Wind power plant sending-out line fault detection method and device based on quantum morphology

Abstract

The invention discloses a wind power plant sending-out line fault detection method and device based on quantum morphology. The method comprises the following steps: acquiring three-phase current signals, and performing normalization processing on the three-phase current signals to obtain a plurality of first signal data points corresponding to each three-phase current; determining a quantum state corresponding to the first signal data point according to the first signal data point; according to all the quantum states corresponding to each three-phase current under each time window, obtaining a corresponding quantum form gradient extreme value; and calculating standard deviations of all quantum form gradient extreme values under each time window, and when an absolute difference value of the standard deviations corresponding to any two adjacent time windows is greater than a first threshold value, determining that the sending-out line of the wind power plant has a fault. According to the invention, the method can avoid the repeated design of structural elements, improves the fault detection robustness of the transmission line of the wind power plant, and enables the signal to be mapped to the quantum space, thereby capturing more signal features, and improving the fault detection accuracy.

Description

Technical Field

[0001] The present application relates to the field of power grid fault detection, and in particular to a method and device for detecting wind farm transmission line faults based on quantum morphology. Background Art

[0002] As an important form of clean energy, wind power generation has made great progress in recent years. Fault detection of wind farm transmission lines is a key link to ensure the reliability of wind power grid-connected systems.

[0003] Mathematical morphology has been widely used in the fields of fault detection and power quality analysis due to its simple principle and fast operation speed. However, traditional mathematical morphology operations require the use of structural elements to perform operations, such as determining the minimum (erosion) and maximum (expansion) of local areas. The structural elements used rely on subjective experience design, which limits its robustness in application to a certain extent. The main reason is that the above-mentioned traditional mathematical morphology methods are limited by the shape and size of the structural elements. Different structural elements may lose the detailed information of the signal, and the design of the structural elements will directly affect the effect of morphological filtering. In addition, when the algorithm needs to be adjusted to adapt to new fault conditions, the existing technology needs to redesign the structural elements, resulting in poor robustness in wind farm transmission line fault detection.

[0004] Therefore, when faced with the problem of wind farm transmission line fault detection, how to improve the robustness of the detection method while ensuring computational efficiency is a technical problem that needs to be solved. Summary of the invention

[0005] The present application provides a method and device for detecting wind farm transmission line faults based on quantum morphology, so as to solve the technical problem of how to improve the robustness of the detection method while ensuring computational efficiency when facing the problem of wind farm transmission line fault detection.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a method for detecting a wind farm transmission line fault based on quantum morphology, comprising:

[0007] Collecting three-phase current signals, and performing normalization processing on the three-phase current signals to obtain a plurality of first signal data points corresponding to each three-phase current;

[0008] Determining, based on the first signal data point, a quantum state corresponding to the first signal data point;

[0009] According to all the quantum states corresponding to the three-phase currents in each time window, the corresponding quantum morphology gradient extreme value is obtained;

[0010] The standard deviation of all quantum morphological gradient extreme values ​​in each time window is calculated. When the absolute difference of the standard deviations corresponding to any two adjacent time windows is greater than a first threshold, it is determined that there is a fault in the wind farm transmission line.

[0011] Compared with the prior art, the embodiments of the present application have the following beneficial effects: in order to avoid the problems of low efficiency and low robustness caused by redesigning structural elements, by using quantum computing to simulate the expansion and corrosion operations in mathematical morphological operations, it is possible to avoid repeatedly constructing different structural elements to process fault current signals. While reducing the computational complexity, it is only necessary to adjust the angles and sequences of quantum states to achieve morphological change operations, thereby effectively improving the flexibility of signal processing. In addition, the faulty three-phase current signal is mapped to a higher-dimensional quantum state space to capture more signal features. When there are complex patterns in the signal, the high-dimensional representation can provide richer information and improve the accuracy of subsequent fault detection.

[0012] In some embodiments of the first aspect of the present application, determining the quantum state corresponding to the first signal data point according to the first signal data point includes:

[0013] encoding the first signal data point as a quantum state rotation angle;

[0014] The quantum state corresponding to the first signal data point is determined according to the quantum state rotation angle through the quantum rotation gate.

