CN113238890B - Current sampling loop abnormity diagnosis method and device based on dynamic record data - Google Patents
Current sampling loop abnormity diagnosis method and device based on dynamic record data Download PDFInfo
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Abstract
The invention discloses a current sampling loop abnormity diagnosis method and device based on dynamic record data, wherein the method comprises the following steps: responding to dynamic recording data acquired under the same power grid disturbance triggering condition, and accurately aligning the dynamic recording data; responding to the acquired dynamic recording data of a certain branch in the aligned dynamic recording data, and performing homologous detection on the dynamic recording data of the certain branch based on the channel identification similarity; judging whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection; and if the dynamic recording data of one branch is abnormal, performing differential flow detection on the dynamic recording data comprising the dynamic recording data of one branch.
Description
Technical Field
The invention belongs to the technical field of current sampling loop abnormity diagnosis, and particularly relates to a current sampling loop abnormity diagnosis method and device based on dynamic record data.
Background
With the continuous expansion of the scale of a power grid and the continuous promotion of the construction of an unattended substation, especially when a power failure fault occurs in a power system, a large amount of multi-source, heterogeneous and dispersive heterogeneous data are delivered, a person in charge and monitoring is difficult to judge the fault property and take effective measures according to experience and intuition, and the traditional data application has low intelligent analysis degree and prominent bottleneck. When the power grid is in a high-quality new normal state, the intelligent application requirement of big data is urgent, and the improvement of the intelligent level is urgently needed particularly in the aspects of system linkage, prospective precontrol, auxiliary decision and the like.
The electric power system is used as a huge and highly complex dynamic system, the fault process is generally divided into a front part, a middle part and a rear part, the duration time in the process is usually short, the fault is isolated by a protection and safety automatic device, and power grid personnel mainly carry out work around the front part and the rear part. The main and sub stations provided by power grid dispatching and provided with protection information, fault recording information and the like always undertake the rapid auxiliary diagnosis work after the power grid fault occurs, the rapid and accurate diagnosis conclusion has great significance for reducing the electric energy interruption time and providing power supply reliability, and the prevention before the fault occurs depends on field inspection and patrol. On one hand, the method is limited by the steady-state environment of a daily power grid, on the other hand, the method is limited by on-site troubleshooting means and conditions, equipment hidden dangers (belonging to diagnosis before failure) and particularly loop hidden dangers are often difficult to expose and discover, and the urgency and importance of safety early warning work before failure are further illustrated by the fact that tripping events of the power grid caused by secondary equipment hidden dangers account for more than 60% of statistics. Therefore, the large-scale advanced diagnosis of the power grid fault is urgently needed.
Disclosure of Invention
The present invention provides a method and an apparatus for diagnosing an abnormality of a current sampling loop based on dynamic recording data, which are used for solving at least one of the above technical problems.
In a first aspect, the present invention provides a current sampling loop abnormality diagnosis method based on dynamic record data, which is used for a conventional substation where there is a unique current loop abnormality point and a dynamic record file is not missing, and includes: responding to dynamic recording data acquired under the same power grid disturbance triggering condition, and accurately aligning the dynamic recording data; responding to the obtained certain branch dynamic record data in the aligned dynamic record data, and performing homologous detection on the certain branch dynamic record data based on the channel identification similarity, wherein the channel identification similarity obtaining step comprises: reading the names of all sampling channels of the dynamic recording data, and confirming the channel phase according to the channel phase identification of the wave recording channel; extracting all analog quantity channel identifiers of phase A, phase B or phase C, identifying key bytes of a line in the channel identifiers, and extracting and storing character strings in front of the key bytes of the line at one time; calculating a correction distance according to the minimum operation times of converting one character string M into another character string N until the edited character strings are completely the same, wherein the operation comprises insertion, deletion and replacement; sequentially calculating the correction distances of any two channel identifier character strings according to the sequence of the character strings from left to right, wherein the correction distances are the channel identifier similarity; judging whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection; and if the dynamic recording data of one branch is abnormal, performing differential flow detection on the dynamic recording data comprising the dynamic recording data of one branch.
