CN117648603A - Method and device for predicting running trend of traction substation equipment - Google Patents
Method and device for predicting running trend of traction substation equipment Download PDFInfo
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Abstract
The application provides a method and a device for predicting running trend of traction substation equipment, wherein the method comprises the following steps: acquiring equipment operation data of the electric equipment to be tested; extracting target operation data related to a preset operation state related index from the equipment operation data; constructing a target trend prediction model based on the target operation data; inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals; and determining the predicted operation condition of the power equipment to be tested in the time interval according to the operation data prediction result. By constructing a target trend prediction model, real-time monitoring and trend prediction of the running of the traction substation equipment are realized, the running efficiency and reliability of the equipment are improved, various relevant data of the equipment are integrated by utilizing big data, accurate guidance of the equipment trend prediction is realized, and accurate management of the substation is realized.
Description
Technical Field
The invention relates to the technical field of intelligent power supply of heavy-load railways, in particular to a method and a device for predicting running trend of traction substation equipment.
Background
The traction substation is one of important power equipment in the power system, and the normal operation of the traction substation is of great significance to the stable operation of the power system.
In the operation and maintenance aspect of a traction substation, along with the improvement of the automation and informatization level of a railway power system, the original someone duty mode is gradually changed into an unmanned duty mode, the difficulty and pain still exist in the aspects of defect positioning and multi-dimensional trend prediction of equipment, operators still rely on carried single, single-station data materials, operation regulations, operation instruction books and other offline and limited data materials to carry out operation, the on-site research and judgment depends on the skill level of the operators, and the support of an expert system is lacked. In summary, the traditional operation monitoring method of the traction substation equipment has the problems of low efficiency, insufficient instantaneity and the like.
Disclosure of Invention
The invention provides a method and a device for predicting the running trend of traction substation equipment, which are used for predicting the running state of power equipment to be tested and early warning when the power equipment to be tested runs abnormally.
In a first aspect, the present invention provides a method for predicting an operational trend of a traction substation device, including:
acquiring equipment operation data of the electric equipment to be tested;
extracting target operation data related to a preset operation state related index from the equipment operation data;
constructing a target trend prediction model based on the target operation data;
inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals;
and determining the predicted operation condition of the power equipment to be tested in the time interval according to the operation data prediction result.
Optionally, acquiring device operation data of the electrical device to be tested includes:
acquiring initial equipment operation data of the electric equipment to be tested;
identifying and eliminating invalid data and error data in the initial equipment operation data;
preprocessing the rejected equipment operation data to obtain the equipment operation data.
Optionally, constructing a target trend prediction model based on the target operation data includes:
inputting the target operation data into an initially constructed trend prediction model to generate a corresponding operation condition type;
determining a training error according to the actual operation category label and the sample category of the target operation data in the next time interval;
based on the training error, the trend prediction model is adjusted through a back propagation algorithm to obtain an optimal network parameter, and the target trend prediction model is generated by adopting the optimal network parameter.
Optionally, determining, according to the operation data prediction result, a predicted operation condition of the electrical device to be tested in the time interval includes:
acquiring a preset running state threshold value related to the running state related index;
and comparing the operation state prediction result with a corresponding operation state threshold value to determine the predicted operation state of the power equipment to be detected.
Optionally, determining, according to the operation state prediction result, a predicted operation condition of the electrical device to be tested in the time interval includes:
and if the predicted running state of the power equipment to be detected is abnormal, sending out abnormal early warning.
In a second aspect, the present invention provides a device for predicting an operation trend of a traction substation device, including:
the acquisition module is used for acquiring equipment operation data of the electric equipment to be tested;
the extraction module is used for extracting target operation data related to a preset operation state related index from the equipment operation data;
the building module is used for building a target trend prediction model based on the target operation data;
the prediction module is used for inputting the acquired real-time equipment operation data of the power equipment to be detected into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals;
and the running state determining module is used for determining the predicted running state of the power equipment to be tested in the time interval according to the running data prediction result.
