CN115936428B - Incremental power distribution network external damage prevention constant value optimizing system - Google Patents
Incremental power distribution network external damage prevention constant value optimizing system Download PDFInfo
- Publication number
- CN115936428B CN115936428B CN202211457984.7A CN202211457984A CN115936428B CN 115936428 B CN115936428 B CN 115936428B CN 202211457984 A CN202211457984 A CN 202211457984A CN 115936428 B CN115936428 B CN 115936428B
- Authority
- CN
- China
- Prior art keywords
- data
- abnormal
- module
- monitoring
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an incremental power distribution network external damage prevention constant value optimization system, relates to the field of power distribution networks, and aims to solve the problem of external damage time constant value protection. The incremental power distribution network external damage prevention value optimizing system comprises a parameter data acquisition system, an acquisition data analysis system, an abnormal data calculation system and a data operation characteristic processing system, wherein the minimum short-circuit impedance value in power data is used for generating the maximum short-circuit current after short-circuit, the maximum short-circuit impedance value is generally used for verifying the stability of electrical equipment, and the minimum short-circuit current is generated after short-circuit in a small-mode decision module. Labeling the first abnormal data and the second abnormal data as final effective abnormal data, ensuring the definition and accuracy of the final effective abnormal data, timely calling the position of the equipment, and alarming according to the requirement when calling the abnormal position, so that the equipment at the abnormal position can be effectively protected.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to an incremental power distribution network external damage prevention constant value optimization system.
Background
The distribution network is composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive compensators, some auxiliary facilities and the like, and plays a role in distributing electric energy in the power network. The existing distribution network has the following problems when being broken outwards:
1. when abnormal data is found, the specific position of the abnormal data cannot be positioned, abnormal equipment cannot be protected at the first time, and the damage range is enlarged.
2. The failure to perform effective data calculation on the data in the abnormal data results in a decrease in the reliability of the data and the failure to perform self-judgment on the abnormal data.
3. When the power fails, key characteristics cannot be obtained quickly, and when the power equipment is used, the current sensitivity is high, so that misoperation is easy to occur.
Disclosure of Invention
The invention aims to provide an incremental power distribution network external damage prevention fixed value optimizing system, which has the advantages that the minimum short-circuit impedance value in power data is provided, the short-circuit current generated after short-circuit occurs is maximum, the system is generally used for verifying the stability of electrical equipment, and in a small-mode decision module, the short-circuit impedance value in the power data is maximum, and the short-circuit current generated after short-circuit occurs is minimum. The first abnormal data and the second abnormal data are marked as final effective abnormal data, so that the definition and accuracy of the final effective abnormal data are ensured, the position of the equipment is timely adjusted, an alarm can be given according to the requirement when the abnormal position is adjusted, the equipment at the abnormal position can be effectively protected, and the problems in the prior art can be solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the incremental power distribution network anti-external breaking value optimizing system comprises a parameter data acquisition system, an acquisition data analysis system, an abnormal data calculation system and a data operation characteristic processing system;
the parameter data acquisition system is used for acquiring the reference interface parameters of different equipment and counting the reference interface parameters of the different equipment by monitoring;
the acquired data analysis system is used for carrying out anomaly detection on the reference interface parameters based on statistics in the parameter data acquisition system and positioning the reference interface parameter equipment with the detected anomaly;
the abnormal data analysis system is used for carrying out data analysis based on the abnormal data detected in the collected data analysis system and calculating a safety index in the abnormal data;
the abnormal data computing system is used for computing the data of the abnormal data based on the abnormal data which is analyzed by the abnormal data analyzing system and computing the effective data in the abnormal data;
the data operation characteristic processing system is used for carrying out data processing based on effective data in the abnormal data computing system and carrying out solution decision on the extracted data;
Wherein, gather data analysis system includes:
the monitoring data analysis module is used for:
drawing a two-dimensional rectangular coordinate system, extracting target values of sub-monitoring data of each module, and carrying out visual display on the two-dimensional rectangular coordinate system based on the target values to obtain a parameter change curve of module interface parameters of each module in a monitoring time period;
acquiring reference interface parameters of interfaces of all modules, and acquiring reference lines of all modules in a two-dimensional rectangular coordinate system based on the reference interface parameters;
overlapping the parameter change curve with the reference line, determining an abnormal parameter change curve based on an overlapping result, and obtaining abnormal data based on the abnormal parameter change curve;
the abnormal interface positioning module is used for:
inputting the sub-monitoring data of the abnormal data into a preset fault diagnosis model for analysis to obtain the fault type and the fault position of the abnormal data;
the abnormal data storage module is used for:
the fault type and the fault position of the abnormal data are stored independently;
wherein, abnormal data analysis system includes:
a data reading module for:
Grouping and packaging abnormal data of each device;
counting according to a target value of which the importance of the interface parameter of each packed data is greater than or equal to a preset threshold value;
the data comparison module is used for:
acquiring historical transmission success data of parameter data of each equipment port, analyzing the historical transmission success data to determine the integrity and the safety of the historical transmission success data, and evaluating threat risk indexes and vulnerability risk indexes of equipment interface parameters according to the integrity and the safety;
calculating the security index of the equipment interface by using a preset risk assessment system according to the threshold value of the target value of each equipment interface parameter, the threat risk index and the vulnerability risk index of the equipment interface parameter
The grade classification module is used for:
calculating security indexes of different equipment interfaces according to a preset risk assessment system, and grading the security indexes according to data values of the security indexes;
wherein the abnormal data computing system comprises:
a data calculation module for:
based on threat risk index and vulnerability risk index data, comparing the data value with normal threshold data, and obtaining a data difference numerical sequence
The judging module is used for:
The data difference value sequence is obtained, the value sequence is periodically detected, and whether the value sequence is a periodic sequence or not is judged;
