CN115436864A - Method, system and medium for diagnosing abnormal state of capacitive voltage transformer - Google Patents
Method, system and medium for diagnosing abnormal state of capacitive voltage transformer Download PDFInfo
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Abstract
The invention discloses a method, a system and a medium for diagnosing the abnormal state of a capacitor voltage transformer, wherein the method comprises the steps of obtaining the state data of a target capacitor voltage transformer and the same type of capacitor voltage transformers; and calculating a transverse fault detection index of the target capacitive voltage transformer, determining whether the target capacitive voltage transformer has a fault according to the transverse fault detection index, and if the target capacitive voltage transformer has the fault, longitudinally comparing the state data of the target capacitive voltage transformer in the time dimension to determine a fault type corresponding to the target capacitive voltage transformer. The method can solve the problem of real-time evaluation of the healthy running state of the capacitor voltage transformer, can realize the state diagnosis of the capacitor voltage transformer based on horizontal and longitudinal information fusion, effectively reduces the misdiagnosis probability of single-target monitoring, and has the advantages of high diagnosis accuracy and easy acquisition of fault information.
Description
Technical Field
The invention relates to a device state diagnosis technology, in particular to a method, a system and a medium for diagnosing an abnormal state of a capacitor voltage transformer.
Background
Compared with an electromagnetic voltage transformer, the capacitive voltage transformer has the advantages of high insulating strength, small volume, light weight, no ferromagnetic resonance generation and the like, is gradually replacing the electromagnetic voltage transformer, is widely applied to voltage measurement, power measurement, relay protection and carrier communication in a power system, and the measurement result of the capacitive voltage transformer is an important basis for normal work of secondary metering, relay protection, monitoring equipment and the like and is of great importance to safe temperature operation of a power grid. However, due to the influence of raw materials, manufacturing experience, system overvoltage and other factors, a capacitive voltage transformer may have a capacitive voltage divider fault, an intermediate transformer fault, a lightning arrester fault, a damper fault and the like in the operation process, so how to effectively monitor the capacitive voltage transformer and timely find and eliminate the operation fault of the capacitive voltage transformer has important significance for safe and reliable operation of a power grid.
Therefore, real-time monitoring of the capacitive voltage transformer is particularly important in maintaining temperature safe operation of a power grid, the healthy operation state of the capacitive voltage transformer is evaluated in real time, when the capacitive voltage transformer breaks down, the capacitive voltage transformer can be checked immediately, fault types can be judged accurately, the fault is alarmed, maintenance personnel can obtain fault information more quickly, and therefore the problem can be solved quickly. However, the traditional monitoring and diagnosing method for the capacitive voltage transformer has the problems of single criterion, inappropriate operation monitoring times, difficult acquisition of fault information and the like.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the invention aims to solve the problem of real-time evaluation of the healthy running state of the capacitive voltage transformer, can realize the state diagnosis of the capacitive voltage transformer based on horizontal and longitudinal information fusion, effectively reduces the misdiagnosis probability of single-target monitoring, and has the advantages of high diagnosis accuracy and easy acquisition of fault information.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for diagnosing the abnormal state of a capacitor voltage transformer comprises the following steps:
s101, acquiring state data of a target capacitive voltage transformer and capacitive voltage transformers of the same type;
s102, calculating a transverse fault detection index of a target capacitive voltage transformer according to state data of the target capacitive voltage transformer and capacitive voltage transformers of the same type, determining whether the target capacitive voltage transformer fails according to the transverse fault detection index, and skipping to the step S103 if the target capacitive voltage transformer fails;
s103, longitudinally comparing the state data of the target capacitor voltage transformer in the time dimension to determine the fault type corresponding to the target capacitor voltage transformer.
Optionally, step S101 includes, before step S101, a step of determining the same type of target capacitive voltage transformer: acquiring the number of the same-level capacitive voltage transformers in a single-phase line where the target capacitive voltage transformer is located, and if the number exceeds a set value, taking the same-level capacitive voltage transformers in the single-phase line where the target capacitive voltage transformers are located as the same-type capacitive voltage transformers of the target capacitive voltage transformers; and otherwise, taking the same-level capacitive voltage transformers in the three-phase line where the target capacitive voltage transformer is positioned as the same-type capacitive voltage transformers of the target capacitive voltage transformers.
