CN105160604B - Power grid equipment state pre-warning platform based on abnormal operation condition identification - Google Patents
Power grid equipment state pre-warning platform based on abnormal operation condition identification Download PDFInfo
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Abstract
The present invention provides a power grid equipment state pre-warning platform based on abnormal operation condition identification, which is characterized in that: the platform comprises the following modules: a data interface module, a data preprocessing module, an equipment state threshold-crossing analysis module, an equipment abnormal operation condition identification module and a state pre-warning platform. According to the present invention, the power grid equipment state pre-warning platform starts from the improvement of an application level for equipment operation data analysis, researches a method for equipment threshold-crossing analysis and abnormal operation condition identification, realizes the scientific judgment on the change of an equipment state, and researches the abnormal operation condition identification, thereby improving the application level for power grid operation data analysis.
Description
Technical Field
The invention relates to the field of electric power system analysis and automatic control, in particular to a power grid equipment state early warning platform based on abnormal working condition identification.
Background
The equipment operation data, particularly the operation data when the equipment abnormally operates or changes states, contains rich equipment condition information. The data not only can reflect the running state and the historical state of the equipment, but also can be used as a reference for decision making, the running state of the equipment can be tracked in time and in the whole course, and the problem after-treatment is changed into prevention in advance.
At present, effective analysis means and unified management are lacked for equipment operation data, the operation state and the change process of the equipment cannot be mastered from a deeper perspective, so that the data application level is low, rich state information contained in the equipment operation data cannot be really mined, and the data analysis work is difficult to develop.
Therefore, the operation data of the equipment is analyzed, the identification model of the abnormal operation working condition of the equipment is established, the potential risk of the equipment can be found from the data, the early warning of the state of the equipment is realized, the operation state of the equipment can be accurately mastered through the statistical analysis of the abnormal operation data of the equipment, and a guide basis is provided for the development of preventive tests, state maintenance and other work.
Disclosure of Invention
In order to achieve the above object, the power grid equipment state early warning platform based on abnormal working condition recognition provided by the invention is constructed from the perspective of multidimensional analysis of equipment operation data, realizes standardized preprocessing and analysis of the operation data, and realizes equipment early warning
The purpose of the invention can be realized by the following technical scheme:
the utility model provides a power grid equipment state early warning platform based on abnormal conditions discernment which characterized in that: the platform comprises the following modules: the system comprises a data interface module, a data preprocessing module, an equipment state out-of-limit analysis module, an equipment abnormal operation condition recognition module and a state early warning module; wherein,
the data interface module accesses various power grid operation data from other systems through a standardized interface; the power grid operation data comprise active power, reactive power, current and oil temperature of a main transformer. The other systems include a dispatch automation system. The normalized interface is, for example, webservice.
The data preprocessing module respectively establishes corresponding data preprocessing strategies for the equipment operation data, wherein the data preprocessing strategies comprise a data rationality checking strategy and a data interpolation strategy, so that the missing and error conditions of various operation data are automatically corrected, and the integrity of the data is guaranteed;
the equipment state out-of-limit analysis module respectively sets corresponding threshold values for various operation data of the equipment, and when the value exceeds the threshold value, automatic judgment of data out-of-limit is realized; the step of respectively setting corresponding threshold values for various operation data of the equipment comprises the step of setting an out-of-limit threshold value of 120 ℃ for the temperature of the main transformer, so that the out-of-limit analysis of the temperature is realized.
The abnormal working condition recognition module selects typical abnormal operation data of the equipment as a training sample, establishes an equipment abnormal operation working condition neural network recognition model, judges the current operation working condition of the equipment by using the recognition model and realizes abnormal working condition recognition; the identification model is an abnormal working condition identification model based on load change rate analysis.
The out-of-limit analysis module and the abnormal working condition identification module output equipment out-of-limit results, and the abnormal working condition identification module outputs abnormal working condition results to the state early warning module, so that comprehensive early warning of equipment based on running data analysis is realized.
The invention has the following beneficial effects:
the invention provides a power grid equipment state early warning platform based on abnormal working condition recognition, which is used for researching equipment out-of-limit analysis and abnormal working condition recognition methods from the aspect of improving equipment operation data analysis application level, realizing scientific judgment of equipment state change, researching recognition of abnormal operation working conditions and improving the power grid operation data analysis application level. In addition, a state early warning strategy based on equipment state out-of-limit and abnormal working condition analysis is established, state early warning based on equipment operation data is achieved, and equipment early warning capacity is greatly improved.
Drawings
Fig. 1 is an overall schematic diagram of a power grid equipment state early warning platform based on abnormal condition identification according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is an overall schematic diagram of a power grid equipment state early warning platform based on abnormal working condition recognition, which comprises a data interface module, a data preprocessing module, an equipment state out-of-limit analysis module, an equipment abnormal operation working condition recognition module and a state early warning module, and is used for realizing power grid equipment state early warning based on abnormal working condition recognition and state baseline analysis and completing the following state early warning process:
step 1: and the data interface module is accessed to various kinds of operation data of the power grid equipment from other systems such as a dispatching automation system and the like through standardized interfaces such as webservice and the like.
