CN116258603A - Method and system for early warning based on equipment state of power system - Google Patents
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Abstract
The method and the system realize the automatic development of the early warning process based on the equipment state of the power system without human intervention, simplify the development process of equipment state early warning application, reduce the development difficulty, improve the intellectualization of the early warning application, reduce the cost development of the early warning application and effectively realize the management and resource sharing of the early warning algorithm and the model.
Description
Technical Field
The application relates to the technical field of power systems, in particular to a method and a system based on power system equipment state early warning.
Background
With the continuous expansion of the power grid construction scale, the informatization level of the power grid construction is continuously improved, and the operation state monitoring and analysis of power system equipment are increasingly emphasized. In order to ensure reliable operation of the power grid, the state of the operation equipment of the power system needs to be analyzed and evaluated by combining massive operation data, the development trend of the future state of the equipment is predicted, and the equipment which is possibly faulty is early-warned in advance, so that the aim of overhauling in advance and reducing or avoiding the fault is fulfilled.
The current equipment state early warning application lacks management and sharing of early warning algorithms and models on one hand, and does not form a unified operation and scheduling framework on the other hand. The traditional equipment state analysis application is independent, a single application needs to consider the scheduling and management of a series of processes such as data preparation, parameter configuration, model training, result comparison, algorithm optimization, data prediction and the like, the application development difficulty is high, the redundancy work is more, and accumulation and sharing of an early warning algorithm and a model cannot be effectively formed, so that the evolution of the equipment state early warning application is influenced. How to comb the flow and characteristics of the state analysis and early warning of the equipment, integrate the unified process of data extraction and data analysis, provide a general early warning analysis application operation and scheduling management framework, and is a problem which needs to be solved continuously at present.
Aiming at the problems, the method and the system for uniformly analyzing and processing the equipment data of the power system and sending out early warning are particularly important.
Disclosure of Invention
The embodiment of the application provides a method and a system for early warning based on the state of equipment of a power system, so as to solve the problems.
In one embodiment of the present application, a method for early warning based on a state of a power system device is provided, the method comprising the steps of:
s100, an early warning algorithm library is established, wherein the early warning algorithm library comprises a basic data algorithm and a mathematical algorithm which is adapted to early warning of power system equipment;
s200, training data is acquired, wherein the training data comprises a data set which is acquired from a traditional relational database, a big data platform and a time sequence library and is suitable for the early warning algorithm library, and the data set is divided into a training data set and a verification data set;
s300, an initial early warning algorithm model is established, wherein the initial early warning algorithm model is established by selecting an algorithm applicable to the training data set from an early warning algorithm library;
s400, training an initial early warning algorithm model, wherein the initial early warning algorithm model is trained by using the training data set;
s500, issuing an early warning algorithm model, wherein the early warning algorithm model is issued after the initial early warning algorithm model is trained; a kind of electronic device with high-pressure air-conditioning system
S600, early warning the state of the power system equipment, wherein the early warning of the state of the power system equipment is performed by using the early warning algorithm model, and whether the early warning of the abnormal state of the power system equipment is generated is judged according to a prediction result.
In an embodiment, the steps S100, S200, S300, S400, S500 and S600 are automatically performed according to a preset operation program.
In an embodiment, the preset operation program includes a periodic operation program and a periodic operation program, where the periodic operation program is a periodic operation for the steps S100, S200, S300, S400, S500 and S600, and the periodic operation program is a timed operation for the steps S100, S200, S300, S400, S500 and S600 according to a preset time point.
In one embodiment, the step S400 includes:
s401, configuring initial parameters of an initial early warning algorithm model;
s402, starting a training task, inputting the training data set into the initial early warning algorithm model, and training the early warning algorithm model to obtain a training result;
s403, verifying the training result by using the verification data set to obtain optimal parameters and obtain an optimal training result; a kind of electronic device with high-pressure air-conditioning system
S404, obtaining the early warning algorithm model according to the optimal training result.
In an embodiment, the step S402 further includes:
s4021, recording training conditions of the training task, wherein the training conditions comprise training success and training failure, executing step S403 when the training is successful, returning to executing step S401 until the training is successful when the training is failed, recording the training failure condition in an abnormal log of the training task, and sending an abnormal alarm.
