CN118628290B - Fault data processing method and system for eddy current self-driven voltage sag control device - Google Patents
Fault data processing method and system for eddy current self-driven voltage sag control device Download PDFInfo
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Abstract
The application provides a fault data processing method and a system of an eddy self-driven voltage sag treatment device, and relates to the technical field of voltage sag treatment; the method comprises the steps of receiving historical working data of a plurality of eddy current self-driven voltage sag management devices, marking a plurality of abnormal segmented branches according to the sag fault data and the historical working data, counting the corresponding abnormal voltage sag frequency and a plurality of abnormal voltage sag currents of each abnormal segmented branch, and matching corresponding device model information to be replaced in a preset device model database based on a plurality of target abnormal voltage sag currents corresponding to target abnormal segmented branches if the abnormal voltage sag frequency corresponding to the target abnormal segmented branches is larger than a preset abnormal frequency threshold. The application enhances the electricity consumption condition of the segmented branch and has the problem of suitability with the model of the eddy current self-driven voltage sag treatment device.
Description
Technical Field
The invention relates to the technical field of voltage sag management, in particular to a fault data processing method and system of an eddy current self-driven voltage sag management device.
Background
Voltage sag of a power distribution grid refers to a phenomenon in which the voltage in the grid suddenly drops to a certain extent and returns to normal in a short time. Such temporary voltage drops may be caused by various reasons, such as sudden increases in grid load, transient current surges at the start-up of the electrical equipment, short circuit faults, etc. Voltage sags can affect equipment and electrical appliances in a power distribution system, possibly causing equipment failure or data loss.
In the related art, an eddy current self-driven voltage sag treatment device mainly comprises a circuit breaker K1, an eddy current self-driven quick switch K2 and a bus maintaining impedance Z, wherein the circuit breaker K1 is connected with the eddy current self-driven quick switch K2 in series, one end of the circuit breaker K1 is connected with the bus, one end of the eddy current self-driven quick switch K2 is connected with a sectionalized branch, and the bus maintaining impedance Z is connected on the eddy current self-driven quick switch K2 in parallel and is used for switching to the bus maintaining impedance Z to be connected between the sectionalized branch and the bus when a voltage sag fault in a branch circuit is detected.
In the process of quickly adopting the eddy current self-driven voltage sag treatment device in a segmented manner, the electricity consumption condition of the segmented branch circuit and the working condition of the eddy current self-driven voltage sag treatment device are often summarized in time, so that the current electricity consumption condition of the segmented branch circuit can be possibly caused, and the problem of weak suitability exists between the current electricity consumption condition and the model of the eddy current self-driven voltage sag treatment device.
Disclosure of Invention
In order to facilitate the enhancement of the electricity consumption condition of the segmented branch and the problem of suitability between the model of the eddy current self-driven voltage sag treatment device, the application provides a fault data processing method and system of the eddy current self-driven voltage sag treatment device.
In a first aspect, the present application provides a fault data processing method for an eddy current self-driven voltage sag management device, which adopts the following technical scheme:
a fault data processing method of an eddy current self-driven voltage sag management device, the method comprising:
receiving sag fault data of a plurality of segmented branches, wherein the sag fault data comprise voltage sag time and voltage sag current;
Receiving historical work data of a plurality of vortex self-driven voltage sag management devices, wherein the historical work data comprise a plurality of historical work cut-off times and corresponding historical work cut-off currents;
marking a plurality of abnormal segmented branches according to the sag fault data and the historical working data, wherein voltage sag faults which cannot be processed or are processed by the eddy current self-driven voltage sag management device in an error mode exist in the abnormal segmented branches;
Counting each abnormal sectional branch, and when the eddy current self-driven voltage sag treatment device fails to process or mistreats the voltage sag fault, correspondingly performing abnormal voltage sag frequency and a plurality of abnormal voltage sag currents;
If the abnormal voltage sag frequency corresponding to the target abnormal section branch is larger than a preset abnormal frequency threshold, matching corresponding device model information to be replaced in a preset device model database based on a plurality of target abnormal voltage sag currents corresponding to the target abnormal section branch.
Optionally, the matching, in a preset device model database, corresponding to-be-replaced device model information based on the multiple target abnormal voltage dip currents corresponding to the target abnormal segment branches includes:
Generating fault-tolerant voltage sag current according to the abnormal frequency threshold and a plurality of target abnormal voltage sag currents;
And matching corresponding equipment model information to be replaced in a preset device model database based on the fault-tolerant voltage sag current.