[0015] Compared with the prior art, the above embodiment has the following beneficial effects: by encoding the signal data into the quantum state rotation angle, the effect of the morphological operation can be flexibly changed by subsequently adjusting the quantum state rotation angle and sequence, without the need to redesign the structural elements like the traditional mathematical morphology method, thereby effectively improving the robustness of the detection method; at the same time, the signal is mapped to a high-dimensional quantum state space, so as to capture more signal features and improve the accuracy of subsequent detection.

[0016] In some embodiments of the first aspect of the present application, encoding the first signal data point as a quantum state rotation angle includes:

[0017] The encoding formula of the first signal data point is specifically:

[0018]

[0019] Wherein, θ is the quantum state rotation angle; x is the first signal data point.

[0020] In some embodiments of the first aspect of the present application, determining the quantum state corresponding to the first signal data point according to the quantum state rotation angle through a quantum rotating gate includes:

[0021] The formula for obtaining the quantum state is specifically:

[0022]

[0023] Among them, |ψ i > is the quantum state corresponding to the i-th first signal data point; θ i is the quantum state rotation angle of the i-th first signal data point; rx(*) is the quantum rotation gate; |0> and |1> are the two basic states of the quantum bit.

[0024] In some embodiments of the first aspect of the present application, the step of obtaining the corresponding quantum morphology gradient extreme value according to all the quantum states corresponding to the three-phase currents in each time window includes:

[0025] According to the quantum state, obtaining a first tensor product of each two adjacent quantum states;

[0026] Performing quantum expansion and quantum corrosion operations on the first tensor product respectively by controlling the NOT gate to obtain corresponding second tensor products respectively;

[0027] According to all the second tensor products corresponding to each three-phase current in each time window, the corresponding quantum morphological gradient extreme value is obtained.

[0028] Compared with the prior art, the above embodiment has the following beneficial effects: by controlling the NOT gate, the expansion and corrosion operation and the quantum entanglement operation are directly implemented, the morphological changes in the current signal can be captured, the sensitivity of fault feature recognition in different time windows is enhanced, and the signal processing process is more flexible.

[0029] In some embodiments of the first aspect of the present application, obtaining a first tensor product of two adjacent quantum states according to the quantum state includes:

[0030] The calculation formula of the first tensor product is:

[0031]

[0032] Among them, |Ψ> is the first tensor product; |ψ1> and |ψ2> are two adjacent quantum states; α, β, γ and δ are the probability amplitudes of the quantum bit; |0> and |1> are the two basic states of the quantum bit; |00>, |01>, |10>, |11> are the four joint ground states of the two quantum bits.

[0033] In some embodiments of the first aspect of the present application, the step of performing a quantum expansion operation and a quantum corrosion operation on the first tensor product by controlling a NOT gate to obtain corresponding second tensor products respectively includes:

[0034] When performing an expansion operation, the first bit of the joint ground state in the first tensor product is used as a control bit, and the second bit is used as a target bit; when performing an erosion operation, the first bit of the joint ground state in the first tensor product is used as a target bit, and the second bit is used as a control bit;

[0035] Through the control NOT gate, the bits of each joint ground state in the first tensor product are bit-flipped according to the control bit and the target bit of the operation to be performed as needed, so as to obtain the corresponding second tensor product.

[0036] Compared with the prior art, the above embodiment has the following beneficial effects: by expanding the high-order state to the low-order state, and by controlling the bit to affect the state of the subsequent bit, the information can be extended to the next bit; by corroding the low-order state to the high-order state, the state of the next bit is reversed to the state of the previous bit, thereby achieving information reduction. Through the above operations, the flexibility of quantum operations is improved, and the ability to handle complex tasks is enhanced.

[0037] In some embodiments of the first aspect of the present application, obtaining the corresponding quantum morphological gradient extreme value according to all the second tensor products corresponding to each three-phase current in each time window includes:

[0038] Obtaining a quantum morphological gradient according to the corresponding second tensor product obtained by the quantum expansion operation and the quantum erosion operation;

[0039] According to all the quantum morphological gradients corresponding to the three-phase currents in each time window, the corresponding quantum morphological gradient extreme value is determined.