In one embodiment, the step S500 includes:
s501, recording the release state of the release early warning model, wherein the release state comprises release success and release failure, when release is successful, executing step S500, and when release is failed, recording the release failure state in an abnormal log of a release task and sending out simultaneous abnormal alarms.
In an embodiment of the present application, a system based on power system equipment status pre-warning is also provided, including:
the system comprises a configuration algorithm library module, a warning algorithm library module and a warning algorithm library module, wherein the configuration algorithm library module is configured to establish a warning algorithm library;
a data acquisition module configured to acquire training data;
the initial early warning algorithm model building module is configured to select an algorithm suitable for the training data from an early warning algorithm library and build an initial early warning algorithm model;
an initial pre-warning algorithm model training module configured to train an initial algorithm model by using a training data set;
the early warning algorithm model issuing module is configured to issue an early warning algorithm model obtained after training is completed;
the power system equipment state early warning module is configured to issue early warning of abnormal power system equipment state;
in an embodiment, the system further comprises:
the task operation module is configured to automatically operate the configuration algorithm library module, the data acquisition module, the initial early warning algorithm model building module, the initial early warning algorithm model training module and the power system equipment state early warning module according to a preset operation program.
In an embodiment, the system further includes a log recording module, where the log recording module is configured to record the operation states of the initial early warning algorithm model training module and the early warning algorithm model publishing module.
The beneficial effects of this application lie in:
the method and the system for early warning based on the power system equipment state can automatically develop the early warning process based on the power system equipment state without human intervention, simplify the development process of equipment state early warning application, reduce the development difficulty, ensure that application developers only need to pay attention to the research and development of the equipment state early warning algorithm, greatly improve the intellectualization of early warning application, reduce the development cost of early warning application, and effectively realize the management and resource sharing of the early warning algorithm and the model.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of a method based on power plant operational status in accordance with an embodiment of the present application;
FIG. 2 is a schematic diagram of a system based on power plant operating conditions in accordance with an embodiment of the present application;
fig. 3 is a task operation flow chart based on power system equipment status early warning in an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. 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, are intended to be within the scope of the present application.
Fig. 1 is a flowchart of a method based on an operation state of an electrical device according to an embodiment of the present application, specifically including the following steps:
s100, an early warning algorithm library is established, and the early warning algorithm library comprises a basic data algorithm and a mathematical algorithm which is adapted to early warning of power system equipment;
s200, training data is acquired, wherein the training data comprises a data set which is acquired from a traditional relational database, a big data platform and a time sequence library and is suitable for an early warning algorithm library, and the data set is divided into a training data set and a verification data set;
s300, an initial early warning algorithm model is established, wherein the initial early warning algorithm model is established by selecting an algorithm suitable for training a data set from an early warning algorithm library, and the selection principle is that an early warning algorithm mathematical model most suitable for a service scene is selected according to priori knowledge;
s400, training an initial early warning algorithm model, wherein the initial early warning algorithm model is trained by using the training data set;
s500, issuing an early warning algorithm model, wherein the early warning algorithm model is issued after the initial early warning algorithm model is trained; a kind of electronic device with high-pressure air-conditioning system
S600, early warning the state of the power system equipment, wherein the early warning of the state of the power system equipment is performed by using the early warning algorithm model, and whether the early warning of the abnormal state of the power system equipment is generated is judged according to a prediction result.
In S100, the mathematical algorithm adapted to the early warning of the power system device may be understood as a mathematical algorithm after verification and parameter tuning according to the application requirement of the early warning;
in S200, the data set applicable to the early warning algorithm library is obtained from the conventional relational database, the big data platform and the permission library, and it can be understood that the data set applicable to the early warning algorithm library is automatically extracted from the conventional relational database, the big data platform and the time sequence library according to a uniform format by reading the model configuration table, where the data set includes the meter data, the equipment state data, the equipment environment data, the generated data and the like of the electric power system equipment, and the historical data or the current measured data of a past characteristic time period;
in S300, the algorithm suitable for the training data set is selected, which can be understood as a mathematical algorithm with higher fitness for the training data set, which is selected by continuous experimental results and combining experience of professionals.