Optionally, the status data in step S101 includes secondary voltage data of the capacitive voltage transformer collected at set interval time t; the step S102 of calculating the transverse fault detection index of the target capacitive voltage transformer includes:
s201, aiming at a target capacitor voltage transformer and capacitor voltage transformers of the same type thereof, respectively obtaining m secondary voltage data U sampled in a current time window T 1 ～U m Calculating centroid as secondary voltage data U cen,i ；
S202, according to the secondary voltage data U of the target capacitor voltage transformer and the same type of capacitor voltage transformers cen,i And calculating the voltage deviation delta U of the target capacitor voltage transformer cen,t ；
S203, according to the voltage deviation delta U cen,t Calculating the secondary voltage change rate delta U of the target capacitor voltage transformer t ％。
Optionally, the centroid is calculated as the secondary voltage data U in step S201 cen,i The functional expression of (a) is:
in the above formula, U cen,i Secondary voltage data, U, for any ith capacitive voltage transformer 1 ～U m Respectively obtaining m secondary voltage data sampled by the ith capacitance voltage transformer in the current time window T, wherein m is the number of the secondary voltage data sampled in the current time window T; voltage offset Δ U in step S202 cen,t The formula of the calculation function is:
ΔU cen,t ＝|U cen,t-1 -U cen,t |
in the above formula, U cen,t-1 When the value of the index t of the target capacitive voltage transformer is 0, the capacitive voltage transformer adjacent to the target capacitive voltage transformer is the nth capacitive voltage transformer, n is the total number of the target capacitive voltage transformer and the same type of capacitive voltage transformers, and U is the total number of the target capacitive voltage transformer and the same type of capacitive voltage transformers cen,t Secondary voltage data of the target capacitive voltage transformer; the secondary voltage change rate Δ U in step S203 t The% of the calculated function is expressed as:
in the above formula,. DELTA.U cen,t Is the voltage offset, U, of the target capacitive voltage transformer cen,t And the secondary voltage data of the target capacitor voltage transformer.
Optionally, the step S102 of determining whether the target capacitive voltage transformer fails according to the lateral fault detection indicator includes: judging the secondary voltage change rate delta U of the target capacitor voltage transformer t And whether the% is satisfied in a specified change rate interval, if so, determining that the target capacitive voltage transformer is not in fault, otherwise, determining that the target capacitive voltage transformer is in fault, wherein the specified change rate interval is as follows:
in the above formula, k is the capacitance voltage division ratio of the target capacitor voltage transformer in normal condition, k 1 The capacitance voltage division ratio k of the target capacitance voltage transformer when the upper capacitor is broken down by 2% 2 The capacitance voltage division ratio of the target capacitance voltage transformer when the lower capacitor is broken down by 2 percent is shown, and the capacitance voltage division ratio comprises the following components:
wherein, C 1 Upper section capacitance, C, of the target capacitive voltage transformer 2 Is the lower section capacitance of the target capacitance voltage transformer. Optionally, the performing the longitudinal comparison in step S103 to determine the fault type corresponding to the target capacitor voltage transformer includes:
s301, inputting secondary voltage data obtained by sampling a target capacitor voltage transformer in a specified time window into a trained machine learning model to obtain a predicted secondary voltage U at the current moment pd ；
S302, according to the delta U ps ＝|U pd -U ps I calculating the predicted secondary voltage U at the current moment pd Actual secondary voltage data U ps Voltage deviation Δ U therebetween ps And shifting the voltage by Δ U ps Divided by actual secondary voltage data U ps Obtaining the secondary voltage change rate delta U of the target capacitive voltage transformer at the current moment p ％；
S303, judging whether the current time and a plurality of continuous secondary voltage change rates of the target capacitor voltage transformer before the current time exceed a set threshold value, and if yes, judging that the fault type of the target capacitor voltage transformer is a capacitor breakdown type fault; otherwise, judging the fault type of the target capacitor voltage transformer to be aging or moisture fault.
Alternatively, the target power is determined in step S303After the fault type of the capacitor voltage transformer is a capacitor breakdown type fault, further determining the number h of the broken capacitors: judging the type of the secondary voltage data input into the machine learning model in step S301, if the secondary voltage data is phase voltage data, determining the type of the secondary voltage data according to the delta U p Determining the number h of the broken capacitors of the target capacitor voltage transformer by using% = (h-1) × 100%; if the secondary voltage data is line voltage data, determining the number h of the broken capacitors of the target capacitor voltage transformer according to the following formula:
wherein, delta U p % is the secondary voltage change rate of the target capacitor voltage transformer at the current moment.
Optionally, the state data in step S101 includes temperature rise data of the target capacitive voltage transformer; step S103 further includes determining that the electromagnetic unit of the target capacitive voltage transformer has a fault when the temperature rise data of the target capacitive voltage transformer exceeds a set value.
In addition, the invention also provides a diagnostic system for the abnormal state of the capacitor voltage transformer, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the diagnostic method for the abnormal state of the capacitor voltage transformer.
In addition, the present invention also provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and the computer program is programmed or configured by a microprocessor to execute the method for diagnosing the abnormal state of the capacitor voltage transformer.
Compared with the prior art, the invention mainly has the following advantages: acquiring state data of a target capacitive voltage transformer and capacitive voltage transformers of the same type; the method comprises the steps of calculating a transverse fault detection index of a target capacitive voltage transformer, determining whether the target capacitive voltage transformer has a fault according to the transverse fault detection index, and longitudinally comparing state data of the target capacitive voltage transformer in a time dimension to determine a fault type corresponding to the target capacitive voltage transformer if the target capacitive voltage transformer has the fault.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a capacitor voltage transformer according to an embodiment of the present invention.
Fig. 3 is a schematic flow chart of fault type determination according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a basic structure of a system according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the method for diagnosing an abnormal state of a capacitive voltage transformer in the present embodiment includes:
s101, acquiring state data of a target capacitive voltage transformer and capacitive voltage transformers of the same type;
s102, calculating a transverse fault detection index of a target capacitive voltage transformer according to state data of the target capacitive voltage transformer and capacitive voltage transformers of the same type, determining whether the target capacitive voltage transformer fails according to the transverse fault detection index, and skipping to the step S103 if the target capacitive voltage transformer fails;
s103, longitudinally comparing the state data of the target capacitor voltage transformer in the time dimension to determine the fault type corresponding to the target capacitor voltage transformer.