Specifically, the device operation data, taking a transformer as an example, includes: load level, temperature rise level, overvoltage conditions (lightning overvoltage, operation overvoltage), short circuit conditions in the near region, operating environment (environmental temperature, humidity, pollution degree) and the like;
step 2: and the data preprocessing module is used for establishing corresponding data preprocessing strategies for the equipment operation data respectively, automatically correcting the conditions of deletion, errors and the like of various operation data and ensuring the integrity of the data.
And step 3: the method comprises the steps of adopting an analytic hierarchy process, comprehensively considering the influence of various operation data of the equipment and the like on the state of the equipment, setting out-of-limit threshold values and weights of the operation data according to experience and related technical indexes, establishing an equipment state out-of-limit analysis model, and judging whether the equipment has the state out-of-limit condition currently;
and 4, step 4: the abnormal operation working condition analysis of equipment, at first comb and analysis to the adverse condition to equipment state influence, include: determining an estimation method for the influence of poor industrial control on the service life of equipment, such as overload, overvoltage and short circuit in a near area; and secondly, determining the state loss relation of the equipment with bad working conditions in different time periods by combining the state loss relation of the equipment in different time periods and the state loss relation of the equipment in different time periods, and evaluating the running working conditions of the equipment. The invention divides the running state of the equipment into 4 states, which are respectively: a normal operation state, an operable state, a reliability decline state and a dangerous state;
and 5: the state early warning module performs state early warning on the equipment based on the equipment operation data by combining the equipment operation data out-of-limit analysis result, the abnormal working condition analysis result and the like; according to the equipment state baseline analysis result, aiming at different equipment state out-of-limit conditions, a differentiated operation and maintenance strategy is formulated, and the equipment operation and maintenance management level is improved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention may be implemented by software plus a necessary hardware platform, and may also be implemented by hardware entirely. With this understanding in mind, all or part of the technical solutions of the present invention that contribute to the background can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments or some parts of the embodiments of the present invention.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (6)
1. The utility model provides a power grid equipment state early warning platform based on abnormal conditions discernment which characterized in that: the platform comprises the following modules: the system comprises a data interface module, a data preprocessing module, an equipment state out-of-limit analysis module, an equipment abnormal operation condition recognition module and a state early warning module; wherein,
the data interface module accesses various kinds of operation data of the equipment from other systems through a standardized interface;
the data preprocessing module respectively establishes corresponding data preprocessing strategies for various operation data of the equipment, wherein the data preprocessing strategies comprise a data rationality checking strategy and a data interpolation strategy, so that the missing and error conditions of various operation data of the equipment are automatically corrected, and the integrity of various operation data of the equipment is ensured;
the equipment state out-of-limit analysis module respectively sets corresponding threshold values for various operation data of the equipment, and when the various operation data of the equipment exceed the threshold values, automatic data out-of-limit judgment is realized;
the device abnormal operation condition recognition module selects typical abnormal operation data of the device as a training sample, establishes a device abnormal operation condition neural network recognition model, judges the current operation condition of the device by using the device abnormal operation condition neural network recognition model, and realizes abnormal condition recognition;
the equipment state out-of-limit analysis module and the abnormal working condition recognition module output equipment out-of-limit results, and the equipment abnormal working condition recognition module outputs abnormal working condition results to the state early warning module, so that comprehensive early warning of the equipment based on operation data analysis is realized.
2. The platform of claim 1, wherein: the various operation data of the equipment comprise active power, reactive power, current and oil temperature of a main transformer.
3. The platform of claim 1, wherein: the other systems include a dispatch automation system.
4. The platform of claim 1, wherein: the normalized interface is a webservice.
5. The platform of claim 1, wherein: the step of respectively setting corresponding threshold values for various operation data of the equipment comprises the step of setting an out-of-limit threshold value of 120 ℃ for the temperature of the main transformer, so that the out-of-limit analysis of the temperature is realized.
6. The platform of claim 1, wherein: the neural network identification model of the abnormal operation condition of the equipment is an abnormal condition identification model based on load change rate analysis.
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| CN108280590A (en) * | 2018-02-09 | 2018-07-13 | 青海电研科技有限责任公司 | Photovoltaic plant online evaluation early warning system and method |
| CN110065866A (en) * | 2019-04-25 | 2019-07-30 | 永大电梯设备（中国）有限公司 | A kind of integrated forecasting system of the exception monitoring based on SMT |
| CN113722973A (en) * | 2020-05-25 | 2021-11-30 | 中国石油化工股份有限公司 | Correction system and correction method of computer simulation model |
| CN112649696A (en) * | 2020-10-26 | 2021-04-13 | 国网河北省电力有限公司邢台供电分公司 | Power grid abnormal state identification method |
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