In one embodiment, S400 further comprises the steps of:
s401, configuring initial parameters of an initial early warning algorithm model;
s402, starting a training task, inputting the training data set into the initial early warning algorithm model, and training the early warning algorithm model to obtain a training result; wherein, S402 further includes: recording training conditions of the training task, wherein the training conditions comprise training success and training failure, executing step S403 when the training is successful, returning to executing step S401 when the training is failed until the training is successful, recording the training failure condition in an abnormal log of the training task, and sending an abnormal alarm.
S403, verifying the training result by using the verification data set to obtain optimal parameters and obtain an optimal training result; a kind of electronic device with high-pressure air-conditioning system
S404, obtaining the early warning algorithm model according to the optimal training result.
In one embodiment, steps S100, S200, S300, S400, S500 and S600 are automatically performed according to a preset operation program. The preset operation program may include a periodic operation program, which may be understood as steps S100, S200, S300, S400, S500, and S600 are operated at a certain periodicity, and a periodic operation program, which may be understood as steps S100, S200, S300, S400, S500, and S600 are operated at a certain timing according to a preset time point.
In an embodiment, step S500 further includes:
s501, recording the release state of the release early warning model, wherein the release state comprises release success and release failure, when release is successful, executing step S500, and when release is failed, recording the release failure state in an abnormal log of a release task and sending out simultaneous abnormal alarms.
In an embodiment, the acquiring training data further includes performing data cleaning on the acquired training data, removing abnormal data, and splitting the acquired data, wherein 80% of the data is selected as training data for training the early warning algorithm model, 20% of the data is used as verification data for performing data comparison and verification on the early warning algorithm model, and performing training result scoring.
Fig. 2 is a schematic diagram of a system based on an operation state of an electrical device according to an embodiment of the present application, where the system includes:
a configuration algorithm library module configured to establish an early warning algorithm library;
a data acquisition module configured to acquire training data;
the initial early warning algorithm model building module is configured to select an algorithm suitable for the training data from an early warning algorithm library and build an initial early warning algorithm model;
an initial pre-warning algorithm model training module configured to train an initial algorithm model by using the training data set;
the early warning algorithm model issuing module is configured to issue an early warning algorithm model obtained after training is completed;
and the power system equipment state early warning module is configured to issue early warning of abnormal power system equipment states.
In one embodiment, the system further comprises: the task operation module is configured to automatically operate the configuration algorithm library module, the data acquisition module, the initial early warning algorithm model building module, the initial early warning algorithm model training module and the power system equipment state early warning module according to a preset operation program.
In an embodiment, the system further includes a log recording module, where the log recording module is configured to record the operation states of the initial early warning algorithm model training module and the early warning algorithm model publishing module.
Fig. 3 is a task operation flowchart of early warning based on a state of a device in a power system according to an embodiment of the present application. The specific task operation steps are as follows:
1) Starting a starting task, selecting an early warning algorithm with high fit degree from the established early warning algorithm database, and establishing an initial early warning algorithm model;
2) Training data preparation and splitting, namely, a data set which is obtained from a traditional relational database, a big data platform and a time sequence library and is suitable for the early warning algorithm library is prepared, and the data set is split into a training data set and a verification data set;
3) Configuring initial parameters of an initial early warning algorithm model;
4) Starting a training task, inputting the training data set into the initial early warning algorithm model, training the early warning algorithm model, grading a training result by using a verification data set when training is successful, obtaining an optimal training grading result by adjusting parameters, and returning to execute the step 3) until the optimal training grading result is obtained when training fails, recording the condition of training failure in an abnormal log and issuing an abnormal log warning;
5) And issuing an early warning algorithm model according to the optimal training scoring result. When the release is successful, predicting the equipment state data by using the early warning algorithm model, judging whether to send out early warning according to a prediction result, and ending the task; when the release fails, the state of the release failure is recorded in an abnormal log of the release task, a simultaneous abnormal alarm is sent out, and the task is ended.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, such as the division of the units, is merely a logical function division, and may be implemented in another manner, for example, multiple units or components may be combined or may be 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 through some interfaces, units or modules, or may be in electrical or other forms.
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 each embodiment of the present application 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 foregoing is merely a preferred embodiment of the present application and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present application and are intended to be comprehended within the scope of the present application.
