CN118280069A - GIS-based power grid disaster monitoring and early warning system - Google Patents
GIS-based power grid disaster monitoring and early warning system Download PDFInfo
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Abstract
The invention discloses a GIS-based power grid disaster monitoring and early warning system, which comprises a data monitoring unit, a parameter processing unit, a networking acquisition unit, a networking processing unit, an association processing unit, an early warning analysis unit and an interface display unit, wherein the data monitoring unit is connected with the parameter processing unit; the invention relates to the technical field of power grid monitoring, which comprises the steps of acquiring real-time monitoring data of power grid facilities in a target area through a data monitoring unit, reflecting the running state of the power grid facilities in real time and accurately, and predicting weather conditions possibly influencing the power grid facilities through processing and analyzing the weather data, so that preventive measures are taken in advance, meanwhile, utilizing a pre-trained early warning analysis model to perform data analysis and processing, the power grid facilities possibly having problems can be automatically identified, early warning signals are generated, and the power grid facilities can be effectively prevented or reduced from being failed due to the change of the weather conditions through the real-time monitoring and early warning of the power grid facilities, so that the normal running of the power grid facilities is ensured.
Description
Technical Field
The invention relates to the technical field of power grid monitoring, in particular to a power grid disaster monitoring and early warning system based on GIS.
Background
With the rapid development of power systems, the health of the grid facilities plays a vital role in the stable operation of the whole power system. However, due to the influence of environmental factors, such as geological conditions, climate change and the like, the power grid facilities may have phenomena of settlement, deviation and the like, and the stability and the safety of the power grid facilities are seriously affected. Therefore, real-time monitoring and early warning of the grid facilities is of paramount importance. The traditional power grid facility monitoring method mainly depends on manual inspection and periodical maintenance, and the method is low in efficiency and cannot realize real-time monitoring and early warning. In addition, because the geographical position of the power grid facility is complex, the manual inspection and maintenance difficulty is high, and the conditions of missed inspection and false inspection are easy to occur.
In recent years, the development of GIS (geographic information system) technology provides a new solution for monitoring and early warning of power grid facilities. The GIS technology can acquire the geographical position information of the power grid facilities in real time, so that the real-time monitoring and early warning of the power grid facilities are realized. However, the existing monitoring and early warning system for the power grid facilities based on the GIS mainly depends on single monitoring data, and cannot comprehensively reflect the health condition of the power grid facilities.
Therefore, the GIS-based power grid disaster monitoring and early warning system is developed, various monitoring data such as sedimentation data, offset data and meteorological data can be obtained and processed in real time, and the health condition of power grid facilities is comprehensively evaluated, so that the real-time monitoring and early warning of the power grid facilities are realized, and the problem to be solved is currently urgently.
Disclosure of Invention
The invention aims to provide a GIS-based power grid disaster monitoring and early warning system, which solves the technical problems in the background technology.
The aim of the invention can be achieved by the following technical scheme:
a GIS-based power grid disaster monitoring and early warning system comprises:
the data monitoring unit is used for acquiring monitoring data of power grid facilities in the target area, wherein the monitoring data comprises sedimentation parameters and offset parameters;
The parameter processing unit is used for processing and analyzing the sedimentation parameters and the offset parameters collected in a preset monitoring period, and then calculating a first parameter occupation coefficient corresponding to the sedimentation parameters and a second parameter occupation coefficient corresponding to the offset parameters on each time node through a preset calculation formula;
The network acquisition unit is used for acquiring meteorological data in a target area through an internet technology, wherein the meteorological data comprises temperature parameters, humidity parameters, wind speed parameters and rainfall, and the meteorological data is divided into historical meteorological data and forecast meteorological data by taking the current time as a reference;
The networking processing unit is used for carrying out standardized processing on the historical meteorological data and the forecast meteorological data according to a preset standardized processing mode;
The association processing unit is used for carrying out association processing on the meteorological data and the monitoring data after parameter processing, and the method is as follows: taking the sedimentation parameters of the first time node at the beginning stage of the monitoring period as reference parameters, then calculating the stage sedimentation differences between other time nodes except the first time node and the reference parameters, calculating corresponding stage offset differences in a similar way, establishing a mapping relation between the first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation differences and the offset differences and the standardized temperature, humidity, wind speed and rainfall data set, and inputting the mapping relation into a pre-trained early warning analysis model;
The early warning analysis unit is used for carrying out early warning analysis on power grid facilities in the target area by combining the standardized predicted meteorological data; the method comprises the following steps: obtaining an optimal matching result from the predicted meteorological data and the historical meteorological data, obtaining a corresponding first parameter occupation coefficient, a corresponding second parameter occupation coefficient, a corresponding stage sedimentation difference and a corresponding stage offset difference according to the matching result, calculating a judging coefficient in a future monitoring period through a preset formula, comparing the judging coefficient with a preset judging threshold value, and generating an early warning signal when the judging coefficient is larger than or equal to the judging threshold value.
