CN116125361B - Voltage transformer error evaluation method, system, electronic equipment and storage medium - Google Patents
Voltage transformer error evaluation method, system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a voltage transformer error evaluation method, which comprises the following steps: constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation; preprocessing the multi-feature historical data set, and constructing an offline modeling data set based on a preprocessing result; establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model; constructing a multi-feature real-time data set based on the secondary output amplitude data and the phase data of the voltage transformer to be tested, which are acquired in real time; constructing an online monitoring data set based on the light load period amplitude of the multi-feature real-time data set; and performing fault early warning based on the Q statistics of the online monitoring dataset. The invention is based on the same-group three-phase relationship and the high-low voltage same-phase relationship, can eliminate the influence of frequent switching of a large load on three-phase unbalance when an industrial user uses electricity, and remarkably improves the online evaluation accuracy of the abnormal transformer with metering error in the load transformer substation of the load of the industrial user.
Description
Technical Field
The invention relates to the technical field of electric power metering, in particular to a voltage transformer error assessment method, a system, electronic equipment and a storage medium.
Background
The Voltage Transformer (VT) is metering equipment for converting high voltage into low voltage, is the only voltage measurement data source for accurate trade settlement, fair and fair trade and economic and technical index assessment among power generation companies, power grid companies and power users, and the fair and fair of the electric energy trade depends on the metering accuracy of the voltage transformer.
However, after the voltage transformer is in service for a long time, the metering performance of the voltage transformer is easily affected by multiple factors to deteriorate or even be out of alignment, and the out of alignment voltage transformer can cause deviation in electric energy calculation so as to influence fairness and fairness of huge electric energy trade.
Currently, voltage transformers are tested once every 4 years by adopting a power failure test method according to the test regulations of JJG 1021-2007 electric transformer and JJG 314-2010 voltage transformer for measurement. However, voltage transformers are large-scale and still increase at a rate of 5% per year, and current blackout verification methods face multiple elucidation difficulties: 1. the power failure verification and on-line operation of the equipment are different in time measuring state, the power failure verification result cannot fully reflect the accuracy of on-line operation, and misalignment of a voltage transformer with normal power failure verification can occur during network access operation; 2. the current power grid has a few power outage windows and short time, and frequent power outage also can seriously influence the power supply reliability, so that a large number of voltage transformers are difficult to make a power outage verification plan; 3. the equipment used in the power outage verification site is large in variety, heavy and low in operation and maintenance efficiency.
Currently, a voltage transformer error evaluation method based on information physical fusion is widely focused on the advantage of strong engineering applicability. According to the method, a voltage transformer group with an electric physical connection existing in the same transformer substation is taken as an evaluation object, information contained in real-time large-scale data of secondary measurement voltage output by the voltage transformer group is cooperatively analyzed through a multivariate statistical method, the measurement data are converted into monitoring statistics, real-time states of a tracking constraint relation are mined, and online evaluation of individual operation error changes of the voltage transformers is achieved through an information physical fusion method. However, the three-phase voltage balance is greatly influenced by load factors, and the method is only suitable for high-voltage substations of resident users with relatively stable loads, and the problem that evaluation deviation is remarkably increased occurs in the substations facing to loads of industrial users such as steel plants, smelting plants and the like, so that the problem of how to further improve the fault early warning capability of a voltage transformer with metering errors in the load substations of the industrial users is to be solved.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a voltage transformer error evaluation method, a system, electronic equipment and a storage medium, which are used for solving the problem of how to further improve the fault early warning capability of the voltage transformer with metering errors in a load transformer substation for bearing industrial users.
According to a first aspect of the present invention, there is provided a voltage transformer error evaluation method, comprising:
constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation;
preprocessing the multi-feature historical data set based on a preset preprocessing flow, and constructing an offline modeling data set based on the obtained light load period historical amplitude data;
establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model;
based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference;
preprocessing the multi-feature real-time data set based on the preset preprocessing flow, and constructing an online monitoring data set based on the obtained light load period real-time amplitude data;
and calculating Q statistics of the online monitoring data set, and judging the fault of the voltage transformer to be tested when the Q statistics are larger than the control limit.
On the basis of the technical scheme, the invention can also make the following improvements.
Preferably, the construction of the same set of three-phase imbalance historical data setsUnbComprising:
;
;
wherein,,、/>、/>the measured amplitudes of the three-phase voltage transformers VT of the same group are respectively +.>The mean value of the amplitude values is measured for three-phase VT.