Optionally, the generating fault-tolerant voltage sag current according to the abnormal frequency threshold and the plurality of target abnormal voltage sag currents includes:
sequencing a plurality of target abnormal voltage sag currents, and generating an extraction sequence number according to the abnormal frequency threshold;
Obtaining a target self-driving current value of the eddy current self-driving voltage sag treatment device in the target abnormal section branch;
If the target self-driven current value is larger than a plurality of target abnormal voltage sag currents, screening fault-tolerant voltage sag currents according to the extraction sequence numbers in a descending order;
and if the target self-driven current value is smaller than a plurality of target abnormal voltage sag currents, screening fault-tolerant voltage sag currents according to the extraction sequence numbers in a sequence from large to small.
Optionally, the method further comprises:
receiving historical electricity consumption data of each segmented branch, wherein the historical electricity consumption data comprises a plurality of historical electricity consumption powers which are averaged in a preset unit time;
receiving estimated electric equipment construction data under a plurality of segmented branches, wherein the estimated electric equipment construction data comprises equipment type to be built of estimated construction equipment and estimated electric power, and the equipment type to be built comprises conventional electric type and high-power electric type;
obtaining the estimated self-driving current value of a target segmented branch according to the target estimated electric equipment construction data and the target historical electric data of the target segmented branch;
and matching corresponding optimized equipment model information in the device model database according to the estimated self-driving current value.
Optionally, the obtaining the estimated self-driving current value of the target segment branch according to the target estimated electric equipment construction data and the target historical electric utilization data of the target segment branch includes:
Generating estimated electricity consumption data according to the target estimated electricity consumption equipment construction data and the target historical electricity consumption data, wherein the estimated electricity consumption data comprises the type of equipment to be constructed and estimated total electricity consumption power;
and based on the estimated power consumption data, matching corresponding estimated self-driving current values in the historical power consumption data corresponding to the plurality of segmented branches.
Optionally, the matching, based on the estimated power consumption data, the corresponding estimated self-driving current value in the historical power consumption data corresponding to the plurality of segmented branches includes:
And if the type of the equipment to be built in the estimated power consumption data is a conventional power consumption type, matching a corresponding estimated self-driving current value in the historical power consumption data based on the estimated total power consumption, or taking a preset self-driving current value as an estimated self-driving current value.
Optionally, the matching, based on the estimated power consumption data, the corresponding estimated self-driving current value in the historical power consumption data corresponding to the plurality of segmented branches includes:
If the type of the equipment to be built in the estimated power consumption data is a high-power consumption type, matching two adjacent historical power consumption data in a plurality of historical power consumption data based on the estimated total power consumption;
acquiring a self-driving current enhancement coefficient according to the two adjacent historical power consumption data;
And calculating and generating a predicted self-driving current value according to the self-driving current enhancement coefficient and the predicted total power consumption.
In a second aspect, the application provides a fault data processing system of an eddy current self-driven voltage sag management device, which adopts the following technical scheme:
a fault data processing system for an eddy current self-driven voltage sag remediation device, the system comprising:
The information receiving module is used for receiving sag fault data of a plurality of segmented branches, wherein the sag fault data comprise voltage sag time and voltage sag current;
The information receiving module is used for receiving historical work data of the vortex self-driven voltage sag management device, wherein the historical work data comprise a plurality of historical work cut-off times and corresponding historical work cut-off currents;
The abnormal identification module is used for marking a plurality of abnormal segmented branches according to the sag fault data and the historical working data, and the abnormal segmented branches have voltage sag faults which cannot be processed or are processed by the eddy current self-driven voltage sag management device in an error mode;
The abnormal identification module is used for counting each abnormal sectional branch, and when the eddy current self-driven voltage sag treatment device fails to process or mistreats the voltage sag fault, the corresponding abnormal voltage sag frequency and a plurality of abnormal voltage sag currents;
The equipment model matching module is used for matching corresponding device model information to be replaced in a preset device model database based on a plurality of target abnormal voltage sag currents corresponding to the target abnormal section branch if the abnormal voltage sag frequency corresponding to the target abnormal section branch is larger than a preset abnormal frequency threshold.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme:
An intelligent terminal comprising a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to realize the processing of the intelligent terminal in the fault data processing method of the vortex self-driven voltage sag management device according to the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions loaded and executed by a processor to implement the processing of a smart terminal in a fault data processing method of an eddy current self-driven voltage sag management device according to the first aspect.