a sequence analysis module for:
after the numerical sequence is determined to be a periodic sequence, dividing the numerical sequence according to the period to obtain a plurality of groups of identical first subsequences, judging whether all numerical values in the first subsequences are larger than a preset numerical value, if so, extracting first abnormal numerical values larger than the preset numerical value in the first subsequences, determining adjacent time intervals among the first abnormal numerical values, judging whether the time intervals are in a preset time interval range, if so, taking the first abnormal numerical values and the time intervals as first abnormal data, otherwise, determining that the first abnormal numerical values are invalid;
when the numerical sequence is determined to be an aperiodic sequence, clustering the numerical sequence by using a one-dimensional clustering method to obtain a plurality of division points, dividing the numerical sequence by using the plurality of division points to obtain a plurality of groups of different second subsequences, acquiring a third subsequence with a value larger than a preset value from the second subsequences, determining an abnormal time interval of the third subsequence based on the position of the third subsequence in the numerical sequence, acquiring a fourth subsequence with an abnormal time interval of an adjacent third subsequence within the preset time interval range, and taking the fourth subsequence and the abnormal time interval as second abnormal data;
The abnormal data acquisition module is used for:
the first abnormal data is marked periodically to obtain first effective abnormal data, the second abnormal data is marked aperiodically to obtain second effective abnormal data, and the first effective abnormal data and the second effective abnormal data are used as final effective abnormal data to be sent to an abnormal classification module;
wherein, data operation feature processing system still includes:
and the relay processing module is used for:
acquiring topological structure information and preset operation mode information of a power distribution network, and node attributes of each power distribution node of the power distribution network;
determining a distribution topology weight value of each distribution node according to node attributes of each distribution node, the topology structure information and preset operation mode information;
calculating a basic value of each relay quantity in the power data based on the distribution topology weight value of each distribution node;
acquiring time sequence characteristic information corresponding to the power data;
extracting time sequence data of each relay quantity from the power data according to the time sequence characteristic information;
determining electricity utilization characteristic information of each relay power based on time sequence data of the relay power;
taking the time sequence data and the basic value of each relay as model input samples, and simultaneously taking the electricity utilization characteristic information of each relay as model output samples to train a preset network model so as to obtain an identification model of each relay;
Acquiring target electricity utilization characteristics corresponding to target time sequence data of each key characteristic in the key characteristic subset by utilizing an identification model of each relay quantity;
acquiring a first operation characteristic of each relay quantity according to the target electricity utilization characteristic of each key characteristic;
acquiring the change condition of the target electricity utilization characteristic of each relay quantity in the electric power data, and determining the electricity utilization change rule of each relay quantity according to the change condition;
and confirming the load quantity with the similarity of the electricity change rule larger than or equal to a preset threshold value as the similar relay, and confirming the second target operation characteristic of any relay quantity in each type of relay as the final operation characteristic of the relay.
Preferably, the parameter data acquisition system comprises:
the parameter interface extraction module is used for:
acquiring functional attributes of a line, a transformer, a reactor and a capacitor, and extracting reference interface parameters of an interface based on the functional attributes;
the interface monitoring module is used for:
determining monitoring indexes of equipment with various attributes based on the reference interface parameters, and generating a monitoring plug-in according to preset monitoring plug-in generation rules based on the monitoring indexes, wherein the monitoring plug-in carries a dynamic function library;
The monitoring interface docking module is used for:
extracting configuration parameters of the monitoring plug-in, adjusting the configuration parameters of the monitoring plug-in through each preset function in the dynamic function library based on reference interface parameters of each equipment interface, and obtaining a monitoring interface corresponding to the monitoring plug-in based on an adjustment result;
the monitoring data statistics module is used for:
sending a monitoring request to a module interface of each module based on the monitoring interface, and butting a monitoring interface corresponding to the monitoring plug-in with a device interface of the device when the response information of the module interface is received;
acquiring a monitoring log file of each module according to a preset time interval through the monitoring plug-in based on the docking result, and extracting data characteristics of monitoring data in the monitoring log file;
and carrying out classification statistics on the monitoring data based on the data characteristics to obtain sub-monitoring data of the equipment.
Preferably, the parameter interface extraction module includes:
an extraction sub-module for:
extracting reference interface parameters of interfaces of the circuit, the transformer, the reactor and the capacitor based on the acquired functional attributes of the circuit, the transformer, the reactor and the capacitor;
a marking sub-module for:
Adding a specific identification mark to the functional attribute which has completed the reference interface parameter extraction;
and a discrimination sub-module for:
after receiving an extraction instruction of the reference interface parameters, inquiring each functional attribute according to the instruction content, removing the functional attribute with the identification mark from the extraction range, and adjusting the extraction range;
an encryption sub-module for:
and before data transmission, packing and encrypting the reference interface parameters in the adjusted extraction range.
Preferably, the data run feature processing system includes:
the power data acquisition module is used for:
redundancy and dimension reduction are carried out on the effective abnormal data based on the abnormal data in the abnormal data calculation module, and finally the electric power data is obtained
The feature extraction module is used for:
extracting the characteristics of the power data, and acquiring an initial characteristic set of the power data according to an extraction result;
extracting key features related to the relay from the initial feature set and integrating the key features into a key feature subset;
the operation mode decision module is used for:
and extracting short-circuit impedance value data in the extracted power data, and respectively making decisions on short-circuit impedance values with different magnitudes.
Preferably, the operation mode decision module further includes:
the big mode decision module is used for:
the minimum short-circuit impedance value data in the power data are frequently extracted;
the small mode decision module is used for:
the largest short-circuit impedance value data among the power data is often extracted.
