CN114678962B - Distributed array temperature measurement abnormal data transmission monitoring system based on power internet of things - Google Patents
Distributed array temperature measurement abnormal data transmission monitoring system based on power internet of things Download PDFInfo
- Publication number
- CN114678962B CN114678962B CN202210566111.3A CN202210566111A CN114678962B CN 114678962 B CN114678962 B CN 114678962B CN 202210566111 A CN202210566111 A CN 202210566111A CN 114678962 B CN114678962 B CN 114678962B
- Authority
- CN
- China
- Prior art keywords
- transformer
- magnetic induction
- detection surface
- induction intensity
- circle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 50
- 230000005540 biological transmission Effects 0.000 title claims abstract description 15
- 238000012544 monitoring process Methods 0.000 title claims abstract description 15
- 238000009529 body temperature measurement Methods 0.000 title claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 122
- 238000001514 detection method Methods 0.000 claims abstract description 110
- 230000008569 process Effects 0.000 claims abstract description 99
- 230000006698 induction Effects 0.000 claims abstract description 88
- 238000012706 support-vector machine Methods 0.000 claims abstract description 36
- 238000012549 training Methods 0.000 claims description 22
- 238000004804 winding Methods 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 14
- 238000000926 separation method Methods 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 238000004861 thermometry Methods 0.000 claims description 2
- 239000011159 matrix material Substances 0.000 description 11
- 230000005856 abnormality Effects 0.000 description 8
- 239000013598 vector Substances 0.000 description 8
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 7
- 230000008859 change Effects 0.000 description 7
- 230000000694 effects Effects 0.000 description 4
- 230000002441 reversible effect Effects 0.000 description 3
- 239000006185 dispersion Substances 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000005355 Hall effect Effects 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
- G01R33/02—Measuring direction or magnitude of magnetic fields or magnetic flux
- G01R33/06—Measuring direction or magnitude of magnetic fields or magnetic flux using galvano-magnetic devices
- G01R33/07—Hall effect devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
- Condensed Matter Physics & Semiconductors (AREA)
- Protection Of Transformers (AREA)
Abstract
The invention relates to a distributed array temperature measurement abnormal data transmission monitoring system based on an electric power internet of things, and belongs to the technical field of magnetic variables. The system can acquire the magnetic induction intensity corresponding to the transformer; according to the magnetic induction intensity and the temperature difference value, a temperature difference value sequence and a magnetic induction intensity sequence corresponding to each circle of the detection surface are constructed and obtained; calculating to obtain a corresponding local structural index in the operation process of the transformer according to the temperature difference sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface; and inputting the local structural index into a target support vector machine classifier, and judging whether the transformer is abnormal in operation or not by using the target support vector machine classifier. The invention realizes the real-time judgment of whether the running state of the transformer is abnormal in the running process of the transformer according to the magnetic induction intensity.
Description
Technical Field
The invention relates to the technical field of magnetic variables, in particular to a distributed array temperature measurement abnormal data transmission monitoring system based on the power internet of things.
Background
With the increasingly accelerated urbanization process, the urban scale is continuously enlarged, the electricity demand of different industries and fields is increasingly increased, more substations need to be constructed to meet the demand of electricity consumption, but the urban land is very tight, and a reasonable transformer substation building land is difficult to obtain, so in the face of the situation, an underground transformer substation needs to be constructed; the main buildings of the underground transformer substation are underground, for example, a main transformer or other main electrical equipment is underground, and only a small part of the main transformer or other main electrical equipment is arranged on the ground; because the underground transformer substation is in a relatively closed space, the heat dissipation problem of the underground transformer substation is an important problem influencing the safety of the underground transformer substation, and the transformer is used as main equipment in the underground transformer substation and needs to pay more attention to the running state of the transformer; because a part of electromagnetic energy is converted into heat energy in the operation of the transformer, and a part of the heat energy is dissipated to the environment, the environmental temperature around the transformer is abnormal when the transformer fails or operates abnormally, and the safety problem of the underground transformer substation is possibly affected, so that the monitoring of the environmental temperature in the operation process of the transformer in the underground transformer substation is very important, namely whether the operation abnormality occurs in the operation process of the transformer is very important.
Disclosure of Invention
The invention provides a distributed array temperature measurement abnormal data transmission monitoring system based on an electric power internet of things, which is used for solving the problem that the prior method cannot reliably monitor the environment temperature of a transformer in an underground substation during operation, and adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a distributed array thermometry abnormal data transmission monitoring system based on an internet of things for electric power, including a memory and a processor, where the processor executes a computer program stored in the memory to implement the following steps:
acquiring the temperature difference of each first target position and the magnetic induction intensity of each second target position in the running process of the transformer to be detected;
according to the position of each first target position on the detection surface and the temperature difference value, constructing and obtaining a temperature difference value sequence corresponding to each circle of the detection surface; according to the position of each second target position on the detection surface and the magnetic induction intensity, constructing and obtaining a magnetic induction intensity sequence corresponding to each circle of the detection surface; calculating to obtain a corresponding local structural index in the running process of the transformer according to the temperature difference sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface;
and inputting the local structural index into a target support vector machine classifier, and judging whether the transformer runs abnormally or not by using the target support vector machine classifier.