In a second aspect, the present invention provides a current sampling loop abnormality diagnosis apparatus based on dynamic record data, which is used for a conventional substation where there is a unique current loop abnormality point and a dynamic record file is not missing, and includes: the alignment module is configured to respond to dynamic recording data acquired under the same power grid disturbance triggering condition and accurately align the dynamic recording data; the homologous detection module is configured to perform homologous detection on a certain branch dynamic record data based on a channel identifier similarity in response to obtaining the certain branch dynamic record data in the aligned dynamic record data, wherein the channel identifier similarity obtaining step includes: reading the names of all sampling channels of the dynamic recording data, and confirming the channel phase according to the channel phase identification of the wave recording channel; extracting all analog quantity channel identifiers of phase A, phase B or phase C, identifying key bytes of a line in the channel identifiers, and extracting and storing character strings in front of the key bytes of the line at one time; calculating a correction distance according to the minimum operation times of converting one character string M into another character string N until the edited character strings are completely the same, wherein the operation comprises insertion, deletion and replacement; sequentially calculating the correction distances of any two channel identifier character strings according to the sequence of the character strings from left to right, wherein the correction distances are the channel identifier similarity; the judging module is configured to judge whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection; and the difference stream detection module is configured to perform difference stream detection on the dynamic record data including the certain branch dynamic record data if the certain branch dynamic record data is abnormal.
In a third aspect, an electronic device is provided, comprising: the system comprises at least one processor and a memory which is connected with the at least one processor in a communication mode, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor so as to enable the at least one processor to execute the steps of the current sampling loop abnormity diagnosis method based on the dynamic record data according to any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the steps of the current sampling loop abnormality diagnosis method based on dynamically recorded data according to any one of the embodiments of the present invention.
The current sampling loop abnormity diagnosis method and device based on dynamic record data can meet the one-click type and full-automatic loop diagnosis early warning under the condition of large-scale and mass recording data files, breaks through the function limitation of the existing monitoring system, and realizes 24-hour all-weather uninterrupted equipment deep hidden danger monitoring with wider coverage abnormity types, more sensitive response time and no need of manual intervention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a current sampling loop abnormality diagnosis method based on dynamic recording data according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for comparing similarity of identifiers of homologous channels of dynamic recording files of a conventional substation according to an embodiment of the present invention;
fig. 3 is a block diagram of a current sampling loop abnormality diagnosis apparatus based on dynamic recording data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flow chart of a current sampling loop abnormality diagnosis method based on dynamic recorded data according to the present application is shown.
As shown in fig. 1, in S101, in response to dynamic recording data obtained under the same grid disturbance trigger condition, the dynamic recording data is accurately aligned, where the specific step of determining whether the dynamic recording data is a recording file triggered by the same grid disturbance includes: calculating the standard time of each recording file based on the self time of the corresponding device in the automatic calling dynamic recording data list; recording the real time of the dynamic recording data according to the time stamp of the dynamic recording data, and subtracting the standard time from the real time to obtain a time difference value corresponding to the dynamic recording data; and if the time difference is not greater than the preset threshold, dynamically recording data under the same power grid disturbance trigger.
In this embodiment, dynamic recording data is acquired under the same grid disturbance triggering condition, where the dynamic recording data includes an a/B set of line protection recording files of all branches of a conventional station bus, a line opposite side a/B set of protection recording files, an a/B set of main transformer protection recording files, an a/B set of bus protection recording files, and a dynamic recording device recording file, and after the dynamic recording data is acquired, the dynamic recording data is accurately aligned based on an abrupt change adaptive discrimination method.
In S102, in response to acquiring a certain branch dynamic record data of the aligned dynamic record data, performing homologous detection on the certain branch dynamic record data based on a channel identifier similarity, where the acquiring step of the channel identifier similarity includes: reading the names of all sampling channels of the dynamic recording data, and confirming the channel phase according to the channel phase identification of the wave recording channel; extracting all analog quantity channel identifiers of phase A, phase B or phase C, identifying key bytes of a line in the channel identifiers, and extracting and storing character strings in front of the key bytes of the line at one time; calculating a correction distance according to the minimum operation times of converting one character string M into another character string N until the edited character strings are completely the same, wherein the operation comprises insertion, deletion and replacement; and sequentially calculating the correction distances of any two channel identifier character strings according to the sequence of the character strings from left to right, wherein the correction distances are the channel identifier similarity.