Preferably, the data comparison module obtains the integrity coefficient and the safety coefficient of the historical transmission success data by assigning according to the integrity and the safety condition of the analysis of the historical transmission success data;
the threat risk index for the data device interface parameters is calculated using the following algorithm:
in the above formula, τ represents a threat risk index of the data device interface parameter; n represents the number of historical transmission success data; omega i A complete coefficient representing the ith historical transmission success data;
representing the whole coefficient mean value of all the historical transmission success data; />
A security coefficient representing the ith historical transmission success data; />
The safety coefficient average value of all the historical transmission success data is represented;
calculating vulnerability risk indexes of interface parameters of the data equipment by adopting the following algorithm:
in the above formula, μ represents a vulnerability risk index of the data device interface parameter; gamma ray 1 Integrity weights representing vulnerability assessment; gamma ray 2 And the security weight of vulnerability assessment is represented.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides an incremental power distribution network anti-external damage constant value optimizing system, which is used for effectively acquiring monitoring indexes of equipment, generating corresponding monitoring plug-ins through the monitoring indexes, adjusting configuration information of the monitoring plug-ins, realizing the butt joint with interfaces of corresponding modules, acquiring working data of different modules through the monitoring plug-ins according to the butt joint result, analyzing the working data of different equipment, accurately analyzing fault types and fault positions of abnormal data, effectively grasping the working state of the equipment, conveniently and timely adjusting the positions of the equipment when the modules are abnormal, alarming according to requirements when the abnormal positions are adjusted, effectively protecting the equipment at the abnormal positions, and preventing the damage range from being enlarged.
2. The invention provides an incremental power distribution network external damage prevention fixed value optimizing system, wherein a first abnormal value and a time interval are used as first abnormal data, the redundancy of the abnormal data is reduced, the accuracy of the obtained first abnormal data is ensured, a non-periodic sequence is clustered by a one-dimensional clustering method to obtain a plurality of partition points, the partition of the partition points is more accurate and objective, after the non-periodic sequence is partitioned, the value and the abnormal time interval are judged to finally obtain second abnormal data, the accuracy of the obtained second abnormal data is ensured, and finally, the first abnormal data and the second abnormal data are marked to be used as final effective abnormal data, so that the definition and the accuracy of the finally obtained effective abnormal data are ensured, thereby providing basis for realizing the judgment and decision of the value of the data, facilitating the self judgment of the data, and realizing the selectivity, the sensitivity and the reliability of the abnormal protection of the power distribution network.
3. The invention provides an incremental power distribution network external damage prevention fixed value optimizing system, which can uniformly count the operation characteristics of relays of the same type by classifying and processing the relay power, does not need to count the operation characteristics of a single relay, and improves the working efficiency. The method is generally used for verifying the sensitivity of the relay protection device, so that the sensitivity of the current can be reduced.
Drawings
FIG. 1 is a schematic overall flow chart of the present invention;
FIG. 2 is a schematic diagram of a parameter data acquisition system module according to the present invention;
FIG. 3 is a schematic diagram of a system for analyzing collected data according to the present invention;
FIG. 4 is a schematic diagram of an anomaly data analysis system according to the present invention;
FIG. 5 is a schematic diagram of an anomaly data computing system module according to the present invention;
FIG. 6 is a schematic diagram of a data run feature processing system module according to the present invention.
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.
In order to solve the problem that in the prior art, when abnormal data is found, the specific position of the abnormal data cannot be located, abnormal equipment cannot be protected at the first time, and the damage range is enlarged, please refer to fig. 1-2, the present embodiment provides the following technical scheme:
the incremental power distribution network anti-external breaking value optimizing system comprises a parameter data acquisition system, an acquisition data analysis system, an abnormal data calculation system and a data operation characteristic processing system; the parameter data acquisition system is used for acquiring the reference interface parameters of different equipment and counting the reference interface parameters of the different equipment by monitoring; the acquired data analysis system is used for carrying out anomaly detection on the reference interface parameters based on statistics in the parameter data acquisition system and positioning the reference interface parameter equipment with the detected anomaly; the abnormal data analysis system is used for carrying out data analysis based on the abnormal data detected in the collected data analysis system and calculating a safety index in the abnormal data; the abnormal data computing system is used for computing the data of the abnormal data based on the abnormal data which is analyzed by the abnormal data analyzing system and computing the effective data in the abnormal data; and the data operation characteristic processing system is used for carrying out data processing based on the effective data in the abnormal data computing system and carrying out solution decision on the extracted data.
The parameter data acquisition system comprises: the parameter interface extraction module is used for: acquiring functional attributes of a line, a transformer, a reactor and a capacitor, and extracting reference interface parameters of an interface based on the functional attributes; the interface monitoring module is used for: determining monitoring indexes of equipment with various attributes based on the reference interface parameters, and generating a monitoring plug-in based on the monitoring indexes according to preset monitoring plug-in generation rules, wherein the monitoring plug-in carries a dynamic function library monitoring interface docking module and is used for: extracting configuration parameters of the monitoring plug-in, adjusting the configuration parameters of the monitoring plug-in through each preset function in the dynamic function library based on reference interface parameters of each equipment interface, and obtaining a monitoring interface corresponding to the monitoring plug-in based on an adjustment result; the monitoring data statistics module is used for: sending a monitoring request to a module interface of each module based on the monitoring interface, and butting a monitoring interface corresponding to the monitoring plug-in with a device interface of the device when the response information of the module interface is received; acquiring a monitoring log file of each module according to a preset time interval through the monitoring plug-in based on the docking result, and extracting data characteristics of monitoring data in the monitoring log file; and carrying out classification statistics on the monitoring data based on the data characteristics to obtain sub-monitoring data of the equipment.
The collected data analysis system comprises: the monitoring data analysis module is used for: drawing a two-dimensional rectangular coordinate system, extracting target values of sub-monitoring data of each module, and carrying out visual display on the two-dimensional rectangular coordinate system based on the target values to obtain a parameter change curve of module interface parameters of each module in a monitoring time period; acquiring reference interface parameters of interfaces of all modules, and acquiring reference lines of all modules in a two-dimensional rectangular coordinate system based on the reference interface parameters; overlapping the parameter change curve with the reference line, determining an abnormal parameter change curve based on an overlapping result, and obtaining abnormal data based on the abnormal parameter change curve; the abnormal interface positioning module is used for: inputting the sub-monitoring data of the abnormal data into a preset fault diagnosis model for analysis to obtain the fault type and the fault position of the abnormal data; the abnormal data storage module is used for: and independently storing the fault type and the fault position of the abnormal data.
