CN116681255A - Transformer operation monitoring system based on Internet of things - Google Patents
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Abstract
The invention discloses a transformer operation monitoring system based on the Internet of things, which belongs to the technical field of transformer monitoring and comprises a tag module, a monitoring module and an analysis module; the tag module is used for obtaining a hazard level tag and a risk level tag corresponding to equipment information and surrounding environment information of the transformer, wherein the equipment information comprises equipment model, responsible area power consumption data, service life and maintenance records; the monitoring module is used for remotely monitoring the transformers, installing corresponding monitoring equipment for each transformer, and monitoring each transformer in real time through the installed monitoring equipment to obtain corresponding monitoring data; inputting the obtained monitoring data into a preset monitoring display model for real-time display; the analysis module is used for carrying out real-time analysis on the display data of each transformer in the monitoring display model, determining each monitoring item, setting a parameter safety interval corresponding to each monitoring item, and identifying the display data corresponding to each monitoring item in the monitoring display model in real time.
Description
Technical Field
The invention belongs to the technical field of transformer monitoring, and particularly relates to a transformer operation monitoring system based on the Internet of things.
Background
A transformer is a device for changing an ac voltage using the principle of electromagnetic induction, and its main components are a primary coil, a secondary coil, and an iron core (magnetic core). The main functions of the transformer are as follows: voltage conversion, current conversion, impedance conversion, isolation, voltage stabilization (magnetic saturation transformer), and the like, can be classified into: power transformers and special transformers. In electrical equipment and wireless circuits, transformers are often used for step-up and step-down voltages, matching impedances, safety isolation, etc. In a generator, either the coil movement through a magnetic field or the movement of a magnetic field through a fixed coil, a potential is induced in the coil, both of which are the same in value but vary in the amount of flux that intersects the coil, which is the principle of mutual induction. A transformer is a device that converts voltage, current and impedance using electromagnetic mutual induction.
Transformers are widely applied, such as power transformers, distribution transformers, dry transformers, amorphous alloy transformers and coiled iron core transformers, and in rural areas, large-scale power transmission and transformation transformers are common, and faults, such as circuit faults, winding faults, transformer oil leakage faults, junction temperature faults, high faults and the like, occur in the use of the transformers; at present, the power system mainly adopts a mode of collecting power load data on site of a transformer, so that the safety is not ensured, the labor load is high, and the rural power grid is basically managed in a mode of 'equipment plus people' all the time; therefore, in order to realize intelligent monitoring of rural transformers, the invention provides a transformer operation monitoring system based on the Internet of things.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a transformer operation monitoring system based on the Internet of things.
The aim of the invention can be achieved by the following technical scheme:
a transformer operation monitoring system based on the Internet of things comprises a tag module, a monitoring module and an analysis module;
the label module is used for carrying out hazard grade labels and risk grade labels corresponding to equipment information and surrounding environment information of the transformer, wherein the equipment information comprises equipment model, responsible area power consumption data, service life and maintenance records.
Further, the risk level label setting method comprises the following steps:
setting corresponding risk values according to the obtained equipment information, matching corresponding risk levels from preset risk level intervals through the calculated risk values, and generating corresponding risk level labels according to the matched risk levels.
Further, the risk value setting method includes:
identifying equipment information of each transformer, analyzing the identified equipment information through a preset equipment analysis model to obtain corresponding equipment risk values and load risk values, marking the obtained equipment risk values and load risk values as SC and CF respectively, and calculating corresponding risk values FPS according to a risk assessment formula FPS=b1×SC+b2×CF, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
Further, the method for setting the hazard class label comprises the following steps:
identifying corresponding risk values and risk levels, acquiring environment information of the transformer, setting corresponding hazard values according to the acquired environment information and the risk levels, matching the corresponding hazard levels from preset hazard level intervals through the calculated hazard values, and generating corresponding hazard level labels according to the matched hazard levels.
Further, the method for setting the hazard value comprises the following steps:
and according to the obtained risk level matching corresponding adjustment coefficient, analyzing the obtained environmental information to obtain a corresponding environmental hazard value, respectively marking the obtained environmental hazard value and adjustment coefficient as WF and t, and calculating the corresponding hazard value WH according to a hazard evaluation formula WH=t×WF.
The monitoring module is used for remotely monitoring the transformers, installing corresponding monitoring equipment for each transformer, and monitoring each transformer in real time through the installed monitoring equipment to obtain corresponding monitoring data; and inputting the obtained monitoring data into a preset monitoring display model for real-time display.
Further, the setting method of the monitoring display model comprises the following steps:
and acquiring the position and equipment information of each monitored transformer, setting a corresponding transformer layout according to the acquired position and equipment information of the transformer, processing the transformer layout, and establishing a corresponding monitoring display model based on a visualization technology.