Claims (9)
1. A method for early warning based on a state of a device in an electric power system, the method comprising the steps of:
s100, an early warning algorithm library is established, wherein the early warning algorithm library comprises a basic data algorithm and a mathematical algorithm which is adapted to early warning of power system equipment;
s200, training data is acquired, wherein the training data comprises a data set which is acquired from a traditional relational database, a big data platform and a time sequence library and is suitable for the early warning algorithm library, and the data set is divided into a training data set and a verification data set;
s300, an initial early warning algorithm model is established, wherein the initial early warning algorithm model is established by selecting an algorithm applicable to the training data set from an early warning algorithm library;
s400, training an initial early warning algorithm model, wherein the initial early warning algorithm model is trained by using the training data set;
s500, issuing an early warning algorithm model, wherein the early warning algorithm model is issued after the initial early warning algorithm model is trained; a kind of electronic device with high-pressure air-conditioning system
S600, early warning the state of the power system equipment, wherein the early warning of the state of the power system equipment is performed by using the early warning algorithm model, and whether the early warning of the abnormal state of the power system equipment is generated is judged according to a prediction result.
2. The method for early warning based on the state of power system equipment according to claim 1, wherein the steps S100, S200, S300, S400, S500 and S600 are automatically performed according to a preset operation program.
3. The method for early warning based on the state of power system equipment according to claim 2, wherein the preset operation program includes a periodic operation program and a periodic operation program, the periodic operation program is periodically operated for the steps S100, S200, S300, S400, S500 and S600, and the periodic operation program is periodically operated for the steps S100, S200, S300, S400, S500 and S600 according to a preset time point.
4. The method of claim 1, wherein the step S400 includes:
s401, configuring initial parameters of an initial early warning algorithm model;
s402, starting a training task, inputting the training data set into the initial early warning algorithm model, and training the early warning algorithm model to obtain a training result;
s403, verifying the training result by using the verification data set to obtain optimal parameters and obtain an optimal training result; a kind of electronic device with high-pressure air-conditioning system
S404, obtaining the early warning algorithm model according to the optimal training result.
5. The method of power system equipment status based on early warning of claim 4, wherein the step S402 further comprises:
s4021, recording training conditions of the training task, wherein the training conditions comprise training success and training failure, executing step S403 when the training is successful, returning to executing step S401 until the training is successful when the training is failed, recording the training failure condition in an abnormal log of the training task, and sending an abnormal alarm.
6. The method of claim 1, wherein the step S500 includes:
s501, recording the release state of the release early warning model, wherein the release state comprises release success and release failure, when release is successful, executing step S500, and when release is failed, recording the release failure state in an abnormal log of a release task and sending out simultaneous abnormal alarms.
7. A system for power system based device status pre-warning, comprising:
the system comprises a configuration algorithm library module, a warning algorithm library module and a warning algorithm library module, wherein the configuration algorithm library module is configured to establish a warning algorithm library; a data acquisition module configured to acquire training data;
the initial early warning algorithm model building module is configured to select an algorithm suitable for the training data from an early warning algorithm library and build an initial early warning algorithm model;
an initial pre-warning algorithm model training module configured to train an initial algorithm model by using a training data set;
the early warning algorithm model issuing module is configured to issue an early warning algorithm model obtained after training is completed; a kind of electronic device with high-pressure air-conditioning system
And the power system equipment state early warning module is configured to issue early warning of abnormal power system equipment state.
8. A system for power system equipment status based pre-warning as in claim 7, further comprising:
the task operation module is configured to automatically operate the configuration algorithm library module, the data acquisition module, the initial early warning algorithm model building module, the initial early warning algorithm model training module and the power system equipment state early warning module according to a preset operation program.
9. The power system state warning-based system of claim 7, further comprising a logging module for logging the operational state of the initial warning algorithm model training module and the warning algorithm model publishing module.
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| CN116662307A (en) * | 2023-07-25 | 2023-08-29 | 苏州盈天地资讯科技有限公司 | Intelligent early warning method, system and equipment based on multi-source data fusion |
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| CN116662307A (en) * | 2023-07-25 | 2023-08-29 | 苏州盈天地资讯科技有限公司 | Intelligent early warning method, system and equipment based on multi-source data fusion |
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