Specifically, the preset monitoring plug-in generation rule is set in advance, and is used for generating the monitoring plug-in, so that corresponding monitoring is carried out on different devices, the monitoring plug-in is a monitoring tool for monitoring the working process of each device, the working data of the devices are obtained, the dynamic function library is a monitoring plug-in carried by the monitoring plug-in, the purpose is to timely adjust the configuration information of the monitoring plug-in, the configuration parameters can be the working power of the monitoring plug-in, the compatibility with the devices and the like, the purpose of the monitoring interface is to interface with the working interfaces of all the modules, the purpose of the monitoring interface is to conveniently collect the corresponding working data, the preset time interval is set in advance, the function attribute of the device is used for intermittently collecting the working data of the devices, the reference interface parameters of the line, the transformer, the reactor and the capacitor are obtained, the effective acquisition of the monitoring index of the devices is realized, the corresponding monitoring plug-in is generated through the monitoring index, the configuration information of the monitoring plug-in is adjusted, the interface of the corresponding module is accordingly realized, the working data of the different modules are obtained through the monitoring plug-in according to the butt joint result, the working data of the different modules are accurately analyzed by the monitoring plug-in, the working data of the different devices are conveniently obtained, the working data of the devices are conveniently analyzed by the aid of the monitoring plug-in the different modules, the fault positions are accurately analyzed when the fault positions of the devices are in the abnormal position is effectively and the abnormal position is effectively analyzed, and can be effectively and can be prevented when the abnormal position is in the abnormal position can be effectively, and can be effectively and can be prevented.
The parameter interface extraction module comprises:
an extraction sub-module for:
extracting reference interface parameters of interfaces of the circuit, the transformer, the reactor and the capacitor based on the acquired functional attributes of the circuit, the transformer, the reactor and the capacitor;
a marking sub-module for:
adding a specific identification mark to the functional attribute which has completed the reference interface parameter extraction;
and a discrimination sub-module for:
after receiving an extraction instruction of the reference interface parameters, inquiring each functional attribute according to the instruction content, removing the functional attribute with the identification mark from the extraction range, and adjusting the extraction range;
an encryption sub-module for:
before data transmission, the reference interface parameters in the adjusted extraction range are packaged, and encryption processing is carried out by adopting a preset encryption strategy.
The marking submodule is used for adding a specific identification mark to the functional attribute which has been extracted by the reference interface parameter, after receiving an extraction instruction of the reference interface parameter, the screening submodule is used for inquiring each functional attribute according to the instruction content, and removing the functional attribute with the identification mark from the extraction range, so that the extraction range is adjusted, repeated extraction of data can be avoided, the data transmission quantity is reduced, the occupation of network transmission resources is reduced, the smooth operation of data transmission is ensured, the transmission efficiency is improved, the chaotic probability of the transmission data and the data management difficulty are reduced, and the management and the use of the data are facilitated; before data transmission, the encryption sub-module is used for carrying out packing encryption processing on the reference interface parameters in the adjusted extraction range, so that the data transmission safety in the extraction process is improved.
In order to solve the problems that in the prior art, effective data calculation cannot be performed on data in abnormal data, resulting in reduced reliability of the data, and self-judgment cannot be performed on the abnormal data, please refer to fig. 4-5, the present embodiment provides the following technical scheme:
the abnormal data analysis system includes: a data reading module for: grouping and packaging abnormal data of each device; counting according to a target value of which the importance of the interface parameter of each packed data is greater than or equal to a preset threshold value; the data comparison module is used for: acquiring historical transmission success data of parameter data of each equipment port, analyzing the historical transmission success data to determine the integrity and the safety of the historical transmission success data, and evaluating threat risk indexes and vulnerability risk indexes of equipment interface parameters according to the integrity and the safety; calculating the security index of the equipment interface by using a preset risk assessment system according to the threshold value of the target value of each equipment interface parameter, the threat risk index and the vulnerability risk index of the equipment interface parameter; the grade classification module is used for: and calculating the security indexes of different equipment interfaces according to a preset risk assessment system, and grading the security indexes according to the data values of the security indexes.
The anomaly data computing system includes: a data calculation module for: comparing the data value with normal threshold data based on threat risk index and vulnerability risk index data, and obtaining a data difference numerical sequence; the judging module is used for: the data difference value sequence is obtained, the value sequence is periodically detected, and whether the value sequence is a periodic sequence or not is judged; a sequence analysis module for: and after the numerical sequence is determined to be a periodic sequence, dividing the numerical sequence according to the period to obtain a plurality of groups of identical first subsequences, judging whether all numerical values in the first subsequences are larger than a preset numerical value, if so, extracting first abnormal numerical values larger than the preset numerical value in the first subsequences, determining adjacent time intervals among the first abnormal numerical values, judging whether the time intervals are in a preset time interval range, if so, taking the first abnormal numerical values and the time intervals as first abnormal data, and otherwise, determining that the first abnormal numerical values are invalid. When the numerical sequence is determined to be an aperiodic sequence, clustering the numerical sequence by using a one-dimensional clustering method to obtain a plurality of division points, dividing the numerical sequence by using the plurality of division points to obtain a plurality of groups of different second subsequences, acquiring a third subsequence with a value larger than a preset value from the second subsequences, determining an abnormal time interval of the third subsequence based on the position of the third subsequence in the numerical sequence, acquiring a fourth subsequence with an abnormal time interval of an adjacent third subsequence within the preset time interval range, and taking the fourth subsequence and the abnormal time interval as second abnormal data; the abnormal data acquisition module is used for: and marking the first abnormal data periodically to obtain first effective abnormal data, marking the second abnormal data non-periodically to obtain second effective abnormal data, and sending the first effective abnormal data and the second effective abnormal data as final effective abnormal data to an abnormal classification module.