Has the advantages that: the method comprises the steps that the temperature difference values of all first target positions and the magnetic induction intensity of all second target positions in the operation process of a transformer to be detected are used as the basis for obtaining the temperature difference value sequence and the magnetic induction intensity sequence corresponding to all circles of a detection surface; taking the temperature difference sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface as a basis for calculating to obtain a corresponding local structural index in the running process of the transformer; the local structural index and the target support vector machine classifier are used as a basis for judging whether the operation of the transformer is abnormal or not in the operation process; the invention realizes the real-time judgment of whether the running state of the transformer is abnormal in the running process of the transformer according to the magnetic induction intensity, and the invention can relatively reliably monitor the running state of the transformer in the underground transformer substation.
Preferably, the method for acquiring the temperature difference of each first target position and the magnetic induction intensity of each second target position in the operation process of the transformer to be detected includes:
obtaining a first temperature detection surface and a second temperature detection surface corresponding to the transformer according to the axis of the primary winding and the axis of the secondary winding of the transformer core; a plurality of temperature sensors are distributed on the temperature detection surface;
recording the position of each temperature sensor on a first temperature detection surface corresponding to the transformer as each first target position;
acquiring a position corresponding to the first target position on a second temperature detection surface corresponding to the transformer, and recording the position as a matching position of each first target position on a first temperature detection surface corresponding to the transformer;
calculating the absolute value of the difference between the temperature value of each first target position on the first temperature detection surface corresponding to the transformer and the temperature value of the corresponding matching position;
recording the absolute value of the difference as the temperature difference corresponding to each first target position in the running process of the transformer;
arranging a Hall sensor on a shell surface of the transformer close to the N-level of the transformer magnet to acquire magnetic induction intensity, marking the shell surface as a magnetic induction intensity detection surface corresponding to the transformer, and arranging the Hall sensor on the magnetic induction intensity detection surface;
and recording the positions of the Hall sensors on the magnetic induction intensity detection surface as second target positions, and obtaining the magnetic induction intensity of the second target positions in the operation process of the transformer.
Preferably, the method for obtaining the first temperature detection surface and the second temperature detection surface corresponding to the transformer according to the axis of the primary winding and the axis of the secondary winding of the transformer core includes:
forming a plane according to the axis of the primary winding and the axis of the secondary winding, respectively arranging a temperature detection surface at the same distance on two sides of the plane, wherein the two temperature detection surfaces are arranged on the outer sides of the winding coils, the two temperature detection surfaces are respectively provided with a temperature sensor, and the positions and the number of the temperature sensors arranged on the two temperature detection surfaces are the same and are in one-to-one correspondence;
and recording the two temperature detection surfaces corresponding to the transformer as a first temperature detection surface and a second temperature detection surface corresponding to the transformer respectively.
Preferably, the training process of the target support vector machine classifier comprises: judging whether the local structural indexes of the training samples corresponding to the operation processes of the transformers are abnormal by using a phase space analysis method, and marking the training samples corresponding to the operation processes of the transformers; and training the support vector machine classifier based on the corresponding training samples in the marked transformer operation process.
Preferably, the method for determining whether the local structural index of the corresponding training sample in the operation process of each transformer is abnormal by using a phase space analysis method includes:
for a training sample corresponding to any transformer operation process:
calculating the standard deviation of the tracking index corresponding to each observation time in the running process of the transformer by taking the target running time period of the transformer as one observation time of the phase space;
calculating a structural separation index corresponding to each section of observation time according to the standard deviation of the tracking index corresponding to each section of observation time;
and judging whether the structural separation index is increased for more than two times in the running process of the transformer, and if so, judging that the local structural index of the running process of the transformer is abnormal.
Preferably, the method for calculating and obtaining the corresponding local structural index in the operation process of the transformer according to the temperature difference sequence and the magnetic induction sequence corresponding to each circle of the detection surface comprises the following steps:
the local structural index of each turn is calculated using the following formula:
wherein,
is as follows
The local structural index of the loop is,
is as follows
The temperature difference value sequence corresponding to the ring,
is as follows
The magnetic induction intensity sequence corresponding to the circle, STD is standard deviation, range is range, F is diagonal sampling function,
is shown as
The number of pairs of symmetric elements corresponding to the circle;
and obtaining a corresponding local structural index in the running process of the transformer according to the local structural index of each circle.
Drawings
To more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the following description will be made
While the drawings necessary for the embodiment or prior art description are briefly described, it should be apparent that the drawings in the following description are merely examples of the invention and that other drawings may be derived from those drawings by those of ordinary skill in the art without inventive step.