The construction of the high-low voltage in-phase difference historical data setSubComprising:
；
wherein,,、/>、/>measuring phases of three-phase VT of the high-voltage side of the transformer, respectively,>、/>、/>the measuring phases of the three phases VT of the low voltage side of the transformer are respectively.
Preferably, the step of preprocessing the multi-feature historical data set based on a preset preprocessing flow and constructing an offline modeling data set based on the obtained light-load period historical amplitude data includes:
performing differential and normalization processing on the multi-feature historical data set to obtain a processed two-dimensional array；
For the two-dimensional arrayAnd performing light-load and heavy-load sensing, and constructing the offline modeling data set based on the light-load period amplitude.
Preferably, the pair of the two-dimensional arraysThe step of sensing the light load and the heavy load comprises the following steps:
based on density clustering algorithm, the two-dimensional array is subjected toClustering separation is carried out to obtain a data cluster +.>；
In a data clusterThe corresponding multiple acquisition amplitude time intervals are the intervals with the most continuous datat sel The light load period is set.
Preferably, the step of normalizing includes:
；
wherein,,Difis first order differential data.
Preferably, the step of establishing a Q statistic calculation model according to the offline modeling dataset and calculating a control limit thereof includes:
performing standardization processing on the offline modeling data to obtain a standardized modeling data set；
Modeling the normalized modeling datasetConversion to Q statistic, calculation of the normalized modeling dataset +.>Covariance of (2)RFor the covarianceRAs singular value decomposition;
based on principal component analysis, the standardized modeling data setMiddle measurement sample->Decomposed into principal components->And residual component->；
A control limit for the Q statistic is calculated based on the residual component.
Preferably, the step of calculating the Q statistic of the online monitoring dataset, and determining that the voltage transformer to be tested has a fault when the Q statistic is greater than the control limit includes:
calculating the contribution values of a plurality of variables in the Q statisticQ xi And setting a variable with the highest contribution value as an abnormal variable based on a Q contribution graph method, and sending out a fault warning based on the abnormal variable.
According to a second aspect of the present invention, there is provided a voltage transformer error evaluation system comprising:
the historical data module is used for constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on the historical amplitude data and the historical phase data of the transformer substation;
the offline data module is used for preprocessing the multi-characteristic historical data set based on a preset preprocessing flow and constructing an offline modeling data set based on the obtained light-load period historical amplitude data;
the limit value calculation module is used for establishing a Q statistic calculation model according to the offline modeling data set and calculating the control limit of the Q statistic calculation model;
the real-time data module is used for constructing a multi-characteristic real-time data set of the same three-phase imbalance and high-low voltage in-phase difference based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time;
the online data module is used for preprocessing the multi-feature real-time data set based on the preset preprocessing flow and constructing an online monitoring data set based on the obtained light-load period real-time amplitude data;
and the fault judging module is used for calculating the Q statistic of the online monitoring data set and judging the fault of the voltage transformer to be tested when the Q statistic is larger than the control limit.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor for implementing the steps of any one of the voltage transformer error assessment methods of the first aspect described above when executing a computer management class program stored in the memory.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer management class program which, when executed by a processor, implements the steps of any one of the voltage transformer error assessment methods of the first aspect described above.
The invention provides a voltage transformer error evaluation method, a system, electronic equipment and a storage medium, wherein the method comprises the following steps: constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation; preprocessing the multi-feature historical data set, and constructing an offline modeling data set based on a preprocessing result; establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model; based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference; constructing an online monitoring data set based on the multi-feature real-time data set light load period amplitude; and calculating Q statistics of the online monitoring data set, and judging the faults of the voltage transformer to be tested when the Q statistics are larger than the control limit. According to the invention, under the framework of information physical fusion, the switching state of an industrial user is adaptively identified by utilizing multiple characteristic parameters of voltage measurement values, the load is perceived to be light and heavy, the light-load period data which is stably cut out by the industrial user is screened out, the influence of frequent switching of a large load on three-phase unbalance when the industrial user uses electricity is eliminated, and the identification capability of a metering error abnormal transformer in a load transformer station of the load of the industrial user is obviously improved.