The method comprises the steps of firstly, periodically judging a numerical sequence, analyzing the periodic sequence and a non-periodic sequence in different modes, ensuring the accuracy of the obtained first abnormal data, dividing the periodic sequence into the same first subsequence, judging the numerical value of the first subsequence and judging the time interval with a first abnormal value, simultaneously judging the time interval when the numerical value meets the abnormal requirement, judging the time interval to be longer than the preset time interval to represent the time between two first abnormal constant values, failing to be taken as effective abnormal data, taking the first abnormal value and the time interval as the first abnormal data, reducing the redundancy of the abnormal data, ensuring the accuracy of the obtained first abnormal data, adopting different sequence analysis modes for the non-periodic sequence, particularly, firstly, carrying out clustering operation on the numerical sequence through a one-dimensional clustering method to obtain a plurality of dividing points, enabling the dividing of the dividing points to be more accurate and objective, providing a dividing basis for subsequent abnormal analysis, extracting a third subsequence which is larger than the preset numerical value after dividing the non-periodic sequence, and marking the third subsequence according to the third abnormal value, and finally, marking the second abnormal data in the same time interval as the second abnormal data in the second abnormal sequence, and finally, marking the second abnormal data in the same time interval as the second abnormal data, namely the second abnormal data, and finally marking the second abnormal data in the time interval, namely the second abnormal data, the first effective abnormal data and the second effective abnormal data are used as final effective abnormal data, so as to ensure the definition and accuracy of the final effective abnormal data, thereby providing basis for realizing the judgment and decision of the numerical value of the data, facilitating the self-judgment of the data, firstly, periodically judging the numerical sequence, analyzing the periodic sequence and the non-periodic sequence in different modes to ensure the emphasis and the efficiency of sequence analysis, dividing the periodic sequence into the same first subsequence to carry out numerical judgment and time interval, ensuring that the numerical value meets the abnormal requirement, judging the time interval, wherein the time interval exceeding the preset time interval indicates that the time between the two first abnormal constant values is longer and can not be used as effective abnormal data, taking the first abnormal value and the time interval as the first abnormal data, reducing the redundancy of the abnormal data, ensuring the accuracy of the obtained first abnormal data, firstly, carrying out clustering operation on the numerical sequence by a one-dimensional clustering method on the non-periodic sequence to obtain a plurality of division points, the division of the division points is more accurate and objective, and after the non-periodic sequence is divided, the same, judging the numerical value and the abnormal time interval to finally obtain second abnormal data, ensuring the accuracy of the obtained second abnormal data, and finally, labeling the first abnormal data and the second abnormal data as final effective abnormal data, ensuring the definition and accuracy of the final effective abnormal data, therefore, a basis is provided for realizing the judgment and decision of the numerical value of the data, the self-judgment of the data is facilitated, and the selectivity, the sensitivity and the reliability of the abnormal protection of the power distribution network are realized.
In order to solve the problems that in the prior art, when power fails, key features cannot be obtained rapidly, and when power equipment is in use, current sensitivity is high, and misoperation is very easy to occur, please refer to fig. 6, the embodiment provides the following technical scheme:
the data operation feature processing system comprises: the power data acquisition module is used for: redundancy and dimension reduction are carried out on the effective abnormal data based on the abnormal data in the abnormal data calculation module, and finally electric power data are obtained; the feature extraction module is used for: extracting the characteristics of the power data, and acquiring an initial characteristic set of the power data according to an extraction result; extracting key features related to the relay from the initial feature set and integrating the key features into a key feature subset; the operation mode decision module is used for: extracting short-circuit impedance value data in the extracted power data, and respectively making decisions on short-circuit impedance values with different magnitudes, wherein the data run feature processing system further comprises: and the relay processing module is used for: acquiring topological structure information and preset operation mode information of a power distribution network, and node attributes of each power distribution node of the power distribution network; determining a distribution topology weight value of each distribution node according to node attributes of each distribution node, the topology structure information and preset operation mode information; calculating a basic value of each relay quantity in the power data based on the distribution topology weight value of each distribution node; acquiring time sequence characteristic information corresponding to the power data; extracting time sequence data of each relay quantity from the power data according to the time sequence characteristic information; determining electricity utilization characteristic information of each relay power based on time sequence data of the relay power; taking the time sequence data and the basic value of each relay as model input samples, and simultaneously taking the electricity utilization characteristic information of each relay as model output samples to train a preset network model so as to obtain an identification model of each relay; acquiring target electricity utilization characteristics corresponding to target time sequence data of each key characteristic in the key characteristic subset by utilizing an identification model of each relay quantity; acquiring a first operation characteristic of each relay quantity according to the target electricity utilization characteristic of each key characteristic; acquiring the change condition of the target electricity utilization characteristic of each relay quantity in the electric power data, and determining the electricity utilization change rule of each relay quantity according to the change condition; and confirming the load quantity with the similarity of the electricity change rule larger than or equal to a preset threshold value as the similar relay, and confirming the second target operation characteristic of any relay quantity in each type of relay as the final operation characteristic of the relay. The operation mode decision module further comprises: the big mode decision module is used for: the minimum short-circuit impedance value data in the power data are frequently extracted; the small mode decision module is used for: the largest short-circuit impedance value data among the power data is often extracted.
Specifically, redundancy and dimension reduction are carried out on electric power data to ensure that the electric power data are displayed in a more visual data form, then feature extraction is carried out on the processed electric power data, the features of each electricity utilization dimension of the electric power data are extracted, an initial feature set of the electric power data is obtained according to the extraction result, key features related to relays are counted and integrated to obtain a key feature subset, a preset distribution weight value of each distribution node is obtained through the node attribute of each distribution node connected with a distribution network, namely the electric power resource distribution weight of the distribution node, then the basic value of each relay electric quantity is calculated according to the distribution weight of each distribution node, the time sequence data of each relay electric quantity is extracted from the electric power data, namely the electricity utilization feature time sequence change sequence data of each relay electric quantity is determined according to the extraction result, an identification model of the relay electric quantity is constructed according to the electricity utilization feature time sequence change sequence data and the electricity utilization feature information of each relay electric quantity, a final operation feature can be determined according to the initial feature of each relay electric quantity, the final feature can be further classified, the classification feature can be further determined according to the classification feature of the initial feature of each relay electric quantity, the electric power can be obtained according to the classification feature of the final feature of the electric power data, the identification model of each relay can be constructed to rapidly acquire the target electricity utilization characteristic corresponding to the target time sequence data of each key characteristic in the key characteristic subset, so that the operation characteristic of the relay can be accurately determined, the data acquisition efficiency and accuracy are improved, furthermore, the operation characteristics of the relays of the same type can be uniformly counted through classifying the relay, the operation characteristic statistics of a single relay is not needed, the working efficiency is improved, wherein in a large-mode decision module, the minimum short-circuit impedance value in the electric power data is used for generating the largest short-circuit current after short-circuit, the stability of electric equipment is generally checked, in a small-mode decision module, the largest short-circuit impedance value in the electric power data is used for generating the smallest short-circuit current after short-circuit. The method is generally used for verifying the sensitivity of the relay protection device, so that the sensitivity of the current can be reduced.