Fig. 1 is a flowchart of an operation abnormality judgment method in a transformer operation process of a distributed array temperature measurement abnormality data transmission monitoring system based on the power internet of things.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present invention belong to the protection scope of the embodiments of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment provides a distributed array temperature measurement abnormal data transmission monitoring system based on an electric power internet of things, which is described in detail as follows:
as shown in fig. 1, the distributed array temperature measurement abnormal data transmission monitoring system based on the power internet of things comprises a memory and a processor, wherein the processor executes a computer program stored in the memory, and the judgment of the transformer operation abnormality comprises the following steps:
and S001, acquiring the temperature difference of each first target position and the magnetic induction intensity of each second target position in the operation process of the transformer to be detected.
The specific process of acquiring the temperature difference values of the first target positions and the magnetic induction intensity of the second target positions in the running process of the transformer is as follows:
in this embodiment, because a part of electromagnetic energy is converted into heat energy in the operation process of the transformer, and a part of the heat energy is dissipated to the environment, and when the transformer is abnormal in operation, the temperature around the transformer may be abnormal, in this embodiment, the temperature in the operation process of the transformer is obtained as one of the bases for reflecting the operation state of the transformer, and the transformer is a single-phase transformer, the mechanical skeleton of the single-phase transformer is an iron core, the iron core of the transformer is a rectangular iron core, one side of the iron core is provided with a primary winding, the other side of the iron core is provided with a secondary winding, the axis of the primary winding and the axis of the secondary winding form a plane, two temperature detection surfaces are respectively arranged at the same distance on two sides of the plane, the two temperature detection surfaces are arranged on the outer sides of the winding coils, each temperature detection surface is respectively provided with a temperature sensor, and the positions and the number of the temperature sensors arranged on the two temperature detection surfaces are the same and are in a one-to-one correspondence relationship; therefore, the transformer corresponds to the two temperature detection surfaces, and the two temperature detection surfaces corresponding to the transformer are respectively marked as a first temperature detection surface and a second temperature detection surface corresponding to the transformer; recording the position of each temperature sensor on a first temperature detection surface corresponding to the transformer as each first target position; acquiring a position corresponding to the first target position on a second temperature detection surface corresponding to the transformer, and recording the position as a matching position of each first target position on the first temperature detection surface corresponding to the transformer; acquiring a temperature value of the matched position; calculating absolute values of differences between temperature values of the first target positions on the first temperature detection surface corresponding to the transformer and temperature values of corresponding matched positions; recording the absolute value of the difference as the temperature difference corresponding to each first target position on each first temperature detection surface in the transformer; therefore, the temperature difference of each first target position in the running process of the transformer can be obtained through the process.
Because the magnetic induction intensity is a physical quantity describing the strength and direction of a magnetic field, and the magnetic induction intensity and the exciting current are in a positive correlation relationship based on the Hall effect, when the single-phase transformer works, if the transformer runs abnormally, the exciting current can be changed or the transformer has copper loss and iron loss, the magnetic induction intensity can be influenced, so that the change of the magnetic induction intensity of the transformer can reflect the running state of the transformer, and the ambient temperature of the transformer can also be abnormal; therefore, in this embodiment, the hall sensors are arranged on the shell surface of the transformer close to the N-level transformer of the transformer magnet to acquire magnetic induction intensity, the shell surface is marked as a magnetic induction intensity detection surface corresponding to the transformer, the hall sensors are arranged on the magnetic induction intensity detection surface, the hall sensors are uniformly distributed in an array manner on the magnetic induction intensity detection surface, the number of the sensors on the magnetic induction intensity detection surface is the same as that on the corresponding first temperature detection surface, and the arrangement of the sensors on each detection surface is also similar; in the embodiment, the positions of all the hall sensors on the magnetic induction intensity detection surface are recorded as second target positions; therefore, the magnetic induction intensity of each second target position in the operation process of the transformer can be obtained.
Step S002, constructing and obtaining a temperature difference value sequence corresponding to each circle of the detection surface according to the position of each first target position on the detection surface and the temperature difference value; according to the position of each second target position on the detection surface and the magnetic induction intensity, constructing and obtaining a magnetic induction intensity sequence corresponding to each circle of the detection surface; and calculating to obtain a corresponding local structural index in the running process of the transformer according to the temperature difference sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface.
The specific process of calculating the corresponding local structural index in the running process of the transformer is as follows:
constructing a temperature difference matrix and a magnetic induction intensity matrix according to the temperature difference of each first target position and the magnetic induction intensity of each second target position in the operation process of the transformer; the temperature sensors are uniformly distributed in an array manner on the temperature detection surface, the Hall sensors are also uniformly distributed in an array manner on the magnetic induction intensity detection surface, and the sensors on the temperature detection surface and the sensors on the magnetic induction intensity detection surface are distributed similarly, so that the number of rows and the number of columns of the corresponding magnetic induction intensity matrix and the corresponding temperature difference matrix are the same in the operation process of the transformer; therefore, in the present embodiment, a magnetic induction intensity detection surface is regarded as a detection surface, and positions of sensors corresponding to an i-th row and a j-th column in a corresponding temperature difference matrix in an operation process of a transformer are regarded as positions of sensors corresponding to the i-th row and the j-th column in the corresponding magnetic induction intensity matrix; therefore, it can be considered that each second target position on the magnetic induction intensity detection surface corresponding to the transformer corresponds to two sensors, namely, a temperature sensor and a hall sensor, that is, a magnetic induction intensity matrix and a temperature difference matrix corresponding to the detection surface of the transformer.