Optionally, the acquiring module includes:
an initial data acquisition sub-module, configured to acquire initial equipment operation data of the electrical equipment to be tested;
the rejecting sub-module is used for identifying and rejecting invalid data and error data in the initial equipment operation data;
and the preprocessing sub-module is used for preprocessing the rejected equipment operation data to obtain the equipment operation data.
Optionally, the building module includes:
the input sub-module is used for inputting the target operation data into the initially constructed trend prediction model to generate a corresponding operation condition type;
the error determination submodule is used for determining training errors according to actual operation category labels and sample categories of the target operation data at the next time interval;
and the optimal parameter determination submodule is used for adjusting the trend prediction model through a back propagation algorithm based on the training error to obtain optimal network parameters, and generating the target trend prediction model by adopting the optimal network parameters.
In a third aspect, the present application provides an electronic device comprising a processor and a memory storing computer readable instructions which, when executed by the processor, perform the steps of the method as provided in the first aspect above.
In a fourth aspect, the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method as provided in the first aspect above.
From the above technical scheme, the invention has the following advantages:
the invention provides a method and a device for predicting the running trend of traction substation equipment, wherein the method comprises the following steps: acquiring equipment operation data of the electric equipment to be tested; extracting target operation data related to a preset operation state related index from the equipment operation data; constructing a target trend prediction model based on the target operation data; inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals; and determining the predicted operation condition of the power equipment to be tested in the time interval according to the operation data prediction result. By constructing a target trend prediction model, real-time monitoring and trend prediction of the running of the traction substation equipment are realized, the running efficiency and reliability of the equipment are improved, various relevant data of the equipment are integrated by utilizing big data, accurate guidance of the equipment trend prediction is realized, and accurate management of the substation is realized.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting an operational trend of a traction substation device according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a second embodiment of a method for predicting an operational trend of a traction substation device according to the present invention;
fig. 3 is a block diagram of an embodiment of a prediction apparatus for running trend of a traction substation device according to the present invention.
Detailed Description
The embodiment of the invention provides a method and a device for predicting the running trend of traction substation equipment, which are used for predicting the running state of power equipment to be tested and early warning when the power equipment to be tested runs abnormally.
In order to make the objects, features and advantages of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in detail below with reference to the accompanying drawings, and it is apparent that the embodiments described below are only some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for predicting an operation trend of a traction substation device according to an embodiment of the present invention, including:
s101, acquiring equipment operation data of electric equipment to be tested;
s102, extracting target operation data related to a preset operation state related index from the equipment operation data;
s103, constructing a target trend prediction model based on the target operation data;
s104, inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain operation data prediction results corresponding to a plurality of time intervals;
s105, according to the operation data prediction result, determining the predicted operation condition of the power equipment to be tested in the time interval.
The method for predicting the running trend of the traction substation equipment provided by the embodiment of the invention comprises the following steps: acquiring equipment operation data of the electric equipment to be tested; extracting target operation data related to a preset operation state related index from the equipment operation data; constructing a target trend prediction model based on the target operation data; inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals; and determining the predicted operation condition of the power equipment to be tested in the time interval according to the operation data prediction result. By constructing a target trend prediction model, real-time monitoring and trend prediction of the running of the traction substation equipment are realized, the running efficiency and reliability of the equipment are improved, various relevant data of the equipment are integrated by utilizing big data, accurate guidance of the equipment trend prediction is realized, and accurate management of the substation is realized.
Referring to fig. 2, fig. 2 is a flowchart illustrating a second embodiment of a method for predicting an operation trend of a traction substation device according to the present invention, including:
s201, acquiring initial equipment operation data of the power equipment to be tested;
the method of the embodiment of the invention is applied to a traction substation digital twin system, and before the method is executed, a data acquisition layer, a data processing layer and a data application layer are firstly built, so that the traction substation digital twin system is formed and is used for converging all account information of a power substation, and the information comprises information such as primary equipment, secondary equipment, an intelligent sensor, an intelligent terminal and the like, and comprises the following steps: the electric station number, the electric station name, the equipment number, the equipment name, the equipment type, the voltage level, the equipment model, the equipment manufacturer, the delivery date, the operation maintenance unit and the like provide a solid foundation guarantee for reliable, stable and safe operation of the substation. Then, the operation data of the traction substation equipment is collected through the data collection layer, and the method comprises the following steps: leading in equipment maintenance records, and registering equipment history defects; binding the device with the sensor, collecting sensor information, and processing the collected data, including data cleaning, feature extraction, data normalization and the like. Finally, a trend prediction model is established by using the processed data; the prediction results are presented to the user through the data application layer.