The embodiment provides a specific technical scheme for implementing integrity and security assessment, and the data comparison module obtains the integrity coefficient and the security coefficient of the historical transmission success data by assigning values according to the integrity and security conditions of the analysis of the historical transmission success data; the integrity and security assessment is as follows:
the threat risk index for the data device interface parameters is calculated using the following algorithm:
in the above formula, τ represents a threat risk index of the data device interface parameter; n represents the number of historical transmission success data; omega i A complete coefficient representing the ith historical transmission success data;
representing the whole coefficient mean value of all the historical transmission success data; />
A security coefficient representing the ith historical transmission success data; />
The safety coefficient average value of all the historical transmission success data is represented;
calculating vulnerability risk indexes of interface parameters of the data equipment by adopting the following algorithm:
in the above formula, μ represents a vulnerability risk index of the data device interface parameter; gamma ray 1 Integrity weights representing vulnerability assessment; gamma ray 2 Security weight representing vulnerability assessment, and gamma 1 + 2 ＝1。
The historical transmission success data is analyzed, corresponding assignment is carried out according to the analyzed integrity and safety conditions, so that the integrity coefficient and the safety coefficient of the historical transmission success data are obtained, for example, the assignment of the integrity coefficient and the safety coefficient can be carried out in the range of [0,1], the higher the integrity coefficient of the better assignment of the integrity condition is, the higher the safety coefficient of the better assignment of the safety condition is, and the highest assignment of the integrity coefficient and the safety coefficient is 1; the evaluation method has the advantages that a data foundation is laid for quantification of integrity and safety evaluation through assignment, and then the quantitative calculation is respectively carried out by adopting the algorithm to obtain threat risk indexes and vulnerability risk indexes, so that the objectivity and reliability of the evaluation are improved; taking the influence differences of integrity and security on threat risk indexes and vulnerability risk indexes into consideration, adopting different algorithms on the threat risk indexes and the vulnerability risk indexes, and introducing an integrity weight and a security weight into vulnerability risk index calculation so as to balance the influence degree differences of the integrity and the security on the vulnerability risk, wherein the integrity weight and the security weight are preset; the algorithm has the advantages of small calculated amount, less point resources, easy operation and implementation and high efficiency.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. The incremental power distribution network anti-external breaking value optimizing system comprises a parameter data acquisition system, an acquisition data analysis system, an abnormal data calculation system and a data operation characteristic processing system;
The parameter data acquisition system is used for acquiring the reference interface parameters of different equipment and counting the reference interface parameters of the different equipment by monitoring;
the acquired data analysis system is used for carrying out anomaly detection on the reference interface parameters based on statistics in the parameter data acquisition system and positioning the reference interface parameter equipment with the detected anomaly;
the abnormal data analysis system is used for carrying out data analysis based on the abnormal data detected in the collected data analysis system and calculating a safety index in the abnormal data;
the abnormal data computing system is used for computing the data of the abnormal data based on the abnormal data which is analyzed by the abnormal data analyzing system and computing the effective data in the abnormal data;
the data operation characteristic processing system is used for carrying out data processing based on effective data in the abnormal data computing system and carrying out solution decision on the extracted data;
wherein, gather data analysis system includes:
the monitoring data analysis module is used for:
drawing a two-dimensional rectangular coordinate system, extracting target values of sub-monitoring data of each module, and carrying out visual display on the two-dimensional rectangular coordinate system based on the target values to obtain a parameter change curve of module interface parameters of each module in a monitoring time period;
Acquiring reference interface parameters of interfaces of all modules, and acquiring reference lines of all modules in a two-dimensional rectangular coordinate system based on the reference interface parameters;
overlapping the parameter change curve with the reference line, determining an abnormal parameter change curve based on an overlapping result, and obtaining abnormal data based on the abnormal parameter change curve;
the abnormal interface positioning module is used for:
inputting the sub-monitoring data of the abnormal data into a preset fault diagnosis model for analysis to obtain the fault type and the fault position of the abnormal data;
the abnormal data storage module is used for:
the fault type and the fault position of the abnormal data are stored independently;
wherein, abnormal data analysis system includes:
a data reading module for:
grouping and packaging abnormal data of each device;
counting according to a target value of which the importance of the interface parameter of each packed data is greater than or equal to a preset threshold value;
the data comparison module is used for:
acquiring historical transmission success data of parameter data of each equipment port, analyzing the historical transmission success data to determine the integrity and the safety of the historical transmission success data, and evaluating threat risk indexes and vulnerability risk indexes of equipment interface parameters according to the integrity and the safety;
Calculating the security index of the equipment interface by using a preset risk assessment system according to the threshold value of the target value of each equipment interface parameter, the threat risk index and the vulnerability risk index of the equipment interface parameter;
the grade classification module is used for:
calculating security indexes of different equipment interfaces according to a preset risk assessment system, and grading the security indexes according to data values of the security indexes;
wherein the abnormal data computing system comprises:
a data calculation module for:
comparing the data value with normal threshold data based on threat risk index and vulnerability risk index data, and obtaining a data difference numerical sequence;
the judging module is used for:
the data difference value sequence is obtained, the value sequence is periodically detected, and whether the value sequence is a periodic sequence or not is judged;
a sequence analysis module for:
after the numerical sequence is determined to be a periodic sequence, dividing the numerical sequence according to the period to obtain a plurality of groups of identical first subsequences, judging whether all numerical values in the first subsequences are larger than a preset numerical value, if so, extracting first abnormal numerical values larger than the preset numerical value in the first subsequences, determining adjacent time intervals among the first abnormal numerical values, judging whether the time intervals are in a preset time interval range, if so, taking the first abnormal numerical values and the time intervals as first abnormal data, otherwise, determining that the first abnormal numerical values are invalid;