And respectively accessing the reading of the sensor in a shape like a Chinese character 'hui' on the basis of the matrix, wherein the access mode of the shape like the Chinese character 'hui' is as follows: from the smallest unit in the center, for example, 2 \ 10005; 2, one circle is expanded outward to obtain the samples in the outer circle (containing 4 × 4-2 × 2=12 samples), and so on, the samples in each circle are obtained. When the width or height of one circle cannot be extended continuously, only the number of samples existing in the extended circle is calculated.
The magnetic induction intensity of each position on the magnetic induction intensity detection surface is similar when the transformer normally operates, and the temperature difference values of corresponding positions on the first temperature detection surface and the second temperature detection surface are similar, but when the transformer is frequently used, some abnormal phenomena may occur on elements of the transformer, so that the transformer is abnormal in the operation process, namely, the heat energy generated during the operation of the transformer is abnormal or the magnetic induction intensity during the operation of the transformer is abnormal, and therefore the distributed array temperature measurement abnormal data transmission monitoring system monitors the transmission operation of abnormal data.
The magnetic induction intensity of one circle can be obtained based on one circle of visit every time
And temperature difference
Where a is the index per turn.
For one turn of the reading, the local structural index per turn is calculated
The calculation formula is as follows:
wherein,
is a first
The local structural index of the loop is,
is as follows
The temperature difference value sequence corresponding to the ring,
is as follows
The magnetic induction intensity sequence corresponding to the circle, STD is standard deviation, range is range, F is diagonal sampling function, and F collects one element each time and opposite elements of the element in the diagonal direction symmetrical around the center; since the operating environment of the transformer should be uniform, the course reflected by the magnetic induction should also be as uniform as possible.
Represents the first
The mean value of the difference between the magnetic induction intensities of all two symmetrical circles,
denotes the first
Number of pairs of symmetric elements corresponding to the circle. When the average value is larger, the process is not uniform, so that the standard deviation of the temperature difference value which is not uniform is increased, and the local structural index in the running process of the transformer is reflected; when the index is too large, the index means that one circle of the detection surface cannot be operated in a uniform environment, and the phenomenon of large local difference is reflected.
So far, a corresponding local structural index vector in the running process of the transformer is obtained based on the local structural index of each circle
，
And N is the number of sampled turns of the detection surface in the running process of the transformer.
And S003, inputting the local structural index into a target support vector machine classifier, and judging whether the transformer is abnormal in operation by using the target support vector machine classifier.
In the embodiment, the support vector machine classifier is used for judging whether the transformer has abnormal operation in the operation time period; the support vector machine classifier needs to be trained for use; therefore, training samples need to be labeled, and in order to improve the accuracy of the classifier of the support vector machine, the embodiment uses a phase space analysis method as a basis for labeling the training samples, and the labeling types are divided into two types, one type is normal, and the other type is abnormal; although the judgment of whether the transformer is abnormal or not can be realized based on the phase space method, when the abnormality analysis is performed based on the phase space analysis method, it is required to ensure that the change of the phase space is continuous, and the records corresponding to the transformer in the next sample operation time period need to be inverted, and the real-time judgment of whether the transformer is abnormal or not cannot be realized. The following explains the relevant procedure:
because a general phase space reconstruction is generally in a uniform embedding mode, but cannot be applied to array data of a transformer, the embodiment is based on local structure analysis edge effect, so that the data non-uniformity phenomenon in the array can be continuously represented by the systematic evolution rule of the data non-uniformity phenomenon; therefore, for any transformer training sample, the embodiment performs characterization processing on the corresponding sample local structural index, and then performs spatial domain expansion on the temperature difference and the evolution of the magnetic induction intensity of each position of the corresponding detection surface of the transformer in the operation process of the transformer according to a polar coordinate mode, thereby realizing an improved spatial bending effect.
The phase space is constructed such that the local structural index can be used as a space for all possible states during operation of the transformer. Under the observation of manual participation, after a sample is ensured to be normally loaded in the operation process of the transformer for one time, the beginning time t =0 is set, and a vector of the sample is established through local structurality
Wherein N is the number of local structures, namely the number of sampling turns; selecting delay time parameter by mutual information method
And selecting an embedded dimension parameter m by using a false adjacent point method, wherein the phase space reconstruction mode is as follows:
at this point, the phase space of the N local structures with the local structural index change is reconstructed and used as the reference phase space
。
The local structural index of each substructure can be obtained each time the readings of the magnetic induction density matrix and the temperature difference matrix are updated
Updating data points
In which N is of partial structureThe number, i.e. the number of sampling turns. Minimum analysis Interval
The setting time is manually specified, and the setting time of the embodiment can be 10 seconds or 20 seconds; using the space of reference phase
Same delay time
And embedding dimension
Reconstruction
The phase space of the temporal local structural index reconstructs the current phase space in the same way as described above:
...
so far, the phase space U in the running process of the transformer can be obtained.