[0040] Compared with the prior art, the above embodiment has the following beneficial effects: the second tensor product generated by the quantum expansion and corrosion operations can be used to calculate the quantum morphological gradient, providing richer morphological information for signal processing, so that the changes of the current signal in different time windows can more accurately reflect the characteristic changes in line fault detection.

[0041] In some embodiments of the first aspect of the present application, the calculating the standard deviation of all quantum morphological gradient extreme values ​​in each time window includes:

[0042] The calculation formula of the standard deviation is:

[0043]

[0044] Where k = α, β, i = 0, 1, α represents the maximum value, β represents the minimum value, 0 represents the previous time window of two adjacent time windows, and 1 represents the next time window of two adjacent time windows; E aki ,Ebki ,E cki They represent the extreme values ​​of the quantum morphological gradients of the three-phase currents A, B, and C in different time windows; Represents standard deviation.

[0045] In a second aspect, an embodiment of the present application further provides a wind farm transmission line fault detection device based on quantum morphology, comprising: a first signal data point acquisition module, a quantum state acquisition module, a quantum morphology gradient extreme value acquisition module, and a fault judgment module;

[0046] The first signal data point acquisition module is used to collect three-phase current signals, and perform normalization processing on the three-phase current signals to obtain a number of first signal data points corresponding to each three-phase current;

[0047] The quantum state acquisition module is used to determine the quantum state corresponding to the first signal data point according to the first signal data point;

[0048] The quantum morphology gradient extreme value acquisition module is used to acquire the corresponding quantum morphology gradient extreme value according to all the quantum states corresponding to each three-phase current in each time window;

[0049] The fault judgment module is used to calculate the standard deviation of all quantum morphological gradient extreme values ​​in each time window. When the absolute difference between the standard deviations corresponding to any two adjacent time windows is greater than a first threshold, it is determined that there is a fault in the wind farm transmission line. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic flow chart of a method for detecting a wind farm transmission line fault based on quantum morphology provided in some embodiments of the present application;

[0051] Figure 2 A wind farm transmission route wiring diagram provided in some embodiments of the present application;

[0052] Figure 3 This is a schematic structural diagram of a wind farm transmission line fault detection device based on quantum morphology provided in some embodiments of the present application;

[0053] Figure 4 This is a schematic structural diagram of another wind farm transmission line fault detection device based on quantum morphology provided in some embodiments of the present application. DETAILED DESCRIPTION

[0054] Mathematical morphology has been widely used in fields such as fault detection and power quality analysis due to its simple principles and fast operation speed. However, traditional mathematical morphology operations require the use of structural elements to perform operations, such as determining the minimum (erosion) and maximum (dilation) of local areas. The structural elements used rely on subjective experience design, which limits its robustness in application to a certain extent.

[0055] In order to solve the above technical problems, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] Embodiment 1

[0057] Please refer to Figure 1 , a wind farm transmission line fault detection method based on quantum morphology provided in an embodiment of the present application, including S10 to S40, specifically:

[0058] S10: Collect three-phase current signals, and perform normalization processing on the three-phase current signals to obtain a plurality of first signal data points corresponding to each three-phase current.

[0059] Preferably, in some embodiments of the present application, a three-phase voltage and current transformer is preferably used to collect current signals to obtain the three-phase current signal i a ,i b ,i c .

[0060] Preferably, in some embodiments of the present application, the first signal data point is obtained by the following preferred implementation:

[0061] S11: resample the collected three-phase current signal, preferably set the resampling frequency to 500 Hz, and set the number of sampling points in one cycle to N. c , then the number of sampling points N of a data acquisition window (also understood as a time window) is set to the number of sampling points of a cycle, that is, N = N c ;

[0062] S12: For each three-phase current signal i in the data acquisition window a ,i b ,i c , respectively perform the minimum-maximum normalization (Min-Max Normalization) so that the range of each three-phase current signal is scaled to the [0,1] interval, specifically:

[0063]

[0064] Among them, x norm is the first signal data point; x is the three-phase current signal; min(x) and max(x) are the minimum and maximum values ​​of the three-phase current signal in the corresponding data acquisition window. It should be noted that if x=i a , then min(x) and max(x) should also correspond to i a The minimum and maximum values ​​within the corresponding data collection window.

[0065] S20: Determine a quantum state corresponding to the first signal data point according to the first signal data point.