It should be noted that, in the present embodiment, attention is paid to processing in which the target capacitor voltage transformer fails, and therefore, if the target capacitor voltage transformer does not fail in step S102, a desired operation is selected or no operation is performed as necessary. The operations required for selection are generally direct selection jump step S101, delayed jump step S101 or direct exit according to actual needs. For example, when the method for diagnosing the abnormal state of the capacitive voltage transformer according to the present embodiment is applied to monitoring of the capacitive voltage transformer, if the steps S101 to S103 are executed at regular time, the skipping step S101 and the delay skipping step S101 are generally selected directly when no fault occurs. If the method for diagnosing the abnormal state of the capacitor voltage transformer is applied to the one-time detection of the capacitor voltage transformer, the capacitor voltage transformer can be directly withdrawn when no fault occurs. In addition, in the present embodiment, steps S101 to S103 describe the method for diagnosing the abnormal state of the capacitive voltage transformer in terms of a single target capacitive voltage transformer, and on the basis of the state data obtained in step S101, steps S102 and S103 may also be performed on any one of the target capacitive voltage transformer and the same type of capacitive voltage transformer to determine whether a fault occurs, and determine the type of the fault, where the difference only relates to traversal of multiple capacitive voltage transformers, but in the processing link of traversal, the steps S102 and S103 are still repeated.
In the method for diagnosing the abnormal state of the capacitive voltage transformer, the key for determining whether the target capacitive voltage transformer fails through the transverse fault detection index is the determination of the same type of capacitive voltage transformer, and in order to improve the accuracy for determining whether the target capacitive voltage transformer fails according to the transverse fault detection index, the step S101 of the embodiment includes the step of determining the same type of capacitive voltage transformer of the target capacitive voltage transformer: acquiring the number of the same-level capacitive voltage transformers in a single-phase line where the target capacitive voltage transformer is located, and if the number exceeds a set value, taking the same-level capacitive voltage transformers in the single-phase line where the target capacitive voltage transformers are located as the same-type capacitive voltage transformers of the target capacitive voltage transformers; and otherwise, taking the same-level capacitive voltage transformers in the three-phase line where the target capacitive voltage transformer is positioned as the same-type capacitive voltage transformers of the target capacitive voltage transformer.
Since steps S102 and S103 both require data at different time intervals, when the step S101 acquires status data of the target capacitive voltage transformer and the same type of capacitive voltage transformer, it actually includes acquiring status data of the target capacitive voltage transformer and the same type of capacitive voltage transformer at different time intervals. Collecting state data of a plurality of capacitive voltage transformer devices in a single-phase circuit of the same power grid structure at different time intervals; the abnormal state index range of each state data and the abnormal state index range of the abnormal state model and the abnormal state index range of the abnormal model at different time points can be compared for judging whether to carry out fault diagnosis; the method can be used for diagnosing different fault types by comparing each monitoring index with corresponding fault index combinations and index ranges under different fault types of the fault model; and comparing the various state data with various parameter indexes and index ranges in the fault parameter model to evaluate the fault degree.
The state data of the capacitor voltage transformer can be selected only according to actual needs. For example, as an optional implementation manner, the state data in step S101 in this embodiment includes secondary voltage data of the capacitive voltage transformer collected at set time intervals t; the step S102 of calculating the transverse fault detection index of the target capacitive voltage transformer includes:
s201, aiming at a target capacitor voltage transformer and capacitor voltage transformers of the same type thereof, respectively obtaining m secondary voltage data U sampled in a current time window T 1 ～U m Calculating centroid as secondary voltage data U cen,i ；
S202, according to the secondary voltage data U of the target capacitor voltage transformer and the same type of capacitor voltage transformers cen,i Calculating the voltage deviation delta U of the target capacitor voltage transformer cen,t ；
S203, according to the voltage deviation delta U cen,t Calculating the secondary voltage change rate delta U of the target capacitor voltage transformer t ％。
In this embodiment, the centroid is calculated in step S201 as the secondary voltage data U cen,i The functional expression of (a) is:
in the above formula, U cen,i Secondary voltage data, U, for any ith capacitive voltage transformer 1 ～U m M secondary voltage data sampled by the ith capacitance voltage transformer in the current time window T are respectively obtained, and m is the number of the secondary voltage data sampled in the current time window T.
In this embodiment, the voltage deviation Δ U in step S202 cen,t The formula of the calculation function is:
ΔU cen,t ＝|U cen,t-1 -U cen,t |
in the above formula, U cen,t-1 The secondary voltage data of the capacitor voltage transformer adjacent to the target capacitor voltage transformer is obtained, when the value of the index t of the target capacitor voltage transformer is 0, the capacitor voltage transformer adjacent to the target capacitor voltage transformer is the nth capacitor voltage transformer, n is the total number of the target capacitor voltage transformer and the same type of capacitor voltage transformers, and U is the total number of the target capacitor voltage transformer and the same type of capacitor voltage transformers cen,t And the secondary voltage data of the target capacitor voltage transformer. The total number of the target capacitor voltage transformer and the capacitor voltage transformers of the same type is n, so that the voltage offset of the n capacitor voltage transformers is as follows:
ΔU cen,1 ＝|U cen,2 -U cen,1 |
ΔU cen,2 ＝|U cen,3 -U cen,2 |
ΔU cen,3 ＝|U cen,4 -U cen,3 |
...