Drawings
FIG. 1 is a flow chart of a voltage transformer error evaluation method provided by the invention;
fig. 2 is a schematic diagram of a primary wiring diagram of the 220kV step-down transformer substation provided by the invention;
FIG. 3 is a full flow chart of a voltage transformer error evaluation method provided by the invention;
FIG. 4 is a waveform diagram of three-phase imbalance and high-low voltage phase difference of historical data provided by the invention;
FIG. 5 is a graph of the result of the light load data clustering screening provided by the invention;
fig. 6 is a schematic diagram of a distribution interval of screening data in overall data according to the present invention;
FIG. 7 is a graph showing the comparison of the fault recognition effect of the method of the present invention with that of the conventional method;
FIG. 8 is a plot of fault localization Q statistics contribution provided by the present invention;
FIG. 9 is a schematic diagram of a voltage transformer error evaluation system according to the present invention;
fig. 10 is a schematic diagram of a possible hardware structure of an electronic device according to the present invention;
fig. 11 is a schematic hardware structure of a possible computer readable storage medium according to the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a voltage transformer error evaluation method provided by the present invention, as shown in fig. 1, the method includes:
step S100: constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation;
it should be noted that, the execution body of the method of this embodiment may be a computer terminal device having functions of data processing, network communication, and program running, for example: computers, tablet computers, etc.; the present embodiment is not limited to this, and may be a server device having the same similar function, or may be a cloud server having a similar function. For ease of understanding, this embodiment and the following embodiments will be described by taking a server device as an example.
In a specific implementation, the step of constructing the multi-feature historical dataset based on the historical amplitude data and the historical phase data includes:
step S101: collecting high voltage on two sides of transformer substation just after operation or after week inspectionGroup and low pressure->Group (same voltage metering node three-phase voltage transformer is a group, ">) Historical data of Voltage Transformer (VT) secondary output signal, selecting amplitude of one day +.>And phase->Data (preferably data at the nearest monitoring date at 0 to 24),，/>，，/>wherein j is a group numberN is the data amount of one day, +.>And->Three-phase amplitude and phase +.>。
Step S102: according to the describedCalculating three-phase imbalance of two groups of VTWherein->Calculated by the formulas (1) and (2). According to any one set of phases of the high voltage side +.>And low-voltage side arbitrary set of phases +.>，/>,Preferably in the high-pressure first group +.>And low-voltage first group->Calculating the in-phase difference of VT>Wherein->Calculated by equation (3).
；(1)
；(2)
； (3)
Step S200: preprocessing the multi-feature historical data set based on a preset preprocessing flow, and constructing an offline modeling data set based on the obtained light load period historical amplitude data;
it should be noted that the steps of the preset preprocessing flow include performing differential and normalization processing on the multi-feature historical dataset.
The preset pretreatment flow may include:
step S201: performing differential and normalization processing on the multi-feature historical data set to obtain a processed two-dimensional array；
In a specific implementation, the calculation is performedAnd->Is a first order differential vector of (a)And。
step S202: for the two-dimensional arrayAnd performing light-load and heavy-load sensing, and constructing the offline modeling data set based on the light-load period amplitude.
In a specific implementation, the method comprises the following steps: for the saidAnd->Performing normalization processing as shown in formula (4) to obtain data set +.>And->Then use said->And->Composing two-dimensional data->。
Step S203: based on density clustering algorithm, the two-dimensional array is subjected toClustering separation is carried out to obtain a data cluster +.>；
In specific implementation, a density clustering method, preferably a DBSCAN method, is adopted to cluster and separate clusters of which the same group of three-phase imbalance change and high-low voltage in-phase difference change are both near 0 point from data。
；(4)
Step S204: in a data clusterThe corresponding multiple acquisition amplitude time intervals are the intervals with the most continuous datat sel The light load period is set.
In a specific implementation, willCorresponding to the amplitude +.>Sequence of->In the method, the interval with the most continuous data is screened out>，/>Constructing a modeling dataset。
Step S300: establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model;
in a specific implementation, the step of constructing the Q statistic calculation model and calculating the control limit thereof specifically includes:
step S301: performing standardization processing on the offline modeling data to obtain a standardized modeling data set；
In a specific implementation, the modeling datasetPerforming normalization processing as shown in formula (5) to obtain normalized modeling data set +.>。
(5)
Where m is the data volume of the modeling dataset X,as the mean value vector of the X,for the X standard deviation matrix, wherein +.>Standard deviation of the ith measurement variable for X.
