CN117648630A - Fault identification method, device, equipment and medium for power transmission line - Google Patents
Fault identification method, device, equipment and medium for power transmission line Download PDFInfo
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Abstract
The embodiment of the disclosure relates to a fault identification method, device, equipment and medium for a power transmission line, wherein the method comprises the following steps: acquiring a first group of three-phase current signals at one end of a power transmission line and a second group of three-phase current signals at the other end of the power transmission line; performing multi-type modal decomposition on each phase of current signals in the first group of three-phase current signals and the second group of three-phase current signals to obtain a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set comprises multi-type modal components corresponding to the three-phase current signals; according to the modal component set, calculating the cepstrum distance of each phase of current signal in the preset modal component in the first group of three-phase current signals and the second group of three-phase current signals; and determining the fault type of the power transmission line according to the cepstrum distance corresponding to each phase of current signal. In the technical scheme, the fault type of the power transmission line can be accurately identified, the identification efficiency of the fault type is improved, and the method has important significance for the safety of a power system.
Description
Technical Field
The disclosure relates to the technical field of electric power safety, and in particular relates to a fault identification method, device, equipment and medium for a power transmission line.
Background
Transmission lines are an important component of an electrical power system, and their normal operation is critical to maintaining the stability and reliability of the electrical power system. However, various faults may occur in the power transmission line due to natural disasters, human factors, equipment aging, and the like, resulting in power disasters. After the occurrence of the power disasters, the type of the power transmission line fault is timely and accurately identified, so that the emergency response and the disaster resistance of the power system are improved.
In the related art, when a power transmission line fails, the failure type of the power transmission line needs to be manually checked, however, the working environment in a power system is complex and changeable, and the accuracy rate and the efficiency of a mode of manually determining the failure type of the power transmission line are low.
Disclosure of Invention
In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a method, an apparatus, a device, and a medium for identifying a fault of a power transmission line, which can accurately identify a fault type of the power transmission line, improve the identification efficiency of the fault type, and have important significance for safety of a power system.
The embodiment of the disclosure provides a fault identification method for a power transmission line, which comprises the following steps: acquiring a first group of three-phase current signals at one end of a power transmission line and a second group of three-phase current signals at the other end of the power transmission line; performing multi-type modal decomposition on each phase current signal in the first group of three-phase current signals and the second group of three-phase current signals to obtain a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set comprises multi-type modal components corresponding to the three-phase current signals; calculating the cepstrum distance of each phase of current signals in the preset modal component in the first group of three-phase current signals and the second group of three-phase current signals according to the modal component set; and determining the fault type of the power transmission line according to the cepstrum distance corresponding to the current signals of each phase.
The embodiment of the disclosure also provides a fault recognition device for the power transmission line, which comprises: the first acquisition module is used for acquiring a first group of three-phase current signals at one end of the power transmission line and a second group of three-phase current signals at the other end of the power transmission line; the second acquisition module is used for carrying out multi-type modal decomposition on each phase of current signals in the first group of three-phase current signals and the second group of three-phase current signals so as to acquire a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set comprises multi-type modal components corresponding to the three-phase current signals; the calculation module is used for calculating the cepstrum distance of each phase of current signal in the preset modal component in the first group of three-phase current signals and the second group of three-phase current signals according to the modal component set; and the determining module is used for determining the fault type of the power transmission line according to the cepstrum distance corresponding to the current signals of each phase.
The embodiment of the disclosure also provides an electronic device, which comprises: 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 a fault identification method for a power transmission line according to an embodiment of the present disclosure.
The embodiment of the disclosure also provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is used for executing the fault identification method of the power transmission line.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the fault identification scheme of the power transmission line, a first group of three-phase current signals at one end of the power transmission line and a second group of three-phase current signals at the other end of the power transmission line are obtained, multi-class modal decomposition is conducted on each phase of current signals in the first group of three-phase current signals and the second group of three-phase current signals, so that a modal component set of the first group of three-phase current signals and a modal component set of the second group of three-phase current signals is obtained, wherein the modal component set contains multi-class modal components corresponding to the three-phase current signals, the cepstrum distance of each phase of current signals in a preset class of modal components is calculated according to the modal component set, and then the fault type of the power transmission line is determined according to the cepstrum distance corresponding to each phase of current signals. In the technical scheme, the fault type of the power transmission line can be accurately identified, the identification efficiency of the fault type is improved, and the method has important significance for the safety of a power system.