In summary, the present application includes at least one of the following beneficial technical effects:
The method comprises the steps of collecting sag fault data of each segmented branch and historical working data of an eddy current self-driven voltage sag treatment device, analyzing working efficiency of the eddy current self-driven voltage sag treatment device in each segmented branch, screening segmented branches with more working errors of the eddy current self-driven voltage sag treatment device, marking the segmented branches as abnormal segmented branches, and further allocating proper equipment models by a plurality of target abnormal voltage sag currents corresponding to the abnormal segmented branches, so that the efficiency of the eddy current self-driven voltage sag treatment device on voltage sag fault treatment is improved, the possibility of fault treatment errors of the eddy current self-driven voltage sag treatment device is reduced, and the problem of suitability between the power consumption condition of the segmented branches and the models of the eddy current self-driven voltage sag treatment device is further solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a related art eddy current self-driven voltage sag control device.
Fig. 2 is a schematic flow chart of a fault data processing method of an eddy current self-driven voltage sag management device according to an embodiment of the application.
Fig. 3 is a schematic flow chart of matching model information of a device to be replaced according to an embodiment of the present application.
Fig. 4 is a schematic flow chart of generating fault tolerant voltage dip current according to an embodiment of the present application.
Fig. 5 is a schematic flow chart of matching and optimizing equipment model information according to an embodiment of the present application.
Fig. 6 is a flowchart illustrating a method for generating a predicted self-driving current value according to an embodiment of the present application.
Fig. 7 is a flowchart illustrating another method for generating a predicted self-driving current value according to an embodiment of the present application.
FIG. 8 is a flowchart illustrating another embodiment of generating a predicted self-driving current value.
FIG. 9 is a system block diagram of a fault data processing system of an eddy current self-driven voltage sag remediation device according to an embodiment of the present application.
Reference numerals illustrate 801, an information receiving module, 802, an abnormality identifying module, 803, an equipment model matching module, 804, an information matching module, 805, a data calculating module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail with reference to the accompanying drawings 1 to 9.
The embodiment of the application provides a fault data processing method of an eddy current self-driven voltage sag treatment device, which can be applied to a fault data processing system of the eddy current self-driven voltage sag treatment device, wherein an execution main body of the method can be an intelligent terminal in the fault data processing system of the eddy current self-driven voltage sag treatment device.
In the embodiment of the application, the voltage sag fault processing of the eddy current self-driven voltage sag treatment device is taken as an example in the voltage sag process of the power distribution network, and other conditions are similar and are not repeated one by one.
The process flow shown in fig. 2 will be described in detail with reference to the specific embodiments, and the following may be included:
S101, receiving sag fault data of a plurality of segmented branches, wherein the sag fault data comprise voltage sag time and voltage sag current.
In implementation, an intelligent terminal in the fault data processing system collects and sorts fault processing data loaded with the eddy current self-driven voltage sag management device.
Specifically, the intelligent terminal receives sag fault data fed back by a plurality of segmented branches, and vortex self-driving voltage sag management devices are arranged on the segmented branches. The intelligent terminal collects and compares the sag fault data so as to adjust the model of the vortex self-driven voltage sag treatment device which can be improved in each segmented branch.
The sag processing data includes a plurality of voltage sag times and corresponding voltage sag currents.
S102, receiving historical work data of a plurality of vortex self-driven voltage sag management devices, wherein the historical work data comprise a plurality of historical work cut-off times and corresponding historical work cut-off currents.
In the implementation, the intelligent terminal collects the historical work data of each vortex self-driven voltage sag treatment device fed back by the vortex self-driven voltage sag treatment device, and the historical work data comprises the corresponding historical work cut-off time and historical work cut-off current when the vortex self-driven voltage sag treatment device cuts off a line in a segmented branch circuit in the process of each voltage sag.
S103, marking a plurality of abnormal section branches according to the sag fault data and the historical working data, wherein the abnormal section branches have voltage sag faults which cannot be processed or are processed by the eddy current self-driven voltage sag management device in an error mode.