For the
A certain vector in the time phase space
Finding in reference phase space
A vector nearest to it
In which
。
Based on the above processing method, all data points are updated every time sampling is performed
Taking T as an observation time interval, and obtaining a structure separation index in the observation time interval T
. Specifically, for the phase space, there are:
computing
And vector
The farthest distance is
Design phase space weights
. Order to
Self increment by 1, continue to calculate
Up to
. Then, utilize
All tracking functions corresponding to all vectors in the time phase space are calculated
Tracking index of time phase space:
wherein q (n) is a weight function,
，
is composed of
The correlation dimension of the time phase space. Observing the phase space for a period of time, e.g. 60s, 6 times for P values, T =6, each P value corresponding to 10s, calculating
Average of T tracking indexes
And standard deviation of
。
The tracking index reveals the minimum unit of the transformer operation process
The course of the change is an indicator of the phase space state over an observation time interval T. When the state index changes greatly in a period of time, the environment on which the transformer depends changes obviously. Generally, when the error causes slow difference change of each area, the local structural index fluctuates in the running process of the transformer; therefore, standard deviation
Will be small so that the 3 sigma criterion can be used to estimate the variation tolerable in the quality of the transformer operation. In the embodiment, the structural separation index is obtained based on the phase space analysis in the operation process of the transformer
。
The above results in a phase space analysis result during one transformer operation, but is not applicable to multiple times. Because the tracking index is in the ending period, each index of the system is different from that when a sample corresponding to a brand new transformer operation process is put in, for example, the sample temperature difference in the brand new transformer operation process may be low, and the sample temperature difference at the ending time may be high, so that the change of a phase space needs to be ensured to be continuous, and the data in the next transformer operation process is subjected to time reverse order processing, so that the two transformer operation processes form a cycle, the phase space analysis is ensured to be continuous and infinite, the structure separation index is analyzed more accurately, and the influence caused by the transformer operation process is avoided. Therefore, according to the above method, when the next transformer runs, all records need to be processed in a reverse order, and the last sample is taken as the time t =0, so that the continuous change process is ensured, and by analogy, all analyses are performed alternately in a positive order and a reverse order, thereby forming a cycle.
Although the phase space can track the operation process of the transformer to find the abnormality, in view of the need of constructing the cyclic process, not every process can be performed in real time, so that the present embodiment performs SVM sample construction based on two classes of the structure discrete code, and initializes the SVM, and the specific process is as follows:
and determining data influencing whether abnormal operation occurs in the operation process of the transformer, and constructing a corresponding local structural index w.
The structure discrete code is a vector of the local structural index represented by each instant detection surface, and the structure separation index L corresponding to the detection surface can be calculated based on the vector of the local structural index represented by each instant detection surface.
And obtaining two levels of structure discrete codes based on L, and marking the structure discrete codes with high dispersion and the structure discrete codes with low dispersion. The marking process is as follows: structural separation index during transformer operation
After two times of measurement and updating, the value is larger than the previous value L, which means that three adjacent structure discrete codes can reflect the phenomenon of abnormal operation in the operation process of the transformer, and the value is marked as B. Otherwise, the other samples are marked as A, which represents that the operation process is normal.
The sample comprises all encountered data conditions in the transformer operation process, the data are approximate complete data, the classification of the sample is determined, and an SVM classifier is trained based on the classification.
Training the support vector machine classifier to obtain a trained target support vector machine classifier:
the embodiment is based on two types of automatic analysis, adopts a supervised classification method and uses a Support Vector Machine (SVM) classifier, and classifies the transformer based on the characteristic parameters expressed above in the operation process of the transformer. The SVM classifier has the advantages that linear or nonlinear classification can be performed by using a hyperplane and kernel function mode, and the result is more accurate, so that the transformer abnormal operation detection and classification method based on the fuzzy multi-class SVM is used in the embodiment.
The calculation process of the SVM in this embodiment is: dividing hyperplanes with different abnormal degrees, calculating intervals among different abnormalities, analyzing hyperplane conditions when the intervals are maximum, and summarizing an optimal hyperplane. And classifying the compounded rubber data finished in the running process of the transformer. The detailed operation steps belong to the prior art and are not described in detail.