In this embodiment, after a certain branch of the aligned dynamic recording data is obtained, the homologous detection is performed on the certain branch of the dynamic recording data based on the channel identifier similarity, so that the problem of unified management of the bottom-layer recording data can be greatly solved, the phenomenon of matching errors caused by irregular and non-unified homologous channel naming modes is reduced, and the method has universality and generalizability in the field of dynamic recording of power systems.
In S103, it is determined whether the certain branch dynamic record data is abnormal based on the result of the homology detection.
In S104, if the certain branch dynamic recording data is abnormal, the difference stream detection is performed on the dynamic recording data including the certain branch dynamic recording data.
In the method of the embodiment, firstly, after a system working power supply is started, the system regularly scans the updating condition of a built-in information table/directory structure body of a database, and a data source is taken from each stage of a scheduling fault recording master station and a protection information system; then, after obtaining the corresponding protection wave recording and wave recording device files, the unified power grid disturbance triggers to obtain the dynamic recording data of the protection and wave recording devices; then, compensating the combination time difference of the wave recording files based on the time compensation table to judge whether the starting scanning conditions of the same power grid disturbance are met; then, realizing accurate alignment of the data of the required dynamic recording file based on a mutation variable self-adaptive judging method; according to the voltage class classification, carrying out accurate comparison and calculation on the data of the alternating current sampling loop by adopting homologous detection as a main criterion and adopting differential current detection as an auxiliary criterion; and finally, automatically outputting a diagnosis report time, an abnormal interval name, an abnormal phase name, an abnormal wave recording graph and the like according to the current abnormality diagnosis conclusion, and displaying in a scheduling management information large area.
In some optional embodiments, the specific step of the differential stream detection includes: reading fault phase and non-fault phase current recording channel data of a recording file, extracting fundamental wave components of each recording channel by adopting a conventional Fourier algorithm to be used as a difference current detection reference waveform, and calculating a cycle backwards in a data window; calculating a full-wave effective value by adopting a closed loop current vector sum, wherein a detection threshold of the current effective value is 0.01In, and calculating a 10000Hz recording time period of a time coverage recording file; judging whether the full-wave effective value reaches a preset alarm threshold value and whether the continuous accumulated time exceeds the preset time; and if the effective value of the full wave reaches a preset alarm threshold value and the continuous accumulated time exceeds the preset time, sending an abnormal alarm signal.
Specifically, the differential flow detection method comprises four types of lines, buses, main transformers and dynamic records, and specifically comprises the following steps:
1) line differential flow detection method
The current difference is calculated by adopting a percentage algorithm dif = (IM + IN-IC)/max [ (IM-IMC), (IN-INC)]Wherein IMC is the measured capacitance current of the normal operation of the local side of the circuit, INC is the measured capacitance current of the normal operation of the opposite side of the circuit, IM is the current data of the local side of the circuit, IN is the current data of the opposite side of the circuit, IC is the measured capacitance current of the circuit IN the normal operation, the uncompensated differential current is obtained IN the normal operation or is obtained according to a formula
In the method, wherein,
representing the capacitance current of each phase of the line to ground;
representing the voltage of the phase on the M side of the line,
representing zero sequence voltage on the M side of the line;
a positive sequence capacitive reactance value representing the full length of the line;
zero sequence capacitive reactance value representing the whole length of the line;
representing the N-side phase voltage of the line;
representing the zero sequence voltage on the N side of the line.
2) Bus differential flow detection method
The current difference calculation adopts a percentage algorithm dif = (ID1-ID2)/max (ID1, ID2), wherein ID1=
Wherein, in the step (A),
for all branch currents on the bus of the station, ID1 is a first set of bus differential protection differential flows, ID2 is a second set of bus differential protection differential flows, and the algorithm is identical to ID 1.