Step S302: modeling the normalized modeling datasetConversion to Q statistic, calculation of the normalized modeling dataset +.>Covariance of (2)RFor the covarianceRAs singular value decomposition;
in a specific implementation, the data setConversion to Q statistic, calculation of modeling dataset +.>Covariance of (2)Singular value decomposition is performed on the R:
;(6)
wherein,,for unitary matrix>Is a diagonal matrix and satisfies +.>。
Is->A set of standard bases of space, front +.>Linear independent vectorsConstitutes principal component space->Back->A linear independent vector->Constitutes residual space->The number of principal elements k is typically determined from the variance accumulation and the percentage.
Step S303: based on principal component analysis, the standardized modeling data setMiddle measurement sample->Decomposed into principal components->And residual component->；
In a specific implementation, the samples are measured using Principal Component Analysis (PCA)Decomposed into principal components->And residual component：
;(7)
Wherein,,，/>，/>and->Respectively->At->And->Projection onto a projection plane.
Step S304: a control limit for the Q statistic is calculated based on the residual component.
In a specific implementation, a Q statistic control limit for monitoring residual components is calculated：
;(8)
Wherein the control limitThe Q control limit is indicated when the confidence level is a, and the monitoring process can be considered normal when the Q statistic is within the control limit. When the residual components obey normal distribution, the control limit calculation formula is:
;(9)
wherein,,，/>，/>is a threshold value that is normally too much distributed under the confidence level a.
Step S400: based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference;
in specific implementation, secondary output amplitude and phase data of a voltage transformer VT to be detected are collected, and after reaching a preset data length, a same group of three-phase imbalance and high-low voltage in-phase difference multi-characteristic data set is constructed.
The specific steps for constructing the same-group three-phase imbalance and high-low voltage same-phase difference multi-characteristic data set comprise the following steps:
step S401: collecting secondary outputs of two groups of Voltage Transformers (VT) monitored by a transformer substation, and generating continuously updated time sequence output amplitude valuesAnd phase->When reaching the preset length of data +.>Preferably, the data amount acquired in one day is a preset length, and the acquired amplitude and phase data are generated, wherein +.>The acquired amplitude and phase data reaching the preset length are +.> 。
Step S402: according to the describedCalculating three-phase imbalance of VTWherein->Calculated by the formulas (1) and (2). According to said->Andcalculating the in-phase difference of VT>Wherein->Calculated by equation (3).
Step S500: preprocessing the multi-feature real-time data set based on the preset preprocessing flow, and constructing an online monitoring data set based on the obtained light load period real-time amplitude data;
in a specific implementation, the step of constructing the online monitoring dataset includes:
step S501: calculating the saidAnd->Is +.>And->。
Step S502: for the saidAnd->Performing normalization processing as shown in formula (4) to obtain data set +.>And->Then use said->And->Composing two-dimensional data->Adopting a density clustering method to cluster and separate clusters of which the same group of three-phase imbalance change and high-low voltage in-phase difference change are near 0 point from data>。
Step S503: will beCorresponding to the amplitude +.>Sequence of->In the method, the interval with the most continuous data is screened out>，/>Constructing an on-line monitoring dataset。
Step S600: and calculating Q statistics of the online monitoring data set, and judging the fault of the voltage transformer to be tested when the Q statistics are larger than the control limit.
In a specific implementation, the Q statistic of the online monitoring dataset is calculated, if the Q statistic is larger than the control limit, the fault is judged, a fault alarm of a stage corresponding to the segment is sent out, and if the Q statistic is not out of limit, the monitoring of the voltage transformer to be tested is continued.
Wherein, the step of carrying out fault warning based on the Q statistic of the online monitoring data set comprises the following steps:
step S601: the light load on-line monitoring data set screened in the ith sectionPerforming normalization processing as shown in formula (5) to obtain normalized modeling data set +.>。
Step S602: the data set is processed according to formulas (6), (7), (8)Conversion to monitoring statistics->。
Step 603: the monitoring statistics are processedAnd control limit->Comparing, when statistics->And if the control limit is exceeded, judging that the operation is abnormal.
Step S604: calculating the contribution values of a plurality of variables in the Q statisticQ xi Setting a variable with the highest contribution value as an abnormal variable based on a Q contribution graph method, and based on the abnormal variableThe constant volume issues a fault warning.