The analysis module is used for carrying out real-time analysis on the display data of each transformer in the monitoring display model, determining each monitoring item, setting a parameter safety interval corresponding to each monitoring item, identifying the display data corresponding to each monitoring item in the monitoring display model in real time, comparing the identified display data with the corresponding parameter safety interval, and judging whether the warning requirement is met.
Further, safety evaluation of display data is carried out according to the hazard grade label and the risk grade label corresponding to each transformer.
Further, the method for carrying out security evaluation on the display data according to the hazard level label and the risk level label comprises the following steps:
identifying display data corresponding to each monitoring item in real time, calculating a difference value between each display data and a corresponding parameter safety interval, identifying a hazard grade label and a risk grade label corresponding to the transformer, analyzing the obtained difference value, the hazard grade label and the risk grade label of each monitoring item to obtain a dynamic coefficient corresponding to each monitoring item, and marking the obtained difference value and the corresponding dynamic coefficient as CZi and ci respectively, wherein i=1, 2, … …, n and n are positive integers; according to the safety formulaCalculating a corresponding safety value, and when the calculated safety value is greater than a threshold value X1, reaching the warning requirement; otherwise, it is normal.
Compared with the prior art, the invention has the beneficial effects that:
through the mutual coordination among the tag module, the monitoring module and the analysis module, the remote monitoring of each transformer in rural environments is realized, and the problems that the existing power system mainly adopts a mode of collecting power load data of the transformer on site, and is not safe and has large labor load are solved; meanwhile, corresponding safety risks and hazard conditions are fully considered in the monitoring process, and subsequent targeted monitoring is achieved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. 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.
As shown in FIG. 1, the transformer operation monitoring system based on the Internet of things comprises a tag module, a monitoring module, an analysis module,
The label module is used for giving corresponding hazard grade labels and risk grade labels according to actual conditions of equipment information and surrounding environment information of the transformer and is used for rapidly knowing risk hazard conditions of the transformer, specifically, corresponding risk values of the transformer are firstly evaluated according to the equipment information of the transformer, corresponding risk grades are matched according to preset risk grade intervals, and corresponding risk grade labels are generated according to the matched risk grades; knowing the probability of corresponding hazard such as fire hazard occurrence through the risk value, and evaluating the corresponding hazard value by combining the severity degree of the hazard occurrence analysis with the surrounding environment; if no households exist around the transformer, more combustible materials exist under the transformer, and when a fire disaster occurs, the control cannot be performed in time; matching corresponding hazard grades according to preset hazard grade intervals, and generating corresponding hazard grade labels according to the matched hazard grades; wherein, each risk level interval and hazard level interval are set through possible risk values and hazard values through discussion of an expert group.
The risk value evaluation method comprises the following steps:
identifying equipment information of each transformer, wherein the equipment information comprises equipment model numbers, charge area electricity data, service life and maintenance records, each level of equipment information is evaluated from two angles of load risk and equipment risk to obtain corresponding load risk values and equipment risk values, the equipment risk values are set according to the equipment model numbers, the service life and the maintenance records, the load risk values are set according to the equipment model numbers, the charge area electricity data and the equipment risk values, namely the equipment risk values are required to be analyzed firstly and then separated, a corresponding equipment analysis model is established based on a CNN network or a DNN network, a corresponding training set is established in a manual mode to train, the training set comprises various equipment information, the corresponding set equipment risk values and the corresponding set equipment risk values, and the equipment information is analyzed through the equipment analysis model after the training is successful to obtain the corresponding equipment risk values and the corresponding load risk values; because neural networks are prior art in the art, the specific setup and training process is not described in detail in this disclosure; and respectively marking the obtained equipment risk value and the load risk value as SC and CF, and calculating corresponding risk values according to a risk assessment formula FPS=b1×SC+b2×CF, wherein b1 and b2 are both proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
The hazard value evaluation method comprises the following steps:
acquiring environmental information such as image data acquired during historical maintenance, maintenance and installation of each transformer, and acquiring corresponding data from corresponding positions if no related data exists, so as to acquire the environmental information; acquiring corresponding risk values, matching corresponding adjustment coefficients according to the acquired risk values, setting the corresponding adjustment coefficients for each risk level in a manual mode, and performing corresponding matching, wherein the adjustment coefficients are set according to the size of the risk level, and the larger the risk level is, the more risk conditions are likely to occur, and the larger the adjustment coefficients are; establishing a corresponding hazard analysis model based on a CNN network or a DNN network, establishing a corresponding training set by a manual mode for training, analyzing the environmental information through the hazard analysis model after successful training to obtain a corresponding environmental hazard value, marking the obtained environmental hazard value and an adjustment coefficient as WF and t respectively, and calculating the corresponding hazard value according to a hazard evaluation formula WH=t×WF.
