pictures to be fitted and sound signal vectors respectively, i and j are integers, N represents signal parameter factors, the ith and jth pictures and sound signal vectors are fitted through N iterations, and when the final fitting value S is between 0 and 1, a fitting result is output:
wherein,abbreviated as X, denotes the output fitting result value, the signal () function is used to convert the fitting factor to an integer, X ij Representing a fitting factor, c representing a constant coefficient;
the analytic model takes similarity maximization as a target optimization function:
where l represents the number of iterations, θ t Representing a historical fault data signal, t representing time;
the output value y of the analytical model for the pre-determined fault information includes,
y＝x ij (θ t X+c)+D i ·D j 。
2. the distribution transformer early warning information acquisition method according to claim 1, wherein: the fault information may include information such as,
sound judgment fault of transformer: the noise during phase failure, poor contact or falling of a voltage regulating tap changer, loosening of foreign matters and a penetrating screw rod, dirt and crack of a high-voltage bushing of the transformer, broken iron core grounding wire, internal discharge, broken wire or short circuit of an external circuit, overload of the transformer, overhigh voltage and short circuit of a winding can all generate noise;
image judgment fault of transformer: the transformer oil temperature becomes high, exceeding the normal temperature.
3. The distribution transformer early warning information acquisition method according to claim 2, wherein: the data acquisition time interval is 3s.
4. The distribution transformer early warning information acquisition method according to claim 3, wherein: constraint condition of the target optimization function is theta t Greater than 0.8.
5. The method for acquiring the distribution transformer early warning information according to claim 4, wherein the method comprises the following steps: the judgment standard for judging whether the collected data information is fault information is as follows:
when the output value y is larger than 2.3, judging that the fault occurs, and generating fault early warning information.
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| CN202111375522.6A CN114157023B (en) | 2021-11-19 | 2021-11-19 | Distribution transformer early warning information acquisition method |
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| CN202111375522.6A CN114157023B (en) | 2021-11-19 | 2021-11-19 | Distribution transformer early warning information acquisition method |
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