The data acquisition layer acquires operation data of traction substation equipment through equipment such as a sensor; the data processing layer processes the acquired data, including data cleaning, feature extraction, data normalization and the like; the data application layer uses the processed data for equipment operation assessment and trend prediction, and presents the assessment result and the prediction result to a user.
In the embodiment of the invention, data collection is the first step of trend prediction of the operation data of the power equipment, and aims to acquire comprehensive, accurate and real-time operation data of the power equipment. The data is specifically initial equipment operation data, including parameters such as current, voltage, temperature, pressure and the like, and can be collected by means of on-site sensors, monitoring systems and the like.
S202, identifying and eliminating invalid data and error data in the initial equipment operation data;
s203, preprocessing the rejected equipment operation data to obtain the equipment operation data;
in the embodiment of the invention, the collected initial equipment operation data often has the problems of deletion, abnormality, repetition and the like, and data cleaning and preprocessing are required. The purpose of data cleansing is to delete invalid data and erroneous data, such as outliers, null values, etc. The purpose of data preprocessing is to normalize, discretize, etc. the data for better feature extraction and training using models.
S204, extracting target operation data related to a preset operation state related index from the equipment operation data;
in the embodiment of the invention, feature extraction and selection are one of key steps of power equipment operation data trend prediction. The purpose of the feature extraction is to extract features related to a preset operation state of the electric power equipment, such as a current waveform, a temperature change trend, and the like, from the equipment operation data, thereby obtaining target operation data. The purpose of feature selection is to select features related to a prediction target, remove irrelevant features, reduce complexity of the model and improve prediction accuracy of the model.
S205, inputting the target operation data into an initially constructed trend prediction model to generate a corresponding operation condition type;
s206, determining a training error according to the actual operation type label and sample type of the target operation data in the next time interval;
s207, based on the training error, adjusting the trend prediction model through a back propagation algorithm to obtain an optimal network parameter, and generating the target trend prediction model by adopting the optimal network parameter;
in embodiments of the present invention, after features are extracted and selected, trend prediction models need to be constructed and trained to make predictions of power plant operational data. Common models include linear regression models, support vector machine models, neural network models, and the like. When constructing a model, proper model types and parameter settings need to be selected according to actual problems and data characteristics. When training the model, the trend prediction model needs to be trained and optimized by using target operation data so as to improve the prediction precision and generalization capability of the trend prediction model.
Meanwhile, prediction and evaluation are one of core steps of power equipment operation data trend prediction. Through the trained trend prediction model, the future running state of the power equipment can be predicted, the prediction result is compared with the actual running data, and the prediction precision and stability of the model are evaluated. If the prediction result of the model has a large difference from the actual running data, the trend prediction model needs to be adjusted and optimized.
S208, inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain operation data prediction results corresponding to a plurality of time intervals;
in the embodiment of the invention, after the running data of the real-time equipment is input into the target trend prediction model, the running data prediction result in the first time interval is obtained, then the running data prediction result is input into the target trend prediction model, the running data prediction result in the second time interval is obtained, and the steps are repeated until the time interval of the obtained running data prediction result meets the time interval preset by the system.
S209, acquiring a preset running state threshold value related to the running state related index;
s210, comparing the operation state prediction result with a corresponding operation state threshold value, and determining the predicted operation state of the power equipment to be detected;
in an alternative embodiment, after comparing the operation state prediction result with a corresponding operation state threshold value to determine the predicted operation condition of the electrical device under test, the method further includes:
and if the predicted running state of the power equipment to be detected is abnormal, sending out abnormal early warning.