ΔU cen,n ＝|U cen,1 -U cen,n |
in the above formula,. DELTA.U cen,1 ～ΔU cen,n Respectively, the voltage offset, U, of the 1 st to nth capacitive voltage transformers cen,1 ～U cen,n Respectively 1 st to n th capacitance voltagesSecondary voltage data of the transformer.
In this embodiment, the secondary voltage change rate Δ U in step S203 t The% of the calculated function is expressed as:
in the above formula,. DELTA.U cen,t Is the voltage offset, U, of the target capacitive voltage transformer cen,t For the secondary voltage data of the target capacitive voltage transformer, the secondary voltage change rate of the n capacitive voltage transformers is:
...
in the above formula,. DELTA.U 1 ％～ΔU n % is the secondary voltage change rate, delta U, of the 1 st to the n th capacitance voltage mutual inductors respectively cen,1 ～ΔU cen,n Respectively, the voltage offset, U, of the 1 st to nth capacitive voltage transformers cen,1 ～U cen,n Respectively are the secondary voltage data of the 1 st to the nth capacitor voltage transformers.
In this embodiment, the step S102 of determining whether the target capacitive voltage transformer fails according to the transverse fault detection indicator includes: judging the secondary voltage change rate delta U of the target capacitor voltage transformer t Whether or not% is within a specified interval of rate of changeAnd if yes, judging that the target capacitor voltage transformer is not in fault, otherwise, judging that the target capacitor voltage transformer is in fault, wherein the appointed change rate interval can be appointed according to an empirical value.
As shown in fig. 2, one end of a capacitor voltage transformer is connected to the secondary voltage side, the other end is grounded through a carrier device, the capacitor voltage transformer is formed by connecting an upper capacitor and a lower capacitor in series, a capacitor C1 close to the secondary voltage side in fig. 2 is the upper capacitor, a capacitor C2 close to the carrier device side is the lower capacitor, a middle node UC1 between the upper capacitor and the upper capacitor is grounded to one terminal AT of the secondary side of a transformer T, the other terminal XT of the secondary side of the transformer T is grounded through a fuse F, and the fuse F is connected in parallel with an inductor L. U1 is the total voltage of the upper capacitor and the lower capacitor connected in series. As an optional implementation mode, in this embodiment, referring to the requirement that the initial value of capacitance is less than or equal to + -2% (warning value) in "Condition Overhaul test procedure for Power Transmission and transformation Equipment", let the upper capacitor be C 1 Lower segment of capacitor C 2 Then, it can be known that the capacitance voltage division ratio when the target capacitive voltage transformer is normal is:
when the upper capacitor breaks down by 2%, the voltage division ratio of the capacitor is as follows:
so that the upper threshold limit DeltaU of the rate of change of the secondary voltage a % is (this is the threshold value of the voltage unbalance rate calculated from the capacitance of the CVT, divided into an upper limit and a lower limit, by comparing the measured voltage data with the threshold value):
when the current capacitor breaks down by 2%, the voltage division ratio of the capacitor is as follows:
so that the lower threshold limit DeltaU of the rate of change of the secondary voltage b % is:
similarly, when the upper power saving capacity and the lower power saving capacity are reduced by 2%, the change rates of the secondary voltages are respectively shown as follows:
when the equipment normally operates, the secondary side voltage fluctuation range of the capacitor voltage transformer is within the upper limit and the lower limit of the set threshold value because of C 2 Far greater than C 1 Sequence relation Δ U a '％<ΔU b ％<0<ΔU a ％<ΔU b '% is evident, with phase voltage intervals ranging from:
the corresponding line voltage is represented by a voltage division ratio, and the change rate interval can be as follows:
in the above formula, k is the capacitance voltage division ratio of the target capacitor voltage transformer in normal condition, k 1 The capacitance of the target capacitance voltage transformer is divided when the upper capacitor is broken down by 2%Pressure ratio, k 2 The capacitance voltage division ratio of the target capacitance voltage transformer when the lower capacitor is broken down by 2 percent is shown, and the capacitance voltage division ratio comprises the following components:
wherein, C 1 Upper section capacitance, C, of the target capacitive voltage transformer 2 The lower section of the capacitor is the target capacitor of the capacitor voltage transformer.