As a further scheme of the invention: the specific mode of the parameter processing unit is as follows:
The method comprises the steps of SS1, selecting a plurality of time nodes with the same time interval according to time sequence in a pre-selected monitoring period, and acquiring corresponding sedimentation parameters from the plurality of time nodes;
meanwhile, the sedimentation parameters are marked as C i, i=1, 2 and … … n, and n represents the number of time nodes;
SS2, by Calculating a corresponding sedimentation proportion CB i of sedimentation parameters on each time node in the monitoring period; wherein i is not n and all sedimentation ratios CBi do not contain CB1;
SS3, by Calculating to obtain a deviation value U of the sedimentation duty ratio of the group of CB 1 to CB n;
In the method, in the process of the invention, Expressed as an average corresponding to all sedimentation ratios CB i;
SS4, then compares U with a preset discrete threshold U y:
If U is larger than U y, the deviation value of the sub item is larger, then corresponding CB i values are deleted in sequence from large to small according to |CB i-CBp |, and the residual deviation value U is correspondingly calculated until U is smaller than or equal to U y;
Extracting undeleted CB i and corresponding C i, and calculating average value C p of corresponding C i of undeleted CB i;
SS5, replacing the sedimentation parameter C i deleted from the corresponding time node with C p in the sedimentation parameters C i corresponding to all the time nodes;
SS6, by Calculating a first parameter occupation coefficient E1i corresponding to the sedimentation parameters on each time node, wherein the sedimentation parameter C i with the corresponding time node deleted is selected as C p;
And SS7, acquiring offset parameters on a plurality of time nodes, and calculating a second parameter occupation coefficient E2 i corresponding to the offset parameters on each time node according to the mode of SS1-SS 6.
As a further scheme of the invention: the temperature parameter, the humidity parameter and the wind speed parameter are regional data acquired in real time, and the rainfall is point position data acquired periodically;
the regional data refers to related data acquired by taking a target region as a standard range;
the point location data refers to related data acquired at each acquisition point by setting up a plurality of acquisition points in a target area.
As a further scheme of the invention: the historical meteorological data is selected for standardized processing in the following mode:
SX1, dividing a pre-selected monitoring period into a plurality of standard time periods, wherein the standard time periods are determined according to the interval time of two adjacent time nodes, and the number of the standard time periods is n-1;
SX2, acquiring all corresponding temperature parameters, humidity parameters, wind speed parameters and rainfall in a corresponding standard period;
SX3, temperature parameter, humidity parameter, wind speed parameter, and it adopts the quartile spacing method to carry out standardization processing, selects the temperature parameter to carry out the mode of processing as follows:
Step31, sequencing the temperature parameters in order from small to large to form a temperature sequence table;
step32, selecting a first quartile Q1 and a third quartile Q3 from all voltage parameters in the temperature sequence table;
step33, calculating to obtain a quartile interval IQR through iqr=q3-Q1, wherein the quartile interval IQR reflects the discrete degree of the middle 50% data;
step34, calculating standardized judgment values Rmin and Rmax respectively through formulas rmin=q1-t×iqr and rmax=q3+t×iqr, wherein t is a fixed value;
Step35, extracting a temperature parameter smaller than Rmin, a temperature parameter larger than Rmax and a temperature parameter larger than or equal to Rmin and smaller than or equal to Rmax in a standard period, and respectively calculating average values of the temperature parameters to obtain W1, W2 and W3;
then, combining all the average values into a temperature set W0E [ W1, W2 and W3];
similarly, humidity sets S0E [ S1, S2 and S3] and wind speed sets F0E [ F1, F2 and F3] corresponding to the humidity parameters and the wind speed parameters are obtained respectively;
the temperature set, the humidity set and the wind speed set are standard data after standardized processing;
the standardized processing mode of SX4 and rainfall is specifically as follows: and carrying out average value calculation on rainfall information acquired by the plurality of acquisition points, and recording the calculation result as rainfall average value of a standard period, namely standard data after standardized processing.
As a further scheme of the invention: the specific way of the association process is as follows:
SZ1, selecting power grid facilities at the same geographic position, and recording a sedimentation parameter corresponding to a first time node at the beginning stage of a monitoring period as a reference parameter;
SZ2, subtracting the reference parameter from the sedimentation parameters corresponding to each time node in all time nodes except the first time node at the beginning stage of the monitoring period to obtain corresponding stage sedimentation differences, wherein the number of the stage sedimentation differences is n-1;
similarly, calculating a corresponding stage offset difference;
SZ3, respectively establishing mapping relations between the stage sedimentation differences of the corresponding time nodes and the temperature sets, the humidity sets, the wind speed sets and the rainfall average values obtained through standardized processing, and importing the mapping relations into a pre-trained early warning analysis model;
SZ4, simultaneously establishing a mapping relation among the first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation difference and the stage offset difference of the corresponding time nodes, and importing the mapping relation into a pre-trained early warning analysis model.