Eighty percent of the obtained transformer operation data samples are used as training samples, the rest twenty percent of the obtained transformer operation data samples are used as test samples, a SVM classifier is carried out by using the training sample data corresponding to the transformer operation process, the values of the parameters of the classifier are changed, various corresponding parameter values when the classification performance of the transformer operation effect classifier reaches the best are calculated, the classifier finishes parameter training till the classifier finishes parameter training, the rest twenty percent of the transformer operation data test samples are put into the classifier, the classification effect is tested, whether the classification is correct or not is judged, whether the correct rate meets the requirement or not is judged, and otherwise, the parameters of the classifier are continuously modified until the correct rate meets the use requirement.
Therefore, a trained SVM classifier can be obtained and recorded as a target SVM classifier, and two classifications of transformer operation quality can be realized based on the target SVM classifier, wherein one classification is a normal condition and the other classification is a slight abnormal condition. Since a serious abnormal situation is generally unlikely to occur, the present embodiment does not consider a serious abnormal situation.
Therefore, the embodiment can judge whether the abnormal operation phenomenon occurs in the operation process of the transformer to be detected based on the obtained target support vector machine classifier; therefore, the local structural index corresponding to the running process of the transformer to be detected is input into the target support vector machine classifier, and whether the abnormal running phenomenon occurs in the running process of the transformer to be detected is judged by using the target support vector machine classifier.
Putting the local structural indexes corresponding to the operation process of the transformer to be detected into an SVM (support vector machine) to carry out 2 classification through calculation, and obtaining a result of whether the operation process of the transformer to be detected has abnormal operation; in particular, the situation is marked as normal
Slight abnormality mark
For being allocated to
The transformer operation process of the group generates some slight transformer operation process abnormity in the transformer operation process, and the abnormity possibly occurs in the performance of the transformer element. The method and the device reduce the time cost of manual observation, and solve the problem of how to reasonably judge the abnormal operation of the samples of the multiple transformer operation processes in all the possibilities of the chaotic system in the space of the temperature and the transformer operation processes.
Has the beneficial effects that: in the embodiment, the temperature difference values of the first target positions and the magnetic induction intensities of the second target positions in the operation process of the transformer to be detected are used as the basis for obtaining the temperature difference value sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface; taking the temperature difference sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface as a basis for calculating to obtain a corresponding local structural index in the running process of the transformer; the local structural index and the target support vector machine classifier are used as a basis for judging whether the operation of the transformer is abnormal or not in the operation process; according to the method and the device, the real-time judgment on whether the running state of the transformer is abnormal in the running process of the transformer is realized according to the magnetic induction intensity, and the running state of the transformer in the underground substation can be monitored relatively reliably.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (4)
1. A distributed array temperature measurement abnormal data transmission monitoring system based on an electric power Internet of things comprises a memory and a processor, and is characterized in that the processor executes a computer program stored in the memory to realize the following steps:
acquiring the temperature difference value of each first target position and the magnetic induction intensity of each second target position in the operation process of the transformer to be detected;
according to the position of each first target position on a first temperature detection surface and the temperature difference value, constructing and obtaining a temperature difference value sequence corresponding to each circle of the first temperature detection surface; according to the position of each second target position on the magnetic induction intensity detection surface and the magnetic induction intensity, constructing and obtaining a magnetic induction intensity sequence corresponding to each circle of the magnetic induction intensity detection surface; calculating to obtain a corresponding local structural index in the operation process of the transformer according to the temperature difference sequence corresponding to each circle of the first temperature detection surface and the magnetic induction intensity sequence corresponding to each circle of the magnetic induction intensity detection surface;
inputting the local structural index into a target support vector machine classifier, and judging whether the transformer is abnormal in operation by using the target support vector machine classifier;
the method for acquiring the temperature difference of each first target position and the magnetic induction intensity of each second target position in the operation process of the transformer to be detected comprises the following steps:
obtaining a first temperature detection surface and a second temperature detection surface corresponding to the transformer according to the axis of the primary winding and the axis of the secondary winding of the transformer core; a plurality of temperature sensors are distributed on the temperature detection surface;
recording the position of each temperature sensor on a first temperature detection surface corresponding to the transformer as each first target position;
acquiring a position corresponding to the first target position on a second temperature detection surface corresponding to the transformer, and recording the position as a matching position of each first target position on a first temperature detection surface corresponding to the transformer;