Therefore, in order to improve the accuracy of fault determination, in this embodiment, the specified change rate interval for determining that the target capacitive voltage transformer has a fault is:
in step S103, a longitudinal comparison is performed, that is, a policy of finding information difference in history data of a single device is performed, specifically, the history data of the secondary voltage of a CVT device in a certain bus of a certain power station is compared with current data, so as to obtain the secondary voltage change information of the CVT device. As shown in fig. 3, the performing the longitudinal comparison in step S103 to determine the fault type corresponding to the target capacitive voltage transformer in the embodiment includes:
s301, inputting secondary voltage data obtained by sampling a target capacitor voltage transformer in a specified time window into a trained machine learning model to obtain a predicted secondary voltage U at the current moment pd ；
S302, according to the delta U ps ＝|U pd -U ps I calculate the predicted secondary voltage U at the current time pd Actual secondary voltage data U ps Voltage deviation Δ U therebetween ps And shift the voltage by Δ U ps Divided by actual secondary voltage data U ps Obtaining the secondary voltage change rate delta U of the target capacitor voltage transformer at the current moment p ％；
S303, judging whether the current time and a plurality of continuous secondary voltage change rates before the current time of the target capacitor voltage transformer exceed a set threshold value, and if yes, judging that the fault type of the target capacitor voltage transformer is a capacitor breakdown type fault; otherwise, judging the fault type of the target capacitor voltage transformer to be aging or moisture fault.
Because the CVT device in the circuit is more or less affected by the system, environment, and other factors, a certain secondary voltage fluctuation situation is generated, which inevitably generates interference to the fault determination, and is easy to cause erroneous determination, in step S301 of this embodiment, the secondary voltage data obtained by sampling the target capacitive voltage transformer in the specified time window is input into the trained machine learning model to obtain the predicted secondary voltage U at the current time pd To improve the accuracy of predicting the secondary voltage (ideal value). The machine learning model may adopt an existing machine learning model as needed, for example, in this embodiment, voltage prediction is specifically adopted by a Long Short Term Memory neural network (LSTM) prediction method, a sample to be measured is predicted by the prediction model, voltage data of a certain period of time is input into the model, predicted voltage data may be obtained, a secondary side voltage change rate is obtained by actual voltage data and the predicted voltage data, and the secondary side voltage change rate is compared with a threshold value to perform fault diagnosis.
Considering that the secondary voltage of the device fluctuates due to uncertain factors such as environment and power load, step S303 determines whether the current time and a plurality of consecutive (specifically, selectable according to actual needs) secondary voltage change rates before the current time of the target capacitive voltage transformer both exceed a set threshold, and if yes, determines that the fault type of the target capacitive voltage transformer is a capacitive breakdown type fault; otherwise, judging the fault type of the target capacitor voltage transformer to be aging or moisture fault.
Referring to fig. 3, after determining that the fault type of the target capacitor voltage transformer is a capacitor breakdown type fault in step S303, the present embodiment further includes determining the number h of broken capacitors. According to the change rate of the phase voltage and the line voltage, the capacitor breakdown number of the capacitor voltage transformer equipment can be determined (if a fault capacitor voltage transformer is detected on a certain bus, the breakdown capacitor number in the capacitor voltage transformer can be determined according to the formula). If the number h of the broken capacitors of the capacitor voltage transformer is as follows:
wherein n is 2 Is the number of cells in the initial lower capacitor, n' 2 The number of units after the breakdown of the lower capacitor (if the lower capacitor is not broken down, the number is unchanged), N is the total number of the initial capacitor units, and N' is the total number of the capacitor units after the breakdown. Therefore, the amount h of the broken-down capacitor can be reversely deduced according to the change rate of the phase voltage and the line voltage. Specifically, determining the number h of broken capacitances in this embodiment includes: judging the type of the secondary voltage data input into the machine learning model in step S301, and if the secondary voltage data is phase voltage data, determining the type according to the delta U p Determining the number h of the broken capacitors of the target capacitor voltage transformer by using% = (h-1) × 100%; if the secondary voltage data is line voltage data, determining the number h of the broken capacitors of the target capacitor voltage transformer according to the following formula:
wherein, delta U p % is the secondary voltage change rate of the target capacitor voltage transformer at the current moment.
Referring to fig. 3, as an alternative implementation manner, the status data in step S101 in this embodiment includes temperature rise data of the target capacitive voltage transformer; step S103 further includes determining that the electromagnetic unit of the target capacitive voltage transformer has a fault when the temperature rise data of the target capacitive voltage transformer exceeds a set value. Furthermore, other abnormal state monitoring can be carried out according to the requirement, including monitoring indexes such as capacitance values, equipment temperature, equipment dielectric loss, operation environment humidity and vibration intensity, and the like, and judgment can be carried out in cooperation with secondary side voltage change rate indexes, so that the misjudgment rate is reduced. The abnormal state index range is a numerical interval in which each comprehensive index is positioned when the equipment is in an abnormal state, and the threshold value of the relevant interval is set to refer to parameter indexes or experimental data. And when the monitoring index exceeds or is lower than the abnormal state index, judging the fault. The fault index combination and the index range are characteristics of different indexes when different faults occur or are located in a characteristic interval, and when the monitoring indexes meet the fault characteristics of the monitoring indexes and a certain index or several indexes are located in the fault characteristic interval, type diagnosis is carried out on the faults. Based on the fault index combination and the index range, the occurrence degree of the abnormal state index and different fault types are distinguished, the fault grade is divided, the monitoring index is compared with the fault grade index, the fault grade is judged, and therefore the priority of fault processing is divided.