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The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of a fault identification method of a power transmission line according to an embodiment of the disclosure;
fig. 2 is a schematic diagram of a power transmission line according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a preset recognition model according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a fault recognition device of a power transmission line according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
In order to solve the above-mentioned problems, an embodiment of the present disclosure provides a fault identification method for a power transmission line, in which, in consideration of that different types of faults of the power transmission line may be reflected by a cepstrum distance, where the cepstrum distance includes a current waveform, a voltage waveform, a power characteristic, and the like, the fault type is identified by extracting characteristic information of a fault signal and then analyzing and classifying the extracted characteristic information according to a deep learning method. The method has the advantages of strong instantaneity and high accuracy, and is suitable for complex and changeable working environments in the power system.
The method is described below in connection with specific examples.
Fig. 1 is a flow chart of a fault identification method for a power transmission line according to an embodiment of the present disclosure, where the method may be performed by a fault identification device for a power transmission line, where the device may be implemented by software and/or hardware, and may generally be integrated in an electronic device. As shown in fig. 1, the method includes:
step 101, a first group of three-phase current signals at one end of a power transmission line and a second group of three-phase current signals at the other end of the power transmission line are obtained.
In one embodiment of the present disclosure, since the difference between the input and output of the power transmission circuit that fails is large, in this embodiment, a first set of three-phase current signals at one end of the electric line and a second set of three-phase current signals at the other end of the electric line may be obtained so as to further determine the type of failure of the power transmission line, where in the embodiment of the present disclosure, the three-phase current signals may be defined as A, B, C three-phase current signals of one cycle each before and after the collection of the failure at both ends of the line.
Step 102, performing multi-type modal decomposition on each phase current signal in the first group of three-phase current signals and the second group of three-phase current signals to obtain a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set contains multi-type modal components corresponding to the three-phase current signals.
In one embodiment of the present disclosure, multi-class modal decomposition is performed on each phase current signal in the first set of three-phase current signals and the second set of three-phase current signals to obtain a modal component set of the first set of three-phase current signals and the second set of three-phase current signals, wherein the modal component set includes multi-class modal components of each phase current signal in the first set of three-phase current signals and multi-class modal components of each phase current signal in the second set of three-phase current signals.
The specific class number corresponding to the multi-class modal decomposition can be set according to scene requirements, fault type identification is performed through the multi-class modal components, and accurate determination of fault type identification is guaranteed, for example, when the specific class number K corresponding to the multi-class modal decomposition is 3, the modal component set comprises three types of modal components IMF1, IMF2 and IMF3 of each phase current signal in the first group of three-phase current signals, and three types of modal components IMF1, IMF2 and IMF3 of each phase current signal in the second group of three-phase current signals.
In the actual application process, the mode of performing multi-type modal decomposition on each phase current signal in the first set of three-phase current signals and the second set of three-phase current signals may be any algorithm capable of performing modal decomposition on the current signals, which is not limited herein.
In some possible implementations, a variational modal decomposition (Variational Mode Decomposition, VMD) may be employed to derive multi-class modal components of the respective phase current signals.
In this embodiment, a preset variation modal decomposition function is determined, and an optimal solution calculation function of multi-class modal decomposition is determined according to the preset variation modal decomposition function. In one embodiment of the present disclosure, the preset variation modal decomposition function is the following formula (1), where f (t) is an input signal, K is a classification number of multi-class modal decomposition, u k (t)、ω k Respectively represent the k-type modal components and the central frequency delta (t) thereof as pulse functions,and represents the gradient and convolution calculations, respectively, j being the complex unit, s.t being the constraint:
further, performing iterative computation on the optimal solution computation function, and determining multi-class modal components corresponding to multi-class modal decomposition according to the corresponding iterative computation result when the iterative computation result meets a preset convergence condition. In this embodiment, the variational modal decomposition is a time-frequency analysis method based on a variational non-recursive form, in this embodiment, the modal decomposition is converted into a variational decomposition problem, an optimal solution of a variational model is searched through an iterative process, optimization is performed by means of an alternate direction multiplier method, and finally a series of modal components with limited features are extracted simultaneously.