In implementation, the intelligent terminal in the fault data processing system compares and compares the sag fault data with the historical working data so as to screen out records of unreacted or error processing of the eddy current self-driven voltage sag management device when the voltage sag fault occurs in the plurality of segmented branches, wherein the segmented branches with the voltage sag fault but unreacted or error processing of the eddy current self-driven voltage sag management device are marked as the non-abnormal segmented branches.
The unreacted method for obtaining the eddy current self-driven voltage sag treatment device includes the steps of comparing a plurality of corresponding voltage sag times under the same sectional branch with a plurality of historical work cut-off times, and if a certain voltage sag time does not exist, regarding sag fault data corresponding to the voltage sag time in the current sectional branch as unreacted voltage sag faults of the eddy current self-driven voltage sag treatment device, wherein the sag fault data are called abnormal sag fault data.
Comparing corresponding historical work cut-off time in the same sectional branch, if the historical cut-off time appears for multiple times in a preset continuous time period, regarding corresponding sag fault data in the continuous time period in the current sectional branch as voltage sag fault wrongly processed by the vortex self-driven voltage sag treatment device, wherein the sag fault data are called abnormal sag fault data.
In the present application, the untreated and error treated are respectively for short-circuit failure unrecognized and load access error recognized.
S104, counting each abnormal sectional branch, and when the eddy current self-driven voltage sag treatment device fails to process or wrongly processes the voltage sag fault, correspondingly reducing the frequency of the abnormal voltage sag and reducing the currents of the abnormal voltage sag.
In implementation, the intelligent terminal counts the corresponding abnormal sag fault data in each abnormal section branch when the eddy current self-driven voltage sag treatment device does not process or erroneously processes the voltage sag fault, so as to calculate the number of the corresponding abnormal sag fault data of each abnormal section branch, and meanwhile, the voltage sag current of the abnormal sag fault data is called as the abnormal voltage sag current.
S105, if the abnormal voltage sag frequency corresponding to the target abnormal section branch is larger than a preset abnormal frequency threshold, matching corresponding device model information to be replaced in a preset device model database based on a plurality of target abnormal voltage sag currents corresponding to the target abnormal section branch.
In implementation, the intelligent terminal in the fault data processing system compares the abnormal voltage dip frequency corresponding to each abnormal section branch with a preset abnormal frequency threshold value so as to screen out the abnormal section branches with the abnormal voltage dip frequency larger than the abnormal frequency threshold value, and one abnormal section branch screened out by the intelligent terminal is called a target abnormal section branch.
Further, the fault data processing system screens out a plurality of target abnormal voltage sag currents with the number equal to that of the abnormal frequency threshold value from the plurality of target abnormal voltage sag currents corresponding to the target abnormal segmentation branch circuits.
The screening method can be that the target self-driving current value of the eddy current self-driving voltage sag treatment device in the target abnormal section branch is firstly obtained, and then the screening is carried out according to the distance between the multiple target abnormal voltage sag currents and the self-driving current value.
And then matching the screened target abnormal voltage sag current closest to the target self-driving current value with corresponding equipment model information to be replaced in a preset device model database.
The method comprises the steps of collecting sag fault data of each segmented branch and historical working data of an eddy current self-driven voltage sag treatment device, analyzing working efficiency of the eddy current self-driven voltage sag treatment device in each segmented branch, screening segmented branches with more working errors of the eddy current self-driven voltage sag treatment device, marking the segmented branches as abnormal segmented branches, and further allocating proper equipment models by a plurality of target abnormal voltage sag currents corresponding to the abnormal segmented branches, so that the efficiency of the eddy current self-driven voltage sag treatment device on voltage sag fault treatment is improved, and the possibility of fault treatment errors of the eddy current self-driven voltage sag treatment device is reduced.
Optionally, in step S105, there is also a process as shown in fig. 3, and specific operation contents are as follows:
s201, generating fault-tolerant voltage sag current according to the abnormal frequency threshold and a plurality of target abnormal voltage sag currents.
In implementation, an intelligent terminal in the fault data processing system sorts a plurality of target abnormal voltage sag currents of the target abnormal section branch according to the size, and then screens out one target abnormal voltage sag current from the target abnormal voltage sag currents according to an abnormal frequency threshold value, wherein the screened target abnormal voltage sag current is called fault-tolerant voltage sag current.