When the numerical sequence is determined to be an aperiodic sequence, clustering the numerical sequence by using a one-dimensional clustering method to obtain a plurality of division points, dividing the numerical sequence by using the plurality of division points to obtain a plurality of groups of different second subsequences, acquiring a third subsequence with a value larger than a preset value from the second subsequences, determining an abnormal time interval of the third subsequence based on the position of the third subsequence in the numerical sequence, acquiring a fourth subsequence with an abnormal time interval of an adjacent third subsequence within the preset time interval range, and taking the fourth subsequence and the abnormal time interval as second abnormal data;
the abnormal data acquisition module is used for:
the first abnormal data is marked periodically to obtain first effective abnormal data, the second abnormal data is marked aperiodically to obtain second effective abnormal data, and the first effective abnormal data and the second effective abnormal data are used as final effective abnormal data to be sent to an abnormal classification module;
wherein, data operation feature processing system still includes:
and the relay processing module is used for:
Acquiring topological structure information and preset operation mode information of a power distribution network, and node attributes of each power distribution node of the power distribution network;
determining a distribution topology weight value of each distribution node according to node attributes of each distribution node, the topology structure information and preset operation mode information;
calculating a basic value of each relay quantity in the power data based on the distribution topology weight value of each distribution node;
acquiring time sequence characteristic information corresponding to the power data;
extracting time sequence data of each relay quantity from the power data according to the time sequence characteristic information;
determining electricity utilization characteristic information of each relay power based on time sequence data of the relay power;
taking the time sequence data and the basic value of each relay as model input samples, and simultaneously taking the electricity utilization characteristic information of each relay as model output samples to train a preset network model so as to obtain an identification model of each relay;
acquiring target electricity utilization characteristics corresponding to target time sequence data of each key characteristic in the key characteristic subset by utilizing an identification model of each relay quantity;
acquiring a first operation characteristic of each relay quantity according to the target electricity utilization characteristic of each key characteristic;
Acquiring the change condition of the target electricity utilization characteristic of each relay quantity in the electric power data, and determining the electricity utilization change rule of each relay quantity according to the change condition;
and confirming the load quantity with the similarity of the electricity change rule larger than or equal to a preset threshold value as the similar relay, and confirming the second target operation characteristic of any relay quantity in each type of relay as the final operation characteristic of the relay.
2. The incremental power distribution network anti-outward breaking constant value optimization system according to claim 1, wherein: the parameter data acquisition system comprises:
the parameter interface extraction module is used for:
acquiring functional attributes of a line, a transformer, a reactor and a capacitor, and extracting reference interface parameters of an interface based on the functional attributes;
the interface monitoring module is used for:
determining monitoring indexes of equipment with various attributes based on the reference interface parameters, and generating a monitoring plug-in according to preset monitoring plug-in generation rules based on the monitoring indexes, wherein the monitoring plug-in carries a dynamic function library;
the monitoring interface docking module is used for:
extracting configuration parameters of the monitoring plug-in, adjusting the configuration parameters of the monitoring plug-in through each preset function in the dynamic function library based on reference interface parameters of each equipment interface, and obtaining a monitoring interface corresponding to the monitoring plug-in based on an adjustment result;
The monitoring data statistics module is used for:
sending a monitoring request to a module interface of each module based on the monitoring interface, and butting a monitoring interface corresponding to the monitoring plug-in with a device interface of the device when the response information of the module interface is received;
acquiring a monitoring log file of each module according to a preset time interval through the monitoring plug-in based on the docking result, and extracting data characteristics of monitoring data in the monitoring log file;
and carrying out classification statistics on the monitoring data based on the data characteristics to obtain sub-monitoring data of the equipment.
3. The incremental power distribution network anti-outward breaking constant value optimization system according to claim 2, wherein: the parameter interface extraction module comprises:
an extraction sub-module for:
extracting reference interface parameters of interfaces of the circuit, the transformer, the reactor and the capacitor based on the acquired functional attributes of the circuit, the transformer, the reactor and the capacitor;
a marking sub-module for:
adding a specific identification mark to the functional attribute which has completed the reference interface parameter extraction;
and a discrimination sub-module for:
after receiving an extraction instruction of the reference interface parameters, inquiring each functional attribute according to the instruction content, removing the functional attribute with the identification mark from the extraction range, and adjusting the extraction range;
An encryption sub-module for:
and before data transmission, packing and encrypting the reference interface parameters in the adjusted extraction range.
4. The incremental power distribution network anti-outward breaking constant value optimization system according to claim 1, wherein: the data operation feature processing system comprises:
the power data acquisition module is used for:
redundancy and dimension reduction are carried out on the effective abnormal data based on the abnormal data in the abnormal data calculation module, and finally electric power data are obtained;
the feature extraction module is used for:
extracting the characteristics of the power data, and acquiring an initial characteristic set of the power data according to an extraction result;
extracting key features related to the relay from the initial feature set and integrating the key features into a key feature subset;
the operation mode decision module is used for:
and extracting short-circuit impedance value data in the extracted power data, and respectively making decisions on short-circuit impedance values with different magnitudes.
5. The incremental power distribution network anti-outward breaking constant value optimization system according to claim 4, wherein: the operation mode decision module further comprises:
the big mode decision module is used for:
the minimum short-circuit impedance value data in the power data are frequently extracted;
The small mode decision module is used for:
the largest short-circuit impedance value data among the power data is often extracted.