The monitoring module is used for remotely monitoring the transformer, and the specific process is as follows:
firstly, installing corresponding monitoring equipment for each transformer, and collecting monitoring data of each transformer in real time through the monitoring equipment, wherein the monitoring equipment monitors the transformers by using the existing related equipment; acquiring the position and equipment information of each monitored transformer, and establishing a corresponding monitoring display model according to the position and equipment information of the transformer; and inputting the collected monitoring data of each transformer into a monitoring display model for real-time display.
The method for establishing the corresponding monitoring display model according to the transformer position and the equipment information comprises the following steps:
the method comprises the steps of obtaining a current map, marking the positions of transformers in the map, inserting corresponding equipment information at the corresponding positions to form a transformer layout, and processing the transformer layout, specifically, according to actual needs, for example, removing unnecessary legends and the like in the map, or not processing, and keeping the transformer layout as it is; establishing a corresponding monitoring display model according to the transformer layout diagram and the visualization technology; typically a two-dimensional display model, and if desired, a three-dimensional visualization model may be created.
The analysis module is used for analyzing the display data of each transformer in the monitoring display model in real time, judging whether the monitoring model has safety and other problems, determining which monitoring items are contained according to the complementary monitoring equipment, setting parameter safety intervals corresponding to the monitoring items, and setting the parameter safety intervals according to the requirements of the monitoring items of the transformer; and identifying display data corresponding to each monitoring item in the monitoring display model in real time, comparing the identified display data with the corresponding parameter safety interval, and judging whether the warning requirement is met.
In one embodiment, because actual conditions of different transformers are different, the threshold values of the monitoring safety of the transformers are different, if the monitoring is performed according to the same standard, the operation parameters of some transformers reach the warning requirement, but the corresponding parameter safety intervals are not reached, so that early warning is not timely, and potential safety hazards are caused; in this embodiment, more accurate analysis and judgment are performed by combining the corresponding hazard level label and risk level label; the correction is mainly carried out in the following three directions;
first kind: generating interval correction coefficients corresponding to all monitoring items of the transformer according to the hazard grade label and the risk grade label corresponding to the transformer, correcting the parameter safety interval corresponding to each monitoring item through the obtained interval correction coefficients, and checking through the corrected parameter safety interval; the method comprises the steps of establishing a corresponding correction analysis model based on a CNN (computer numerical network) or a DNN (digital network), establishing a corresponding training set by a manual mode for training, wherein the training set comprises possible combinations of various hazard grade labels and risk grade labels and corresponding set interval correction coefficients, analyzing by the correction analysis model after successful training to obtain the corresponding interval correction coefficients, and setting the interval correction coefficients corresponding to each combination by the manual mode and summarizing the interval correction coefficients into a corresponding interval correction coefficient matching table when the possible combinations of the hazard grade labels and the risk grade labels are fewer, so that corresponding matching is carried out.
Second kind: determining corresponding parameter correction coefficients by analyzing the hazard level label and the risk level label, and performing real-time correction on the monitored parameters, and comparing the corrected display data with corresponding parameter safety intervals; the method comprises the steps of establishing a corresponding parameter analysis model based on a CNN (computer numerical network) or a DNN (digital network), establishing a corresponding training set by a manual mode for training, wherein the training set comprises possible combinations of various hazard grade labels and risk grade labels and corresponding parameter correction coefficients, analyzing by the parameter analysis model after successful training to obtain the corresponding parameter correction coefficients, and setting the parameter correction coefficients corresponding to each combination by the manual mode and summarizing the parameter correction coefficients into a corresponding parameter correction coefficient matching table when the possible combinations of the hazard grade labels and the risk grade labels are fewer, so that corresponding matching is carried out.
Third kind: identifying display data corresponding to each monitoring item in real time, calculating a difference value between each display data and a corresponding parameter safety interval, wherein the difference value refers to a difference value between lower boundaries of the parameter safety intervals, namely, the boundary of the parameter safety interval can be reached by adding the difference value; identifying a corresponding hazard level label and a risk level label of the transformer, analyzing the obtained difference value of each monitoring item, the hazard level label and the risk level label to obtain a dynamic coefficient corresponding to each monitoring item, specifically establishing a corresponding safety analysis model based on a CNN network or a DNN network, establishing a corresponding training set in a manual mode for training, wherein the training set comprises the simulated set difference value of the monitoring item, the hazard level label and the risk level label and the corresponding set dynamic coefficient, and analyzing the safety analysis model after successful training to obtain the corresponding dynamic coefficient; marking the obtained monitoring item difference value and the corresponding dynamic coefficient as CZi and ci respectively, wherein i=1, 2, … … and n, n being a positive integer; according to the safety formulaCalculating a corresponding safety value, and when the calculated safety value is greater than a threshold value X1, reaching the warning requirement; otherwise, it is normal.