Specifically, in step S201, there is also a process as shown in fig. 4, and specific operation contents are as follows:
S301, sorting the plurality of target abnormal voltage sag currents, and generating extraction sequence numbers according to the abnormal frequency threshold.
In implementation, the intelligent terminal in the fault data processing system sorts the plurality of target abnormal voltage sag currents corresponding to the target segment branches according to the magnitude. At the same time, the fault data processing system takes the abnormal frequency threshold value as the extraction sequence number.
S302, obtaining a target self-driving current value of the eddy current self-driving voltage sag treatment device in the target abnormal section branch.
In implementation, the intelligent terminal acquires a self-driving current value corresponding to the eddy current self-driving voltage sag management device in the target abnormal section branch, where the self-driving current value is called as a target self-driving current value in S105. And comparing the target self-driving current value with a plurality of target abnormal voltage sag currents, wherein the comparison result at least comprises the following two conditions, namely, the situation that the target self-driving current value is compared with the plurality of target abnormal voltage sag currents of the target abnormal sectional branch is respectively corresponding to the situation that the short circuit fault is not processed and the situation that the large load is connected into an error is processed because the voltage sag current caused by the short circuit fault in the normal sectional branch is larger than the voltage sag current caused by the large load.
S303, if the target self-driving current value is larger than the plurality of target abnormal voltage sag currents, the fault-tolerant voltage sag currents are screened out according to the extraction sequence numbers in the descending order.
In implementation, the intelligent terminal compares the self-driving current value corresponding to the target abnormal section branch with a plurality of target abnormal voltage sag currents, if the target self-driving current value is larger than the plurality of target abnormal voltage sag currents, the plurality of target abnormal voltage sag currents are sequenced from small to large, then one of the target abnormal voltage sag currents is screened out from the plurality of arrayed target abnormal voltage sag currents according to the extraction sequence number, and the screened target abnormal voltage sag current is called fault-tolerant voltage sag current.
S304, if the target self-driving current value is smaller than the plurality of target abnormal voltage sag currents, fault-tolerant voltage sag currents are screened out according to the extraction sequence numbers in the sequence from large to small.
In implementation, the intelligent terminal compares the self-driving current value corresponding to the target abnormal section branch with a plurality of target abnormal voltage sag currents, if the target self-driving current value is smaller than the plurality of target abnormal voltage sag currents, the plurality of target abnormal voltage sag currents are sequenced from large to small, then one of the target abnormal voltage sag currents is screened out from the plurality of arrayed target abnormal voltage sag currents according to the extraction sequence number, and the screened target abnormal voltage sag current is called fault-tolerant voltage sag current.
S202, matching corresponding equipment model information to be replaced in a preset device model database based on fault-tolerant voltage sag current.
In implementation, a device model database is prestored in an intelligent terminal of the fault data processing system, equipment model information of different models of the vortex self-driven voltage sag management device is stored in the device model database, and the equipment model information at least comprises self-driven current values.
The intelligent terminal screens equipment model information with the closest self-driving current value in the device model database according to fault-tolerant voltage sag current values, and the screened equipment model information is called equipment model information to be replaced.
Optionally, in the present application, there is also a process as shown in fig. 5, and specific operation contents are as follows:
S401, receiving historical electricity consumption data of each segmented branch, wherein the historical electricity consumption data comprises a plurality of historical electricity consumption powers which are averaged in a preset unit time.
In implementation, the intelligent terminal of the fault data processing system is used for optimizing and adjusting the eddy current self-driven voltage sag treatment device on the branched path in order to be convenient for combining construction planning. The intelligent terminal is preset with unit time so as to divide the electricity consumption condition of each segmented branch in a stepwise manner, wherein the unit time can be one day, one week and the like.
The intelligent terminal firstly receives historical electricity consumption data on each segmented branch, each segmented branch refers to a segmented branch provided with an eddy current self-driven voltage sag treatment device, and the historical electricity consumption data at least comprises historical electricity consumption power of the segmented branch which is averaged in a plurality of unit time.
S402, receiving estimated electric equipment construction data under a plurality of segmented branches, wherein the estimated electric equipment construction data comprises the type of equipment to be built of estimated construction equipment and estimated electric power, and the type of equipment to be built comprises the conventional electric type and the high-power electric type.