As a further scheme of the invention: mapping is implemented based on any one of a vector machine, convolutional neural network, or recurrent neural network.
As a further scheme of the invention: the mode of the early warning analysis unit is as follows:
ST1, importing the temperature parameter, the humidity parameter, the wind speed parameter and the rainfall in the standardized predicted meteorological data into a pre-trained early warning analysis model;
ST2, the early warning analysis model matches the similarity of the predicted meteorological data and the historical meteorological data establishing the mapping relation, and the similarity matching mode is as follows:
selecting temperature parameters in the predicted meteorological data, carrying out difference analysis calculation on a temperature set obtained by the temperature parameters in the predicted meteorological data and a temperature set obtained by the temperature parameters in the historical meteorological data in an early warning analysis model, and obtaining an analysis judgment value;
Then acquiring a corresponding temperature set of the historical meteorological data with the minimum analysis judgment value as a matching result;
ST3, obtaining a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference and a stage offset difference of corresponding mapping according to the matching result;
And by analogy, a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference and a stage offset difference which are obtained in each standard period in a future monitoring period are obtained;
The first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation difference and the stage offset difference obtained in each standard period in the future monitoring period are re-marked as E1j, E2j, JCj, JPj, j=1, 2 and … … m, and m represents the number of each standard period in the future monitoring period;
ST4, through the formula Calculating a judging coefficient PD in a future monitoring period;
Wherein d1 and b1 represent sedimentation parameters obtained by a sedimentation sensor and offset parameters obtained by an offset sensor when the power grid facilities are in accordance, which are fixed values, d2 and b2 represent sedimentation parameters and offset parameters obtained by the sedimentation sensor and the offset sensor for the corresponding power grid facilities at the current time node, and beta 1 and beta 2 are preset duty ratio coefficients;
ST5, then, compares the determination coefficient PD in the future monitoring period with a preset determination threshold value PDy:
if PD is more than or equal to PDy, generating an early warning signal;
if PD is less than PDy, no early warning signal is generated.
As a further scheme of the invention: the formula for the difference analysis calculation is as follows:
Wherein Wy is represented as an analysis determination value, W1, W2, W3 are represented as values in a temperature set obtained by predicting temperature parameters in the weather data, W01, W02, W03 are represented as values in a temperature set obtained by predicting temperature parameters in the weather data, and α1, α2, α3 are preset scale factors.
As a further scheme of the invention: the data monitoring unit also acquires GIS information corresponding to the power grid facilities by using a GIS technology, wherein the GIS information is expressed as the geographic position of the power grid facilities.
As a further scheme of the invention: the interface display unit is used for displaying the early warning signals through the PC end and the mobile end, acquiring corresponding GIS information of the power grid facilities according to the early warning signals, and displaying the GIS information to related personnel.
The invention has the beneficial effects that:
real-time and accuracy: the real-time monitoring data of the power grid facilities in the target area, including the sedimentation parameters and the offset parameters, are obtained through the data monitoring unit, so that the running state of the power grid facilities can be accurately reflected in real time.
Predictability: the system can process and analyze meteorological data, including temperature parameters, humidity parameters, wind speed parameters, rainfall, etc., to predict meteorological conditions that may have an impact on the grid facilities, thereby taking precautions ahead of time.
Intelligent: the system utilizes a pre-trained early warning analysis model to perform data analysis and processing, can automatically identify power grid facilities possibly having problems, and generates early warning signals.
And (3) visualization: through the interface display unit, early warning signals can be displayed on the PC end and the mobile end, and corresponding GIS information of the power grid facilities is obtained according to the early warning signals, so that related personnel can intuitively know the running state and the geographic position of the power grid facilities.
Disaster prevention and reduction: through the real-time monitoring and early warning of the power grid facilities, the faults of the power grid facilities caused by the change of meteorological conditions can be effectively prevented or reduced, and the normal operation of the power grid facilities is ensured.
The operation and maintenance efficiency is improved: through automatic analysis and early warning of the system, the number of times and time of manual inspection can be reduced, and the operation and maintenance efficiency is improved.
Providing decision support: the prediction result and the early warning information provided by the system can provide important support for the decision-making of operation and maintenance personnel, and help them to plan maintenance work better.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of a GIS-based power grid disaster monitoring and early warning system of the present invention.
FIG. 2 is a schematic diagram of a related process flow of a GIS-based power grid disaster monitoring and early warning system according to the present invention;
Fig. 3 is a schematic diagram of an early warning analysis flow of the GIS-based power grid disaster monitoring and early warning system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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.