In some possible embodiments, the optimal solution computing function for the multi-class modal decomposition may be the following equation (2), including:
wherein lambda is Lagrange multiplier, alpha is a quadratic penalty factor, L ({ u) k },{ω k And }, lambda) is an augmented lagrangian function, and in the embodiment, a quadratic penalty factor alpha and a lagrangian multiplier lambda are introduced to obtain an optimal solution of the constraint variation problem, and the constraint variation problem is converted into an unconstrained variation problem.
In this embodiment, the optimal solution calculation function is subjected to iterative calculation, and the result of each iterative calculation includes the following formula (3), formula (4) and formula (5) during the iterative calculation,wiener filtering for modal components, u k (t) represents a modal component of the time domain, +.>Is the center frequency of the k-type modal component, omega is the centerFrequency, n is the number of iterations, γ is the noise tolerance:
further, when the iterative calculation result meets a preset convergence condition, determining a multi-type modal component corresponding to the multi-type modal decomposition according to the corresponding iterative calculation result, where in some possible embodiments, the preset convergence condition is the following formula (6), where ε is a preset error threshold:
that is, in this embodiment, when the iteration result satisfies the preset convergence condition, the corresponding iteration calculation result is used as the modal component of the corresponding class.
Step 103, calculating the cepstrum distance of each phase of current signal in the preset modal component in the first group of three-phase current signals and the second group of three-phase current signals according to the modal component set.
In an embodiment of the present disclosure, a cepstrum distance of each phase current signal in the first set of three-phase current signals and the second set of three-phase current signals is calculated for a preset class modal component, where the preset class modal component is different in a corresponding class in different scenes, for example, in the above embodiment, the preset class modal component may be the first modal component IMF1 obtained by calculation, in this embodiment, the cepstrum distance of the modal component IMF1 of the two-end in-phase fault current of the obtained input line is calculated, the cepstrum distance value of the fault phase is large, and the cepstrum distance value of the non-fault phase is small.
It should be noted that, in different application scenarios, the manner of calculating the cepstrum distance of each phase current signal in the preset class of modal components in the first group of three-phase current signals and the second group of three-phase current signals according to the modal component set is different, in some possible implementation manners, an energy spectrum function of each phase current signal in the preset class of modal components is determined according to the modal component set, and fourier-level cepstrum information of the energy spectrum function is determined, where the fourier-level cepstrum information includes the following formula (7):
further, the Fourier-level cepstrum information is calculated according to a preset algorithm to obtain the cepstrum distance of each phase of current signals in the preset modal components in the first group of three-phase current signals and the second group of three-phase current signals.
In this embodiment, the preset algorithm is a calculation algorithm of the mean square value of the spectrum, and the following formula (8) is referred to:
wherein S (ω) and S' (ω) are energy spectrum functions of a pair of modal components belonging to the same phase current signal in the first set of three-phase current signals and the second set of three-phase current signals, c n And c' n The cepstrum coefficients of S (ω) and S' (ω) are represented respectively,the cepstrum distances of S (ω) and S' (ω) are represented.
And 104, determining the fault type of the power transmission line according to the cepstrum distance corresponding to each phase of current signal.
In one embodiment of the disclosure, the fault type of the power transmission line is determined according to the cepstrum distance corresponding to each phase of current signal, so that the fault type of the power transmission line can be accurately identified, the identification efficiency of the fault type is improved, and the method has important significance for the safety of a power system.