In implementation, the intelligent terminal receives the corresponding estimated electric equipment construction data under each subsection branch, wherein the estimated electric equipment construction data comprises the type of equipment to be built of the estimated construction equipment and the estimated electric power, so that the fault data processing system can estimate the electric consumption condition of the estimated construction equipment conveniently. The types of devices to be built herein include a normal electricity type and a high-power electricity type.
S403, obtaining the estimated self-driving current value of the target subsection branch according to the target estimated electric equipment construction data and the target historical electric utilization data of the target subsection branch.
In the implementation, one of the segmented branches is used for illustration, and other segmented branches are similar to the one segmented branch, wherein the selected segmented branch is called a target segmented branch, the corresponding estimated electric equipment construction data is called target estimated electric equipment construction data, and the corresponding historical electric data is called target historical electric data.
The intelligent terminal superimposes estimated power consumption of the construction data of the target estimated electric equipment and historical power consumption in the historical power consumption data of the target to calculate and generate estimated power consumption, then matches the estimated power consumption with the historical power consumption data of a plurality of the historical power consumption data with the historical power consumption data which are close to each other, and uses the self-driving current value corresponding to the eddy current self-driving voltage sag management device in the segmented branch corresponding to the matched historical power consumption data as an estimated self-driving current value.
S404, matching corresponding optimized equipment model information in a device model database according to the estimated self-driving current value.
In implementation, the intelligent terminal predicts the self-driving current value, screens equipment model information with the closest self-driving current value in the device model database, and the screened equipment model information is called optimized equipment model information.
Optionally, in step S403, there is also a process as shown in fig. 6, and a specific process flow is as follows:
S501, estimated electricity utilization data is generated according to the estimated electricity utilization data of the target electric equipment and the historical electricity utilization data of the target, and the estimated electricity utilization data comprises the type of the equipment to be constructed and the estimated total electricity utilization power.
In implementation, the intelligent terminal superimposes estimated power consumption in the estimated electric equipment construction data of the target and historical power consumption in the historical power consumption data of the target, calculates and generates estimated total power consumption, and gathers the estimated total power consumption and the type of equipment to be built in the estimated electric equipment construction data of the target to generate estimated power consumption data.
S502, based on the estimated power consumption data, matching corresponding estimated self-driving current values in historical power consumption data corresponding to the plurality of segmented branches.
In implementation, one implementation mode of matching the estimated self-driving current value by the intelligent terminal is as follows:
The intelligent terminal is used for estimating estimated total power in the power consumption data, matching the closest historical power consumption in the historical power consumption data corresponding to the plurality of sectional branches, and matching the self-driving current value of the eddy current self-driving voltage sag management device on the sectional branches corresponding to the historical power consumption, wherein the self-driving current value is called estimated self-driving current value.
Optionally, in step S502, there is also a process as shown in fig. 7, and specific operation contents are as follows:
S601, if the type of the equipment to be built in the estimated power consumption data is the conventional power consumption type, a corresponding estimated self-driving current value is matched in the historical power consumption data based on the estimated total power consumption, or the preset self-driving current value is used as the estimated self-driving current value.
In the implementation, if the intelligent terminal identifies that the type of the equipment to be built is the conventional electricity type in the estimated electricity data, the intelligent terminal firstly screens the sectional branches of the conventional electricity type from the plurality of sectional branches, and the electricity type corresponding to the sectional branches can be data in advance by a user or can be determined by load attributes in the sectional branches.
Then the intelligent terminal compares the estimated total power consumption with the historical power consumption in the historical power consumption data corresponding to the plurality of sectional branches of the conventional power consumption type, and the comparison result comprises the following two types:
in the first case, the estimated self-driving current is estimated by taking the self-driving current corresponding to the eddy current self-driving voltage sag management device on the segmented branch circuit corresponding to the first historical electric power as the self-driving current when the historical electric power with the difference smaller than the preset power value exists between the estimated total electric power and the plurality of the current historical electric powers which are screened out currently.
And secondly, estimating the total power consumption and the plurality of historical power consumption screened currently, wherein if no historical power consumption with the difference value smaller than the preset power value exists, taking the preset self-driving current value as the estimated self-driving current value.
Optionally, in step S502, there is also a process as shown in fig. 8, and specific operation contents are as follows:
And S701, if the type of the equipment to be built in the estimated power utilization data is a high-power utilization type, matching two adjacent historical power utilization data in the historical power utilization data based on the estimated total power utilization.