calculating the absolute value of the difference between the temperature value of each first target position on the first temperature detection surface corresponding to the transformer and the temperature value of the corresponding matching position;
recording the absolute value of the difference as the temperature difference corresponding to each first target position in the running process of the transformer;
arranging a Hall sensor on a shell surface of the transformer close to the N-level of the transformer magnet to acquire magnetic induction intensity, marking the shell surface as a magnetic induction intensity detection surface corresponding to the transformer, and arranging the Hall sensor on the magnetic induction intensity detection surface;
recording the position of each Hall sensor on the magnetic induction intensity detection surface as each second target position, and obtaining the magnetic induction intensity of each second target position in the running process of the transformer;
the method for obtaining the first temperature detection surface and the second temperature detection surface corresponding to the transformer according to the axis of the primary winding and the axis of the secondary winding of the transformer core comprises the following steps:
forming a plane according to the axis of the primary winding and the axis of the secondary winding, respectively arranging a temperature detection surface at the same distance on two sides of the plane, wherein the two temperature detection surfaces are arranged on the outer sides of the winding coils, the two temperature detection surfaces are respectively provided with a temperature sensor, and the positions and the number of the temperature sensors arranged on the two temperature detection surfaces are the same and are in one-to-one correspondence;
recording the two temperature detection surfaces corresponding to the transformer as a first temperature detection surface and a second temperature detection surface corresponding to the transformer respectively;
the method for constructing and obtaining the temperature difference value sequence corresponding to each circle of the first temperature detection surface according to the position of each first target position on the first temperature detection surface and the temperature difference value comprises the following steps:
reading the temperature difference value of each first target position on the first temperature detection surface in a zigzag mode, specifically: firstly, reading the temperature difference of each first target position on a minimum circle consisting of 2 multiplied by 2 first target positions at the central position of a first temperature detection surface to obtain a temperature difference sequence of the minimum circle, then sequentially expanding one circle outwards on the basis of the minimum circle, reading the temperature difference of each first target position on each outer expansion circle to obtain a temperature difference sequence of each outer expansion circle, and when the width or height of one circle cannot be expanded outwards, only reading the temperature difference of the first target position on the outer expansion circle, and finally constructing to obtain a temperature difference sequence corresponding to each circle of the first temperature detection surface;
the method for constructing and obtaining the magnetic induction intensity sequence corresponding to each circle of the magnetic induction intensity detection surface according to the position of each second target position on the magnetic induction intensity detection surface and the magnetic induction intensity comprises the following steps:
reading the magnetic induction intensity of each second target position on the magnetic induction intensity detection surface in a square-shaped mode, and specifically comprises the following steps: firstly, reading the magnetic induction intensity of each second target position on a minimum circle consisting of 2 multiplied by 2 second target positions at the central position of a magnetic induction intensity detection surface to obtain a magnetic induction intensity sequence of the minimum circle, then expanding the minimum circle outwards in sequence for one circle, reading the magnetic induction intensity of each second target position on each external expansion circle to obtain a magnetic induction intensity sequence of each external expansion circle, when the width or height of one circle cannot be expanded outwards continuously, only reading the magnetic induction intensity of the second target position existing on the external expansion circle, and finally constructing and obtaining the magnetic induction intensity sequence corresponding to each circle of the magnetic induction intensity detection surface.
2. The power internet of things-based distributed array thermometry anomaly data transmission monitoring system according to claim 1, wherein the training process of the target support vector machine classifier comprises: judging whether the local structural indexes of the training samples corresponding to the operation processes of the transformers are abnormal by using a phase space analysis method, and marking the training samples corresponding to the operation processes of the transformers; and training the support vector machine classifier based on the corresponding training samples in the marked transformer operation process.
3. The power internet of things-based distributed array temperature measurement abnormal data transmission monitoring system of claim 2, wherein the method for judging whether the local structural index of the corresponding training sample in the operation process of each transformer is abnormal by using a phase space analysis method comprises the following steps:
for a training sample corresponding to any transformer operation process:
calculating the standard deviation of the tracking index corresponding to each observation time in the running process of the transformer by taking the target running time period of the transformer as one observation time period of a phase space;
calculating the structural separation index corresponding to each observation time according to the standard deviation of the tracking index corresponding to each observation time;
and judging whether the structural separation index is increased for more than two times in the running process of the transformer, and if so, judging that the local structural index of the running process of the transformer is abnormal.