For example, as shown in fig. 2 (in which, in fig. 2, two ends of the power transmission line are respectively M and N, and the current signal transmitted is f), when the power transmission circuit is a 220Kv/50Hz double-end power transmission line model, the line length is 100km, and the system sampling frequency is 10kHz, the corresponding barrier types may include: the fault location (single-phase earth fault (Ag), two-phase earth fault (ABg), three-phase earth fault (ABCg) and the like), fault distance (including 10km, 30km, 60km, 80km and the like), fault initial angle (30 °, 60 °, 90 ° and the like), transition resistance (0.1Ω, 10Ω, 100deg.Ω) and the like, of course, in order to ensure accuracy of fault type identification in the present disclosure, a corresponding transmission line model may be built on simulation software, a specific fault of the transmission line model may be set, and whether the fault type of the transmission line model built by the embodiment of the present disclosure is consistent with a specific fault set in advance may be determined, and an algorithm or model related in the present disclosure may be adjusted when the fault type is inconsistent, so as to ensure accuracy of identification of the fault identification method of the present disclosure.
It should be noted that, in some possible implementations, a fault feature set is generated according to the current signals of each phase, for example, a fault feature data set is constructed according to the cepstrum distance values of the fault currents of the a phase, the B phase and the C phase obtained in the above embodiments, and the fault feature set is input into a preset recognition model to determine the fault type.
In this embodiment, the preset recognition model may include a convolution layer, a pooling layer, a residual error model, a full connection layer, and the like, the fault feature set is input into the convolution layer and the pooling layer of the preset recognition model to obtain candidate fault features of the fault feature set, the candidate reference features are input into the residual error module of the preset recognition model to obtain target fault features, global average pooling processing is performed on the target fault features through the preset recognition model to obtain target fault feature vectors, and then the target fault feature vectors are input into the full connection layer of the preset recognition model to obtain fault types of the power transmission line.
The residual module of the preset recognition model may include a plurality of convolution networks of preset sizes connected in series, where the mapping function of the residual module, the preset sizes, and the number of convolution networks are all related to the preset recognition model of a specific application. The mapping function of the residual module may be the following formula (9), where x is an input, F (x) is a mapping function of the residual module, and residual module H (x) is a sum of the input x and the mapping function, where F (x) =relu (BN (conv (x))), where ReLu is a modified linear unit activation function, BN represents a batch normalization operation, and conv represents a convolution operation.
H (x) =f (x) +x formula (9)
The preset recognition model may include a residual network ResNet18 model, and the like. ResNet18 is a residual network in deep learning and is mainly characterized in that the problems of gradient elimination and gradient explosion in deep network training are relieved by introducing a residual module, and a network model is shown in figure 3. Referring to fig. 3, the convolution layer of the res net18 model is a 7*7-sized convolution layer, the res net18 model includes 4 residual modules, the core of each residual module is a residual module formed by concatenating two 3×3 convolution networks, the gradient is prevented from disappearing by adding a bypass connection, and the mapping function of the basic residual module refers to the above formula (9).
Therefore, in this embodiment, according to the power transmission line fault type identification method for emergency of power disasters provided by the invention, three-phase fault current signals at two ends of a power transmission line are obtained first; secondly, decomposing the three-phase fault current by utilizing the VMD to obtain K modal components IMF; then, calculating a cepstrum distance value of IMF1 components of the two-end in-phase fault current, and constructing a fault feature set; and finally, importing the fault feature set into a ResNet18 residual error network model to perform training learning, and obtaining the fault type of the power transmission line. Therefore, the power signal is subjected to fault identification by utilizing the feature extraction and deep learning technology, and the fault type identification accuracy is good.
In summary, according to the fault identification method for the power transmission line in the embodiment of the disclosure, a first group of three-phase current signals at one end of the power transmission line and a second group of three-phase current signals at the other end of the power transmission line are obtained, multi-type modal decomposition is performed on each phase of current signals in the first group of three-phase current signals and the second group of three-phase current signals to obtain a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set contains multi-type modal components corresponding to the three-phase current signals, cepstrum distances of each phase of current signals in preset modal components are calculated according to the modal component set, and then the fault type of the power transmission line is determined according to the cepstrum distances corresponding to each phase of current signals. In the technical scheme, the fault type of the power transmission line can be accurately identified, the identification efficiency of the fault type is improved, and the method has important significance for the safety of a power system.