Example 1
Referring to fig. 1 and 3, the present invention is a power grid disaster monitoring and early warning system based on GIS, including:
The data monitoring unit is used for acquiring monitoring data of power grid facilities in the target area;
the monitoring data includes: a sedimentation parameter obtained by a sedimentation sensor and an offset parameter obtained by an offset sensor;
The parameter processing unit is used for processing and analyzing the sedimentation parameters and the offset parameters collected in a preset monitoring period, and then calculating a first parameter occupation coefficient corresponding to the sedimentation parameters and a second parameter occupation coefficient corresponding to the offset parameters on each time node through a preset calculation formula; the method comprises the following steps:
The method comprises the steps of SS1, selecting a plurality of time nodes with the same time interval according to time sequence in a pre-selected monitoring period, and acquiring corresponding sedimentation parameters from the plurality of time nodes;
meanwhile, the sedimentation parameters are marked as C i, i=1, 2 and … … n, and n represents the number of time nodes;
SS2, by Calculating a corresponding sedimentation proportion CB i of sedimentation parameters on each time node in the monitoring period; wherein i is not n and all sedimentation ratios CBi do not contain CB1;
SS3, by Calculating to obtain a deviation value U of the sedimentation duty ratio of the group of CB 1 to CB n;
In the method, in the process of the invention, Expressed as an average corresponding to all sedimentation ratios CB i;
SS4, then compares U with a preset discrete threshold U y:
If U is larger than U y, the deviation value of the sub item is larger, then corresponding CB i values are deleted in sequence from large to small according to |CB i-CBp |, and the residual deviation value U is correspondingly calculated until U is smaller than or equal to U y;
Extracting undeleted CB i and corresponding C i, and calculating average value C p of corresponding C i of undeleted CB i;
SS5, replacing the sedimentation parameter C i deleted from the corresponding time node with C p in the sedimentation parameters C i corresponding to all the time nodes;
SS6, by Calculating a first parameter occupation coefficient E1i corresponding to the sedimentation parameters on each time node, wherein the sedimentation parameter C i with the corresponding time node deleted is selected as C p;
SS7, obtaining offset parameters on a plurality of time nodes, and calculating a second parameter occupation coefficient E2i corresponding to the offset parameters on each time node according to the mode of SS1-SS 6;
in the embodiment, the design of the parameter processing unit enables the GIS-based power grid disaster monitoring and early warning system to be more efficient, accurate and reliable in data processing, and provides powerful technical support for health monitoring and maintenance of power grid facilities;
Through an automatic parameter processing flow, the system can rapidly calculate a first parameter occupation coefficient of sedimentation parameters and a second parameter occupation coefficient of offset parameters on each time node, so that the data processing efficiency is improved; the monitoring data is accurately analyzed through the preset calculation formula, the accuracy of the first parameter occupation coefficient and the second parameter occupation coefficient is ensured, a reliable data base is provided for subsequent early warning analysis, manual intervention is reduced through automatic processing, errors and deviations caused by manual operation are reduced, objectivity and consistency of a data processing result are ensured, the stability and reliability of the monitoring data can be ensured through identification and processing of abnormal values, such as deleting data points with larger deviation values, and influence of the abnormal data on the early warning result is avoided.
The networking acquisition unit is used for acquiring meteorological data in the target area through an internet technology;
the meteorological data comprise temperature parameters, humidity parameters, wind speed parameters and rainfall;
the temperature parameter, the humidity parameter and the wind speed parameter are regional data acquired in real time, and the rainfall is point position data acquired periodically;
the regional data refers to related data acquired by taking a target region as a standard range;
The point location data refers to related data acquired at each acquisition point by setting up a plurality of acquisition points in a target area;
The weather data is based on the current time, the weather data before the current time is defined as historical weather data, the weather data after the current time is defined as forecast weather data, the historical weather data refers to temperature parameters, humidity parameters, wind speed parameters and rainfall which are already generated and monitored in the early stage in the current area range, and the forecast weather data refers to the temperature parameters, humidity parameters, wind speed parameters and rainfall which are forecast in the future appointed period in the current area range;
the networking processing unit is used for acquiring all corresponding temperature parameters, humidity parameters, wind speed parameters and rainfall in a pre-selected monitoring period and carrying out standardized processing on the parameters;
In this embodiment, the normalization process is to process the historical meteorological data in the following manner:
SX1, dividing a pre-selected monitoring period into a plurality of standard time periods, wherein the standard time periods are determined according to the interval time of two adjacent time nodes, and the number of the standard time periods is n-1;
SX2, acquiring all corresponding temperature parameters, humidity parameters, wind speed parameters and rainfall in a corresponding standard period;