Visual display is one of important links of power equipment operation data trend prediction. Through the visualization technology, complex power equipment operation data and model prediction results can be presented to a user, so that the user can better understand and master the operation state and trend of the power equipment.
In the embodiment of the invention, firstly, a key device for monitoring is input into a device account interface, device account data is managed, secondly, sensors required by devices are bound, related thresholds and rules are set, equipment related maintenance records and defect registration information are imported, based on regression prediction models such as Arima, holt-Winter, STL, LSTM, TCN, deepAR and the like, an algorithm is utilized to predict trend of a time sequence of equipment related indexes in a future time period, and according to an operation state prediction result and a corresponding operation state threshold, the predicted operation condition (normal, concerned, serious and crisis) of the power equipment to be tested is determined, so that equipment operation trend prediction is realized.
The method for predicting the running trend of the traction substation equipment provided by the embodiment of the invention comprises the following steps: acquiring equipment operation data of the electric equipment to be tested; extracting target operation data related to a preset operation state related index from the equipment operation data; constructing a target trend prediction model based on the target operation data; inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals; and determining the predicted operation condition of the power equipment to be tested in the time interval according to the operation data prediction result. By constructing a target trend prediction model and adopting a data analysis method based on a digital twin system, real-time monitoring and trend prediction analysis of the operation of traction substation equipment can be realized continuously for 24 hours, the operation and maintenance efficiency of the substation equipment is greatly improved, the operation efficiency and reliability of the equipment are improved, and the lean management of the substation is realized.
Referring to fig. 3, fig. 3 is a block diagram of an embodiment of a prediction apparatus for running trend of a traction substation device according to the present invention, including:
an acquiring module 301, configured to acquire device operation data of a to-be-tested power device;
an extracting module 302, configured to extract, from the device operation data, target operation data related to a preset operation state related index;
a construction module 303, configured to construct a target trend prediction model based on the target operation data;
the prediction module 304 is configured to input the obtained real-time equipment operation data of the electrical equipment to be tested into the target trend prediction model, so as to obtain operation data prediction results corresponding to the electrical equipment to be tested in a plurality of time intervals;
and the operation state determining module 305 is configured to determine a predicted operation state of the electrical equipment to be tested within the time interval according to the operation data prediction result.
In an alternative embodiment, the obtaining module 301 includes:
an initial data acquisition sub-module, configured to acquire initial equipment operation data of the electrical equipment to be tested;
the rejecting sub-module is used for identifying and rejecting invalid data and error data in the initial equipment operation data;
and the preprocessing sub-module is used for preprocessing the rejected equipment operation data to obtain the equipment operation data.
In an alternative embodiment, the building block 303 comprises:
the input sub-module is used for inputting the target operation data into the initially constructed trend prediction model to generate a corresponding operation condition type;
the error determination submodule is used for determining training errors according to actual operation category labels and sample categories of the target operation data at the next time interval;
and the optimal parameter determination submodule is used for adjusting the trend prediction model through a back propagation algorithm based on the training error to obtain optimal network parameters, and generating the target trend prediction model by adopting the optimal network parameters.
In an alternative embodiment, the operating state determination module 305 includes:
the threshold value obtaining sub-module is used for obtaining a preset running state threshold value related to the running state related index;
and the running state prediction sub-module is used for comparing the running state prediction result with a corresponding running state threshold value and determining the predicted running state of the power equipment to be detected.
In an alternative embodiment, the operation state determining module 305 further includes:
and the early warning module is used for sending out abnormal early warning when the predicted running state of the power equipment to be detected is abnormal.
An embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for predicting an operation trend of the traction substation device in any one of the embodiments.