In a specific implementation, the Q statistic exceeding the control limit is brought into a formula (6) to calculate the contribution value of each variable to the Q statisticAnd performing fault diagnosis by adopting a Q contribution graph method, wherein the variable with the highest contribution rate can be regarded as the variable with abnormal change, and a fault alarm is sent to VT corresponding to the variable.
;(10)
Wherein,,is->Is>Component(s)>Is->Is the i-th row vector of (c).
If the condition of overrun does not occur, continuing to monitor the voltage transformer to be tested, and marking the next acquired data number i+1.
The invention provides a voltage transformer error evaluation method, a system, electronic equipment and a storage medium, wherein the method comprises the following steps: constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation; preprocessing the multi-feature historical data set, and constructing an offline modeling data set based on a preprocessing result; establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model; based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference; constructing an online monitoring data set based on the multi-feature real-time data set light load period amplitude; and calculating Q statistics of the online monitoring data set, and judging the faults of the voltage transformer to be tested when the Q statistics are larger than the control limit. According to the invention, under the framework of information physical fusion, the switching state of an industrial user is adaptively identified by utilizing multiple characteristic parameters of voltage measurement values, the load is perceived to be light and heavy, the light-load period data which is stably cut out by the industrial user is screened out, the influence of frequent switching of a large load on three-phase unbalance when the industrial user uses electricity is eliminated, and the identification capability of a metering error abnormal transformer in a load transformer station of the load of the industrial user is obviously improved. Meanwhile, the method can adaptively screen the data of the time period of stable cutting-out of the daily industrial user load of the transformer substation, and provide more reliable modeling and monitoring data sets for the subsequent data driving method.
In a possible application scenario, referring to fig. 2, fig. 2 is a schematic diagram of a primary wiring diagram of a 220kV step-down transformer substation provided by the invention; in the application scene, the transformer substation is a 220kV step-down transformer substation and comprises two voltage levels of 220kV and 110kV, one-time wiring is shown in fig. 2, the operation mode is double bus line-by-line operation, and each bus is provided with a group of VT. In this embodiment, two groups of VT, VT-1 and VT-3 on both sides of the transformer I are taken as examples, and the voltage transformer error evaluation method provided by the present invention is explained.
In order to further explain the online evaluation flow in the present embodiment in the present application scenario, referring to fig. 3, fig. 3 is a full flowchart of a voltage transformer error evaluation method provided by the present invention, in fig. 3, the method in the present embodiment includes:
step S1: and collecting historical amplitude and phase data of the transformer substation, and constructing a multi-characteristic data set of the same group of three-phase imbalance and high-low voltage in-phase difference.
The embodiment selects the transformer substation which has just finished off-line verification, installs a high-precision synchronous acquisition device on two groups of VT secondary outputs of the monitored 220kV bus VT-1 and the 110kV bus VT-3,the accuracy grade of the device is higher than 0.05 grade, the output frequency of the acquired characteristic quantity is 1/s, and for 0.2 grade VT, the error introduced by the acquisition device can be ignored. As the transformer substation just completes verification, the current VT metering performance can be considered to be normal, and the data collected from the first 0 time to the second 24 time can be used as historical data. Wherein the amplitude is，/>，，/>Wherein->j is the group number, j=1 represents VT-1 group, j=2 represents VT-3 group, +.>And->Three-phase amplitude and phase of ith secondary output of jth group VT respectively。
According to the describedCalculation of three-phase imbalance of VT-1Wherein->Calculated by the formulas (1) and (2). According to said->、/>And equation (3) calculating the in-phase difference of VT +.>，Is->The timing is shown in fig. 4.
Step S2: performing differential and normalization processing on the multi-feature data set to form a two-dimensional array, clustering the array to realize the light and heavy load sensing of historical data, and intercepting the light load period amplitude to construct an offline modeling data set。
Calculating the saidAnd->Is a first order differential vector of (a)And. And then->And->Performing normalization processing as shown in formula (4) to obtain data set +.>And->Then use said->And->Constructing two-dimensional dataAdopting a DBSCAN clustering method to cluster and separate the cluster which has the same three-phase imbalance change and high-low voltage in-phase difference change and simultaneously has the smallest from the data>As shown in fig. 5.
Finally, willCorresponding to the amplitude X 0 Sequence of->In the method, the interval with the most continuous data is screened out>，/>Constructing a modeling dataset. Fig. 6 shows the distribution interval of the screening data in the overall data in a three-phase imbalance curve.