SX3, temperature parameter, humidity parameter and wind speed parameter, and adopts a quartile spacing method for standardization treatment, taking temperature parameter as an example:
The method comprises the following steps:
Step31, sequencing the temperature parameters in order from small to large to form a temperature sequence table;
step32, selecting a first quartile Q1 and a third quartile Q3 from all voltage parameters in the temperature sequence table;
step33, calculating to obtain a quartile interval IQR through iqr=q3-Q1, wherein the quartile interval IQR reflects the discrete degree of the middle 50% data;
step34, calculating standardized judgment values Rmin and Rmax respectively through formulas rmin=q1-t×iqr and rmax=q3+t×iqr, wherein t is a fixed value;
Step35, extracting a temperature parameter smaller than Rmin, a temperature parameter larger than Rmax and a temperature parameter larger than or equal to Rmin and smaller than or equal to Rmax in a standard period, and respectively calculating average values of the temperature parameters to obtain W1, W2 and W3;
then, combining all the average values into a temperature set W0E [ W1, W2 and W3];
similarly, humidity sets S0E [ S1, S2 and S3] and wind speed sets F0E [ F1, F2 and F3] corresponding to the humidity parameters and the wind speed parameters are obtained respectively;
the temperature set, the humidity set and the wind speed set are standard data after standardized processing;
The standardized processing mode of SX4 and rainfall is specifically as follows: the rainfall information acquired by the plurality of acquisition points is subjected to mean value calculation, and the calculation result is recorded as rainfall average in a standard period, namely standard data after standardized processing;
In the embodiment, the networking processing unit not only improves the efficiency and accuracy of data processing through standardized processing of meteorological data, but also provides reliable data support for safety monitoring and early warning of power grid facilities, and is beneficial to reducing power grid faults caused by meteorological factors and ensuring stable operation of the power grid;
By dividing the monitoring period into standard time periods and carrying out unified standardized processing for each standard time period, the system can efficiently process a large amount of meteorological data; meanwhile, the quartile space method is adopted for standardization treatment, so that meteorological data such as temperature parameters, humidity parameters, wind speed parameters and the like have better comparability, and comparison analysis is convenient to be carried out between different time periods or different geographic positions; and by identifying and processing abnormal values smaller than Rmin and larger than Rmax, the influence of extreme weather events on the data analysis result is reduced, and the stability and reliability of the data are improved.
The association processing unit is used for carrying out association processing on the meteorological data and the monitoring data after parameter processing;
the association processing mode is as follows:
SZ1, reference parameter determination
Taking the power grid facilities in the same geographic position as an example, recording a sedimentation parameter corresponding to the first time node at the beginning stage of a monitoring period as a reference parameter;
SZ2, calculating stage sedimentation difference and stage offset difference
Then subtracting the reference parameter from the sedimentation parameter corresponding to each time node in all time nodes except the first time node positioned at the beginning stage of the monitoring period to obtain the corresponding stage sedimentation difference, wherein the number of the stage sedimentation differences is n-1;
similarly, calculating a corresponding stage offset difference;
SZ3, establishing a mapping relation and importing an early warning analysis model
Then, respectively establishing a mapping relation between the stage sedimentation difference of the corresponding time node and the obtained temperature set, humidity set, wind speed set and rainfall average value through standardized treatment, and importing the mapping relation into a pre-trained early warning analysis model;
Simultaneously, a mapping relation is established among the first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation difference and the stage offset difference of the corresponding time nodes, and the mapping relation is imported into a pre-trained early warning analysis model;
in this embodiment, the mapping may be based on a variety of methods such as vector machines, convolutional neural networks, or recurrent neural networks;
The networking processing unit is also used for dividing the future monitoring period into a plurality of standard time periods and carrying out standardized processing on the obtained predicted meteorological data, and the mode is the same as the mode of carrying out standardized processing on all corresponding temperature parameters, humidity parameters, wind speed parameters and rainfall which are obtained in the pre-selected monitoring period;
the early warning analysis unit is used for carrying out early warning analysis on power grid facilities in the target area by combining the standardized predicted meteorological data;
the method comprises the following steps:
ST1, data import
The temperature parameter, the humidity parameter, the wind speed parameter and the rainfall in the standardized predicted meteorological data are imported into a pre-trained early warning analysis model;
ST2, similarity matching
The early warning analysis model closely matches the predicted meteorological data with the historical meteorological data establishing a mapping relation;
The similarity matching mode is as follows:
Taking the example of predicting the temperature parameter in the meteorological data:
performing difference analysis calculation on a temperature set obtained by predicting the temperature parameters in the meteorological data and a temperature set obtained by using the temperature parameters in the historical meteorological data in an early warning analysis model, and obtaining an analysis judgment value;
The formula for the difference analysis calculation is as follows:
Wherein Wy is represented as an analysis determination value, W1, W2 and W3 are represented as values in a temperature set obtained by predicting temperature parameters in the meteorological data, W01, W02 and W03 are represented as values in a temperature set obtained by predicting temperature parameters in the meteorological data, and α1, α2 and α3 are preset scale factors;
Then acquiring a corresponding temperature set of the historical meteorological data with the minimum analysis judgment value as a matching result;
ST3, mapping parameter acquisition and future monitoring period analysis
Obtaining a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference and a stage offset difference of the corresponding mapping according to the matching result;
And by analogy, a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference and a stage offset difference which are obtained in each standard period in a future monitoring period are obtained;
The first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation difference and the stage offset difference obtained in each standard period in the future monitoring period are re-marked as E1j, E2j, JCj, JPj, j=1, 2 and … … m, and m represents the number of each standard period in the future monitoring period;
ST4, determination coefficient calculation
By the formulaCalculating a judging coefficient PD in a future monitoring period;
Wherein d1 and b1 represent sedimentation parameters obtained by a sedimentation sensor and offset parameters obtained by an offset sensor when the power grid facilities are in accordance, which are fixed values, d2 and b2 represent sedimentation parameters and offset parameters obtained by the sedimentation sensor and the offset sensor for the corresponding power grid facilities at the current time node, and beta 1 and beta 2 are preset duty ratio coefficients;
ST5, early warning signal generation
Subsequently, the determination coefficient PD in the future monitoring period is compared with a preset determination threshold value PDy:
PD is more than or equal to PDy, generating an early warning signal, indicating that the corresponding power grid facilities in the target area are influenced by weather and abnormal, and needing related personnel to perform preventive measures in advance to maintain the power grid facilities;
PD < PDy, do not generate the early warning signal, indicate the corresponding electric wire netting facility in the goal area is not influenced by the weather, and the subsequent operation of the corresponding electric wire netting facility is normal;
In the embodiment, the design of the early warning analysis unit enables the GIS-based power grid disaster monitoring and early warning system to be more efficient, accurate and reliable in early warning function, and provides powerful technical support for health monitoring and maintenance of power grid facilities
By matching the similarity of the predicted meteorological data and the historical meteorological data, the system can more accurately predict the potential influence of future meteorological conditions on power grid facilities, so that early warning is sent out in advance;
By using the difference analysis and calculation method, the system can effectively process and analyze a large amount of meteorological data, and the efficiency and quality of data processing are improved; by combining key indexes such as a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference, a stage offset difference and the like, the system can evaluate the safety condition of power grid facilities more comprehensively, and fine management is realized;
The operation and maintenance cost is reduced: through accurate early warning analysis, unnecessary maintenance and repair work can be reduced, manpower and material resources are saved, and operation and maintenance cost is reduced; the system can timely generate early warning signals, help operation and maintenance personnel to quickly respond, shorten the response time, and effectively avoid or reduce power grid faults caused by meteorological factors; by monitoring and early warning the power grid facilities in real time, the system is beneficial to preventing and reducing power failure events caused by meteorological reasons and guaranteeing the stability of power supply; the stability of the power grid facilities is directly related to public safety, and the effective operation of the early warning analysis unit is beneficial to improving the life quality and the safety feeling of the public.
Example two
As an embodiment two of the present application, when the present application is specifically implemented, compared with the embodiment one, the technical solution of the present embodiment is different from the embodiment one only in that in the present embodiment, the data monitoring unit further obtains GIS information corresponding to the power grid facility by using the GIS technology, where the GIS information is represented as a geographic location of the power grid facility;
Meanwhile, the system also comprises:
The interface display unit is used for displaying the early warning signals through the PC end and the mobile end, acquiring corresponding GIS information of the power grid facilities according to the early warning signals, and displaying the GIS information to related personnel;
The embodiment integrates the GIS technology, so that the early warning information has geographic position reference, the problem of quick positioning of operation and maintenance personnel is facilitated, meanwhile, the early warning information is displayed through the PC end and the mobile end, and the accessibility of the information and the emergency response efficiency are improved.