Through the mutual coordination among the tag module, the monitoring module and the analysis module, the remote monitoring of each transformer in rural environments is realized, and the problems that the existing power system mainly adopts a mode of collecting power load data of the transformer on site, and is not safe and has large labor load are solved; meanwhile, corresponding safety risks and hazard conditions are fully considered in the monitoring process, and subsequent targeted monitoring is achieved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The transformer operation monitoring system based on the Internet of things is characterized by comprising a tag module, a monitoring module and an analysis module;
the tag module is used for obtaining a hazard level tag and a risk level tag corresponding to equipment information and surrounding environment information of the transformer, wherein the equipment information comprises equipment model, responsible area power consumption data, service life and maintenance records;
the monitoring module is used for remotely monitoring the transformers, installing corresponding monitoring equipment for each transformer, and monitoring each transformer in real time through the installed monitoring equipment to obtain corresponding monitoring data; inputting the obtained monitoring data into a preset monitoring display model for real-time display;
the analysis module is used for carrying out real-time analysis on the display data of each transformer in the monitoring display model, determining each monitoring item, setting a parameter safety interval corresponding to each monitoring item, identifying the display data corresponding to each monitoring item in the monitoring display model in real time, comparing the identified display data with the corresponding parameter safety interval, and judging whether the warning requirement is met.
2. The transformer operation monitoring system based on the internet of things according to claim 1, wherein the risk level tag setting method comprises the following steps:
setting corresponding risk values according to the obtained equipment information, matching corresponding risk levels from preset risk level intervals through the calculated risk values, and generating corresponding risk level labels according to the matched risk levels.
3. The transformer operation monitoring system based on the internet of things according to claim 2, wherein the risk value setting method comprises the following steps:
identifying equipment information of each transformer, analyzing the identified equipment information through a preset equipment analysis model to obtain corresponding equipment risk values and load risk values, marking the obtained equipment risk values and load risk values as SC and CF respectively, and calculating corresponding risk values FPS according to a risk assessment formula FPS=b1×SC+b2×CF, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1.
4. The transformer operation monitoring system based on the internet of things according to claim 3, wherein the method for setting the hazard class label comprises the following steps:
identifying corresponding risk values and risk levels, acquiring environment information of the transformer, setting corresponding hazard values according to the acquired environment information and the risk levels, matching the corresponding hazard levels from preset hazard level intervals through the calculated hazard values, and generating corresponding hazard level labels according to the matched hazard levels.
5. The transformer operation monitoring system based on the internet of things according to claim 4, wherein the method for setting the hazard value comprises the following steps:
and according to the obtained risk level matching corresponding adjustment coefficient, analyzing the obtained environmental information to obtain a corresponding environmental hazard value, respectively marking the obtained environmental hazard value and adjustment coefficient as WF and t, and calculating the corresponding hazard value WH according to a hazard evaluation formula WH=t×WF.
6. The transformer operation monitoring system based on the internet of things according to claim 1, wherein the method for setting the monitoring display model comprises the following steps:
and acquiring the position and equipment information of each monitored transformer, setting a corresponding transformer layout according to the acquired position and equipment information of the transformer, processing the transformer layout, and establishing a corresponding monitoring display model based on a visualization technology.
7. The transformer operation monitoring system based on the internet of things according to claim 1, wherein safety evaluation of display data is performed according to hazard class labels and risk class labels corresponding to the transformers.
8. The transformer operation monitoring system based on the internet of things according to claim 7, wherein the method for performing security assessment on the display data according to the hazard class label and the risk class label comprises:
identifying display data corresponding to each monitoring item in real time, calculating a difference value between each display data and a corresponding parameter safety interval, identifying a hazard grade label and a risk grade label corresponding to the transformer, analyzing the obtained difference value, the hazard grade label and the risk grade label of each monitoring item to obtain a dynamic coefficient corresponding to each monitoring item, and marking the obtained difference value and the corresponding dynamic coefficient as CZi and ci respectively, wherein i=1, 2, … …, n and n are positive integers; according to the safety formulaCalculating a corresponding safety value, and when the calculated safety value is greater than a threshold value X1, reaching the warning requirement; otherwise, it is normal.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117172559A (en) * | 2023-11-02 | 2023-12-05 | 南京怡晟安全技术研究院有限公司 | Risk identification early warning method, system and storage medium for Internet of things data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117172559A (en) * | 2023-11-02 | 2023-12-05 | 南京怡晟安全技术研究院有限公司 | Risk identification early warning method, system and storage medium for Internet of things data |
| CN117172559B (en) * | 2023-11-02 | 2024-02-09 | 南京怡晟安全技术研究院有限公司 | Risk identification early warning method, system and storage medium for Internet of things data |
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