CN114636882A - Digital twin-based transformer magnetic bias detection system and method - Google Patents
Digital twin-based transformer magnetic bias detection system and method Download PDFInfo
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Abstract
The invention discloses a digital twin-based transformer magnetic biasing detection system and a method, the method adopts a digital twin technology to establish a simulation transformer model in a workstation, collects transformer data information, remotely leads the data information into the simulation transformer to form the digital twin transformer, leads the information of the digital twin transformer and the transformer to be consistent, fuses the data information together through a multi-sensor data fusion technology to jointly judge the magnetic biasing information of the transformer, establishes a characteristic curve of neutral point current and transformer core magnetic field intensity in the workstation, superposes the characteristic curve, the transformer information data and the magnetic biasing information on the digital twin transformer, leads a user not to go to the site, looks up the magnetic biasing information of the transformer and other characteristic changes caused by magnetic biasing through the workstation, looks up the influence of the neutral point current on the magnetic biasing of the transformer through the information superposed on the digital twin transformer, and the influence of the bias magnetism of the transformer on the transformer.
Description
Technical Field
The invention belongs to the field of transformer detection, and particularly relates to a digital twin-based transformer magnetic biasing detection system and method.
Background
The phenomenon of transformer dc magnetic bias is mainly caused by dc current intruding into an ac system due to operation of a high voltage dc transmission monopole ground loop, geomagnetic induction current caused by solar magnetic force, dc component flowing into a power grid from power electronic devices such as an inverter and a controller, etc., and dc current flows into a transformer winding. Especially, when the high-voltage power grid operates in a single-pole ground loop mode, a certain potential difference exists between grounding points, and the potential difference can enable a neutral line on one side of the transformer to inject a certain direct current into the transformer. With the development of urban rail transit, stray current leaked to the ground when a locomotive runs invades a neutral point of a transformer along the line, so that a working magnetization curve of the transformer core is deviated, and a direct-current magnetic biasing effect of the transformer is caused. The earth magnetic field changes caused by stray currents of rail transit are significantly periodic and more common than those caused by monopole earth operation of high-voltage direct current transmission and solar magnetic storm.
After the direct current flows into the transformer, direct current magnetic flux is generated in the iron core, so that a series of problems of saturation of the half cycle of the iron core of the transformer, serious distortion of exciting current, large reactive power consumption, increase of vibration noise and the like are caused. The problem of vibration noise caused by direct current magnetic biasing gradually draws attention of people, and the vibration noise of the transformer seriously influences the normal life, physical and mental health of people. The mechanical vibration performance is also one of the technical indexes for evaluating the working state of the transformer, the transformer under long-term abnormal vibration can cause the structure to be loose and the mechanical strength to be reduced, and in severe cases, the structural part can be abraded, the insulating strength can be reduced, and potential safety hazards can be buried. Secondly, the temperature of the transformer is increased due to the direct current magnetic biasing of the transformer, if the transformer runs at a high temperature for a long time, the service life of the internal insulating paper board is shortened, the insulating paper board becomes brittle and is easy to break, the insulating effect is lost, accidents such as breakdown and the like are caused, the insulation of a winding is seriously aged, the degradation of insulating oil is accelerated, and the service life is influenced.
In order to guarantee the stable operation of the transformer in the prior art, the transformer is generally shut down and overhauled regularly, and the temperature change condition of the transformer and the vibration condition of the transformer and the like are mainly detected on site through a manual handheld infrared thermal imager. Secondly, the magnetic biasing condition of the transformer is monitored through various sensors and is transmitted to a monitoring center for monitoring through a wired or wireless transmission mode. However, the labor intensity of workers is increased by checking the operation condition of the transformer on site manually, the cost is high, and the overhaul period is long. And there is a certain risk. The existing sensor detection mode is single, misjudgment is easy to occur, once the sensor goes wrong, detection cannot be performed, the acquired data information is digitally displayed through the sensor detection mode, and a user cannot visually check the magnetic biasing condition of the transformer.
Disclosure of Invention
The invention aims to provide a transformer magnetic biasing detection system and method based on digital twinning, which can utilize a digital twinning technology to establish a simulation transformer model in 3D MAX software according to transformer parameters, a magnetic field inductor, a current sensor, a temperature sensor, a vibration sensor and a noise detector are arranged on a transformer to respectively monitor the iron core magnetic field change information, the neutral point current information, the primary side current information, the secondary side current information, the temperature change information, the vibration information and the generated noise information of the transformer, the data information is fused together through a multi-sensor data fusion technology to jointly judge the magnetic biasing information of the transformer, the detected data information is transmitted to a monitoring center, the data information is automatically introduced into the simulation transformer model at the monitoring center to form the digital twinning transformer, the iron core magnetic field change information of the digital twinning transformer, the noise information of the digital twinning transformer and the like, The neutral point current information, the primary side current information, the secondary side current information, the temperature change information, the vibration information and the generated noise information are consistent with the information of the transformer and can be superposed on the digital twin transformer for display, a user can check the magnetic biasing information of the transformer and other characteristic changes caused by magnetic biasing through a computer end without frequently going to the site, and check the influence of the neutral point current on the magnetic biasing of the transformer and the influence of the magnetic biasing of the transformer on the transformer through the information superposed on the digital twin transformer.
In order to achieve the purpose, the invention adopts the technical scheme that: a transformer magnetic biasing detection method based on digital twinning comprises the following steps:
detecting iron core magnetic field change information, neutral point current information, primary side voltage information, primary side current information, secondary side current information, temperature change information, vibration information and generated noise information of a transformer through a sensor;
secondly, transmitting the detected data information to an industrial personal computer, carrying out signal amplification, filtering, AD conversion and data preprocessing on the acquired data information, and then remotely transmitting the preprocessed data information to a workstation;
thirdly, the workstation establishes a simulation transformer model according to transformer parameters through a digital twinning technology, and then introduces the preprocessed data information into the simulation transformer model to form a digital twinning transformer;
fusing iron core magnetic field change information, primary side current information, secondary side current information, temperature change information, vibration information and generated noise information together by a multi-sensor data fusion technology to judge magnetic biasing information of the transformer, and overlapping the magnetic biasing information on the digital twin transformer to be displayed through a workstation;
and fifthly, establishing a characteristic curve of the neutral point current and the magnetic field intensity of the iron core of the transformer in the workstation, superposing the characteristic curve between the neutral point current and the magnetic field intensity of the iron core on the digital twin transformer, and checking the influence of the neutral point current on the magnetic biasing of the transformer through the information superposed on the digital twin transformer.
Further, in the fourth step, the scheme for judging the magnetic biasing information of the transformer by the multi-sensor data fusion technology is as follows: the magnetic bias condition of the transformer can be judged by fusing parameter information such as iron core magnetic field change information, primary side current information, secondary side current information, temperature change information, vibration information, noise information and the like generated by the magnetic bias of the transformer, the magnetic bias condition of the transformer is detected by establishing a gradient data fusion model, a data fusion center is firstly set, wherein the sensor comprises a voltage sensor, a current sensor, a magnetic field inductor, a temperature sensor, a vibration sensor and a noise detector, the current sensor comprises a first current sensor and a second current sensor, wherein the first current sensor is used for detecting the primary side current information and the secondary side current information of the transformer, the second current sensor is used for detecting current information of a neutral point of the transformer, an intermediate station is arranged between the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor, the noise detector and the data fusion center, data information collected by the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor and the noise detector is correlated in the intermediate station, data level fusion is carried out, local fusion is achieved, feature extraction is carried out on the data information after the local fusion in the data fusion center, data attributes are judged according to feature extraction results, then the judged data information is correlated to carry out decision level fusion, global fusion is achieved, and the result after the fusion is output to judge the magnetic biasing condition of the transformer.
Furthermore, in the fifth step, after the second current sensor detects the current of the neutral point, the influence of the stray current on the magnetic biasing of the transformer is judged by comparing the current with data information collected by the magnetic field sensor.
Furthermore, in the process of judging the influence of the neutral point current on the magnetic bias of the transformer, the magnetic field sensor is used for detecting the change information of the magnetic field of the iron core, the neutral point current information and the change information of the magnetic field of the iron core, which are detected by the second current sensor and the magnetic field sensor, are transmitted to the workstation together, a characteristic curve of the neutral point current and the magnetic field strength of the iron core of the transformer is established in the workstation, the characteristic curve between the neutral point current and the magnetic field strength of the iron core of the transformer is superposed on the digital twin transformer, and when stray current enters the neutral point, the influence of the stray current of the neutral point on the magnetic bias of the transformer can be checked through the information superposed on the digital twin transformer.
Further, when a digital twin transformer is constructed in the third step, specific parameters of the transformer are led into BIM software Revit to generate RVT files, the RVT files are led into 3D max software to be edited, parts identical to BIM models are combined into an object according to element types in the 3D max software, redundant dot and line surfaces are deleted, the combined model is endowed with material textures and characteristic parameters, the edited model is led out from the 3D max software and loaded into an engine, secondary material effect adjustment is carried out according to the material attributes of the transformer, a virtual transformer with the same parameters as the transformer is rendered, a virtual industrial personal computer is established, and data information acquired by a voltage sensor, a current sensor, a magnetic field sensor, a temperature sensor, a vibration sensor and a noise detector is transmitted to the virtual industrial personal computer after being converged by the physical industrial personal computer, and establishing a data interface between the virtual industrial personal computer and the virtual transformer, performing data analysis and scene rendering in the virtual transformer according to the acquired data information, and finally generating a digital twin transformer so that the data information received by the digital twin transformer is consistent with the real characteristic data of the transformer.
Furthermore, in the data level fusion process of the data information collected by the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor and the noise detector, the collected data information is locally fused by adopting a fuzzy logic control method, and X is used for fusing the collected data information1、X2、X3、X4、X5Respectively as fuzzy sets of a first current sensor, a magnetic field sensor, a temperature sensor, a vibration sensor and a noise detector, wherein X1The method comprises the primary side current information and the secondary side current information acquired by a first current sensor at each moment, wherein the primary side current information and the secondary side current information acquired at the kth moment are represented as x1(k)；X2The magnetic field sensor comprises iron core magnetic field change information acquired at each moment of a magnetic field sensor, wherein the iron core magnetic field change information acquired at the kth moment is represented as x2(k)；X3The temperature change information acquired at each moment of the temperature sensor is included, wherein the temperature change information acquired at the kth moment is represented as x3(k)；X4The method comprises vibration information acquired at each moment of a vibration sensor, wherein the vibration information acquired at the kth moment is represented as x4(k)；X5Including noise detector time of dayCollecting generated noise information at the kth moment, wherein the generated noise information collected at the kth moment is represented as x5(k) (ii) a Evaluation of X by closeness1、X2、X3、X4、X5The similarity of the magnetic biasing situation is identified, and at the kth moment, the data matrix among all sensors is as follows:
the elements in the matrix a are the expression of closeness between the sensors, that is:
αmn(k) representing a proximity expression, x, between any two of the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor, and the noise detectorm(k)、xn(k) Respectively representing data information collected by the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor and the noise detector at the time k, wherein when m is 1 and n is 2, x is1(k) Representing data information, x, collected by the first current sensor at time k2(k) Representing the data information collected by the magnetic field inductor at time k, when alphamn(k) When the value is less than the preset value M, considering xm(k) And xn(k) Are not similar, then alpha can be obtainedmn(k) When the value is 0, the following components are available:
the consistency measurement calculation formula of the sampling values of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector is as follows:
in the formula of alphaij(k) For the ith row and jth column of data information, alpha, of the sensor in the data matrix at time kij(k) Is x1(k)、x2(k)、x3(k)、x4(k) Or x5(k)。
The calculation formula of data fusion is expressed as:
in the formula x1(k) Expressed as data information, x, acquired by the first current sensor at time k2(k) Expressed as data information, x, collected by the magnetic field sensor at time k3(k) Expressed as data information, x, collected by the temperature sensor at time k4(k) Expressed as data information, x, collected by the vibration sensor at time k5(k) Expressed as data information, omega, collected by the noise detector at time ki(k) The ith row weight in the data matrix is represented, and according to the information sharing principle, the sum of the information quantity of the optimal fusion estimation can be decomposed into the sum of the information quantities of a plurality of measurement data, namely, one information can be shared by a plurality of subsystems, wherein
Normalizing the fuzzy closeness of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector to obtain respective weights
In the formula, c1(k)，c2(k)，c3(k)，c4(k)，c5(k) Representing a set of non-negative numbers.
Further, in the process of decision-level fusion in the data fusion center, the magnetic biasing situation of the transformer is divided into normal F1Mild degree of F2Severe F3Severe F4Four levels, let Θ be a recognition framework, Ω (Θ) represents the set of all subsets in Θ, and the transformer bias level is represented as:
Ω(Θ)＝{φ，{F1}，{F2}，{F3}，{F4}，{F1,F2}，{F2,F3say, phi is an empty set, assuming Q ═ F1,F2E.g., theta, indicates that Q is either normal F1Either light F2I.e. Ω (Θ) is any one of the states;
after the recognition frame theta is determined, a set function G is defined, and omega (theta) is mapped to the interval [0,1 ]]The number of (a) above, expressed as:
wherein G (Q) is called basic probability assignment function, represents the confidence of the state recognition model to a specific state, and represents the mapping Bel on the recognition framework theta by using the belief function Bel, wherein Q is → [0,1]And satisfies the following conditions:
the next mapping Pl: Ω (Θ) → [0,1 ] is then represented by the plausibility function Pl]And satisfies:
in the formula QcRepresents the complement of Q, pl (Q) represents the plausibility of proposition Q, where pl (Q) 0 represents evidence rejection Q, and pl (Q) 1 represents evidence support Q;
let GiA basic probability assignment function representing the ith set of evidence defined on the recognition framework Θ, where i ═ 1,2.
K represents the normalized coefficient, expressed as:
G1(Qi) As set 1 evidence QiBasic probability assignment function of G2((Ω(Θ))j) As the 2 nd evidence (omega (theta))jG (q) reflects the degree of conflict between two evidences, and when K ≠ 0, represents the combination between the two sets of evidences in the form of an orthogonal sum; when K is 0, the condition that the orthogonal sum does not exist is shown, and the two evidences belong to a contradiction relation;
then a first current sensor, a magnetic field sensor, a temperature sensor, a vibration sensor and a noise sensor are constructedBasic probability assignment function of acoustic detector, and identification results of first current sensor, magnetic field sensor, temperature sensor, vibration sensor and noise detector as five evidences E1、E2、E3、E4、E5While at the same time F is normal1Mild degree of F2Severe F3Severe F4Four grades are used as identification fields, and theta is equal to { F1,F2,F3,F4Form E1、E2、E3、E4、E5A common identification framework, a basic probability assignment function of each evidence is constructed on the identification framework theta, and the basic probability assignment function is calculated according to a formula
Then E can be calculated1、E2、E3、E4、E5The fused basic probability assignment function is used for judging the state of the magnetic biasing condition of the transformer according to the fused basic probability assignment function, for example, E is constructed on an identification frame theta1As shown in table 1, where sample 1 is analyzed and the maximum value of the basic probability assignment function of sample 1 is at F1And indicating that the bias condition of the transformer is in a normal state.
TABLE 1E1Basic probability assignment function of
| Sample(s) | F1 | F2 | F3 | F4 | Sample classes |
| 1 | 0.69 | 0.15 | 0.06 | 0.10 | |
| 2 | 0.45 | 0.43 | 0.06 | 0.06 | |
| 3 | 0.09 | 0.02 | 0.77 | 0.12 | |
| 4 | 0.11 | 0.05 | 0.03 | 0.81 | F4 |
The utility model provides a transformer magnetic biasing detecting system based on digit twin, which comprises a transformer, voltage sensor, current sensor, the magnetic field inductor, a weighing sensor and a temperature sensor, vibration sensor, the noise detector, the industrial computer, 5G module and workstation, install voltage sensor and current sensor on the transformer surface, voltage sensor, current sensor inserts the generating line of transformer respectively, the industrial computer is installed at the transformer surface, 5G module is installed on the industrial computer, install the magnetic field inductor by transformer core, temperature sensor, vibration sensor, the noise detector is installed at the transformer inboard, the magnetic field inductor, temperature sensor, vibration sensor, the noise detector, current sensor and voltage sensor connect the industrial computer respectively, the industrial computer passes through 5G module connection workstation.
Furthermore, the current sensors comprise six first current sensors and six second current sensors, the six first current sensors are respectively used for detecting the primary side current of the transformer and the secondary side current of the transformer, the one second current sensor is used for detecting the current of the neutral point of the transformer, and the three voltage sensors are used for detecting the primary side voltage of the bus of the transformer.
Compared with the prior art, the invention has the following beneficial effects: 1. the invention reflects the running condition of the transformer under the magnetic biasing condition by utilizing the digital twin technology, and transmits the iron core magnetic field change information, the neutral point current information, the primary side current information, the secondary side current information, the temperature change information, the vibration information and the generated noise information in the transformer to the digital twin transformer by adopting the 5G technology, so that the information of the digital twin transformer is consistent with the information of the transformer and can be superposed on the digital twin transformer for display, a user can check the magnetic biasing information of the transformer and other characteristic changes caused by magnetic biasing through a computer end without frequently going to the site, and check the influence of the neutral point current on the magnetic biasing of the transformer and the influence of the magnetic biasing of the transformer on the transformer by superposing the information on the digital twin transformer. The efficiency of fault monitoring is improved through analysis of the transformer simulation data. 2. The data information is fused together through a multi-sensor data fusion technology to judge the magnetic biasing information of the transformer, the system does not rely on the data information collected by one sensor alone to judge the magnetic biasing condition of the transformer, even if one sensor fails, the detection is not affected, and the identification accuracy is effectively improved. 3. The influence of the neutral point current on the magnetic biasing of the transformer is checked through information superposed on the digital twin transformer. The relation of stray current to the bias of the transformer can be corresponded, and particularly, for the transformer beside the rail transit, effective overhaul reference can be provided for workers.
Drawings
FIG. 1 is a block diagram of the components of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a flow chart of the modeling of the digital twin transformer of the present invention;
FIG. 4 is a flow chart of multi-sensor data fusion in accordance with the present invention.
In the figure: 1. the device comprises a voltage sensor, a current sensor 2, a magnetic field sensor 3, a temperature sensor 4, a vibration sensor 5, a noise detector 6, an industrial personal computer 7 and an 8.5G module.
Detailed Description
Referring to fig. 1 to 4, a method for detecting magnetic bias of a transformer based on digital twins includes the following steps:
detecting iron core magnetic field change information, neutral point current information, primary side voltage information, primary side current information, secondary side current information, temperature change information, vibration information and generated noise information of a transformer through a sensor;
secondly, transmitting the detected data information to an industrial personal computer 7, performing signal amplification, filtering, AD conversion and data preprocessing on the acquired data information, and remotely transmitting the preprocessed data information to a workstation;
thirdly, the workstation establishes a simulation transformer model according to the transformer parameters through a digital twinning technology, then leads the preprocessed data information into the simulation transformer model to form a digital twinning transformer,
fusing iron core magnetic field change information, primary side current information, secondary side current information, temperature change information, vibration information and generated noise information together by a multi-sensor data fusion technology to judge magnetic biasing information of the transformer, and overlapping the magnetic biasing information on the digital twin transformer to be displayed through a workstation; therefore, when the transformer magnetic biasing is judged, the transformer magnetic biasing condition is not judged by independently depending on data information collected by one sensor, and the identification accuracy is effectively improved;
and fifthly, establishing a characteristic curve of the neutral point current and the magnetic field intensity of the iron core of the transformer in the workstation, superposing the characteristic curve between the neutral point current and the magnetic field intensity of the iron core on the digital twin transformer, and checking the influence of the neutral point current on the magnetic biasing of the transformer through the information superposed on the digital twin transformer.
As shown in fig. 4, in the fourth step, the scheme for determining the magnetic bias information of the transformer by the multi-sensor data fusion technology is as follows: the magnetic bias condition of the transformer can be judged by fusing parameter information such as iron core magnetic field change information, primary side current information, secondary side current information, temperature change information, vibration information, noise information and the like generated by the magnetic bias of the transformer, a gradient data fusion model is established to detect the magnetic bias condition of the transformer, a data fusion center is set firstly, wherein the sensors comprise a voltage sensor 1, a current sensor 2, a magnetic field inductor 3, a temperature sensor 4, a vibration sensor 5 and a noise detector 6, the current sensor 2 comprises a first current sensor and a second current sensor, wherein the first current sensor is used for detecting the primary current information and the secondary current information of the transformer, the second current sensor is used for detecting the current information of the neutral point of the transformer, an intermediate station is arranged among the first current sensor, the magnetic field inductor 3, the temperature sensor 4, the vibration sensor 5, the noise detector 6 and the data fusion center, the data information collected by the first current sensor, the magnetic field sensor 3, the temperature sensor 4, the vibration sensor 5 and the noise detector 6 are correlated in the intermediate station, then data level fusion is carried out to realize local fusion, and the data information after the local fusion is carried out the feature extraction in the data fusion center, the data attribute is judged according to the feature extraction result, and then, associating the judged data information to perform decision-level fusion so as to realize global fusion, and outputting the fused result to judge the magnetic biasing condition of the transformer.
In the fifth step, after the second current sensor detects the current of the neutral point, the influence of the stray current on the magnetic biasing of the transformer is judged by comparing the current with the data information collected by the magnetic field inductor 3.
In the process of judging the influence of neutral point current on the bias magnetism of the transformer, the magnetic field sensor 3 is used for detecting iron core magnetic field change information, the neutral point current information and the iron core magnetic field change information detected by the second current sensor and the magnetic field sensor are transmitted to a workstation together, a characteristic curve of the neutral point current and the magnetic field intensity of the transformer iron core is established in the workstation, the characteristic curve between the neutral point current and the magnetic field intensity of the iron core is superposed on the digital twin transformer, when stray current is mixed into a neutral point, the influence of the neutral point stray current on the bias magnetism of the transformer can be checked through the information superposed on the digital twin transformer, the change of the magnetic field intensity of the iron core of the transformer along the rail transit is mainly checked when an locomotive passes through the transformer, and then the influence of the stray current on the bias magnetism of the transformer during the operation of the locomotive is obtained.
As shown in fig. 3, when constructing the digital twin transformer in the third step, the specific parameters of the transformer are first introduced into the BIM software Revit to generate an RVT file, the RVT file is introduced into the 3D max software to be edited, the same parts of the BIM model are combined into an object according to the element types in the 3D max software, redundant dot and line surfaces are deleted, the combined model is endowed with material texture and characteristic parameters, the edited model is exported from the 3D max software to be loaded into an engine, secondary material effect adjustment is performed according to the material property of the transformer, a virtual transformer having the same parameters as the transformer is rendered, a virtual industrial personal computer is established, and data information acquired by the voltage sensor 1, the current sensor 2, the magnetic field sensor 3, the temperature sensor 4, the vibration sensor 5 and the noise detector 6 is collected by the entity industrial computer 7 and then transmitted to the virtual industrial computer, and establishing a data interface between the virtual industrial personal computer and the virtual transformer, performing data analysis and scene rendering in the virtual transformer according to the acquired data information, and finally generating a digital twin transformer so that the data information received by the digital twin transformer is consistent with the real characteristic data of the transformer. And the data information collected by the voltage sensor 1 and the current sensor 2 for detecting the bus is also transmitted to the digital twin transformer, and meanwhile, the running voltage and current input by the digital twin transformer are consistent with those input by the transformer, so that the consistency of data in all aspects of the digital twin transformer and the transformer is ensured.
In the data level fusion process, the data information acquired by the first current sensor, the magnetic field inductor 3, the temperature sensor 4, the vibration sensor 5 and the noise detector 6 is locally fused by adopting a fuzzy logic control method, and the X is used for carrying out X fusion on the acquired data information1、X2、X3、X4、X5Respectively as a fuzzy set of a first current sensor, a magnetic field sensor 3, a temperature sensor 4, a vibration sensor 5, a noise detector 6, where X1The method comprises the primary current information and the secondary current information acquired by a first current sensor at each moment, wherein the primary current information and the secondary current information acquired at the kth moment are represented as x1(k)；X2Comprises iron core magnetic field change information collected at each moment of the magnetic field inductor 3, wherein the iron core magnetic field change information collected at the kth moment is represented as x2(k)；X3Comprises temperature change information collected at each moment of the temperature sensor 4, wherein the temperature change information collected at the kth moment is represented as x3(k)；X4Comprises vibration information collected at each moment of the vibration sensor 5, wherein the vibration information collected at the kth moment is represented as x4(k)；X5The noise detector 6 collects generated noise information at each moment, wherein the generated noise information collected at the kth moment is represented as x5(k) (ii) a Evaluation of X by closeness1、X2、X3、X4、X5The similarity of the magnetic biasing situation is identified, and at the kth moment, the data matrix among all sensors is as follows:
the consistency measurement calculation formula of the sampling values of the first current sensor, the magnetic field inductor 3, the temperature sensor 4, the vibration sensor 5 and the noise detector 6 is as follows:
in the formula of alphaij(k) For the ith row and jth column of data information, alpha, of the sensor in the data matrix at time kij(k) Is x1(k)、x2(k)、x3(k)、x4(k) Or x5(k)。
The calculation formula of data fusion is expressed as:
in the formula x1(k) Expressed as data information, x, collected by the first current sensor at time k2(k) Expressed as data information, x, collected by the magnetic field sensor at time k3(k) Expressed as data information, x, collected by the temperature sensor at time k4(k) Expressed as data information, x, collected by the vibration sensor at time k5(k) Expressed as data information, omega, collected by the noise detector at time ki(k) The ith row weight in the data matrix is represented, and according to the information sharing principle, the sum of the information quantity of the optimal fusion estimation can be decomposed into the sum of the information quantities of a plurality of measurement data, namely, one information can be shared by a plurality of subsystems, wherein
Normalizing the fuzzy closeness of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector to obtain respective weights
In the formula, c1(k)，c2(k)，c3(k)，c4(k)，c5(k) Representing a set of non-negative numbers.
During decision-level fusion process of the data fusion center, the magnetic biasing situation of the transformer is divided into normal F1Mild degree of F2Severe F3Severe F4Four levels, let Θ be a recognition framework, Ω (Θ) represents the set of all subsets in Θ, and the transformer bias level is represented as:
Ω(Θ)＝{φ，{F1}，{F2}，{F3}，{F4}，{F1,F2}，{F2,F3say, phi is an empty set, assuming Q ═ F1,F2E.g., theta, indicates that Q is either normal F1Either light F2I.e. Ω (Θ) is any one of the states;
after the recognition frame theta is determined, one is definedSet function G, mapping Ω (Θ) to the interval [0,1 ]]The number of (a) above, expressed as:
wherein G (Q) is called basic probability assignment function, represents the confidence of the state recognition model to a specific state, and represents the mapping Bel on the recognition framework theta by using the belief function Bel, wherein Q is → [0,1]And satisfies the following conditions:
the next mapping Pl: Ω (Θ) → [0,1 ] is then represented by the plausibility function Pl]And satisfies the following conditions:
in the formula QcRepresents the complement of Q, pl (Q) represents the plausibility of proposition Q, where pl (Q) 0 represents evidence rejection Q, and pl (Q) 1 represents evidence support Q;
let GiA basic probability assignment function representing the ith set of evidence defined on the recognition framework Θ, where i ═ 1,2.
K represents the normalized coefficient, expressed as:
G1(Qi) As set 1 evidence QiBasic probability assignment function of G2((Ω(Θ))j) As a 2 nd evidence (Ω (Θ))jG (Q) reflects the degree of conflict between two evidences, and when K is not equal to 0, the combination between two sets of evidences is expressed in the form of orthogonal sum; when K is 0, the condition that the orthogonal sum does not exist is shown, and the two evidences belong to a contradiction relation;
then, basic probability assignment functions of the first current sensor, the magnetic field inductor 3, the temperature sensor 4, the vibration sensor 5 and the noise detector 6 are constructed, and the first current sensor, the magnetic field inductor 3, the temperature sensor 4, the vibration sensor 5 and the noise areThe recognition result of the detector 6 is used as five evidences E1、E2、E3、E4、E5While normalizing F1Mild degree of F2Severe F3Severe F4Four grades are used as identification fields, and theta is equal to { F1,F2,F3,F4Form E1、E2、E3、E4、E5A common identification framework, a basic probability assignment function of each evidence is constructed on the identification framework theta, and the basic probability assignment function is calculated according to a formula
Then E can be calculated1、E2、E3、E4、E5The fused basic probability assignment function is used for judging the state of the transformer magnetic biasing situation according to the fused basic probability assignment function, for example, E is constructed on an identification frame theta1As shown in table 1, where sample 1 is analyzed and the maximum value of the basic probability assignment function of sample 1 is at F1And indicating that the bias condition of the transformer is in a normal state.
TABLE 1E1Basic probability assignment function of
| Sample(s) | F1 | F2 | F3 | F4 | Sample classes |
| 1 | 0.69 | 0.15 | 0.06 | 0.10 | |
| 2 | 0.45 | 0.43 | 0.06 | 0.06 | |
| 3 | 0.09 | 0.02 | 0.77 | 0.12 | |
| 4 | 0.11 | 0.05 | 0.03 | 0.81 | F4 |
Referring to fig. 1 and 2, a transformer magnetic bias detection system based on digital twins comprises a transformer, a voltage sensor 1, a current sensor 2, a magnetic field inductor 3, a temperature sensor 4, a vibration sensor 5, a noise detector 6, an industrial personal computer 7, a 5G module 8 and a workstation, wherein the voltage sensor 1 is installed on an inlet wire of a transformer bus, the voltage sensor 1 and the current sensor 2 are installed on the outer surface of the transformer, the voltage sensor 1 and the current sensor 2 are respectively connected to the transformer bus, the industrial personal computer 7 is installed on the outer surface of the transformer, the 5G module 8 is installed on the industrial personal computer 7, the magnetic field inductor 3 is installed beside a transformer core, the temperature sensor 4, the vibration sensor 5 and the noise detector 6 are installed on the inner side of the transformer, the magnetic field inductor 3, the temperature sensor 4, the vibration sensor 5, the noise detector 6, The current sensor 2 and the voltage sensor 1 are respectively connected with an industrial personal computer 7, and the industrial personal computer 7 is connected with the workstation through a 5G module 8.
The current sensors 2 comprise six first current sensors and six second current sensors, the six first current sensors are respectively used for detecting the primary side current of the transformer and the secondary side current of the transformer, the one second current sensor is used for detecting the current of the neutral point of the transformer, and the three voltage sensors 1 are used for detecting the primary side voltage of a bus of the transformer.
The working principle and the working process of the invention are as follows:
as shown in fig. 1 to 4, a voltage sensor 1 and a current sensor 2 are adopted to respectively collect voltage and current on a bus connected with a transformer, the voltage sensor 1, the current sensor 2, a magnetic field sensor 3, a temperature sensor 4, a vibration sensor 5 and a noise detector 6 transmit respective detected information to an industrial personal computer 7, the industrial personal computer 7 performs operations such as signal amplification, filtering, AD conversion, data preprocessing and the like on the collected data information, and controls a 5G module 8 to transmit data to a workstation through a 5G technology, a simulation transformer model is established in 3D MAX software through a digital twin technology in the workstation according to transformer parameters, the preprocessed data information is automatically introduced into the simulation transformer model to form a digital twin transformer, the data information received by the digital twin transformer is consistent with real characteristic data of the transformer, and then the magnetic field change information of the iron core, the primary current information, the secondary current information, the temperature change information, the vibration information and the generated noise information are fused together by a multi-sensor data fusion technology to jointly judge the magnetic biasing information of the transformer and are superposed on the digital twin transformer for display. The influence of the neutral point current on the magnetic biasing of the transformer is checked through information superposed on the digital twin transformer.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A transformer magnetic biasing detection method based on digital twinning is characterized by comprising the following steps:
detecting iron core magnetic field change information, neutral point current information, primary side voltage information, primary side current information, secondary side current information, temperature change information, vibration information and generated noise information of a transformer through a sensor;
secondly, transmitting the detected data information to an industrial personal computer, carrying out signal amplification, filtering, AD conversion and data preprocessing on the acquired data information, and then remotely transmitting the preprocessed data information to a workstation;
thirdly, the workstation establishes a simulation transformer model according to transformer parameters through a digital twinning technology, and then introduces the preprocessed data information into the simulation transformer model to form a digital twinning transformer;
fusing iron core magnetic field change information, primary side current information, secondary side current information, temperature change information, vibration information and generated noise information together by a multi-sensor data fusion technology to judge magnetic biasing information of the transformer, and overlapping the magnetic biasing information on the digital twin transformer to be displayed through a workstation;
and step five, establishing a characteristic curve of the neutral point current and the magnetic field intensity of the iron core of the transformer in the workstation, superposing the characteristic curve between the neutral point current and the magnetic field intensity of the iron core on the digital twin transformer, and checking the influence of the neutral point current on the magnetic biasing of the transformer through information superposed on the digital twin transformer.
2. The method for detecting the magnetic bias of the transformer based on the digital twin as claimed in claim 1, wherein the method comprises the following steps: the sensor comprises a voltage sensor, a current sensor, a magnetic field inductor, a temperature sensor, a vibration sensor and a noise detector, wherein the current sensor comprises a first current sensor and a second current sensor, the first current sensor is used for detecting primary side current information and secondary side current information of the transformer, and the second current sensor is used for detecting current information of a neutral point of the transformer; and fourthly, in the process of multi-sensor data fusion, firstly setting a data fusion center, arranging an intermediate station between the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor, the noise detector and the data fusion center, associating the data information acquired by the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor and the noise detector in the intermediate station, then carrying out data level fusion to realize local fusion, carrying out feature extraction on the data information after the local fusion in the data fusion center, judging the data attribute according to the feature extraction result, then associating the judged data information to carry out decision level fusion, further realizing global fusion, and outputting the fused result to judge the magnetic biasing condition of the transformer.
3. The method for detecting the magnetic bias of the transformer based on the digital twin as claimed in claim 2, wherein: and in the fifth step, after the second current sensor detects the current of the neutral point, the influence of the stray current on the magnetic biasing of the transformer is judged by comparing the current with the data information collected by the magnetic field sensor.
4. The digital twin-based transformer magnetic bias detection method as claimed in claim 3, wherein: in the process of judging the influence of neutral point current on the magnetic biasing of the transformer, the magnetic field change information of the iron core is detected through the magnetic field inductor, the neutral point current information and the magnetic field change information of the iron core, which are detected by the second current sensor and the magnetic field inductor, are transmitted to a workstation together, a characteristic curve of the neutral point current and the magnetic field intensity of the iron core of the transformer is established in the workstation, the characteristic curve between the neutral point current and the magnetic field intensity of the iron core of the transformer is superposed on the digital twin transformer, and when stray current jumps into the neutral point, the influence of the stray current of the neutral point on the magnetic biasing of the transformer can be checked through the information superposed on the digital twin transformer.
5. The method for detecting the magnetic bias of the transformer based on the digital twin as claimed in claim 2, wherein: when the digital twin transformer is constructed, firstly constructing a model consistent with the transformer, rendering a virtual transformer with the same parameters as the transformer, then constructing a virtual industrial personal computer, converging data information acquired by a voltage sensor, a current sensor, a magnetic field sensor, a temperature sensor, a vibration sensor and a noise detector through an entity industrial personal computer, transmitting the converged data information to the virtual industrial personal computer, establishing a data interface between the virtual industrial personal computer and the virtual transformer, carrying out data analysis and scene rendering in the virtual transformer according to the acquired data information, and finally generating the digital twin transformer so that the data information received by the digital twin transformer is consistent with real characteristic data of the transformer.
6. The digital twin-based transformer magnetic bias detection method as claimed in claim 2, wherein: in the data level fusion process of data information collected by the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector, the collected data information is locally fused by adopting a fuzzy logic control method, and the X-ray fusion method is used for fusing the collected data information locally1、X2、X3、X4、X5Respectively as fuzzy sets of a first current sensor, a magnetic field sensor, a temperature sensor, a vibration sensor and a noise detector, wherein X1IncludedThe method comprises the steps that primary side current information and secondary side current information are collected by a first current sensor at each moment, wherein the primary side current information and the secondary side current information collected at the kth moment are represented as x1(k)；X2The magnetic field sensor comprises iron core magnetic field change information acquired at each moment of a magnetic field sensor, wherein the iron core magnetic field change information acquired at the kth moment is represented as x2(k)；X3The temperature change information acquired at each moment of the temperature sensor is included, wherein the temperature change information acquired at the kth moment is represented as x3(k)；X4The method comprises vibration information acquired at each moment of a vibration sensor, wherein the vibration information acquired at the kth moment is represented as x4(k)；X5The method comprises the steps that generated noise information is collected by a noise detector at each moment, wherein the generated noise information collected at the kth moment is represented as x5(k) (ii) a Evaluation of X by closeness1、X2、X3、X4、X5The similarity of the magnetic biasing situation is identified, and at the kth moment, the data matrix among all sensors is as follows:
the elements in the matrix a are the expression of closeness between the sensors, that is:
αmn(k) representing a proximity expression, x, between any two of the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor, and the noise detectorm(k)、xn(k) Respectively representing data information collected by the first current sensor, the magnetic field sensor, the temperature sensor, the vibration sensor and the noise detector at the time k, wherein when m is 1 and n is 2, x is1(k) Representing data information, x, collected by the first current sensor at time k2(k) Representing the data information collected by the magnetic field inductor at time k, when alphamn(k) When the value is less than the preset value M, considering xm(k) And xn(k) Are not similar, then alpha can be obtainedmn(k) When the value is 0, the following components are available:
the consistency measurement calculation formula of the sampling values of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector is as follows:
in the formula of alphaij(k) For the ith row and jth column of data information, alpha, of the sensor in the data matrix at time kij(k) Is x1(k)、x2(k)、x3(k)、x4(k) Or x5(k)；
The calculation formula of data fusion is expressed as:
in the formula x1(k) Expressed as data information, x, collected by the first current sensor at time k2(k) Expressed as data information, x, collected by the magnetic field sensor at time k3(k) Expressed as data information, x, collected by the temperature sensor at time k4(k) Expressed as data information, x, acquired by the vibration sensor at the moment k5(k) Expressed as data information, omega, collected by the noise detector at time ki(k) The ith row weight in the data matrix is represented, and according to the information sharing principle, the sum of the information quantity of the optimal fusion estimation can be decomposed into the sum of the information quantities of a plurality of measurement data, namely, one information can be shared by a plurality of subsystems, wherein
Normalizing the fuzzy closeness of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector to obtain respective weights
In the formula, c1(k)，c2(k)，c3(k)，c4(k)，c5(k) Representing a set of non-negative numbers.
7. The method for detecting the magnetic bias of the transformer based on the digital twin as claimed in claim 6, wherein: in the process of decision-level fusion in a data fusion center, dividing the magnetic biasing situation of the transformer into normal F1Mild degree of F2Severe F3Severe F4Four levels, let Θ be a recognition framework, Ω (Θ) represents the set of all subsets in Θ, and the transformer bias level is represented as: Ω (Θ) ═ Φ, { F1}，{F2}，{F3}，{F4}，{F1,F2}，{F2,F3Phi is the empty set, let Q ═ F1,F2E θ, indicates that Q is either normal F1Or is mild F2I.e., Ω (Θ) is any one of the states;
after the recognition frame theta is determined, a set function G is defined, and omega (theta) is mapped to the interval [0,1 ]]The number of (a) above, expressed as:
wherein G (Q) is called basic probability assignment function, represents the confidence of the state recognition model to a specific state, and represents the mapping Bel on the recognition framework theta by using the belief function Bel, wherein Q is → [0,1]And satisfies the following conditions:
the next mapping Pl: Ω (Θ) → [0,1 ] is then represented by the plausibility function Pl]And satisfies the following conditions:
in the formula QcRepresents the complement of Q, pl (Q) represents the plausibility of proposition Q, where pl (Q) 0 represents evidence rejection Q, and pl (Q) 1 represents evidence support Q;
let GiA basic probability assignment function representing an ith set of evidences defined on an identification framework Θ, where i ═ 1,2The rate assignment function is expressed as:
k represents the normalized coefficient, expressed as:
G1(Qi) As set 1 evidence QiBasic probability assignment function of G2((Ω(Θ))j) As a 2 nd evidence (Ω (Θ))jG (Q) reflects the degree of conflict between two evidences, and when K is not equal to 0, the combination between two sets of evidences is expressed in the form of orthogonal sum; when K is 0, the condition that the orthogonal sum does not exist is shown, and the two evidences belong to a contradiction relation;
then, establishing basic probability assignment functions of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector, and taking the identification results of the first current sensor, the magnetic field inductor, the temperature sensor, the vibration sensor and the noise detector as five evidences E1、E2、E3、E4、E5While at the same time F is normal1Mild degree of F2Severe F3Severe F4Four grades are used as identification fields, and theta is equal to { F1,F2,F3,F4Form E1、E2、E3、E4、E5A common identification framework, a basic probability assignment function of each evidence is constructed on the identification framework theta, and the basic probability assignment function is calculated according to a formula
Then E can be calculated1、E2、E3、E4、E5And determining which state the magnetic biasing condition of the transformer is in according to the fused basic probability assignment function.
8. A transformer magnetic biasing detection system based on digital twinning is characterized in that: the transformer, voltage sensor, current sensor, magnetic field sensor, a weighing sensor and a temperature sensor, vibration sensor, the noise detection appearance, the industrial computer, 5G module and workstation, install voltage sensor and current sensor on the transformer surface, voltage sensor, current sensor inserts the generating line of transformer respectively, the industrial computer is installed at the transformer surface, 5G module installation is on the industrial computer, install magnetic field sensor by transformer core, temperature sensor, vibration sensor, the noise detection appearance is installed at the transformer inboard, the magnetic field sensor, temperature sensor, vibration sensor, the noise detection appearance, the industrial computer is connected respectively to current sensor and voltage sensor, the industrial computer passes through 5G module connection workstation.
9. The digital twin-based transformer magnetic bias detection system as claimed in claim 8, wherein: the current sensors comprise six first current sensors and six second current sensors, the six first current sensors are respectively used for detecting the primary side current of the transformer and the secondary side current of the transformer, the one second current sensor is used for detecting the current of the neutral point of the transformer, and the three voltage sensors are used for detecting the primary side voltage of the bus of the transformer.
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