Example III
As an embodiment three of the present application, in the implementation of the present application, compared with the first embodiment and the second embodiment, the technical solution of the present embodiment is to combine the solutions of the first embodiment and the second embodiment;
the above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. GIS-based power grid disaster monitoring and early warning system is characterized by comprising:
the data monitoring unit is used for acquiring monitoring data of power grid facilities in the target area, wherein the monitoring data comprises sedimentation parameters and offset parameters;
The parameter processing unit is used for processing and analyzing the sedimentation parameters and the offset parameters collected in a preset monitoring period, and then calculating a first parameter occupation coefficient corresponding to the sedimentation parameters and a second parameter occupation coefficient corresponding to the offset parameters on each time node through a preset calculation formula;
The network acquisition unit is used for acquiring meteorological data in a target area through an internet technology, wherein the meteorological data comprises temperature parameters, humidity parameters, wind speed parameters and rainfall, and the meteorological data is divided into historical meteorological data and forecast meteorological data by taking the current time as a reference;
The networking processing unit is used for carrying out standardized processing on the historical meteorological data and the forecast meteorological data according to a preset standardized processing mode;
The association processing unit is used for carrying out association processing on the meteorological data and the monitoring data after parameter processing, and the method is as follows: taking the sedimentation parameters of the first time node at the beginning stage of the monitoring period as reference parameters, then calculating the stage sedimentation differences between other time nodes except the first time node and the reference parameters, calculating corresponding stage offset differences in a similar way, establishing a mapping relation between the first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation differences and the offset differences and the standardized temperature, humidity, wind speed and rainfall data set, and inputting the mapping relation into a pre-trained early warning analysis model;
The early warning analysis unit is used for carrying out early warning analysis on power grid facilities in the target area by combining the standardized predicted meteorological data; the method comprises the following steps: obtaining an optimal matching result from the predicted meteorological data and the historical meteorological data, obtaining a corresponding first parameter occupation coefficient, a corresponding second parameter occupation coefficient, a corresponding stage sedimentation difference and a corresponding stage offset difference according to the matching result, calculating a judging coefficient in a future monitoring period through a preset formula, comparing the judging coefficient with a preset judging threshold value, and generating an early warning signal when the judging coefficient is larger than or equal to the judging threshold value.
2. The GIS-based power grid disaster monitoring and early warning system according to claim 1, wherein the specific mode of the parameter processing unit is as follows:
The method comprises the steps of SS1, selecting a plurality of time nodes with the same time interval according to time sequence in a pre-selected monitoring period, and acquiring corresponding sedimentation parameters from the plurality of time nodes;
meanwhile, the sedimentation parameters are marked as C i, i=1, 2 and … … n, and n represents the number of time nodes;
SS2, by Calculating a corresponding sedimentation proportion CB i of sedimentation parameters on each time node in the monitoring period; wherein i is not n and all sedimentation ratios CBi do not contain CB1;
SS3, by Calculating to obtain a deviation value U of the sedimentation duty ratio of the group of CB 1 to CB n;
In the method, in the process of the invention, Expressed as the average corresponding to all sedimentation ratios CB i;
SS4, then compares U with a preset discrete threshold U y:
If U is larger than U y, the deviation value of the sub item is larger, then corresponding CB i values are deleted in sequence from large to small according to |CB i-CBp |, and the residual deviation value U is correspondingly calculated until U is smaller than or equal to U y;
Extracting undeleted CB i and corresponding C i, and calculating average value C p of corresponding C i of undeleted CB i;
SS5, replacing the sedimentation parameter C i deleted from the corresponding time node with C p in the sedimentation parameters C i corresponding to all the time nodes;
SS6, by Calculating a first parameter occupation coefficient E1i corresponding to the sedimentation parameters on each time node, wherein the sedimentation parameter C i with the corresponding time node deleted is selected as C p;
And SS7, acquiring offset parameters on a plurality of time nodes, and calculating a second parameter occupation coefficient E2 i corresponding to the offset parameters on each time node according to the mode of SS1-SS 6.
3. The GIS-based power grid disaster monitoring and early warning system according to claim 2, wherein: the temperature parameter, the humidity parameter and the wind speed parameter are regional data acquired in real time, and the rainfall is point position data acquired periodically;
the regional data refers to related data acquired by taking a target region as a standard range;
the point location data refers to related data acquired at each acquisition point by setting up a plurality of acquisition points in a target area.
4. A GIS-based power grid disaster monitoring and early warning system according to claim 3, wherein: the historical meteorological data is selected for standardized processing in the following mode:
SX1, dividing a pre-selected monitoring period into a plurality of standard time periods, wherein the standard time periods are determined according to the interval time of two adjacent time nodes, and the number of the standard time periods is n-1;
SX2, acquiring all corresponding temperature parameters, humidity parameters, wind speed parameters and rainfall in a corresponding standard period;
SX3, temperature parameter, humidity parameter, wind speed parameter, and it adopts the quartile spacing method to carry out standardization processing, selects the temperature parameter to carry out the mode of processing as follows:
Step31, sequencing the temperature parameters in order from small to large to form a temperature sequence table;
step32, selecting a first quartile Q1 and a third quartile Q3 from all voltage parameters in the temperature sequence table;
step33, calculating to obtain a quartile interval IQR through iqr=q3-Q1, wherein the quartile interval IQR reflects the discrete degree of the middle 50% data;
step34, calculating standardized judgment values Rmin and Rmax respectively through formulas rmin=q1-t×iqr and rmax=q3+t×iqr, wherein t is a fixed value;
Step35, extracting a temperature parameter smaller than Rmin, a temperature parameter larger than Rmax and a temperature parameter larger than or equal to Rmin and smaller than or equal to Rmax in a standard period, and respectively calculating average values of the temperature parameters to obtain W1, W2 and W3;
then, combining all the average values into a temperature set W0E [ W1, W2 and W3];
similarly, humidity sets S0E [ S1, S2 and S3] and wind speed sets F0E [ F1, F2 and F3] corresponding to the humidity parameters and the wind speed parameters are obtained respectively;
the temperature set, the humidity set and the wind speed set are standard data after standardized processing;
the standardized processing mode of SX4 and rainfall is specifically as follows: and carrying out average value calculation on rainfall information acquired by the plurality of acquisition points, and recording the calculation result as rainfall average value of a standard period, namely standard data after standardized processing.