[0066] Furthermore, in some embodiments of the present application, determining the quantum state corresponding to the first signal data point according to the first signal data point includes:

[0067] encoding the first signal data point as a quantum state rotation angle;

[0068] The quantum state corresponding to the first signal data point is determined according to the quantum state rotation angle through the quantum rotation gate.

[0069] By encoding signal data into quantum state rotation angles, the effect of morphological operations can be flexibly changed by subsequently adjusting the quantum state rotation angles and sequences. There is no need to redesign structural elements like traditional mathematical morphology methods, which effectively improves the robustness of the detection method. At the same time, the signal is mapped to a high-dimensional quantum state space, so that more signal features can be captured and the accuracy of subsequent detection can be improved.

[0070] Preferably, in some embodiments of the present application, the quantum state of each first signal data point may be determined by the following preferred implementations:

[0071] S21: Initialize electronic circuits and qubits:

[0072] Set the number of qubits in the quantum circuit to be equal to the length of the array corresponding to the first signal data point after S11 to S12 normalizes the three-phase current signal, and initialize an empty quantum circuit for subsequent addition of quantum gate operations. The location of the qubit is:

[0073] |ψ>=α|0>+β|1>

[0074] Among them, |0> and |1> are the two basic states of the qubit; |ψ> is the quantum state; α and β are the probability amplitudes of the qubit, satisfying |α| 2 +|β| 2 =1, where |α| 2represents the probability of observing the quantum state |0>, |β| 2 represents the probability of observing the quantum state |1>.

[0075] S22: Encode the normalized signal data into quantum states:

[0076] S221: Encode each first signal data point x in the data acquisition window as a quantum state rotation angle θ:

[0077]

[0078] S222: The first signal data point after encoding is applied to the quantum bit through the quantum rotating gate. For the initial state of each bit |0>, the quantum state after the rotating gate can be expressed as:

[0079]

[0080] Among them, |ψ i > is the quantum state corresponding to the i-th first signal data point; θ i is the quantum state rotation angle of the i-th first signal data point; rx(*) is the quantum rotation gate; |0> and |1> are the two basic states of the quantum bit.

[0081] S30: According to all the quantum states corresponding to the three-phase currents in each time window, the corresponding quantum morphology gradient extreme value is obtained.

[0082] Further, in some embodiments of the present application, the step of obtaining the corresponding quantum morphology gradient extreme value according to all the quantum states corresponding to the three-phase currents in each time window includes:

[0083] According to the quantum state, obtaining a first tensor product of each two adjacent quantum states;

[0084] Performing quantum expansion and quantum corrosion operations on the first tensor product respectively by controlling the NOT gate to obtain corresponding second tensor products respectively;

[0085] According to all the second tensor products corresponding to each three-phase current in each time window, the corresponding quantum morphological gradient extreme value is obtained.

[0086] By controlling the NOT gate, the expansion and corrosion operations and quantum entanglement operations can be directly implemented, which can capture the morphological changes in the current signal, enhance the sensitivity of fault feature recognition in different time windows, and make the signal processing process more flexible.

[0087] Preferably, in some embodiments of the present application, the first tensor product of each two adjacent quantum states in each data acquisition window can be calculated by the following preferred implementation:

[0088] S31: The first tensor product of two adjacent quantum states can be expressed as follows:

[0089]

[0090] S32: According to the above form, assuming that the state of the first qubit is |ψ1>=α|0>+β|1>, and the state of the second qubit is |ψ2>=γ|0>+δ|1>, then the first tensor product calculation formula of the two qubits is:

[0091]

[0092] Among them, |00>, |01>, |10>, and |11> are the four joint ground states of two qubits.

[0093] Further, in some embodiments of the present application, performing a quantum expansion operation and a quantum corrosion operation on the first tensor product respectively by controlling a NOT gate to obtain a corresponding second tensor product respectively includes:

[0094] When performing an expansion operation, the first bit of the joint ground state in the first tensor product is used as a control bit, and the second bit is used as a target bit; when performing an erosion operation, the first bit of the joint ground state in the first tensor product is used as a target bit, and the second bit is used as a control bit;

[0095] Through the control NOT gate, the bits of each joint ground state in the first tensor product are bit-flipped according to the control bit and the target bit of the operation to be performed as needed, so as to obtain the corresponding second tensor product.