Step S3: modeling data sets from offlineEstablishing a Q statistic calculation model, and calculating the control limit +.>。
According to equation (5), for the modeling datasetPerforming standardization treatment as shown to obtainTo a standardized modeling dataset. Then, the modeling dataset is calculated +.>Covariance +.>And said +.>Singular value decomposition to obtain ++>A set of standard bases of space->The number of principal elements k is 1 in the present problem, the first 1 linear independent vectors of U +.>Constitutes principal component space->The last 2 linear independent vectors +.>Constitutes residual space->,. Then according to formulas (7), (8) and (9), the measurement sample is +.>Decomposed into principal components->And residual component->Obtaining +.>Statistics control Limit->。
Step S4: and acquiring secondary output amplitude and phase data of VT, and constructing the same-group three-phase imbalance and high-low voltage in-phase difference multi-characteristic data set when the data reach a preset data length.
In specific implementation, after generating historical data, continuously collecting two groups of Voltage Transformers (VT) secondary outputs monitored by a transformer substation, and generating continuously updated time sequence output amplitude valuesAnd phase ofGenerating +.sup.th ∈when the data amount 1440 acquired on one day is reached>Segment acquisition amplitude data +.>And->Segment acquisition phase data。
Similar to step S1, according to the followingCalculating three-phase imbalance of VTWherein->Calculated by the formulas (1) and (2). According to the describedCalculating the in-phase difference of VT>Wherein->Calculated by equation (3).
Step S5: and sensing the light load and the heavy load of the acquired data, and intercepting the amplitude of the light load period to construct an on-line monitoring data set.
Similarly to step S2, calculate theAnd->Is +.>And->For the saidAnd->Performing normalization processing as shown in formula (4) to obtain data set +.>And->Then use said->And->Composing two-dimensional data->Adopts DBSCAN clustering methodClustering and separating clusters with the same group of three-phase imbalance change and high-low voltage in-phase difference change near 0 point from data. Will->Corresponding to the amplitude +.>Sequence of->In the method, the interval with the most continuous data is screened out>，/>Constructing an on-line monitoring dataset。
Step S6: and (3) calculating Q statistics of the online monitoring data set, judging that faults occur when the Q statistics are larger than a control limit, giving out fault alarms of the stage corresponding to the segments, and repeating the steps (3-6) if the Q statistics are not exceeded.
Similar to step S3, the online monitoring datasetPerforming normalization processing as shown in formula (5) to obtain normalized modeling data set +.>. Then, the dataset is ++according to formulas (6), (7), (8)>Conversion to monitoring statistics. The monitoring statistics are->And control limit->Comparing, when statistics->And if the control limit is exceeded, judging that the operation is abnormal. Taking the example that about 0.3% of positive error mutation occurs in the phase A at about 100 th point on a certain day as an example, the comparison result of the method and the traditional method is shown in FIG. 7.
Fig. 7 includes Q statistics curves for the day and day before a fault, where the conventional method does not screen modeling and online monitoring data, and cannot distinguish the influence of industrial user load and error drift on three-phase imbalance, and when industrial user load is input, Q statistics exceeds a control limit due to frequent switching of industrial user load, and when industrial user load is switched out, statistics is stably lower than the control limit, and error abnormal change cannot be identified. The Q statistic of the method is basically stabilized below the control limit the day before error abnormal change occurs, and the Q statistic is stabilized beyond the control limit after the error abnormal change, so that fault occurrence can be clearly identified.
After abnormality occurs, the Q statistic exceeding the control limit is brought into a formula (6) to calculate each variable pairContribution value of statistics->Adopts->The contribution graph method performs fault diagnosis as shown in fig. 8.
In FIG. 8, it is determined that the VT of the VT-1A phase with the highest contribution rate is abnormally changed, and a fault alarm is sent to the abnormal VT, so that the abnormal VT is consistent with the actual fault. If at firstIIf the section acquisition data is not abnormal, repeating the steps 3-6, wherein the acquisition data number isI+1。
Compared with the prior art, the invention has the beneficial technical effects that:
(1) The method provided by the invention can be used for adaptively screening the data of the daily substation industrial user load stable cut-out time period, and a more reliable modeling and monitoring data set is provided for a subsequent data driving method.