In conclusion, the method can solve the technical problem of how to evaluate the healthy running state of the capacitive voltage transformer in real time, and the method adopts the capacitive voltage transformer state monitoring based on the transverse and longitudinal information fusion, so that the misdiagnosis probability of single-target monitoring is effectively reduced.
In addition, the invention also provides a diagnostic system for the abnormal state of the capacitor voltage transformer, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the diagnostic method for the abnormal state of the capacitor voltage transformer.
As shown in fig. 4, the abnormal state diagnostic system (monitoring diagnostic system) of the capacitive voltage transformer in the present embodiment includes:
index parameter acquisition layer: the system comprises a monitoring system, a cloud terminal and a monitoring system, wherein the monitoring system is used for collecting monitoring indexes of different monitoring points of a plurality of different capacitor voltage transformer devices in the same power grid structure, mainly comprises CVT (capacitor voltage transformer) secondary voltage data and CVT electromagnetic unit temperature data, and uploads the monitoring indexes to the cloud terminal;
index parameter analysis layer: comparing and analyzing the monitoring indexes collected by the collection layer by using a cloud platform, analyzing and judging whether CVT secondary voltage data and CVT electromagnetic unit temperature data are within a normal threshold range or not, wherein an analysis model comprises an abnormal state model, a fault model and a parameter model;
failure state assessment layer: whether the CVT equipment is in a capacitance unit fault or an electromagnetic unit fault is judged (if the CVT secondary voltage change exceeds a threshold range, the capacitance unit fault is judged), if the temperature of the electromagnetic unit is abnormal through infrared temperature measurement, the electromagnetic unit fault is judged), different fault positions are diagnosed in detail (the capacitance unit fault generally comprises capacitance breakdown, capacitance aging and the like, the electromagnetic unit fault generally comprises iron core short circuit, partial discharge abnormity, turn-to-turn short circuit and the like), and fault indexes and fault types are displayed and output to mobile communication equipment of maintenance personnel to obtain equipment information in real time.
Further, the system for diagnosing the abnormal state of the capacitive voltage transformer (monitoring and diagnosing system) in the embodiment is further connected with a terminal device, and the terminal device can be divided into five modules, namely a data signal acquisition module, a DSP data processing module, an MCU system control module, a data storage module and a human-computer interaction module according to functions, and the main functions of the system comprise:
the data signal acquisition module: the monitoring system is used for receiving the processed monitoring data indexes transmitted by the cloud.
The DSP data processing module: and the system is used for receiving the monitoring data transmitted by the data signal acquisition module and forming an instant visual fault view according to set criteria.
MCU system control module: for taking charge of data control and management.
A data storage module: for storing monitoring data.
A human-computer interaction module: the system is used for realizing interaction with a maintenance worker, and comprises a keyboard control, a display screen and the like.
The capacitive voltage transformer abnormal state diagnostic system in this embodiment can collect the detection index of each different monitoring point through intelligent sensor, through comparing abnormal state model, the fault model, the parameter model with each monitoring index, judge whether equipment is unusual, and have under the unusual condition, judge fault type and trouble size, the real-time supervision to capacitive voltage transformer has been realized, and with equipment real-time fault information transmission to high in the clouds, transmit to maintenance personal's mobile communication equipment by the high in the clouds again, make maintenance personal can be quick, accurate, timely obtain each item fault index information, and carry out corresponding maintenance measure to the trouble. Monitoring indexes, especially secondary side voltage data, of a plurality of different capacitor voltage transformer devices at the same level in a single-phase bus structure of a certain power station in different time periods can be collected, and the monitoring indexes are used for judging breakdown faults of a capacitor element; comparing each monitoring index with the abnormal state index range of the abnormal state model, and setting monitoring times for judging whether to carry out fault diagnosis; comparing each monitoring index with corresponding fault index combinations and index ranges under different fault types of the fault model, and diagnosing different fault types; comparing each monitoring index with each parameter index and index range in the fault parameter model, and judging the fault level and evaluating the fault degree; the monitoring index mainly refers to the difference of the secondary side voltage change rate between different target models and the fluctuation of the secondary side voltage change rate of different time periods of a single model, and the transverse and longitudinal method is used for judging the breakdown fault of the capacitor. The related parameter index setting of the capacitor voltage transformer equipment can be obtained by referring to related data or through historical experience.