[0096] Through the expansion operation, the high-bit state is expanded to the low-bit state, and the state of the subsequent bits is affected by the control bit, so that the information can be extended to the next bit; through the corrosion operation, the low-bit state is affected to the high-bit state, so that the state of the next bit is reversed to the state of the previous bit, and information reduction is achieved. Through the above operations, the flexibility of quantum operations is improved, and the ability to handle complex tasks is enhanced.

[0097] Preferably, in some embodiments of the present application, the second tensor product can be obtained by the following preferred implementation:

[0098] S33: Perform an expansion operation through a controlled-NOT gate (CNOT) to obtain the corresponding second tensor product:

[0099] The control NOT gate is specifically:

[0100]

[0101] After the CNOT gate, if the first bit is |0> (i.e. |00> or |01>), the second bit remains unchanged. If the first bit is |1> (i.e. |10> or |11>), the second bit flips. That is: |00>→|00>, |01>→|01>, |10>→|11>, |11>→|10>.

[0102] For the expansion operation, a controlled NOT gate is used in the forward direction, the first bit is used as the control bit, and the second bit is used as the target bit. After the expansion operation, the first tensor product of two adjacent quantum states becomes the following second tensor product:

[0103] |Ψ′>=αγ|00>+αδ|01>+βγ|11>+βδ|10>

[0104] Among them, |Ψ′> is the second tensor product under the expansion operation.

[0105] S34: Perform the corrosion operation by controlling the NOT gate to obtain the corresponding second tensor product:

[0106] For the corrosion operation, the controlled NOT gate is used in reverse, the second bit is used as the control bit, and the first bit is the target bit. After the corrosion operation, the tensor product of the two adjacent quantum states becomes:

[0107] |Ψ″>=αγ|00>+αδ|11>+βγ|10>+βδ|01>

[0108] Among them, |Ψ″> is the second tensor product under the corrosion operation.

[0109] Further, in some embodiments of the present application, the step of obtaining the corresponding quantum morphological gradient extreme value according to all the second tensor products corresponding to each three-phase current in each time window includes:

[0110] Obtaining a quantum morphological gradient according to the corresponding second tensor product obtained by the quantum expansion operation and the quantum erosion operation;

[0111] According to all the quantum morphological gradients corresponding to the three-phase currents in each time window, the corresponding quantum morphological gradient extreme value is determined.

[0112] The second tensor product generated by quantum expansion and corrosion operations can be used to calculate quantum morphological gradients, providing richer morphological information for signal processing, so that the changes in current signals in different time windows can more accurately reflect the characteristic changes in line fault detection.

[0113] Preferably, in some embodiments of the present application, the quantum morphological gradient extreme value can be obtained by the following preferred implementations:

[0114] S35: Calculate all quantum morphological gradients within the data acquisition window by controlling the NOT gate:

[0115] G(f)=|Ψ′>-|Ψ″>

[0116] Where G(f) is the quantum morphological gradient.

[0117] S36: For the three-phase current signal i a ,i b ,i c , assuming that the corresponding three-phase currents are A, B, and C, according to the above S10 to S35, all quantum morphological gradients corresponding to A, B, and C in each data acquisition window can be obtained. Assuming that G a , G b ,G c They represent the quantum morphological gradient sets of A, B, and C in a certain data acquisition window respectively, and the maximum and minimum values ​​in the sets are obtained in turn as the quantum morphological gradient extreme values ​​of the corresponding three-phase currents in the corresponding data acquisition window.

[0118] S40: Calculate the standard deviation of all quantum morphological gradient extreme values ​​in each time window, and when the absolute difference between the standard deviations corresponding to any two adjacent time windows is greater than a first threshold, determine that a fault exists in the wind farm transmission line.

[0119] Preferably, in some embodiments of the present application, it can be determined whether there is a fault in the wind farm transmission line by the following steps:

[0120] S41: Construct indicators based on all extreme values ​​obtained in S36

[0121]

[0122] Among them, E aki ,E bki ,E cki Respectively represent the quantum morphological gradient extreme values ​​of the three-phase currents A, B, and C in different data acquisition windows; k = α, β, i = 0, 1, α represents the maximum value, β represents the minimum value, assuming that the first signal data point at the current time is the jth point, then 1 represents the data acquisition window from the jth point to the j+Nth point, and 0 represents the data acquisition window from the jNth point to the jth point.