(2) The method provided by the invention is based on the same-group three-phase relationship and the high-low voltage same-phase relationship, can eliminate the influence of frequent switching of a large load on three-phase unbalance when an industrial user uses electricity, and remarkably improves the on-line evaluation accuracy of the abnormal transformer with metering error in the load transformer substation of the load industrial user.
Referring to fig. 9, fig. 9 is a schematic diagram of a voltage transformer error evaluation system according to an embodiment of the present invention, and as shown in fig. 9, a voltage transformer error evaluation system includes a historical data module 100, an offline data module 200, a limit value calculation module 300, a real-time data module 400, an online data module 500, and a fault determination module 600, wherein:
the historical data module 100 is used for constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage phase difference based on the historical amplitude data and the historical phase data of the transformer substation; an offline data module 200, configured to pre-process the multi-feature historical dataset, and construct an offline modeling dataset based on the pre-processing result; the limit value calculation module 300 is configured to establish a Q statistic calculation model according to the offline modeling dataset, and calculate a control limit thereof; the real-time data module 400 is used for constructing a multi-characteristic real-time data set of the same three-phase imbalance and high-low voltage in-phase difference based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time; the online data module 500 is configured to construct an online monitoring data set based on the multi-feature real-time data set light load period amplitude; the fault determination module 600 is configured to calculate a Q statistic of the online monitoring dataset, and determine that the voltage transformer to be tested has a fault when the Q statistic is greater than the control limit.
It can be understood that the voltage transformer error evaluation system provided by the present invention corresponds to the voltage transformer error evaluation method provided in the foregoing embodiments, and relevant technical features of the voltage transformer error evaluation system may refer to relevant technical features of the voltage transformer error evaluation method, which are not described herein.
Referring to fig. 10, fig. 10 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 10, an embodiment of the present invention provides an electronic device including a memory 1310, a processor 1320, and a computer program 1311 stored on the memory 1310 and executable on the processor 1320, the processor 1320 implementing the following steps when executing the computer program 1311:
constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation; preprocessing the multi-feature historical data set, and constructing an offline modeling data set based on a preprocessing result; establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model; based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference; constructing an online monitoring data set based on the multi-feature real-time data set light load period amplitude; and calculating Q statistics of the online monitoring data set, and judging the faults of the voltage transformer to be tested when the Q statistics are larger than the control limit.
Referring to fig. 11, fig. 11 is a schematic diagram of a computer readable storage medium according to an embodiment of the invention. As shown in fig. 11, the present embodiment provides a computer-readable storage medium 1400 on which a computer program 1411 is stored, the computer program 1411, when executed by a processor, implementing the steps of:
constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation; preprocessing the multi-feature historical data set, and constructing an offline modeling data set based on a preprocessing result; establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model; based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference; constructing an online monitoring data set based on the multi-feature real-time data set light load period amplitude; and calculating Q statistics of the online monitoring data set, and judging the faults of the voltage transformer to be tested when the Q statistics are larger than the control limit.
The embodiment of the invention provides a voltage transformer error evaluation method, a system and a storage medium, wherein the method comprises the following steps: constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation; preprocessing the multi-feature historical data set, and constructing an offline modeling data set based on a preprocessing result; establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model; based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference; constructing an online monitoring data set based on the multi-feature real-time data set light load period amplitude; and calculating Q statistics of the online monitoring data set, and judging the faults of the voltage transformer to be tested when the Q statistics are larger than the control limit. According to the invention, under the framework of information physical fusion, the switching state of an industrial user is adaptively identified by utilizing multiple characteristic parameters of voltage measurement values, the load is perceived to be light and heavy, the light-load period data which is stably cut out by the industrial user is screened out, the influence of frequent switching of a large load on three-phase unbalance when the industrial user uses electricity is eliminated, and the identification capability of a metering error abnormal transformer in a load transformer station of the load of the industrial user is obviously improved.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 computer, 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.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A method for evaluating voltage transformer errors, the method comprising:
constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on historical amplitude data and historical phase data of a transformer substation;
preprocessing the multi-feature historical data set based on a preset preprocessing flow, and constructing an offline modeling data set based on the obtained light load period historical amplitude data;
establishing a Q statistic calculation model according to the offline modeling data set, and calculating a control limit of the Q statistic calculation model;
based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time, constructing a multi-characteristic real-time data set of the same group of three-phase imbalance and high-low voltage in-phase difference;
preprocessing the multi-feature real-time data set based on the preset preprocessing flow, and constructing an online monitoring data set based on the obtained light load period real-time amplitude data;
and calculating Q statistics of the online monitoring data set, and judging the fault of the voltage transformer to be tested when the Q statistics are larger than the control limit.