In the implementation of the scheme, when the monitoring index exceeds or is lower than the abnormal state index, the fault is judged, wherein the most important index is the voltage amplitude index of the secondary side of the capacitor voltage transformer, and different line voltages and phase voltage grades have different judgment thresholds, so that the voltage amplitude of the voltage amplitude is different in a non-alarm interval. For example, by calculation, the voltage amplitude non-alarm region of 110kV and below is selected from-10% to +10% of the set value, the voltage amplitude non-alarm region of 220kV is selected from-5% to +5% of the set value, and the voltage amplitude non-alarm region of 500kV is selected from-3% to +3% of the set value. When setting the alarm limit value, attention should be paid to having enough sensitivity, namely the set limit value should be smaller than the calculated value, and the sensitivity is larger than 1; frequent alarm due to disturbance of the power grid voltage is avoided, namely the set limit value is not too low according to the stability level of the power grid voltage, and can be gradually corrected according to the false alarm times. And when the monitoring index exceeds the non-alarm interval, judging the abnormal state. In the implementation of the scheme, monitoring index data of different monitoring points of the capacitor voltage transformer equipment, such as monitoring indexes of secondary side voltage, equipment temperature, capacitance, equipment dielectric loss, operating environment humidity, vibration intensity and the like, are collected through different intelligent sensors. The intelligent sensor is mainly used for monitoring the secondary side voltage change of the capacitor voltage transformer and judging the breakdown fault of the equipment capacitor, and other types of faults such as the fault of an electromagnetic unit element, poor contact of a secondary terminal and the like can be jointly judged by combining an infrared temperature measurement method. In this scheme implementation, through comparing abnormal state model, fault model, parameter model and each item monitoring index, then accessible high in the clouds passes to maintenance personal with monitoring result and fault parameter result or passes to the cloud platform and supply maintenance personal to report to the police, and the cloud platform still can provide the basis for the function of the report form that realizes providing the monitoring data variation curve, and wherein the means of communication is modes such as mobile communication, thing networking, wireless network and optical fiber communication. The intelligent sensor uploads the monitoring indexes to the cloud platform, various index parameters are processed by utilizing the computing capacity of the cloud platform, and the abnormal state model, the fault model and the parameter model are compared to diagnose the fault. The system can be embodied in a terminal storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a mobile device-based system, processing module-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "terminal-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. For example, when the system is configured on a mobile communication device, a maintenance person can quickly obtain related monitoring index information of the capacitor voltage transformer in the power network through the mobile device, and if a fault occurs, the cloud platform can also transmit the fault type and the related fault index to the mobile device so that the maintenance person can accurately and quickly process the fault type and the related fault index. In summary, according to the system for monitoring and diagnosing the abnormal state and the horizontal and vertical information fusion of the capacitive voltage transformer, monitoring index information is uploaded to the cloud end through the intelligent sensor device, various indexes are processed through the cloud platform, and fault information is transmitted to the terminal device, namely, on the mobile communication device of maintenance personnel, the fault information acquisition time is shortened, and the maintenance efficiency is improved.
In addition, the present invention also provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and the computer program is programmed or configured by a microprocessor to execute the method for diagnosing the abnormal state of the capacitor voltage transformer.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (10)
1. A method for diagnosing the abnormal state of a capacitive voltage transformer is characterized by comprising the following steps:
s101, acquiring state data of a target capacitive voltage transformer and capacitive voltage transformers of the same type;
s102, calculating a transverse fault detection index of a target capacitive voltage transformer according to state data of the target capacitive voltage transformer and the same type of capacitive voltage transformers, determining whether the target capacitive voltage transformer is in fault according to the transverse fault detection index, and jumping to the step S103 if the target capacitive voltage transformer is in fault;
s103, longitudinally comparing the state data of the target capacitive voltage transformer in terms of the change of the state data of the target capacitive voltage transformer in the time dimension to determine the fault type corresponding to the target capacitive voltage transformer.
2. The method for diagnosing the abnormal state of the capacitor voltage transformer as claimed in claim 1, wherein step S101 is preceded by the step of determining the same type of capacitor voltage transformer as the target capacitor voltage transformer: acquiring the number of the same-level capacitive voltage transformers in a single-phase line where the target capacitive voltage transformers are located, and if the number exceeds a set value, taking the same-level capacitive voltage transformers in the single-phase line where the target capacitive voltage transformers are located as the same-type capacitive voltage transformers of the target capacitive voltage transformers; and otherwise, taking the same-level capacitive voltage transformers in the three-phase line where the target capacitive voltage transformer is positioned as the same-type capacitive voltage transformers of the target capacitive voltage transformer.
3. The abnormal state diagnostic method of the capacitor voltage transformer according to claim 1, wherein the state data in step S101 includes secondary voltage data of the capacitor voltage transformer collected at set intervals t; the step S102 of calculating the transverse fault detection index of the target capacitive voltage transformer includes:
s201, aiming at a target capacitor voltage transformer and capacitor voltage transformers of the same type thereof, respectively obtaining m secondary voltage data U sampled in a current time window T 1 ～U m Calculating centroid as secondary voltage data U cen,i ；
S202, according to the secondary voltage data U of the target capacitor voltage transformer and the same type of capacitor voltage transformers cen,i And calculating the voltage deviation delta U of the target capacitor voltage transformer cen,t ；
S203, according to the voltage deviation delta U cen,t Calculating the secondary voltage change rate delta U of the target capacitor voltage transformer t ％。
4. The abnormal state diagnosis method for capacitor voltage transformer according to claim 3, wherein the centroid is calculated as the secondary voltage data U in step S201 cen,i The functional expression of (a) is:
in the above formula, U cen,i For any ith capacitive voltage mutual inductanceSecondary voltage data of the device, U 1 ～U m Respectively obtaining m secondary voltage data sampled by the ith capacitance voltage transformer in the current time window T, wherein m is the number of the secondary voltage data sampled in the current time window T; voltage offset Δ U in step S202 cen,t The formula of the calculation function is:
ΔU cen,t ＝|U cen,t-1 -U cen,t |
in the above formula, U cen,t-1 When the value of the index t of the target capacitive voltage transformer is 0, the capacitive voltage transformer adjacent to the target capacitive voltage transformer is the nth capacitive voltage transformer, n is the total number of the target capacitive voltage transformer and the same type of capacitive voltage transformers, and U is the total number of the target capacitive voltage transformer and the same type of capacitive voltage transformers cen,t Secondary voltage data of the target capacitor voltage transformer are obtained; the secondary voltage change rate Δ U in step S203 t The% of the calculated function is expressed as:
in the above formula,. DELTA.U cen,t Is the voltage offset, U, of the target capacitive voltage transformer cen,t And the secondary voltage data of the target capacitor voltage transformer.