In order to achieve the above embodiments, the present disclosure further provides a fault recognition device for a power transmission line.
Fig. 4 is a schematic structural diagram of a fault recognition device for a power transmission line according to an embodiment of the present disclosure, where the device may be implemented by software and/or hardware, and may be generally integrated in an electronic device to perform fault recognition of the power transmission line. As shown in fig. 4, the apparatus includes: a first acquisition module 410, a second acquisition module 420, a calculation module 430, a determination module 440, wherein,
a first obtaining module 410, configured to obtain a first set of three-phase current signals at one end of the power transmission line and a second set of three-phase current signals at the other end of the power transmission line;
the second obtaining module 420 is configured to perform multi-class modal decomposition on each phase current signal in the first set of three-phase current signals and the second set of three-phase current signals, so as to obtain a modal component set of the first set of three-phase current signals and the second set of three-phase current signals, where the modal component set includes multi-class modal components corresponding to the three-phase current signals;
the calculating module 430 is configured to calculate, according to the modal component set, a cepstrum distance of each phase of current signal in a preset modal component in the first group of three-phase current signals and the second group of three-phase current signals;
the determining module 440 is configured to determine a fault type of the power transmission line according to the cepstrum distance corresponding to the current signal of each phase.
The fault recognition device for the power transmission line provided by the embodiment of the disclosure can execute the fault recognition method for the power transmission line provided by any embodiment of the disclosure, has corresponding functional modules and beneficial effects of the execution method, and has similar implementation principles and is not repeated herein.
To achieve the above embodiments, the present disclosure also proposes a computer program product comprising a computer program/instruction which, when executed by a processor, implements the fault identification method of the power transmission line in the above embodiments.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Referring now in particular to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device 500 in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.
As shown in fig. 5, the electronic device 500 may include a processor (e.g., a central processing unit, a graphics processor, etc.) 501 that may perform various suitable actions and processes in accordance with programs stored in a Read Only Memory (ROM) 502 or loaded from a memory 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processor 501, ROM 502, and RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; memory 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the memory 508, or from the ROM 502. When executed by the processor 501, the computer program performs the above-described functions defined in the fault identification method of the power transmission line of the embodiment of the present disclosure.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a first group of three-phase current signals at one end of the power transmission line and a second group of three-phase current signals at the other end of the power transmission line, performing multi-class modal decomposition on each phase current signal in the first group of three-phase current signals and the second group of three-phase current signals to acquire a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set comprises multi-class modal components corresponding to the three-phase current signals, calculating the cepstrum distance of each phase current signal in the first group of three-phase current signals and the second group of three-phase current signals in the preset modal components according to the modal component set, and further determining the fault type of the power transmission line according to the cepstrum distance corresponding to each phase current signal. In the technical scheme, the fault type of the power transmission line can be accurately identified, the identification efficiency of the fault type is improved, and the method has important significance for the safety of a power system.
The electronic device may write computer program code for performing the operations of the present disclosure in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (12)
1. The fault identification method for the power transmission line is characterized by comprising the following steps of:
acquiring a first group of three-phase current signals at one end of a power transmission line and a second group of three-phase current signals at the other end of the power transmission line;
performing multi-type modal decomposition on each phase current signal in the first group of three-phase current signals and the second group of three-phase current signals to obtain a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set comprises multi-type modal components corresponding to the three-phase current signals;
calculating the cepstrum distance of each phase of current signals in the preset modal component in the first group of three-phase current signals and the second group of three-phase current signals according to the modal component set;
and determining the fault type of the power transmission line according to the cepstrum distance corresponding to the current signals of each phase.
2. The method of claim 1, wherein the multi-type modal decomposition of each phase current signal in the first set of three-phase current signals and the second set of three-phase current signals comprises:
determining a preset variation modal decomposition function, and determining an optimal solution calculation function of multi-class modal decomposition according to the preset variation modal decomposition function;
and carrying out iterative computation on the optimal solution computation function, and determining multi-class modal components corresponding to the multi-class modal decomposition according to the corresponding iterative computation result when the iterative computation result meets a preset convergence condition.