In the implementation, if the intelligent terminal identifies that the type of the equipment to be built is the high-power electricity consumption type in the estimated electricity consumption data, the intelligent terminal matches two historical electricity consumption powers closest to the estimated total electricity consumption power from the historical electricity consumption powers in the plurality of historical electricity consumption data, the two matched historical electricity consumption powers become second historical electricity consumption powers, and the two historical electricity consumption data corresponding to the second historical electricity consumption powers are called adjacent historical electricity consumption data.
S702, obtaining a self-driving current enhancement coefficient according to two adjacent historical electricity consumption data.
In the implementation, the intelligent terminal subtracts the two second historical electric power to calculate and generate an electric power difference value.
Then the intelligent terminal identifies whether two adjacent historical electricity utilization data have corresponding device model information to be replaced or not, and at least the following three conditions exist:
The first condition is that two adjacent historical electricity consumption data do not have corresponding model information of the device to be replaced, the fault data processing system subtracts the self-driving current value of the eddy current self-driving voltage sag electric equipment on a segmented branch corresponding to the two adjacent historical electricity consumption data, and calculates a generated self-driving current difference value;
The second condition is that the two adjacent historical electricity consumption data have corresponding model information of the device to be replaced, and then the fault data processing system generates a self-driving current difference value by subtracting the self-driving current value corresponding to the model information of the device to be replaced of the two adjacent historical electricity consumption data;
And thirdly, if one of the two adjacent historical electricity consumption data has the model information of the device to be replaced and the other one does not have the corresponding model information of the device to be replaced, the intelligent terminal generates a self-driving current difference value by subtracting the self-driving current value of the electric equipment with the eddy self-driving voltage sag from the self-driving current value of the segmented branch corresponding to the other adjacent historical electricity consumption data, wherein the self-driving current value corresponds to the model information of the device to be replaced corresponding to the one adjacent historical electricity consumption data.
And the intelligent terminal calculates and generates a self-driving current difference value according to the three conditions, and then calculates and generates a self-driving current enhancement coefficient by dividing the self-driving current difference value by the power consumption difference value.
S703, calculating and generating a predicted self-driving current value according to the self-driving current enhancement coefficient and the predicted total power consumption.
In the implementation, the intelligent terminal obtains the historical power consumption and the estimated total power consumption in the target historical power consumption data to calculate and generate an estimated power difference value, and then the estimated power difference value and the self-driving current enhancement coefficient are multiplied and calculated to generate an estimated self-driving current value.
As shown in fig. 9, the embodiment of the application further discloses a fault data processing system of the eddy current self-driven voltage sag management device, the system comprises:
The information receiving module 801 is configured to receive sag fault data of a plurality of segmented branches, where the sag fault data includes a voltage sag time and a voltage sag current;
The information receiving module 801 is configured to receive historical operation data of a plurality of vortex self-driven voltage sag management devices, where the historical operation data includes a plurality of historical operation cut-off times and corresponding historical operation cut-off currents;
The anomaly identification module 802 is configured to mark a plurality of anomaly segmented branches according to the sag fault data and the historical working data, where there is a voltage sag fault that the eddy current self-driven voltage sag management device fails to process or incorrectly processes;
The anomaly identification module 802 is configured to count each anomaly segmented branch, and when the eddy current self-driven voltage sag management device fails to process or incorrectly processes the voltage sag fault, the anomaly voltage sag frequency and the plurality of anomaly voltage sag currents are corresponding to each anomaly segmented branch;
The equipment model matching module 803 is configured to match corresponding device model information to be replaced in a preset device model database based on a plurality of target abnormal voltage dip currents corresponding to the target abnormal section branch if the abnormal voltage dip frequency corresponding to the target abnormal section branch is greater than a preset abnormal frequency threshold.
Optionally, the fault data processing system is specifically configured to:
The information matching module 804 is configured to generate fault-tolerant voltage sag current according to the abnormal frequency threshold and the multiple target abnormal voltage sag currents;
The equipment model matching module 803 is used for matching corresponding equipment model information to be replaced in a preset device model database based on fault-tolerant voltage sag current.