5. The method for diagnosing the abnormal state of the capacitor voltage transformer according to claim 3, wherein the step S102 of determining whether the target capacitor voltage transformer has a fault according to the transverse fault detection index comprises: judging the secondary voltage change rate delta U of the target capacitor voltage transformer t And whether the% is satisfied in a specified change rate interval, if so, determining that the target capacitive voltage transformer is not in fault, otherwise, determining that the target capacitive voltage transformer is in fault, wherein the specified change rate interval is as follows:
in the above formula, k is the capacitance voltage division ratio of the target capacitor voltage transformer in normal condition, k 1 The capacitance voltage division ratio k of the target capacitance voltage transformer when the upper capacitor is broken down by 2% 2 The capacitance voltage division ratio of the target capacitance voltage transformer when the lower capacitor is broken down by 2 percent is shown, and the capacitance voltage division ratio comprises the following components:
wherein, C 1 Upper section capacitance, C, of the target capacitive voltage transformer 2 The lower section of the capacitor is the target capacitor of the capacitor voltage transformer.
6. The method for diagnosing the abnormal state of the capacitor voltage transformer according to claim 1, wherein the step S103 of performing the longitudinal comparison to determine the fault type corresponding to the target capacitor voltage transformer comprises:
s301, inputting secondary voltage data obtained by sampling a target capacitor voltage transformer in a specified time window into a trained machine learning model to obtain a predicted secondary voltage U at the current moment pd ；
S302, according to the delta U ps ＝|U pd -U ps I calculate the predicted secondary voltage U at the current time pd Actual secondary voltage data U ps Voltage deviation Δ U therebetween ps And shifting the voltage by Δ U ps Divided by actual secondary voltage data U ps Obtaining the secondary voltage change rate delta U of the target capacitor voltage transformer at the current moment p ％；
S303, judging whether the current time and a plurality of continuous secondary voltage change rates of the target capacitor voltage transformer before the current time exceed a set threshold value, and if yes, judging that the fault type of the target capacitor voltage transformer is a capacitor breakdown type fault; otherwise, judging the fault type of the target capacitor voltage transformer to be aging or moisture fault.
7. The method as claimed in claim 6, wherein after determining in step S303 that the fault type of the target capacitive voltage transformer is a capacitance breakdown type fault, the method further comprises determining the number h of broken capacitances: judging the type of the secondary voltage data input into the machine learning model in step S301, if the secondary voltage data is phase voltage data, determining the type of the secondary voltage data according to the delta U p Determining the number h of the broken capacitors of the target capacitor voltage transformer by using% = (h-1) × 100%; if the secondary voltage data is line voltage data, determining the number h of the broken capacitors of the target capacitor voltage transformer according to the following formula:
wherein, delta U p % is the secondary voltage change rate of the target capacitor voltage transformer at the current moment.
8. The abnormal state diagnostic method of the capacitor voltage transformer according to claim 1, wherein the state data in step S101 includes temperature rise data of the target capacitor voltage transformer; step S103 further includes determining that the electromagnetic unit of the target capacitive voltage transformer has a fault when the temperature rise data of the target capacitive voltage transformer exceeds a set value.
9. A diagnostic system for abnormal conditions of a capacitive voltage transformer, comprising a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the diagnostic method for abnormal conditions of a capacitive voltage transformer according to any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored therein, wherein the computer program is used for being programmed or configured by a microprocessor to execute the method for diagnosing the abnormal state of the capacitive voltage transformer according to any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117849691A (en) * | 2024-03-08 | 2024-04-09 | 国网江西省电力有限公司电力科学研究院 | Multi-dimensional collaborative operation monitoring and early warning system and method for capacitive voltage transformer |
| CN118278624A (en) * | 2024-06-04 | 2024-07-02 | 中电装备山东电子有限公司 | Intelligent analysis system for electric power monitoring data based on mutual inductor |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117849691A (en) * | 2024-03-08 | 2024-04-09 | 国网江西省电力有限公司电力科学研究院 | Multi-dimensional collaborative operation monitoring and early warning system and method for capacitive voltage transformer |
| CN117849691B (en) * | 2024-03-08 | 2024-05-14 | 国网江西省电力有限公司电力科学研究院 | Multi-dimensional collaborative operation monitoring and early warning system and method for capacitive voltage transformer |
| CN118278624A (en) * | 2024-06-04 | 2024-07-02 | 中电装备山东电子有限公司 | Intelligent analysis system for electric power monitoring data based on mutual inductor |
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