6. The incremental power distribution network anti-outward breaking constant value optimization system according to claim 1, wherein: the data comparison module obtains the integrity coefficient and the safety coefficient of the historical transmission success data through assignment according to the integrity and the safety condition of the analysis of the historical transmission success data;
the threat risk index for the data device interface parameters is calculated using the following algorithm:
in the above formula, τ represents a threat risk index of the data device interface parameter; n represents the number of historical transmission success data; omega i A complete coefficient representing the ith historical transmission success data;
representing the whole coefficient mean value of all the historical transmission success data;
a security coefficient representing the ith historical transmission success data; />
The safety coefficient average value of all the historical transmission success data is represented;
calculating vulnerability risk indexes of interface parameters of the data equipment by adopting the following algorithm:
in the above formula, μ represents a vulnerability risk index of the data device interface parameter; gamma ray 1 Integrity weights representing vulnerability assessment; gamma ray 2 And the security weight of vulnerability assessment is represented.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211457984.7A CN115936428B (en) | 2022-11-17 | 2022-11-17 | Incremental power distribution network external damage prevention constant value optimizing system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211457984.7A CN115936428B (en) | 2022-11-17 | 2022-11-17 | Incremental power distribution network external damage prevention constant value optimizing system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115936428A CN115936428A (en) | 2023-04-07 |
| CN115936428B true CN115936428B (en) | 2023-06-30 |
Family
ID=86556712
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211457984.7A Active CN115936428B (en) | 2022-11-17 | 2022-11-17 | Incremental power distribution network external damage prevention constant value optimizing system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115936428B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116599217B (en) * | 2023-04-19 | 2023-09-29 | 国网黑龙江省电力有限公司齐齐哈尔供电公司 | Intelligent diagnosis system for power distribution network based on big data driving |
| CN116915500B (en) * | 2023-09-05 | 2023-11-17 | 武汉万数科技有限公司 | Security detection method and system for access equipment |
| CN117152925B (en) * | 2023-10-27 | 2024-01-16 | 吉林省吉能电力通信有限公司 | Big data-based power communication data processing system |
| CN117849700B (en) * | 2024-03-07 | 2024-05-24 | 南京国网电瑞电力科技有限责任公司 | Modular electric energy metering system capable of controlling measurement |
| CN117952564B (en) * | 2024-03-22 | 2024-06-07 | 江西为易科技有限公司 | Scheduling simulation optimization method and system based on progress prediction |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914791A (en) * | 2014-03-17 | 2014-07-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Electrical equipment state maintenance system |
| CN108074021A (en) * | 2016-11-10 | 2018-05-25 | 中国电力科学研究院 | A kind of power distribution network Risk Identification system and method |
| WO2020046286A1 (en) * | 2018-08-29 | 2020-03-05 | General Electronic Company | Integrated cybersecurity risk assessment and state monitoring for electrical power grid |
| CN110297715B (en) * | 2019-07-02 | 2021-09-14 | 北京工业大学 | Online load resource prediction method based on periodic feature analysis |
| CN110909811B (en) * | 2019-11-28 | 2022-10-18 | 国网湖南省电力有限公司 | OCSVM (online charging management system) -based power grid abnormal behavior detection and analysis method and system |
| CN113313371B (en) * | 2021-05-25 | 2023-04-25 | 华南理工大学 | Power distribution network risk assessment method, device, computer equipment and storage medium |
| CN113486078B (en) * | 2021-06-15 | 2023-11-21 | 国网山东省电力公司金乡县供电公司 | Distributed power distribution network operation monitoring method and system |
| CN113344134B (en) * | 2021-06-30 | 2024-04-19 | 广东电网有限责任公司 | Low-voltage distribution monitoring terminal data acquisition abnormality detection method and system |
| CN113839812B (en) * | 2021-09-08 | 2024-04-09 | 深圳华跃云鹏科技有限公司 | Ad hoc network topology construction method |
| CN114358152A (en) * | 2021-12-21 | 2022-04-15 | 国网江苏省电力有限公司苏州供电分公司 | Intelligent power data anomaly detection method and system |
| CN115048591B (en) * | 2022-06-17 | 2023-09-29 | 河北新大长远电力科技股份有限公司 | Visual intelligent display analysis system of distribution network holographic data based on artificial intelligence |
-
2022
- 2022-11-17 CN CN202211457984.7A patent/CN115936428B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN115936428A (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115936428B (en) | Incremental power distribution network external damage prevention constant value optimizing system | |
| CN112162878B (en) | Database fault discovery method and device, electronic equipment and storage medium | |
| CN112199367B (en) | Long-time scale power balance check and big data analysis method and system | |
| CN110909811A (en) | OCSVM (online charging management system) -based power grid abnormal behavior detection and analysis method and system | |
| CN112491805B (en) | Network security equipment management system applied to cloud platform | |
| CN108198408B (en) | Self-adaptive anti-electricity-stealing monitoring method and system based on electricity information acquisition system | |
| CN114997682B (en) | Construction site safety monitoring system and method based on big data | |
| CN115269342B (en) | Monitoring cloud platform based on edge computing and monitoring method thereof | |
| CN113125903A (en) | Line loss anomaly detection method, device, equipment and computer-readable storage medium | |
| CN115664038B (en) | Intelligent power distribution operation and maintenance monitoring system for electrical safety management | |
| CN113071966A (en) | Elevator fault prediction method, device, equipment and storage medium | |
| CN117614978A (en) | Information security communication management system for digital workshop | |
| CN116756966A (en) | Power grid fault early warning method, system, terminal equipment and storage medium | |
| CN110807014B (en) | Cross validation based station data anomaly discrimination method and device | |
| CN109587145B (en) | False data intrusion detection method, device and equipment in power network | |
| CN114978877A (en) | Exception handling method and device, electronic equipment and computer readable medium | |
| CN113934536B (en) | Data acquisition method facing edge calculation | |
| CN113285847A (en) | Communication network anomaly detection method and system of intelligent converter station monitoring system | |
| CN117118665A (en) | Power system data interaction security threat information analysis method | |
| CN117117780A (en) | Circuit breaker anti-blocking method and system based on secondary information fusion of transformer substation | |
| CN109376168A (en) | A kind of big data analysis system of master network equipment | |
| CN115834412A (en) | Network security situation evaluation method and device, electronic equipment and storage medium | |
| CN116049098A (en) | Operational data characteristic analysis method suitable for judging operational state of power distribution network | |
| CN114118208A (en) | Transformer fault judgment method and device based on multivariate information and electronic equipment | |
| CN114997804A (en) | Method and system for cooperatively sensing running state of complex power distribution network information physical system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |