CN117347916A - Winding structure fault positioning method and device based on parameter identification - Google Patents
Winding structure fault positioning method and device based on parameter identification Download PDFInfo
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Abstract
The invention discloses a winding structure fault positioning method and device based on parameter identification. The method comprises the following steps: obtaining actual measurement frequency response data of a transformer winding in different states; constructing a ladder network structure of a transformer winding, and determining initial parameters of elements in the ladder network structure in different states by applying boundary conditions in different states; determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network; constructing an objective function according to the actually measured frequency response data in different states, and adopting a parameter identification algorithm to identify optimal parameters of the ladder-shaped network structure element in different states according to initial parameters, the objective function and association relations between unit parameters and the frequency response data in different states; and determining the diagnosis of the fault type, the fault position and the fault degree of the transformer winding according to the optimal parameters of the ladder network structural element and the normal parameters of the transformer winding in different states.
Description
Technical Field
The invention relates to the technical field of power transformer winding deformation detection, in particular to a winding structure fault positioning method and device based on parameter identification.
Background
The power transformer is a key device in a power system and plays roles of electric energy conversion, phase conversion, isolation, noise reduction and the like. The operation state of the transformer is related to whether the electric energy of the power grid is normally transmitted and effectively distributed, and once the transformer fails, the transformer can cause large-area power failure, so that the normal life of people is influenced, and huge economic loss is brought. Studies have shown that among the numerous fault data occurring in power transformers, accidents due to winding deformation are most common, and winding deformation mainly results from two factors: on one hand, the power transformer is easy to be collided by external force in the transportation process, so that fault hidden dangers such as local concave and local bulge of windings appear in the power transformer, and the power transformer is taken out of operation due to continuous aggravation of hidden dangers after the power transformer is put into operation; on the other hand, in the continuous operation process of the power transformer, the windings of the power transformer tend to deform to different degrees and different types due to the influences of various factors such as short-circuit current impact, overvoltage, abrasion of turn-to-turn insulating paper and the like. The winding deformation defect generally does not cause major accidents in the early stage, but the generated insulation defect is increasingly enlarged along with the continuous operation of the transformer, and finally serious consequences such as the shutdown of the transformer can be caused. How to effectively and accurately detect and diagnose the deformation state of the transformer winding becomes a key problem of transformer faults.
At present, the method for detecting the faults of the transformer mainly comprises the following steps: the frequency response method, the short-circuit impedance method, the sweep frequency impedance method and the like, wherein the frequency response method has the advantages of high test sensitivity, simple and portable equipment, relatively perfect winding deformation criterion and the like as the current commonly used transformer fault detection method, and the frequency response method is often dependent on field experience judgment of staff when fault diagnosis is carried out, so that misjudgment of a transformer winding is easily caused. How to extract useful information reflecting the real deformation state of the winding from the existing frequency response data is the key and difficult point for researching the accurate and quantitative diagnosis method of the deformation of the transformer winding. Therefore, there is an urgent need for a transformer winding diagnostic method capable of accurately and efficiently judging a deformation state of a transformer winding.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a winding structure fault positioning method and device based on parameter identification.
According to one aspect of the present invention, there is provided a winding structure fault locating method based on parameter identification, including:
obtaining measured frequency response data of a transformer winding in different states, wherein the different states comprise a normal state and a fault state of the transformer;
Constructing a ladder network structure of a transformer winding, and determining initial parameters of elements in the ladder network structure in different states by applying boundary conditions in different states;
determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network;
constructing an objective function according to the actually measured frequency response data in different states, and adopting a parameter identification algorithm to identify optimal parameters of the ladder-shaped network structure element in different states according to initial parameters, the objective function and association relations between unit parameters and the frequency response data in different states;
and determining the diagnosis of the fault type, the fault position and the fault degree of the transformer winding according to the optimal parameters of the ladder network structural element and the normal parameters of the transformer winding in different states.
Optionally, the ladder network structure is composed of a high-voltage side circuit model and a low-voltage side circuit model, the nodes of the high-voltage side circuit model/the low-voltage side circuit model are composed of inter-cake inductance and resistance in a serial connection mode and inter-cake capacitance and conductance in a parallel connection mode, the nodes of the high-voltage side circuit model/the low-voltage side circuit model are composed of conductance to ground and capacitance to ground in parallel connection mode, the high-voltage side circuit model and the low-voltage side circuit model are connected through a coupling branch, the coupling branch is connected with the nodes of the high-voltage side circuit model and the low-voltage side circuit model, and the coupling branch comprises coupling capacitance and coupling conductance which are connected in parallel.
Alternatively, the boundary condition is the high voltage winding 1A current, the low voltage winding 0V voltage.
Optionally, determining the association relationship between the unit parameter of each element of the transformer winding unit and the frequency response data by using the voltage-current relationship of the trapezoidal equivalent network includes:
constructing an equation set for each node and each branch of the trapezoid equivalent network by using kirchhoff voltage and current law;
and determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data according to the constructed equation set.
Optionally, the constructed equation set includes:
the 2n impedance branch columns write the KVL equation:
2n+2 node columns write the KCL equation:
wherein n is the number of sections of the trapezoid equivalent network, I m The current of the inductive resistance branch with the number m; u (U) m Is the voltage on the node numbered m; g gm Numbered asConductance between the node of m and ground; c (C) gm A capacitance between the node numbered m and ground; g p,q Is the conductance between the node numbered p and the node numbered q; c (C) p,q Is the capacitance between the node numbered p and the node numbered q.
Optionally, the association relationship between the unit parameter of each element of the transformer winding unit and the frequency response data is:
wherein X (1) is a frequency domain value of a voltage applied to the winding end; x is X 1 (n) is the voltage frequency domain value of the node numbered n in the model; h is the frequency response of the node voltage to the end voltage, numbered n.
Optionally, constructing an objective function according to the actually measured frequency response data and the association relation between the unit parameters and the frequency response data in different states, and identifying the optimal parameters of the ladder network structural element in different states according to the initial parameters and the objective function in different states by adopting a parameter identification algorithm, including:
step 1: constructing an objective function according to the actually measured frequency response data in different states and the simulated frequency response data obtained by simulation, and setting a classification error rate lambda for terminating the gray wolf optimization algorithm;
step 2: initializing whale groups, setting the size of the whale groups to be N, initializing the positions of the whale groups in a feasible domain, and obtaining simulation frequency response data according to the positions of the initialized whale groups and the association relation between unit parameters and frequency response data, wherein the positions of whales are vectors formed by element parameters of a trapezoid equivalent network;
step 3: calculating the objective function value of the whale randomly generated in the step 2 according to the objective function, and recording the position X of the whale with the minimum objective function value best ；
Step 4: judging the position X of whale best If the objective function value of (2) is smaller than lambda, defining iteration times t=1, updating the whale position according to a preset updating formula, and updating the position Out-of-range adjustment is carried out on whales, objective function values of whales are recalculated, and positions X of whales with minimum updated objective function values are recorded best 。
Step 5: judging the updated X obtained in the step 4 best If the objective function value of (2) is less than lambda, if not, let t=t+1, repeating the updating of the whale position of step 4 until the updated whale is at position X best Is less than lambda, X obtained in the last iteration best As an optimal position and converts it into optimal parameters for 7n+3 elements in the ladder equivalent network.
Optionally, the objective function is:
wherein k is the number of resonance points of the frequency response data amplitude-frequency curve, m is the number of resonance points of the frequency response data phase-frequency curve, and H i Is the amplitude value of the ith frequency point in the amplitude-frequency curve resonance points, H p Is the amplitude of the p-th frequency point in the resonance points of the phase frequency curve, (H) i ) Actual measurement For the measured frequency response data, (H) i ) Simulation of Is simulated frequency response data.
Optionally, the update formula is as shown in the formula.
Wherein the three expressions correspond to three actions of whale searching for hunting, surrounding hunting and hunting, respectively, wherein X rand For randomly selected whale positions, a=2ar 1 -a,C＝2r 1 The value of a decreases linearly from 2 to 0, r 1 Is a [0,1 ] ]D= |c×x) best (t)-X(t)|，D'＝|X best (t) -X (t) |, b is a constant, used to define the shape of the spiral, l is a random number in (-1, 1), p is a [0,1 ]]Three behaviors of whale were selected by coefficients a and p.
According to another aspect of the present invention, there is provided a winding structure fault locating device based on parameter identification, including:
the acquisition module is used for acquiring actual measurement frequency response data of the transformer winding in different states, wherein the different states comprise a normal state and a fault state of the transformer;
the construction module is used for constructing a ladder network structure of the transformer winding and determining initial parameters of elements in the ladder network structure under different states by applying boundary conditions of different states;
the first determining module is used for determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network;
the identification module is used for constructing an objective function according to the actually measured frequency response data in different states, adopting a parameter identification algorithm, and identifying optimal parameters of the ladder-shaped network structural element in different states according to the initial parameters, the objective function and the association relation between the unit parameters and the frequency response data in different states;
And the second determining module is used for determining the diagnosis of the fault type, the position and the degree of the transformer winding according to the optimal parameters of the ladder network structural element and the normal parameters of the transformer winding in different states.
According to a further aspect of the present invention there is provided a computer readable storage medium storing a computer program for performing the method according to any one of the above aspects of the present invention.
According to still another aspect of the present invention, there is provided an electronic device including: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any of the above aspects of the present invention.
Therefore, the invention provides a winding structure fault positioning method based on parameter identification, which constructs an equivalent network model of a transformer, introduces COMSOL finite element modeling, combines a parameter identification algorithm, selects a proper objective function, and determines network element parameters under normal conditions of the transformer. On the basis, mathematical indexes are introduced through comparison with normal parameters, the state of the transformer winding is judged, and the transformer winding parameter identification and fault diagnosis through a whale-changing optimization algorithm are realized. The diagnosis of faults is realized by comparing the equivalent network parameters of the transformers, the trapezoid equivalent network equivalent to the transformers in the normal state is used as a reference to be compared with the equivalent network parameters of the faults, and the unit parameters and the normal unit parameters can be compared under the fault state because different fault types can influence different parameters of the unit, so that the diagnosis of the fault types and the fault positions is realized, and the judgment of the fault degree is realized by comparing the difference between the fault types and the normal parameter values. The invention does not depend on the experience judgment of field personnel, realizes the fault diagnosis of the transformer winding only by the difference between parameters and the difference degree, and effectively reduces the probability of misjudgment.
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Exemplary embodiments of the present invention may be more completely understood in consideration of the following drawings:
FIG. 1 is a flow chart of a method for locating faults of a winding structure based on parameter identification according to an exemplary embodiment of the present invention;
FIG. 2 is another flow chart of a method for locating faults of a winding structure based on parameter identification according to an exemplary embodiment of the present invention;
FIG. 3 is a diagram of a transformer A-phase winding frequency response test wiring provided in an exemplary embodiment of the present invention;
FIG. 4 is a diagram of a ladder equivalent network for a transformer according to an exemplary embodiment of the present invention;
FIG. 5 is a graph of an iterative comparison of GA and WOA algorithms provided in an exemplary embodiment of the present invention;
FIG. 6 is a schematic diagram of a device for locating faults in a winding structure based on parameter identification according to an exemplary embodiment of the present invention;
fig. 7 is a structure of an electronic device provided in an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present invention are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present invention, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in an embodiment of the invention may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in the present invention is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations with electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with the terminal device, computer system, server, or other electronic device include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the foregoing, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Exemplary method
Fig. 1 is a flowchart of a winding structure fault locating method based on parameter identification according to an exemplary embodiment of the present invention. The embodiment can be applied to an electronic device, as shown in fig. 1, the winding structure fault locating method 100 based on parameter identification includes the following steps:
step 101, obtaining actual measurement frequency response data of a transformer winding in different states, wherein the different states comprise a normal state and a fault state of the transformer;
102, constructing a ladder network structure of a transformer winding, and determining initial parameters of elements in the ladder network structure in different states by applying boundary conditions in different states;
step 103, determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network;
104, constructing an objective function according to the actually measured frequency response data in different states, and adopting a parameter identification algorithm to identify optimal parameters of the ladder network structure element in different states according to initial parameters, the objective function and association relations between unit parameters and the frequency response data in different states;
and 105, determining the diagnosis of the fault type, position and degree of the transformer winding according to the optimal parameters of the ladder network structural element in different states and the normal parameters of the transformer winding.
Specifically, the fault diagnosis method for the transformer winding in the prior art cannot truly and accurately reflect the real state of the transformer winding. In order to avoid the above problems, the specific flow of the proposed transformer winding fault positioning method based on parameter identification (see fig. 2) is as follows:
step 1, connecting a frequency response tester with a three-phase transformer to obtain actual measurement frequency response data of a transformer winding, wherein a test wiring diagram is shown in fig. 3. The transformer is equivalent to a trapezoidal equivalent network consisting of inductance, capacitance and resistance, as shown in fig. 4. The network consists of a high voltage side and a low voltage side, wherein the high voltage side and the low voltage side are respectively provided with an inter-cake inductance L i Inter-cake capacitance C i,j Inter-cake conductance G i,j Resistance R i Ground lead G gi And to the groundCapacitor C gi The high-voltage side and the low-voltage side are connected through a coupling branch circuit, and the coupling branch circuit comprises a coupling capacitor C i,i+1+n And coupling conductance G i,i+1+n . Where n is the number of sections of the equivalent network. The invention models the three-phase transformer winding through COMSOL software, and applies boundary conditions: the high-voltage winding 1A current and the low-voltage winding 0V voltage are used for equivalent solid power transformer low-voltage side winding short circuit condition, and further the leakage reactance of the transformer winding can be obtained. And obtaining high-low voltage equivalent parameters of the transformer, and determining the definition domain of the parameters through the serial-parallel connection relation of the section number and the network units. This ensures that the equivalent network parameters are more relevant to the actual transformer.
And 2, obtaining the relation between each element of the unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network, and realizing the process by MATLAB programming. According to fig. 4, the current flow direction of the impedance branch is set to be the low node to the high node, and the current flow direction to the ground is the direction of flowing out of the node. Equations can be written for each node and branch column using kirchhoff's voltage-current law. The method comprises the following steps:
The KVL equation is written for each of the 2n impedance branch columns as shown in equation (1), and the KCL equation is written for the 2n+2 node columns as shown in equation (2). The listed equations are organized to obtain:
wherein u= [ U ] 1 ,U 2 …U 2n+2 ] T ，I＝[I 1 ,I 2 …I 2n ] T . T and T 1 Voltage conversion matrix and current conversion matrix containing 0 and 1 respectivelyAnd (3) changing the matrix, and calculating the voltage and the current of the equivalent network through the formula (3).
X is the voltage of each node of the equivalent network and the current of the impedance branch, and since the frequency response method needs to load the voltage at the head end, the voltage value is set to be 1V, that is, X (1) =1, and the rest node voltage and the impedance branch current can be calculated.
Wherein X is 1 To obtain the node voltage and impedance branch current to be calculated, the high-voltage side end voltage value required by the frequency response curve can be obtained by the formula (5), N 1 The first row and first column are deleted from the M matrix. The final frequency response formula obtained is as follows:
x is the voltage at each node of the equivalent network and the current at the impedance branch, which is a vector. The first element of the vector is the voltage at the head-end node. In this embodiment, this head-end node voltage is set to 1V, which is known. The remaining elements from the second element to the last element are all unknown quantities to be solved, and the elements from the second element to the last element are set as a new vector for the convenience of representation and solving, and the new vector is X1. So X1 is less than X, the first element of X is otherwise the same vector.
Step 2, determining the relation between the unit parameters of the trapezoid equivalent network and the frequency response data by using the KCL and KVL laws of the equivalent network, wherein the process is realized by MATLAB programming.
Step 3, introducing a parameter identification algorithm to find equivalent network parameters which are most matched with the measured frequency response data in the feasible domain, and obtaining the parameters which are matched with the actual parametersLadder-like equivalent network of an inter-transformer. The present invention uses whale optimization algorithms to implement this process. The whale optimization algorithm (Whale Optimization Algorithm, WOA) achieves the aim of optimizing searches through whale group searching, surrounding, hunting and other processes. Considering the recognition efficiency of the algorithm, the invention omits the conductance of each branch in the built trapezoid equivalent network, determines that the section number of the equivalent network is 7, namely 52 elements exist in the equivalent network, so each whale represents a 52×1 matrix, namely X= [ X ] 1 ,x 2 …x 52 ]。
Step 301, constructing an objective function of a whale optimization algorithm by using the transformer frequency response data measured in step 1 and the frequency response data of the parameters obtained through simulation, wherein the objective function is constructed in a full frequency band because the scanning frequency of the frequency response method is set to be 1K-1MHz, and the recognition efficiency of the whale optimization algorithm is influenced by constructing the objective function by using the objective function on the basis of the resonance points of the amplitude-frequency information of the frequency response data and the resonance points of the phase-frequency information (see formula (7)). Lambda is set to 5e-5 for the classification error rate at which the wolf optimization algorithm terminates.
In the formula, k is the number of resonance points of the frequency response data amplitude-frequency curve, and m is the number of resonance points of the frequency response data phase-frequency curve.
H i Is the amplitude value of the ith frequency point in the amplitude-frequency curve resonance points, H p The amplitude of the p-th frequency point in the resonance points of the phase frequency curve.
Step 302, initializing whale group, setting the size of whale group to be 50, and initializing whale group position in a feasible domain, wherein the whale position is a vector formed by element parameters of a trapezoid equivalent network.
Step 303, calculating the objective function value of the randomly generated whale, and recording the position of the whale with the minimum objective function value, which is defined as X best 。
Step 304, judge X best If the objective function value of (2) is less than lambda, if not, defining the iteration number t=1, and making the whale position moreThe new updated formula is shown in the formula.
Wherein the three expressions correspond to three actions of whale searching for hunting, surrounding hunting and hunting, respectively, wherein X rand For randomly selected whale positions, a=2ar 1 -a,C＝2r 1 The value of a decreases linearly from 2 to 0, r 1 Is a [0,1 ]]Is a random number of (a) in the memory. D= |c×x best (t)-X(t)|，D'＝|X best (t) -X (t) |, b is a constant that defines the shape of the spiral, and l is a random number in (-1, 1). p is a [0,1 ]]Three behaviors of whale were selected by coefficients a and p. Let p=0.5. When p is smaller than 0.5, A is more than or equal to 1, searching for a prey by whale; when p is less than 0.5, |A| < 1, whale carries out surrounding hunting; when p is greater than 0.5, whale hunting is performed.
And (5) performing out-of-range adjustment on whales after the position updating, and recalculating objective function values of the whales. Setting the whale position with minimum objective function value after position update as X best 。
Step 305, determining the updated X in step 304 best If the objective function value of (2) is less than lambda, if not, let t=t+1, repeating the whale position update of step 304 until updated X best Is less than lambda, X obtained in the last iteration best And as the optimal position, converting the optimal position into 52 element parameters in the trapezoidal equivalent network, and finally obtaining the trapezoidal equivalent network of the actual transformer winding. In order to reduce the calculation amount, the parameters of each unit of the equivalent network are set to be the same, and the obtained parameters of the trapezoid equivalent network with normal A-phase windings are shown in table 1.
Table 1A phase winding normal trapezoidal equivalent network parameters
In the embodiment, the actual parameters of the transformer are compared with the simulation parameters obtained through parameter identification, the difference between the actual parameters and the simulation parameters is compared by introducing an error value, and an error calculation formula is shown in the formula. As can be obtained from table 1, the equivalent network parameters of the transformer obtained by the method are highly consistent with the actual parameters, the maximum error value is only 1.64%, the optimal solution for parameter identification by using the whale optimization algorithm is 2.591e-05, and the identification time is 121min. The high recognition accuracy and the low recognition time prove that the method can perform network equivalence and fault diagnosis.
In addition, in order to prove that the precision and the efficiency of the method are superior to those of other optimization algorithms, the embodiment proves that the parameter identification method based on the whale optimization algorithm has better precision and efficiency through errors, identification time and an iteration chart by comparing the parameter identification method based on the whale optimization algorithm with the parameter identification method based on the genetic algorithm. Simulation parameters obtained by genetic algorithm and errors thereof are shown in table 2.
Table 2A phase winding normal trapezoidal equivalent network parameters (based on genetic algorithm)
Table 2 shows the comparison between the simulation parameters and the actual parameters obtained by the parameter identification method based on the genetic algorithm, and the difference between the simulation parameters and the actual parameters obtained by the algorithm is larger, the maximum error value can reach 10.22%, and the identification effect on the high-voltage inter-cake capacitance, the high-voltage earth capacitance, the low-voltage inter-cake capacitance and the low-voltage earth capacitance is worst. Compared with table 1, the error of the parameter identification method based on the whale algorithm is not more than 2%, while the error of the parameter identification method based on the genetic algorithm is mostly more than 2%, the parameter identification effect is poor, and the misjudgment on the state of the transformer winding is easy to cause. Therefore, the recognition effect based on the whale optimization algorithm is good.
The iteration diagram of the parameter identification method based on the genetic algorithm and the parameter identification iteration diagram based on the whale optimization algorithm are shown in fig. 5, and the iteration diagram proves that compared with the genetic algorithm, the iteration speed of the whale optimization algorithm is higher, and the found optimal solution effect is better than that of the genetic algorithm. The optimal solution for parameter identification by genetic algorithm is 0.0252, and the identification time is 49min. The identification time of the genetic algorithm is faster in terms of identification efficiency, but the accuracy of identification is lower. The recognition time of the whale optimization algorithm is longer and is 2 times of that of the genetic algorithm, but the recognition accuracy is better than the recognition effect of the genetic algorithm, and the found optimal solution is better than the optimal solution of the genetic algorithm. The lower recognition accuracy can lead to misjudgment of fault diagnosis when parameter recognition is carried out, while the recognition time based on the whale optimization algorithm is inferior to that of the genetic algorithm, but the recognition accuracy is superior to that of the genetic algorithm. Therefore, the parameter identification method based on the whale optimization algorithm has the advantage of fault diagnosis.
The method is used for obtaining the actually measured frequency response data of the transformer in different states, respectively simulating faults of normal winding, turn-to-turn short circuit faults, axial displacement and radial displacement of the transformer, obtaining a trapezoid equivalent network in different states, and diagnosing the fault type, position and degree of the transformer winding by using unit parameters obtained in different states. Table 3 shows the parameters of the unit with obvious difference between the equivalent network parameters and the normal parameters of the transformer after the mixed faults are carried out on the transformer windings.
Table 3 main trapezoidal equivalent network parameter contrast under A phase winding mixed fault
By comparing the parameters of the equivalent network in different states with the parameters of the normal trapezoid equivalent network, different winding faults can affect different parameters in the unit, and by comparing whether the parameter values of the inter-cake inductance, the inter-cake capacitance, the resistance and the capacitance to ground are too different from the normal parameters, the judgment of the fault type is realized. The fault degree is judged mainly by comparing the difference degree of parameter values of the inter-cake inductance, the inter-cake capacitance, the resistance and the capacitance to ground with normal parameters, if the difference between the parameters is within 3% -10%, the fault is mild, and if the difference is more than 25%, the fault is obvious, and if the difference is serious. The diagnosis of the fault position is compared with equivalent network units in different states through a normal equivalent network, and the fault positioning is realized by utilizing the units with parameter differences. By comparing with the actual parameters, element parameters with larger errors are selected for fault analysis, as shown in table 3. Since the axial displacement mainly affects the parameters of the inter-cake capacitance, it can be seen that the inter-cake capacitances of the 1 st cell, the 2 nd cell and the 7 th cell are reduced compared with the normal parameters, and the error rates thereof are 21.9%, 4.96% and 12.28%, respectively. Therefore, the upper winding of the A-phase winding is subjected to severe axial displacement, and the lower part of the winding is subjected to relatively obvious axial displacement; similarly, the radial displacement often affects the parameter of the capacitance to ground, and the error value of the parameter of the capacitance to ground of the 1 st unit is 13.67% and 12.44% compared with the normal parameter, which proves that the upper part of the winding has obvious radial displacement; finally, table 3 shows that the inductance of the 5 th and 6 th units of the equivalent network are reduced, and the error from the normal value reaches 7.04% and 3.44%, which proves that the middle lower part of the winding has a slight turn-to-turn short circuit fault. The embodiment proves that the transformer winding fault positioning and diagnosing method based on parameter identification has higher feasibility.
The method mainly comprises the steps of establishing an equivalent network model of the transformer, identifying unit parameters of the equivalent network, and carrying out fault diagnosis and research based on network parameters. An equivalent network model of the transformer is constructed, COMSOL finite element modeling is introduced, a proper objective function is selected in combination with a parameter identification algorithm, and network element parameters of the transformer under normal conditions are determined. On the basis, mathematical indexes are introduced through comparison with normal parameters, the state of the transformer winding is judged, and the transformer winding parameter identification and fault diagnosis through a whale-changing optimization algorithm are realized.
The invention provides a winding structure fault positioning method based on parameter identification, which comprises the steps of enabling a physical transformer to be equivalent to a trapezoid equivalent network consisting of an inductor, a resistor and a capacitor, enabling the network to comprise 2n units, obtaining the mathematical relationship between unit parameters and a frequency response curve of the network by using KCL and KVL laws of the network, and realizing the process through MATLAB programming. And constructing an objective function based on the actually measured frequency response data and the frequency response data obtained by MATLAB programming, introducing an intelligent optimization algorithm to obtain equivalent network element parameters which are most in line with the actually measured frequency response data, and finally realizing the parameter identification of the transformer. According to the invention, the equivalent network parameters in different states are obtained through parameter identification by measuring the actual measurement frequency response data of the transformer in different states. The invention mainly realizes fault diagnosis by comparing the equivalent network parameters of the transformer, takes the equivalent trapezoidal equivalent network of the transformer in a normal state as a reference, compares the equivalent trapezoidal equivalent network with the equivalent network parameters of the fault, and can realize fault type and fault position diagnosis by comparing the unit parameters with the normal unit parameters in the fault state because different fault types can influence different parameters of the unit, and can realize fault degree judgment by comparing the difference with the normal parameter values. The invention does not depend on the experience judgment of field personnel, realizes the fault diagnosis of the transformer winding only by the difference between parameters and the difference degree, and effectively reduces the probability of misjudgment.
Exemplary apparatus
Fig. 6 is a schematic structural diagram of a winding structure fault locating device based on parameter identification according to an exemplary embodiment of the present invention. As shown in fig. 6, the apparatus 600 includes:
the obtaining module 610 is configured to obtain measured frequency response data of the transformer winding in different states, where the different states include a normal state and a fault state of the transformer;
the construction module 620 is configured to construct a ladder network structure of the transformer winding, and determine initial parameters of elements in the ladder network structure in different states by applying boundary conditions in different states;
a first determining module 630, configured to determine an association relationship between a unit parameter of each element of the transformer winding unit and the frequency response data by using a voltage-current relationship of the trapezoidal equivalent network;
the identifying module 640 is configured to construct an objective function according to the actually measured frequency response data in different states, and identify an optimal parameter of the ladder network structural element in different states by adopting a parameter identifying algorithm according to the initial parameters, the objective function and the association relation between the unit parameters and the frequency response data in different states;
a second determining module 650 is configured to determine a diagnosis of the type, location and degree of the fault of the transformer winding according to the optimal parameters of the ladder network structure element and the normal parameters of the transformer winding in different states.
Optionally, the ladder network structure is composed of a high-voltage side circuit model and a low-voltage side circuit model, the nodes of the high-voltage side circuit model/the low-voltage side circuit model are composed of inter-cake inductance and resistance in a serial connection mode and inter-cake capacitance and conductance in a parallel connection mode, the nodes of the high-voltage side circuit model/the low-voltage side circuit model are composed of conductance to ground and capacitance to ground in parallel connection mode, the high-voltage side circuit model and the low-voltage side circuit model are connected through a coupling branch, the coupling branch is connected with the nodes of the high-voltage side circuit model and the low-voltage side circuit model, and the coupling branch comprises coupling capacitance and coupling conductance which are connected in parallel.
Alternatively, the boundary condition is the high voltage winding 1A current, the low voltage winding 0V voltage.
Optionally, the first determining module 630 includes:
the construction submodule is used for constructing an equation set for each node and each branch of the trapezoid equivalent network by using kirchhoff voltage and current law;
and the determining submodule is used for determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data according to the constructed equation set.
Optionally, the constructed equation set includes:
the 2n impedance branch columns write the KVL equation:
2n+2 node columns write the KCL equation:
wherein n is the number of sections of the trapezoid equivalent network, I m The current of the inductive resistance branch with the number m; u (U) m Is the voltage on the node numbered m; g gm Is the conductance between the node numbered m and ground; c (C) gm A capacitance between the node numbered m and ground; g p,q Is the conductance between the node numbered p and the node numbered q; c (C) p,q Is the capacitance between the node numbered p and the node numbered q.
Optionally, the association relationship between the unit parameter of each element of the transformer winding unit and the frequency response data is:
wherein X (1) is a frequency domain value of a voltage applied to the winding end; x is X 1 (n) is the voltage frequency domain value of the node numbered n in the model; h is the frequency response of the node voltage to the end voltage, numbered n.
Optionally, the identification module 640 includes:
step 1: constructing an objective function according to the actually measured frequency response data in different states and the simulated frequency response data obtained by simulation, and setting a classification error rate lambda for terminating the gray wolf optimization algorithm;
step 2: initializing whale groups, setting the size of the whale groups to be N, initializing the positions of the whale groups in a feasible domain, and obtaining simulation frequency response data according to the positions of the initialized whale groups and the association relation between unit parameters and frequency response data, wherein the positions of whales are vectors formed by element parameters of a trapezoid equivalent network;
Step 3: calculating the objective function value of the whale randomly generated in the step 2 according to the objective function, and recording the position X of the whale with the minimum objective function value best ；
Step 4: judging the position X of whale best If the objective function value of the whale is less than lambda, defining the iteration times t=1, updating the position of the whale according to a preset updating formula, performing out-of-range adjustment on the whale after the position is updated, recalculating the objective function value of the whale, and recording the position X of the whale with the minimum objective function value after the update best 。
Step 5: judging the updated X obtained in the step 4 best If the objective function value of (2) is less than lambda, if not, let t=t+1, repeating the updating of the whale position of step 4 until the updated whale is at position X best Is less than lambda, X obtained in the last iteration best As an optimal position and converts it into optimal parameters for 7n+3 elements in the ladder equivalent network.
Optionally, the objective function is:
/>
wherein k is the number of resonance points of the frequency response data amplitude-frequency curve, m is the number of resonance points of the frequency response data phase-frequency curve, and H i Is the amplitude value of the ith frequency point in the amplitude-frequency curve resonance points, H p Is the amplitude of the p-th frequency point in the resonance points of the phase frequency curve, (H) i ) Actual measurement For the measured frequency response data, (H) i ) Simulation of Is simulated frequency response data.
Optionally, the update formula is as shown in the formula.
Wherein the three expressions respectively correspond to whale to search for hunting objects and surround huntingThree behaviors, namely object and hunting, wherein X rand For randomly selected whale positions, a=2ar 1 -a,C＝2r 1 The value of a decreases linearly from 2 to 0, r 1 Is a [0,1 ]]D= |c×x) best (t)-X(t)|，D'＝|X best (t) -X (t) |, b is a constant, used to define the shape of the spiral, l is a random number in (-1, 1), p is a [0,1 ]]Three behaviors of whale were selected by coefficients a and p.
Exemplary electronic device
Fig. 7 is a structure of an electronic device provided in an exemplary embodiment of the present invention. As shown in fig. 7, the electronic device 70 includes one or more processors 71 and memory 72.
The processor 71 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.
Memory 72 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 71 to implement the methods of the software programs of the various embodiments of the present invention described above and/or other desired functions. In one example, the electronic device may further include: an input device 73 and an output device 74, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown).
In addition, the input device 73 may also include, for example, a keyboard, a mouse, and the like.
The output device 74 can output various information to the outside. The output device 74 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, only some of the components of the electronic device relevant to the present invention are shown in fig. 7 for simplicity, components such as buses, input/output interfaces, etc. being omitted. In addition, the electronic device may include any other suitable components depending on the particular application.
Exemplary computer program product and computer readable storage Medium
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary methods" section of this specification.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the invention may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform steps in a method according to various embodiments of the invention described in the "exemplary method" section of the description above.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present invention have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present invention are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be considered as essential to the various embodiments of the present invention. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the invention is not necessarily limited to practice with the above described specific details.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, systems, apparatuses, systems according to the present invention are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, systems, apparatuses, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The method and system of the present invention may be implemented in a number of ways. For example, the methods and systems of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present invention may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.
It is also noted that in the systems, devices and methods of the present invention, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the invention to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (12)
1. The winding structure fault positioning method based on parameter identification is characterized by comprising the following steps of:
obtaining measured frequency response data of a transformer winding in different states, wherein the different states comprise a normal state and a fault state of the transformer;
constructing a ladder network structure of the transformer winding, and determining initial parameters of elements in the ladder network structure under different states by applying boundary conditions of different states;
determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network;
constructing an objective function according to the actually measured frequency response data in different states, and adopting a parameter identification algorithm to identify optimal parameters of the ladder network structural element in different states according to the initial parameters, the objective function and the association relation between the unit parameters and the frequency response data in different states;
And determining the diagnosis of the fault type, the fault position and the fault degree of the transformer winding according to the optimal parameters of the ladder network structural element and the normal parameters of the transformer winding under different states.
2. The method of claim 1, wherein the ladder network structure is composed of a high-voltage side circuit model and a low-voltage side circuit model, wherein a branch between nodes of the high-voltage side circuit model and the low-voltage side circuit model is composed of an inter-cake inductance and a resistance in a serial connection mode and an inter-cake capacitance and a conductance in a parallel connection mode, a branch between nodes of the high-voltage side circuit model and the low-voltage side circuit model is composed of a conductance to ground and a capacitance to ground in a parallel connection mode, the high-voltage side circuit model and the low-voltage side circuit model are connected through a coupling branch, and the coupling branch is connected with the high-voltage side circuit model node and the low-voltage side circuit model node and comprises a coupling capacitance and a coupling conductance which are connected in parallel.
3. The method of claim 1, wherein the boundary condition is a high voltage winding 1A current, a low voltage winding 0V voltage.
4. The method of claim 1, wherein determining the association of the unit parameter of each element of the transformer winding unit with the frequency response data using the voltage-current relationship of the trapezoidal equivalent network comprises:
Constructing an equation set for each node and each branch of the trapezoid equivalent network by using kirchhoff voltage and current law;
and determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data according to the constructed equation set.
5. The method of claim 4, wherein the set of equations constructed comprises:
the 2n impedance branch columns write the KVL equation:
2n+2 node columns write the KCL equation:
wherein n is the number of sections of the trapezoid equivalent network, I m The current of the inductive resistance branch with the number m; u (U) m Is the voltage on the node numbered m; g gm Is the conductance between the node numbered m and ground; c (C) gm A capacitance between the node numbered m and ground; g p,q Is the conductance between the node numbered p and the node numbered q; c (C) p,q Is the capacitance between the node numbered p and the node numbered q.
6. The method of claim 1, wherein the association between the unit parameter and the frequency response data of each element of the transformer winding unit is:
wherein X (1) is a frequency domain value of a voltage applied to the winding end; x is X 1 (n) is the voltage frequency domain value of the node numbered n in the model; h is the frequency response of the node voltage to the end voltage, numbered n.
7. The method of claim 1, wherein constructing an objective function according to the measured frequency response data and the association relation between the unit parameters and the frequency response data in different states, and using a parameter identification algorithm to identify optimal parameters of the ladder network structure element in different states according to the initial parameters and the objective function in different states, comprises:
step 1: constructing an objective function according to the actually measured frequency response data and the simulated frequency response data obtained by simulation under different states, and setting a classification error rate lambda for terminating the gray wolf optimization algorithm;
step 2: initializing whale groups, setting the size of the whale groups to be N, initializing the positions of the whale groups in a feasible domain, and obtaining simulation frequency response data according to the initialized whale group positions and the association relation between the unit parameters and the frequency response data, wherein the positions of whales are vectors formed by element parameters of a trapezoid equivalent network;
step 3: calculating the objective function value of the whale randomly generated in the step 2 according to the objective function, and recording the position X of the whale with the minimum objective function value best ；
Step 4: judging the position X of whale best If the objective function value of the whale is less than lambda, defining the iteration times t=1, updating the position of the whale according to a preset updating formula, performing out-of-range adjustment on the whale after the position is updated, recalculating the objective function value of the whale, and recording the position X of the whale with the minimum objective function value after the update best 。
Step 5: judging the updated X obtained in the step 4 best If the objective function value of (2) is less than lambda, if not, let t=t+1, repeating the updating of the whale position of step 4 until the updated whale is at position X best Is less than lambda, will finallyX obtained in one iteration best As an optimal position and converts it into optimal parameters for 7n+3 elements in the ladder equivalent network.
8. The method of claim 1, wherein the objective function is:
wherein k is the number of resonance points of the frequency response data amplitude-frequency curve, m is the number of resonance points of the frequency response data phase-frequency curve, and H i Is the amplitude value of the ith frequency point in the amplitude-frequency curve resonance points, H p Is the amplitude of the p-th frequency point in the resonance points of the phase frequency curve, (H) i ) Actual measurement For the measured frequency response data, (H) i ) Simulation of Is simulated frequency response data.
9. The method of claim 1, wherein the updated formula is as shown in the equation.
Wherein the three expressions correspond to three actions of whale searching for hunting, surrounding hunting and hunting, respectively, wherein X rand For randomly selected whale positions, a=2ar 1 -a,C＝2r 1 The value of a decreases linearly from 2 to 0, r 1 Is a [0,1 ]]D= |c×x) best (t)-X(t)|，D'＝|X best (t) -X (t) |, b is a constant, used to define the shape of the spiral, l is a random number in (-1, 1), p is a [0,1 ] ]Three behaviors of whale were selected by coefficients a and p.
10. The utility model provides a winding structure fault location device based on parameter identification which characterized in that includes:
the acquisition module is used for acquiring actual measurement frequency response data of the transformer winding in different states, wherein the different states comprise a normal state and a fault state of the transformer;
the construction module is used for constructing a ladder network structure of the transformer winding and determining initial parameters of elements in the ladder network structure under different states by applying boundary conditions of different states;
the first determining module is used for determining the association relation between the unit parameters of each element of the transformer winding unit and the frequency response data by utilizing the voltage-current relation of the trapezoid equivalent network;
the identification module is used for constructing an objective function according to the actually measured frequency response data in different states, adopting a parameter identification algorithm, and identifying optimal parameters of the ladder network structural element in different states according to the initial parameters, the objective function and the association relation between the unit parameters and the frequency response data in different states;
and the second determining module is used for determining the diagnosis of the fault type, the position and the degree of the transformer winding according to the optimal parameters of the ladder network structural element and the normal parameters of the transformer winding in different states.
11. A computer readable storage medium, characterized in that the storage medium stores a computer program for executing the method of any of the preceding claims 1-9.
12. An electronic device, the electronic device comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-9.
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| CN117639282B (en) * | 2024-01-26 | 2024-04-19 | 南京中鑫智电科技有限公司 | Converter transformer valve side sleeve end screen voltage divider frequency domain response processing method and system |
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