3) Main transformer differential flow detection method
The current difference calculation adopts a percentage algorithm dif = (ID1-ID2)/max (ID1, ID2), wherein
，
In the formula (I), the compound is shown in the specification,
representing the phase current of the high-voltage side of the main transformer;
representing the rated voltage of the high-voltage side of the main transformer,
representing the transformation ratio of a current transformer at the high-voltage side of a main transformer;
representing the medium voltage side of the main transformerPhase current;
representing the rated voltage of the medium voltage side of the main transformer;
representing the transformation ratio of a current transformer at the medium-voltage side of a main transformer;
representing the phase current of the low-voltage side of the main transformer;
representing the rated voltage of the low-voltage side of the main transformer;
representing the transformation ratio of a current transformer at the low-voltage side of the main transformer, the invention uniformly takes differential TA wiring at each side of the main transformer as a Y/Y type, takes the low-voltage side d as a reference, performs phase shift and multiplication of balance coefficients at the high-medium-voltage side by software for example, and analyzes, wherein ID1 is a first main transformer protection differential flow, ID2 is a second main transformer protection differential flow, and the algorithm is identical to ID 1.
4) Dynamic recording difference stream detection method
Calculating a full-wave effective value by adopting a percentage algorithm, wherein the expression for calculating the full-wave effective value is as follows:
in the formula (I), the compound is shown in the specification,
all branch currents on the bus or main transformer of the station,
is the effective value of the full wave;
referring to fig. 2, a flowchart of a method for comparing similarity of homologous channel identifiers of dynamic record files of a conventional substation according to an embodiment of the present application is shown.
As shown in fig. 2, step one: identifying channel information;
the channel identifiers comprise two keywords of '220' and 'line', and the channel identifiers are uniformly identified as A.
Step two: and calculating the correction distance, wherein the correction distance is calculated by the least operation times of converting one character string M into another character string N, and the operation comprises 3 methods of inserting, deleting and replacing, wherein the insertion of 1 correction distance corresponds to 1 point, the deletion of 1 correction distance corresponds to 2 points, and the replacement of 1 correction distance corresponds to 3 points until the edited character strings are completely the same.
Step three: comparing the similarity;
and calculating a column according to the corrected distance in the second step, wherein the channel with the minimum similarity score is the homologous channel.
Referring to fig. 3, a block diagram of a current sampling loop abnormality diagnosis apparatus based on dynamic recording data according to the present application is shown.
As shown in fig. 3, the current sampling loop abnormality diagnosing apparatus 200 includes an alignment module 210, a homologous detection module 220, a determination module 230, and a difference current detection module 240.
The alignment module 210 is configured to respond to dynamic recording data acquired under the same grid disturbance triggering condition, and perform accurate alignment on the dynamic recording data; the homology detection module 220 is configured to perform, in response to obtaining a certain branch dynamic record data of the aligned dynamic record data, a homology detection on the certain branch dynamic record data based on a channel identifier similarity, where the obtaining of the channel identifier similarity includes: reading the names of all sampling channels of the dynamic recording data, and confirming the channel phase according to the channel phase identification of the wave recording channel; extracting all analog quantity channel identifiers of phase A, phase B or phase C, identifying key bytes of a line in the channel identifiers, and extracting and storing character strings in front of the key bytes of the line at one time; calculating a correction distance according to the minimum operation times of converting one character string M into another character string N until the edited character strings are completely the same, wherein the operation comprises insertion, deletion and replacement; sequentially calculating the correction distances of any two channel identifier character strings according to the sequence of the character strings from left to right, wherein the correction distances are the channel identifier similarity; a determining module 230 configured to determine whether the dynamic record data of the certain branch is abnormal based on the result of the homologous detection; a difference stream detection module 240 configured to perform difference stream detection on the dynamic record data including the certain branch dynamic record data if the certain branch dynamic record data is abnormal.
It should be understood that the modules depicted in fig. 3 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 3, and are not described again here.
In other embodiments, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions may perform the current sampling loop abnormality diagnosis method based on dynamic log data in any of the above method embodiments;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
responding to dynamic recording data acquired under the same power grid disturbance triggering condition, and accurately aligning the dynamic recording data;
responding to the acquired dynamic recording data of a certain branch in the aligned dynamic recording data, and performing homologous detection on the dynamic recording data of the certain branch based on the channel identification similarity;
judging whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection;
and if the dynamic recording data of one branch is abnormal, performing differential flow detection on the dynamic recording data comprising the dynamic recording data of one branch.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the current sampling loop abnormality diagnostic apparatus based on the dynamic recording data, and the like. Further, the computer-readable storage medium may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely located from the processor, and the remote memory may be connected to the current sampling loop abnormality diagnostic device based on the dynamically recorded data via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device includes: a processor 310 and a memory 320, and fig. 4 illustrates the processor 310 as an example. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 4. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running nonvolatile software programs, instructions and modules stored in the memory 320, namely, implementing the current sampling loop abnormality diagnosis method based on dynamic log data of the above method embodiment. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the current sampling loop abnormality diagnosis device based on the dynamic recording data. The output device 340 may include a display device such as a display screen.