[0123] S42: Compare indicators between data collection windows simultaneously and in real time Set the following detection indicators Determine if there is a fault in the wind farm transmission line:

[0124]

[0125] Setting the Threshold Determine whether a fault has occurred. When the wind farm transmission line is faulty, it is determined that there is a fault.

[0126] In order to better verify the effectiveness of the wind farm transmission line fault detection based on quantum morphology proposed in this application, the following is combined with Figure 2 A wind farm transmission line wiring diagram is shown for further explanation:

[0127] Should Figure 2 The power system simulation model for verifying the effectiveness of the method proposed in this application, in which the bus POC is the protected line, and the simulation model parameters of the wind farm transmission line are: frequency is 60Hz, line length is 60km, power supply rated voltage is 230kV, power supply impedance r s =5Ω,L s =0.06H, the line resistance per unit length is r1=0.013206Ω / km, and the line parallel conductance per unit length is g1=1×10 -11 S / m; the rated voltage of the transformer is 230kV / 33kV; the length of the cable is 2km, with two layers of insulation, the DC resistance per unit length of the outer protective layer is r2=0.101Ω / km, the DC resistance per unit length of the inner layer is r3=0.01288Ω / km, the insulation capacitance per unit length of the outer insulation layer of the cable is c1=0.20559μF / km, and the insulation capacitance per unit length of the inner insulation layer of the cable is c2=0.20559μF / km; the busbar is equipped with current transformers and A / D conversion equipment for sampling at a sampling frequency of 3.85kHz; the sampling signal is resampled at a sampling frequency of 500Hz to obtain a data window, and the data window length N=10.

[0128] If at t=200ms, a phase A grounding fault occurs in the bus POC (Point of Connection), the three-phase current signal is sampled through its current transformer, the signal is resampled to obtain the data window to be processed, and normalized through S11 to S12; the normalized signal is used to initialize the number of quantum bits so that the number of quantum bits is equal to the signal length; the quantum state of each data point is calculated through S21 to S22, and then the tensor product of the quantum states of two adjacent points is calculated through S31 to S32; the tensor product of the quantum states of two adjacent points is forwardly simulated by using the CNOT gate operation through S33 to simulate the expansion operation; through S34, the tensor product of the quantum states of two adjacent points is reversely simulated by using the CNOT gate operation to simulate the corrosion operation; the morphological gradient signal is obtained through S35 and the calculated expansion and corrosion signals (i.e., the second tensor product); through S36, the extreme value of the three-phase morphological gradient signal is obtained, and through S41, the index is constructed. Measuring the extreme values ​​of the three phases, the indicators before the fault can be obtained by calculation. Indicators after failure Compare metrics between data windows in real time The difference is constructed through S42 index Calculation can be obtained Setting Thresholds Due to the existence It is determined that a fault has occurred.

[0129] In summary, a quantum morphological wind farm transmission line fault detection method provided in an embodiment of the present application has the following beneficial effects: in order to avoid the problems of low efficiency and low robustness caused by redesigning structural elements, by using quantum computing to simulate the expansion and corrosion operations in mathematical morphological operations, it is possible to avoid repeatedly constructing different structural elements to process fault current signals. While reducing the computational complexity, it is only necessary to adjust the angles and sequences of quantum states to achieve morphological change operations, thereby effectively improving the flexibility of signal processing. In addition, the faulty three-phase current signal is mapped to a higher-dimensional quantum state space to capture more signal features. When there are complex patterns in the signal, the high-dimensional representation can provide richer information and improve the accuracy of subsequent fault detection.

[0130] Embodiment 2

[0131] refer to Figure 3 , a wind farm transmission line fault detection device based on quantum morphology provided in an embodiment of the present application, can be used to implement the above S10 to S40, wherein, Figure 3 A wind farm transmission line fault detection device based on quantum morphology is shown, comprising a data acquisition module, a signal preprocessing module, a quantum circuit module and a fault detection module; wherein the data acquisition module is used to execute the above S11, the signal preprocessing module is used to execute S12, the quantum circuit module is used to execute S21 to S36, and the fault detection module is used to execute S41 to S42.