3. The method of claim 2, wherein the preset variational modal decomposition function comprises:
wherein K is the classification number of multi-class modal decomposition, u k (t)、ω k Respectively represent the k-type modal components and the central frequency delta (t) thereof as pulse functions,and represents gradient and convolution calculations, respectively;
the optimal solution computing function of the multi-class modal decomposition comprises the following steps:
where λ is the Lagrangian multiplier and α is the quadratic penalty factor.
4. The method of claim 3, wherein the iterative computation of the optimal solution computation function includes:
wherein,wiener filtering for modal components, u k (t) represents a modal component of the time domain, +.>The center frequency of the k-th modal component, γ is the noise tolerance.
5. The method of claim 4, wherein the predetermined convergence condition is:
wherein epsilon is a preset error threshold.
6. The method according to any one of claims 1-5, wherein calculating a cepstrum distance of each phase current signal in a preset class of modal components in the first set of three-phase current signals and the second set of three-phase current signals according to the set of modal components comprises:
determining an energy spectrum function of each phase of current signal in a preset class of modal components according to the modal component set, and determining Fourier-level cepstrum information of the energy spectrum function;
and calculating the Fourier-level cepstrum information according to a preset algorithm to obtain the cepstrum distance of each phase of current signal in the preset class modal component in the first group of three-phase current signals and the second group of three-phase current signals.
7. The function of claim 6, wherein the fourier-level cepstral information comprises:
the preset algorithm comprises the following steps:
wherein S (ω) and S' (ω) are energy spectrum functions of a pair of modal components belonging to the same phase current signal in the first set of three-phase current signals and the second set of three-phase current signals, c n And c' n The cepstrum coefficients of S (ω) and S' (ω) are represented respectively,the cepstrum distances of S (ω) and S' (ω) are represented.
8. The method of claim 1, wherein determining the fault type of the power transmission line according to the cepstrum distance corresponding to the current signals of each phase comprises:
generating a fault feature set according to the current signals of each phase;
inputting the fault feature set into a convolution layer and a pooling layer of a preset identification model to obtain candidate fault features of the fault feature set;
inputting the candidate reference features into a residual error module of the preset identification model to obtain target fault features;
carrying out global average pooling treatment on the target fault characteristics through the preset identification model so as to obtain target fault characteristic vectors;
and inputting the target fault characteristic vector into a full-connection layer of the preset identification model to obtain the fault type of the power transmission line.
9. The method of claim 8, wherein the residual module of the preset recognition model comprises:
a plurality of convolution networks of preset size connected in series, wherein the mapping function of the residual error module is as follows:
H(x)＝F(x)+x
where x is the input, F (x) is the mapping function of the residual block, and residual block H (x) is the sum of the input x and the mapping function, where,
F(x)＝ReLu(BN(conv(x)))，
wherein ReLU is a modified linear unit activation function, BN represents a batch normalization operation, conv represents a convolution operation.
10. A fault identification device for a power transmission line, comprising:
the first acquisition module is used for acquiring a first group of three-phase current signals at one end of the power transmission line and a second group of three-phase current signals at the other end of the power transmission line;
the second acquisition module is used for carrying out multi-type modal decomposition on each phase of current signals in the first group of three-phase current signals and the second group of three-phase current signals so as to acquire a modal component set of the first group of three-phase current signals and the second group of three-phase current signals, wherein the modal component set comprises multi-type modal components corresponding to the three-phase current signals;
the calculation module is used for calculating the cepstrum distance of each phase of current signal in the preset modal component in the first group of three-phase current signals and the second group of three-phase current signals according to the modal component set;
and the determining module is used for determining the fault type of the power transmission line according to the cepstrum distance corresponding to the current signals of each phase.
11. An electronic device, the electronic device comprising:
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 executable instructions to implement the fault identification method of the power transmission line according to any one of the preceding claims 1-9.
12. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program for executing the fault identification method of the electric power transmission line according to any one of the preceding claims 1-9.
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