The device can execute the method provided by the embodiment of the invention and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to a current sampling loop abnormality diagnosis device based on dynamic recording data, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
responding to dynamic recording data acquired under the same power grid disturbance triggering condition, and accurately aligning the dynamic recording data;
responding to the acquired dynamic recording data of a certain branch in the aligned dynamic recording data, and performing homologous detection on the dynamic recording data of the certain branch based on the channel identification similarity;
judging whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection;
and if the dynamic recording data of one branch is abnormal, performing differential flow detection on the dynamic recording data comprising the dynamic recording data of one branch.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments. Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A current sampling loop abnormity diagnosis method based on dynamic record data is used for a conventional transformer substation with a unique current loop abnormity point and a dynamic record file which is not missing, and is characterized by comprising the following steps:
responding to dynamic recording data acquired under the same power grid disturbance triggering condition, and accurately aligning the dynamic recording data, wherein the specific step of judging whether the dynamic recording data are recording files triggered by the same power grid disturbance comprises the following steps:
calculating the standard time of each recording file based on the self time of the corresponding device in the automatic calling dynamic recording data list;
recording the real time of the dynamic recording data according to the time stamp of the dynamic recording data, and subtracting the standard time from the real time to obtain a time difference value corresponding to the dynamic recording data;
if the time difference is not greater than the preset threshold, dynamically recording data under the same power grid disturbance trigger;
responding to the obtained certain branch dynamic record data in the aligned dynamic record data, and performing homologous detection on the certain branch dynamic record data based on the channel identification similarity, wherein the channel identification similarity obtaining step comprises:
reading the names of all sampling channels of the dynamic recording data, and confirming the channel phase according to the channel phase identification of the wave recording channel;
extracting all analog quantity channel identifiers of phase A, phase B or phase C, identifying key bytes of a line in the channel identifiers, and extracting and storing character strings in front of the key bytes of the line at one time;
calculating a correction distance according to the minimum operation times of converting one character string M into another character string N until the edited character strings are completely the same, wherein the operation comprises insertion, deletion and replacement;
sequentially calculating the correction distances of any two channel identifier character strings according to the sequence of the character strings from left to right, wherein the correction distances are the channel identifier similarity, and the calculation of the correction distances specifically comprises the following steps: calculating the correction distance by the least operation times of converting one character string M into another character string N, wherein the operation comprises 3 methods of inserting, deleting and replacing, wherein the distance is 1 point by 1 time of inserting, the distance is 2 points by 1 time of deleting, and the distance is 3 points by 1 time of replacing until the edited character strings are completely the same;
judging whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection;
and if the dynamic recording data of one branch is abnormal, performing differential flow detection on the dynamic recording data comprising the dynamic recording data of one branch.
2. The method for diagnosing the abnormality of the current sampling loop based on the dynamic record data as claimed in claim 1, wherein the dynamic record data includes a/B set of line protection recording files for all branches of the bus of the conventional station, a/B set of protection recording files for the opposite side of the line, a/B set of main transformer protection recording files, a/B set of bus protection recording files and dynamic recording device recording files.
3. The method for diagnosing the abnormality of the current sampling loop based on the dynamic record data as claimed in claim 1, wherein the specific steps of the differential flow detection include:
reading fault phase and non-fault phase current recording channel data of a recording file, extracting fundamental wave components of each recording channel by adopting a conventional Fourier algorithm to be used as a difference current detection reference waveform, and calculating a cycle backwards in a data window;
calculating a full-wave effective value by adopting a closed loop current vector sum, wherein a detection threshold of the current effective value is 0.01In, and calculating a 10000Hz recording time period of a time coverage recording file;
judging whether the full-wave effective value reaches a preset alarm threshold value and whether the continuous accumulated time exceeds the preset time;
and if the effective value of the full wave reaches a preset alarm threshold value and the continuous accumulated time exceeds the preset time, sending an abnormal alarm signal.