[0132] refer to Figure 4 , which is another wind farm transmission line fault detection device based on quantum morphology provided in an embodiment of the present application, includes: a first signal data point acquisition module 11, a quantum state acquisition module 12, a quantum morphology gradient extreme value acquisition module 13 and a fault judgment module 14.

[0133] Furthermore, in some embodiments of the present application, the first signal data point acquisition module 11 is used to collect three-phase current signals and normalize the three-phase current signals to obtain several first signal data points corresponding to each three-phase current; the quantum state acquisition module 12 is used to determine the quantum state corresponding to the first signal data point based on the first signal data point; the quantum morphology gradient extreme value acquisition module 13 is used to obtain the corresponding quantum morphology gradient extreme value based on all the quantum states corresponding to each three-phase current in each time window; the fault judgment module 14 is used to calculate the standard deviation of all quantum morphology gradient extreme values ​​in each time window, and when the absolute difference between the standard deviations corresponding to any two adjacent time windows is greater than the first threshold, it is determined that there is a fault in the wind farm transmission line.

[0134] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention. The wind farm transmission line fault detection device based on quantum morphology provided in the embodiments of the present invention can implement any method item embodiment of the present invention, that is, the wind farm transmission line fault detection method based on quantum morphology provided in Embodiment 1.

[0135] In summary, a quantum morphological wind farm transmission line fault detection device provided in an embodiment of the present application has the following beneficial effects: in order to avoid the problems of low efficiency and low robustness caused by redesigning structural elements, by using quantum computing to simulate the expansion and corrosion operations in mathematical morphological operations, it is possible to avoid repeatedly constructing different structural elements to process fault current signals. While reducing the computational complexity, it is only necessary to adjust the angles and sequences of quantum elements to achieve morphological change operations, thereby effectively improving the flexibility of signal processing; in addition, the faulty three-phase current signal is mapped to a higher-dimensional quantum state space to capture more signal features. When there are complex patterns in the signal, the high-dimensional representation can provide richer information and improve the accuracy of subsequent fault detection.

[0136] Embodiment 3

[0137] Based on the above-mentioned embodiment of the wind farm transmission line fault detection method based on quantum morphology, another embodiment of the present application provides a wind farm transmission line fault detection terminal device based on quantum morphology. The wind farm transmission line fault detection terminal device based on quantum morphology includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the wind farm transmission line fault detection method based on quantum morphology of any embodiment of the present application is implemented.

[0138] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the wind farm transmission line fault detection device based on quantum morphology.

[0139] The wind farm transmission line fault detection device based on quantum morphology can be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The wind farm transmission line fault detection terminal device based on quantum morphology can include, but is not limited to, a processor and a memory.

[0140] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the wind farm transmission line fault detection device based on quantum morphology, and uses various interfaces and lines to connect the various parts of the wind farm transmission line fault detection device based on quantum morphology. The memory may be used to store the computer program and / or module, and the processor implements the various functions of the wind farm transmission line fault detection device based on quantum morphology by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0141] Embodiment 4

[0142] Based on the above-mentioned embodiment of the wind farm transmission line fault detection method based on quantum morphology, another embodiment of the present application provides a storage medium, wherein the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the wind farm transmission line fault detection method based on quantum morphology according to any embodiment of the present application.

[0143] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0144] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for detecting wind farm transmission line faults based on quantum morphology, characterized in that: include: Collecting three-phase current signals, and performing normalization processing on the three-phase current signals to obtain a plurality of first signal data points corresponding to each three-phase current; Determining, based on the first signal data point, a quantum state corresponding to the first signal data point; According to all the quantum states corresponding to the three-phase currents in each time window, the corresponding quantum morphology gradient extreme value is obtained; The standard deviation of all quantum morphological gradient extreme values ​​in each time window is calculated. When the absolute difference of the standard deviations corresponding to any two adjacent time windows is greater than a first threshold, it is determined that there is a fault in the wind farm transmission line.

2. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 1, characterized in that: Determining the quantum state corresponding to the first signal data point according to the first signal data point includes: encoding the first signal data point as a quantum state rotation angle; The quantum state corresponding to the first signal data point is determined according to the quantum state rotation angle through the quantum rotation gate.

3. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 2, characterized in that: The step of encoding the first signal data point into a quantum state rotation angle comprises: The encoding formula of the first signal data point is specifically: Wherein, θ is the quantum state rotation angle; x is the first signal data point.

4. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 2, characterized in that: Determining the quantum state corresponding to the first signal data point according to the quantum state rotation angle through the quantum rotating gate includes: The formula for obtaining the quantum state is specifically: Among them, |ψ i > is the quantum state corresponding to the i-th first signal data point; θ i is the quantum state rotation angle of the i-th first signal data point; rx(*) is the quantum rotation gate; |0> and |1> are the two basic states of the quantum bit.

5. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 1, characterized in that: The step of obtaining the corresponding quantum morphology gradient extreme value according to all the quantum states corresponding to the three-phase currents in each time window includes: According to the quantum state, obtaining a first tensor product of each two adjacent quantum states; Performing quantum expansion and quantum corrosion operations on the first tensor products respectively by controlling the NOT gates to obtain corresponding second tensor products respectively; According to all the second tensor products corresponding to each three-phase current in each time window, the corresponding quantum morphological gradient extreme value is obtained.

6. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 5, characterized in that: The step of obtaining a first tensor product of two adjacent quantum states according to the quantum state includes: The calculation formula of the first tensor product is: Among them, |Ψ> is the first tensor product; |ψ1> and |ψ2> are two adjacent quantum states; α, β, γ and δ are the probability amplitudes of the quantum bit; |0> and |1> are the two basic states of the quantum bit; |00>, |01>, |10>, |11> are the four joint ground states of the two quantum bits.

7. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 5, characterized in that: The step of performing a quantum expansion operation and a quantum corrosion operation on the first tensor product respectively by controlling the NOT gate to obtain the corresponding second tensor product respectively includes: When performing an expansion operation, the first bit of the joint ground state in the first tensor product is used as a control bit, and the second bit is used as a target bit; when performing an erosion operation, the first bit of the joint ground state in the first tensor product is used as a target bit, and the second bit is used as a control bit; Through the control NOT gate, the bits of each joint ground state in the first tensor product are bit-flipped according to the control bit and the target bit of the operation to be performed as needed, so as to obtain the corresponding second tensor product.

8. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 5, characterized in that: The step of obtaining the corresponding quantum morphological gradient extreme value according to all the second tensor products corresponding to each three-phase current in each time window includes: Obtain a quantum morphological gradient according to the corresponding second tensor product obtained by the quantum expansion operation and the quantum erosion operation; According to all the quantum morphological gradients corresponding to the three-phase currents in each time window, the corresponding quantum morphological gradient extreme value is determined.

9. A method for detecting wind farm transmission line faults based on quantum morphology as claimed in claim 5, characterized in that: The calculation of the standard deviation of all quantum morphological gradient extreme values ​​in each time window includes: The calculation formula of the standard deviation is: Where k = α, β, i = 0, 1, α represents the maximum value, β represents the minimum value, 0 represents the previous time window of two adjacent time windows, and 1 represents the next time window of two adjacent time windows; E aki ,E bki ,E cki They represent the extreme values ​​of the quantum morphological gradients of the three-phase currents A, B, and C in different time windows; Represents standard deviation.

10. A wind farm transmission line fault detection device based on quantum morphology, characterized in that: include: A first signal data point acquisition module, a quantum state acquisition module, a quantum morphology gradient extreme value acquisition module, and a fault judgment module; The first signal data point acquisition module is used to collect three-phase current signals, and perform normalization processing on the three-phase current signals to obtain a number of first signal data points corresponding to each three-phase current; The quantum state acquisition module is used to determine the quantum state corresponding to the first signal data point according to the first signal data point; The quantum morphology gradient extreme value acquisition module is used to acquire the corresponding quantum morphology gradient extreme value according to all the quantum states corresponding to each three-phase current in each time window; The fault judgment module is used to calculate the standard deviation of all quantum morphological gradient extreme values ​​in each time window. When the absolute difference between the standard deviations corresponding to any two adjacent time windows is greater than a first threshold, it is determined that there is a fault in the wind farm transmission line.

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