2. The voltage transformer error assessment method according to claim 1, wherein said constructing the same set of three-phase imbalance historical data sets Unb comprises:
;
;
wherein,,、/>、/>the measured amplitudes of the three-phase voltage transformers VT of the same group are respectively +.>Measuring the average value of amplitude values for three-phase VT;
the construction of the high-low voltage in-phase difference historical data set Sub comprises the following steps:
；
wherein,,、/>、/>measuring phases of three-phase VT of the high-voltage side of the transformer, respectively,>、/>、/>respectively isThe phase of the measurement of the low-side three-phase VT of the transformer.
3. The voltage transformer error assessment method according to claim 2, wherein the step of preprocessing the multi-feature historical data set based on a preset preprocessing flow and constructing an offline modeling data set based on the obtained light load period historical amplitude data comprises the steps of:
performing differential and normalization processing on the multi-feature historical data set to obtain a processed two-dimensional array；
For the two-dimensional arrayAnd performing light-load and heavy-load sensing, and constructing the offline modeling data set based on the light-load period amplitude.
4. A method of evaluating a voltage transformer error as claimed in claim 3 wherein said pair of said two-dimensional arraysThe step of sensing the light load and the heavy load comprises the following steps:
based on density clustering algorithm, the two-dimensional array is subjected toClustering separation is carried out to obtain a data cluster +.>；
In a data clusterIn the corresponding multiple acquisition amplitude time intervals, the interval t with the largest continuous data is selected sel When set to light loadSegments.
5. The voltage transformer error assessment method according to claim 4, wherein the step of normalizing comprises:
；
wherein Dif is first order differential data.
6. The voltage transformer error assessment method according to claim 1, wherein said step of establishing a Q statistic calculation model from said offline modeling dataset, calculating a control limit thereof, comprises:
performing standardization processing on the offline modeling data to obtain a standardized modeling data set；
Modeling the normalized modeling datasetConversion to Q statistic, calculation of the normalized modeling dataset +.>As a singular value decomposition for the covariance R;
based on principal component analysis, the standardized modeling data setMiddle measurement sample->Decomposed into principal components->And residual component->；
A control limit for the Q statistic is calculated based on the residual component.
7. The voltage transformer error assessment method according to claim 1, wherein said calculating the Q statistic of the on-line monitoring dataset, after the step of determining that the voltage transformer under test has failed when it is greater than the control limit, comprises:
calculating the contribution value Q of a plurality of variables in the Q statistic xi And setting a variable with the highest contribution value as an abnormal variable based on a Q contribution graph method, and sending out a fault warning based on the abnormal variable.
8. A voltage transformer error assessment system, comprising:
the historical data module is used for constructing a multi-characteristic historical data set of the same group of three-phase imbalance and high-low voltage in-phase difference based on the historical amplitude data and the historical phase data of the transformer substation;
the offline data module is used for preprocessing the multi-characteristic historical data set based on a preset preprocessing flow and constructing an offline modeling data set based on the obtained light-load period historical amplitude data;
the limit value calculation module is used for establishing a Q statistic calculation model according to the offline modeling data set and calculating the control limit of the Q statistic calculation model;
the real-time data module is used for constructing a multi-characteristic real-time data set of the same three-phase imbalance and high-low voltage in-phase difference based on the secondary output amplitude data and the phase data of the voltage transformer to be tested in the transformer substation acquired in real time;
the online data module is used for preprocessing the multi-feature real-time data set based on the preset preprocessing flow and constructing an online monitoring data set based on the obtained light-load period real-time amplitude data;
and the fault judging module is used for calculating the Q statistic of the online monitoring data set and judging the fault of the voltage transformer to be tested when the Q statistic is larger than the control limit.
9. An electronic device comprising a memory, a processor for implementing the steps of the voltage transformer error assessment method according to any one of claims 1-7 when executing a computer management class program stored in the memory.
10. A computer-readable storage medium, having stored thereon a computer-management-class program which, when executed by a processor, implements the steps of the voltage transformer error assessment method of any of claims 1-7.
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| CN117093834B (en) * | 2023-10-19 | 2024-01-02 | 华中科技大学 | Abnormality monitoring method for three-phase voltage system |
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