5. The GIS-based power grid disaster monitoring and early warning system according to claim 4, wherein: the specific way of the association process is as follows:
SZ1, selecting power grid facilities at the same geographic position, and recording a sedimentation parameter corresponding to a first time node at the beginning stage of a monitoring period as a reference parameter;
SZ2, subtracting the reference parameter from the sedimentation parameters corresponding to each time node in all time nodes except the first time node at the beginning stage of the monitoring period to obtain corresponding stage sedimentation differences, wherein the number of the stage sedimentation differences is n-1;
similarly, calculating a corresponding stage offset difference;
SZ3, respectively establishing mapping relations between the stage sedimentation differences of the corresponding time nodes and the temperature sets, the humidity sets, the wind speed sets and the rainfall average values obtained through standardized processing, and importing the mapping relations into a pre-trained early warning analysis model;
SZ4, simultaneously establishing a mapping relation among the first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation difference and the stage offset difference of the corresponding time nodes, and importing the mapping relation into a pre-trained early warning analysis model.
6. The GIS-based power grid disaster monitoring and early warning system according to claim 5, wherein: mapping is implemented based on any one of a vector machine, convolutional neural network, or recurrent neural network.
7. The GIS-based power grid disaster monitoring and early warning system according to claim 5, wherein: the mode of the early warning analysis unit is as follows:
ST1, importing the temperature parameter, the humidity parameter, the wind speed parameter and the rainfall in the standardized predicted meteorological data into a pre-trained early warning analysis model;
ST2, the early warning analysis model matches the similarity of the predicted meteorological data and the historical meteorological data establishing the mapping relation, and the similarity matching mode is as follows:
selecting temperature parameters in the predicted meteorological data, carrying out difference analysis calculation on a temperature set obtained by the temperature parameters in the predicted meteorological data and a temperature set obtained by the temperature parameters in the historical meteorological data in an early warning analysis model, and obtaining an analysis judgment value;
Then acquiring a corresponding temperature set of the historical meteorological data with the minimum analysis judgment value as a matching result;
ST3, obtaining a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference and a stage offset difference of corresponding mapping according to the matching result;
And by analogy, a first parameter occupation coefficient, a second parameter occupation coefficient, a stage sedimentation difference and a stage offset difference which are obtained in each standard period in a future monitoring period are obtained;
The first parameter occupation coefficient, the second parameter occupation coefficient, the stage sedimentation difference and the stage offset difference obtained in each standard period in the future monitoring period are re-marked as E1j, E2j, JCj, JPj, j=1, 2 and … … m, and m represents the number of each standard period in the future monitoring period;
ST4, through the formula Calculating a judging coefficient PD in a future monitoring period;
Wherein d1 and b1 represent sedimentation parameters obtained by a sedimentation sensor and offset parameters obtained by an offset sensor when the power grid facilities are in accordance, which are fixed values, d2 and b2 represent sedimentation parameters and offset parameters obtained by the sedimentation sensor and the offset sensor for the corresponding power grid facilities at the current time node, and beta 1 and beta 2 are preset duty ratio coefficients;
ST5, then, compares the determination coefficient PD in the future monitoring period with a preset determination threshold value PDy:
if PD is more than or equal to PDy, generating an early warning signal;
if PD is less than PDy, no early warning signal is generated.
8. The GIS-based power grid disaster monitoring and early warning system according to claim 7, wherein: the formula for the difference analysis calculation is as follows:
Wherein Wy is represented as an analysis determination value, W1, W2, W3 are represented as values in a temperature set obtained by predicting temperature parameters in the weather data, W01, W02, W03 are represented as values in a temperature set obtained by predicting temperature parameters in the weather data, and α1, α2, α3 are preset scale factors.
9. The GIS-based power grid disaster monitoring and early warning system according to claim 1, wherein: the data monitoring unit also acquires GIS information corresponding to the power grid facilities by using a GIS technology, wherein the GIS information is expressed as the geographic position of the power grid facilities.
10. The GIS-based power grid disaster monitoring and early warning system according to claim 9, wherein: the interface display unit is used for displaying the early warning signals through the PC end and the mobile end, acquiring corresponding GIS information of the power grid facilities according to the early warning signals, and displaying the GIS information to related personnel.
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