An embodiment five further provides a computer storage medium having a computer program stored thereon, the computer program implementing the steps of the method for predicting the running trend of the traction substation device according to any embodiment when executed by the processor.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments provided in the present application, it should be understood that the methods, apparatuses, electronic devices and storage media disclosed in the present application may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a readable storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for predicting an operational trend of a traction substation device, comprising:
acquiring equipment operation data of the electric equipment to be tested;
extracting target operation data related to a preset operation state related index from the equipment operation data;
constructing a target trend prediction model based on the target operation data;
inputting the acquired real-time equipment operation data of the power equipment to be tested into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals;
and determining the predicted operation condition of the power equipment to be tested in the time interval according to the operation data prediction result.
2. The method for predicting an operational trend of a traction substation device according to claim 1, wherein obtaining device operational data of a power device to be measured includes:
acquiring initial equipment operation data of the electric equipment to be tested;
identifying and eliminating invalid data and error data in the initial equipment operation data;
preprocessing the rejected equipment operation data to obtain the equipment operation data.
3. The method for predicting an operational trend of a traction substation device according to claim 1, wherein constructing a target trend prediction model based on the target operational data comprises:
inputting the target operation data into an initially constructed trend prediction model to generate a corresponding operation condition type;
determining a training error according to the actual operation category label and the sample category of the target operation data in the next time interval;
based on the training error, the trend prediction model is adjusted through a back propagation algorithm to obtain an optimal network parameter, and the target trend prediction model is generated by adopting the optimal network parameter.
4. A traction substation equipment operation trend prediction method according to claim 3, wherein determining the predicted operation condition of the electrical equipment to be measured in the time interval according to the operation data prediction result comprises:
acquiring a preset running state threshold value related to the running state related index;
and comparing the operation state prediction result with a corresponding operation state threshold value to determine the predicted operation state of the power equipment to be detected.
5. The method for predicting an operational trend of a traction substation device according to claim 4, wherein comparing the operational state prediction result with a corresponding operational state threshold value, after determining the predicted operational state of the electrical device to be measured, further comprises:
and if the predicted running state of the power equipment to be detected is abnormal, sending out abnormal early warning.
6. A traction substation equipment operation trend prediction device, comprising:
the acquisition module is used for acquiring equipment operation data of the electric equipment to be tested;
the extraction module is used for extracting target operation data related to a preset operation state related index from the equipment operation data;
the building module is used for building a target trend prediction model based on the target operation data;
the prediction module is used for inputting the acquired real-time equipment operation data of the power equipment to be detected into the target trend prediction model to obtain corresponding operation data prediction results in a plurality of time intervals;
and the running state determining module is used for determining the predicted running state of the power equipment to be tested in the time interval according to the running data prediction result.
7. The traction substation equipment operation trend prediction apparatus according to claim 6, wherein the acquisition module includes:
an initial data acquisition sub-module, configured to acquire initial equipment operation data of the electrical equipment to be tested;
the rejecting sub-module is used for identifying and rejecting invalid data and error data in the initial equipment operation data;
and the preprocessing sub-module is used for preprocessing the rejected equipment operation data to obtain the equipment operation data.
8. The traction substation equipment operation trend prediction device according to claim 6, wherein the construction module comprises:
the input sub-module is used for inputting the target operation data into the initially constructed trend prediction model to generate a corresponding operation condition type;
the error determination submodule is used for determining training errors according to actual operation category labels and sample categories of the target operation data at the next time interval;
and the optimal parameter determination submodule is used for adjusting the trend prediction model through a back propagation algorithm based on the training error to obtain optimal network parameters, and generating the target trend prediction model by adopting the optimal network parameters.
9. An electronic device comprising a processor and a memory storing computer readable instructions that, when executed by the processor, perform the method of any of claims 1-5.
10. A storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311587221.9A CN117648603A (en) | 2023-11-24 | 2023-11-24 | Method and device for predicting running trend of traction substation equipment |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311587221.9A CN117648603A (en) | 2023-11-24 | 2023-11-24 | Method and device for predicting running trend of traction substation equipment |
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| CN117648603A true CN117648603A (en) | 2024-03-05 |
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| CN202311587221.9A Pending CN117648603A (en) | 2023-11-24 | 2023-11-24 | Method and device for predicting running trend of traction substation equipment |
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