4. The power internet of things-based distributed array temperature measurement abnormal data transmission monitoring system of claim 1, wherein the method for obtaining the corresponding local structural index in the running process of the transformer through calculation according to the temperature difference sequence and the magnetic induction intensity sequence corresponding to each circle of the detection surface comprises the following steps:
the local structural index of each turn is calculated using the following formula:
wherein,
is as follows
The local structural index of the ring is,
is as follows
The temperature difference value sequence corresponding to the ring,
is as follows
The magnetic induction intensity sequence corresponding to the circle, STD is standard deviation, range is range, F is diagonal sampling function,
denotes the first
The number of pairs of symmetric elements corresponding to the circle;
and obtaining a corresponding local structural index in the running process of the transformer according to the local structural index of each circle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210566111.3A CN114678962B (en) | 2022-05-24 | 2022-05-24 | Distributed array temperature measurement abnormal data transmission monitoring system based on power internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210566111.3A CN114678962B (en) | 2022-05-24 | 2022-05-24 | Distributed array temperature measurement abnormal data transmission monitoring system based on power internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114678962A CN114678962A (en) | 2022-06-28 |
| CN114678962B true CN114678962B (en) | 2023-01-13 |
Family
ID=82079722
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210566111.3A Active CN114678962B (en) | 2022-05-24 | 2022-05-24 | Distributed array temperature measurement abnormal data transmission monitoring system based on power internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114678962B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114925943B (en) * | 2022-07-22 | 2022-09-30 | 江苏布罗信息技术有限公司 | Transformer abnormal state prediction method based on power internet of things |
| CN117545141B (en) * | 2023-11-03 | 2024-07-02 | 深圳市郡宁智能科技有限公司 | Intelligent illumination control method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495320A (en) * | 2011-12-21 | 2012-06-13 | 鞍钢集团矿业公司 | Dynamic monitoring system and on-line monitoring method of transformer operation exception |
| CN105468858A (en) * | 2015-12-01 | 2016-04-06 | 国家电网公司 | Structural transformer fault diagnosis method based on finite element simulation and field test |
| CN114509708A (en) * | 2022-01-06 | 2022-05-17 | 华为数字能源技术有限公司 | Power system, magnetic induction module, magnetic induction intensity detection device, and transformer |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FI20185463A1 (en) * | 2018-05-18 | 2019-11-19 | Enics Ag | Method and system for fault detection |
| CN113011464A (en) * | 2021-02-25 | 2021-06-22 | 沈阳工业大学 | Comprehensive prediction method for running state of transformer based on multi-dimensional data evaluation |
| CN113204922B (en) * | 2021-05-17 | 2023-03-28 | 上海交通大学 | Transformer running state monitoring and predicting method |
-
2022
- 2022-05-24 CN CN202210566111.3A patent/CN114678962B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102495320A (en) * | 2011-12-21 | 2012-06-13 | 鞍钢集团矿业公司 | Dynamic monitoring system and on-line monitoring method of transformer operation exception |
| CN105468858A (en) * | 2015-12-01 | 2016-04-06 | 国家电网公司 | Structural transformer fault diagnosis method based on finite element simulation and field test |
| CN114509708A (en) * | 2022-01-06 | 2022-05-17 | 华为数字能源技术有限公司 | Power system, magnetic induction module, magnetic induction intensity detection device, and transformer |
Non-Patent Citations (1)
| Title |
|---|
| 电力变压器运行状态下振动特性探讨;黄守剑;《电子世界》;20201231;第50-51页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114678962A (en) | 2022-06-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114678962B (en) | Distributed array temperature measurement abnormal data transmission monitoring system based on power internet of things | |
| CN112763929B (en) | Method and device for predicting health of battery monomer of energy storage power station system | |
| Biswas et al. | Electricity theft pinpointing through correlation analysis of master and individual meter readings | |
| CN109659933A (en) | A kind of prediction technique of power quality containing distributed power distribution network based on deep learning model | |
| CN110210681B (en) | Prediction method of PM2.5 value of monitoring station based on distance | |
| CN103954913A (en) | Predication method of electric vehicle power battery service life | |
| Chen et al. | A health index construction framework for prognostics based on feature fusion and constrained optimization | |
| CN113743651A (en) | Electronic voltage transformer error state prediction method based on machine learning and SVM algorithm | |
| CN109598052B (en) | Intelligent ammeter life cycle prediction method and device based on correlation coefficient analysis | |
| CN108830407B (en) | Sensor distribution optimization method in structure health monitoring under multi-working condition | |
| CN117992776B (en) | Real-time prediction method for health state of power grid equipment based on artificial intelligence | |
| CN115408864A (en) | Electronic transformer error state self-adaptive prediction method, system and equipment | |
| Zhang et al. | Partially observable multi-sensor sequential change detection: A combinatorial multi-armed bandit approach | |
| Wang et al. | A sensor network based virtual beam-like structure method for fault diagnosis and monitoring of complex structures with improved bacterial optimization | |
| CN118013361B (en) | Intelligent diagnosis and evaluation system for power supply equipment on railway | |
| CN113536662B (en) | Electronic transformer error state prediction method based on firefly optimized LightGBM algorithm | |
| Ma et al. | Measurement error assessment for smart electricity meters under extreme natural environmental stresses | |
| CN113589172A (en) | Service life estimation method for power grid components | |
| CN117706452A (en) | Online evaluation method of voltage transformer based on KNN-GRU model | |
| CN117330963A (en) | Energy storage power station fault detection method, system and equipment | |
| Deng et al. | Real-time detection of false data injection attacks based on load forecasting in smart grid | |
| CN116432535A (en) | Performance verification error analysis method for electric power metering device considering multiple environmental elements | |
| CN116258467A (en) | Electric power construction management and control system | |
| Yan et al. | Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM | |
| CN118378544B (en) | Smart grid line loss prediction method based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Distributed Array Temperature Measurement Anomaly Data Transmission and Monitoring System Based on the Internet of Things for Power Systems Effective date of registration: 20230829 Granted publication date: 20230113 Pledgee: Jinan Shengfu Branch of Qilu Bank Co.,Ltd. Pledgor: SHANDONG KEHUA ELECTRICAL TECHNOLOGY CO.,LTD. Registration number: Y2023980054393 |