Optionally, the fault data processing system is specifically configured to:
the sequencing module is used for sequencing the plurality of target abnormal voltage sag currents and generating an extraction sequence number according to the abnormal frequency threshold;
the information matching module 804 is configured to obtain a target self-driving current value of the eddy current self-driving voltage sag management device in the target abnormal section branch;
the information matching module 804, if the target self-driving current value is greater than the plurality of target abnormal voltage sag currents, filters out fault-tolerant voltage sag currents according to the serial numbers of the extraction sequences in the order from small to large;
And the information matching module 804 is configured to screen fault-tolerant voltage sag currents according to the extraction sequence numbers in a sequence from large to small if the target self-driving current value is smaller than the plurality of target abnormal voltage sag currents.
Optionally, the fault data processing system may be further configured to:
An information receiving module 801, configured to receive historical electricity consumption data of each segment branch, where the historical electricity consumption data includes a plurality of historical electricity consumption powers averaged in a preset unit time;
The information receiving module 801 is configured to receive estimated electric equipment construction data under a plurality of segment branches, where the estimated electric equipment construction data includes a type of equipment to be built of the estimated construction equipment and estimated electric power, and the type of equipment to be built includes a conventional electric type and a high-power electric type;
The information matching module 804 is configured to obtain a predicted self-driving current value of the target segment branch according to the target predicted power consumption equipment construction data and the target historical power consumption data of the target segment branch;
The equipment model matching module 803 is configured to match corresponding optimized equipment model information in the device model database according to the estimated self-driving current value.
Optionally, the fault data processing system is specifically configured to:
the data calculation module 805 is configured to generate estimated power consumption data according to the target estimated power consumption device construction data and the target historical power consumption data, where the estimated power consumption data includes a type of a device to be constructed and an estimated total power consumption;
the information matching module 804 matches corresponding estimated self-driving current values in the historical electricity consumption data corresponding to the plurality of segmented branches based on the estimated electricity consumption data.
Optionally, the fault data processing system is specifically configured to:
the information matching module 804 matches a corresponding estimated self-driving current value in the historical electricity consumption data based on the estimated total electricity consumption power if the type of the equipment to be built in the estimated electricity consumption data is a conventional electricity consumption type, or takes the preset self-driving current value as the estimated self-driving current value.
Optionally, the fault data processing system is specifically configured to:
The information matching module 804 is configured to match two adjacent historical power consumption data in the plurality of historical power consumption data based on the estimated total power consumption if the type of the equipment to be built in the estimated power consumption data is a high-power consumption type;
The data calculation module 805 is configured to obtain a self-driving current enhancement coefficient according to two adjacent historical electricity consumption data;
the data calculation module 805 is configured to calculate and generate a predicted self-driving current value according to the self-driving current enhancement coefficient and the predicted total power consumption.
The embodiment of the application provides an intelligent terminal. The intelligent terminal may vary considerably in configuration or performance and may include one or more central processors (e.g., one or more processors) and memory, one or more storage media (e.g., one or more mass storage devices) that store applications or data. The memory and storage medium may be transitory or persistent. The program stored on the storage medium may include one or more modules (not shown), each of which may include a series of instruction operations in the intelligent terminal.
The intelligent terminal may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, one or more keyboards, and/or one or more operating systems.
The intelligent terminal may include a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including processing of the intelligent terminal in the fault data processing method for the above-described eddy current self-driven voltage sag management device.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above-described embodiments may be implemented by hardware, or may be implemented by a program for instructing the relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read only memory or the like.
The above embodiments are not intended to limit the scope of the application, so that the equivalent changes of the structure, shape and principle of the application are covered by the scope of the application.
Although the invention is disclosed above, the scope of the invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications will fall within the scope of the invention.
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| LU508721A LU508721B1 (en) | 2024-08-13 | 2024-10-29 | Fault Data Processing Method and System for Eddy Current Self-Driven Voltage Sag Mitigation Device |
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| US12176717B2 (en) * | 2018-10-23 | 2024-12-24 | Mitsubishi Electric Corporation | Grid system, control device, control method for grid system, and power conversion device |
| CN111564896B (en) * | 2020-05-11 | 2023-03-28 | 国家电网有限公司 | Smooth switching and flexible exiting method and device for voltage sag control device |
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| US20240250528A1 (en) * | 2022-03-03 | 2024-07-25 | Sichuan University | Methods and systems for controlling multi-stage voltage sags |
| CN117517856A (en) * | 2023-10-20 | 2024-02-06 | 深圳供电局有限公司 | Voltage sag cause identification method, device, computer equipment and storage medium |
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