4. The method as claimed in claim 1, wherein the differential flow detection comprises line differential flow detection, bus differential flow detection, main transformer differential flow detection and dynamic recording differential flow detection.
5. The method for diagnosing the abnormality of the current sampling loop based on the dynamic recording data as claimed in claim 4, wherein the dynamic recording differential flow detection specifically comprises:
calculating a full-wave effective value by adopting a percentage algorithm, wherein the expression for calculating the full-wave effective value is as follows:
in the formula (I), the compound is shown in the specification,
all branch currents on a bus or a main transformer of the station are dif, and the dif is a full-wave effective value.
6. The method for diagnosing the abnormality of the current sampling loop based on the dynamic recording data as claimed in claim 4, wherein the identification of the interval of the single element of the power grid in the dynamic recording differential current detection specifically comprises the following steps:
reading the naming of each sampling channel of the wave recording file, and confirming the accurate phase according to the channel phase identification of the wave recording channel;
extracting channel information, wherein the channel information comprises an A-phase analog quantity channel identifier, a channel unit, a channel current transformer transformation ratio primary coefficient and a channel current transformer transformation ratio secondary coefficient;
judging whether the bus element is a line current interval of the bus element according to the principle that the channel identifier contains '220' and does not contain 'analog quantity', the channel unit is equal to 'A', and the primary coefficient of the transformation ratio of the channel current transformer is divided by the secondary coefficient of the transformation ratio of the channel current transformer and is less than 5000;
judging whether the bus element is a main transformer current interval of the bus element or not according to the principle that the channel identifier contains a main transformer and does not contain an analog quantity, the channel unit is equal to A, and the primary coefficient of the transformation ratio of the channel current transformer is divided by the secondary coefficient of the transformation ratio of the channel current transformer and is less than 5000;
according to the principle that the channel identifier contains a main transformer and does not contain an analog quantity, and the channel unit is equal to A, determining the current interval of three sides of each main transformer element; and judging whether the main transformer intervals are the same according to the principle that the channel identifiers simultaneously comprise ' 1 ', one ', 2 ', two ', 3 ' or three '.
7. A current sampling loop abnormity diagnosis device based on dynamic record data is used for a conventional transformer substation with a unique current loop abnormity point and a dynamic record file which is not missing, and is characterized by comprising the following components:
the alignment module is configured to respond to dynamic recording data acquired under the same power grid disturbance triggering condition and accurately align the dynamic recording data;
the homologous detection module is configured to perform homologous detection on a certain branch dynamic record data based on a channel identifier similarity in response to obtaining the certain branch dynamic record data in the aligned dynamic record data, wherein the channel identifier similarity obtaining step includes:
reading the names of all sampling channels of the dynamic recording data, and confirming the channel phase according to the channel phase identification of the wave recording channel;
extracting all analog quantity channel identifiers of phase A, phase B or phase C, identifying key bytes of a line in the channel identifiers, and extracting and storing character strings in front of the key bytes of the line at one time;
calculating a correction distance according to the minimum operation times of converting one character string M into another character string N until the edited character strings are completely the same, wherein the operation comprises insertion, deletion and replacement;
sequentially calculating the correction distances of any two channel identifier character strings according to the sequence of the character strings from left to right, wherein the correction distances are the channel identifier similarity, and the calculation of the correction distances specifically comprises the following steps: calculating the correction distance by the least operation times of converting one character string M into another character string N, wherein the operation comprises 3 methods of inserting, deleting and replacing, wherein the distance is 1 point by 1 time of inserting, the distance is 2 points by 1 time of deleting, and the distance is 3 points by 1 time of replacing until the edited character strings are completely the same;
the judging module is configured to judge whether the dynamic record data of a certain branch is abnormal or not based on the result of the homologous detection;
and the difference stream detection module is configured to perform difference stream detection on the dynamic record data including the certain branch dynamic record data if the certain branch dynamic record data is abnormal.
8. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 6.
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