A method, device and electronic equipment for monitoring power quality harmonics

Abstract

The present invention discloses a method, device and electronic equipment for monitoring harmonics of power quality, wherein a method for monitoring harmonics of power quality comprises: collecting power data, including time series data of current and voltage; preprocessing the power data; performing pattern recognition and feature extraction on the preprocessed power data to obtain first data; calculating the energy and intensity of each harmonic component according to the first data; setting energy and intensity thresholds of each harmonic component; if the energy or intensity of the harmonic component exceeds the set threshold, triggering an alarm to notify the operator or the system to make corrections; the present invention realizes high-precision recognition and separation of harmonic signals, which not only improves the accuracy and real-time performance of harmonic detection, but also maintains high detection performance in a noisy environment; in addition, by setting a dynamic threshold, the system can automatically adjust the detection standard according to the real-time power load changes, thereby maintaining higher sensitivity and accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality monitoring, and in particular to a power quality harmonic monitoring method, device and electronic equipment. Background Art

[0002] In modern power systems, the monitoring and control of power quality has become the key to ensuring the stable operation of power systems. With the development of industrial automation and information technology, the nonlinear loads in power systems have increased significantly, resulting in increasingly serious harmonic pollution problems. Harmonics refer to higher-order harmonic components other than the fundamental wave in power systems, which can cause problems such as power loss, equipment overheating, insulation aging, and control system malfunctions. Therefore, it is particularly important to accurately monitor the harmonic components in power quality and take corresponding measures in a timely manner.

[0003] At present, the existing harmonic monitoring technology still has many shortcomings in dealing with the complex environment and dynamic changes of the power system. For example, the traditional method has limitations in the accuracy and real-time performance of high-frequency harmonic detection, and it is difficult to respond quickly and accurately identify. In addition, the existing harmonic monitoring system often relies on linear models in noise filtering, data processing and feature extraction, lacks effective capture and analysis of nonlinear characteristics, making it difficult to ensure the accuracy and reliability of harmonic monitoring. Summary of the invention

[0004] The object of the present invention is to provide a method for monitoring power quality harmonics, so as to solve the problem that the existing harmonic monitoring technology has limitations in terms of accuracy and real-time performance of high-frequency harmonic detection.

[0005] To solve the above technical problems, the present invention provides the following technical solutions, including: collecting electric energy data, including time series data of current and voltage; preprocessing the electric energy data; performing pattern recognition and feature extraction on the preprocessed electric energy data to obtain first data; calculating the energy and intensity of each harmonic component based on the first data; setting energy and intensity thresholds for each harmonic component; if the energy or intensity of the harmonic component exceeds the set threshold, triggering an alarm to notify the operator or the system to make corrections.

[0006] As a preferred solution of the power quality harmonic monitoring method described in the present invention, the preprocessing includes filtering, adjusting the sampling rate and normalization processing.

[0007] As a preferred solution of the power quality harmonic monitoring method described in the present invention, the obtaining of the first data includes: quantum state encoding the preprocessed power data; adjusting the relative phase and amplitude between quantum bits; extracting and analyzing the information of the quantum state through the measurement gate to obtain the first data.

[0008] As a preferred solution of the power quality harmonic monitoring method described in the present invention, wherein: calculating the energy of each harmonic component includes: constructing the Hamiltonian corresponding to n harmonic components according to the harmonic frequency information of the first data , each harmonic component corresponds to an item in the Hamiltonian, which is expressed as the product of the generation and annihilation operators of the corresponding harmonic component, as follows:

[0009]

[0010] In the formula, is the reduced Planck constant, is the natural frequency of the harmonic oscillator corresponding to the nth harmonic component, is the natural frequency of the harmonic oscillator corresponding to the n+th harmonic component, is the generation operator corresponding to the nth harmonic component, is the generation operator corresponding to the n+1th harmonic component, is the annihilation operator corresponding to the nth harmonic component, is the annihilation operator corresponding to the n+1th harmonic component, is the coupling strength, which represents the interaction strength between two harmonic oscillators;

[0011] The Hamiltonian Diagonalize:

[0012] ,

[0013] In the formula, is the diagonalization result, , , , New creation and annihilation operators are used to simplify the calculation process;

[0014] According to the diagonalization result Solve for the energy corresponding to the nth harmonic component ;

[0015] .

[0016] As a preferred solution of the power quality harmonic monitoring method of the present invention, wherein: calculating the intensity of each harmonic component includes: obtaining the amplitude of each harmonic component from the first data ; According to the amplitude Calculate the strength of harmonic components :

[0017] .

[0018] As a preferred solution of the power quality harmonic monitoring method of the present invention, wherein: the energy threshold of each harmonic component is set: the energy threshold of each harmonic component is set according to the total harmonic distortion and learning rate of the harmonic component:

[0019]

[0020] In the formula, is the energy threshold of the nth harmonic component in the i-th iteration, is the energy threshold of the nth harmonic component in the i+1th iteration, is the learning rate of the energy threshold, is the effective value of the fundamental voltage or current, is the signal-to-noise ratio of the nth harmonic component, is the nonlinear index specific to the harmonic component, is the total harmonic distortion of the nth harmonic component, is the desired total harmonic distortion target.

[0021] As a preferred solution of the power quality harmonic monitoring method of the present invention, the setting of the intensity threshold of each harmonic component includes: setting the intensity threshold of each harmonic component according to the total harmonic distortion and learning rate of the harmonic component:

[0022]

[0023] Where: is the intensity threshold of the nth harmonic component in the i+1th iteration, is the intensity threshold of the nth harmonic component in the i-th iteration, is the learning rate of the intensity threshold, is the total harmonic distortion of the nth harmonic component, is the desired total harmonic distortion target, is the maximum allowable total harmonic distortion, It is the influencing factor of the system load and is used to adjust the change of the intensity threshold.

[0024] As a preferred solution of the power quality harmonic monitoring method described in the present invention, it also includes: capturing quantum state evolution data, including energy spectrum characteristics and coherence time of the quantum state; analyzing the evolution path and possible error types of the current quantum bit according to the quantum state evolution data; generating corresponding correction strategies and parameters according to the identified error type and current characteristics of the quantum state; and adjusting the generated correction strategies and parameters according to the actual evolution of the quantum state to optimize the correction effect.

[0025] As a preferred solution of the power quality harmonic monitoring device described in the present invention, wherein: for implementing the power quality harmonic monitoring method described in any of the above items, the power quality harmonic monitoring device includes: an acquisition module, configured to execute acquisition of power data, including time series data of current and voltage; a data processing module, configured to execute preprocessing of the power data; a feature extraction module, configured to execute pattern recognition and feature extraction of the preprocessed power data to obtain first data; a calculation module, configured to execute calculation of the energy and intensity of each harmonic component based on the first data; a threshold setting module, configured to execute setting of energy and intensity thresholds of each harmonic component; a monitoring and early warning module, configured to execute judgment on whether the energy or intensity of the harmonic component exceeds the set threshold, and if so, trigger an alarm to notify the operator or system to make corrections.

[0026] The present invention also provides an electronic device, comprising: one or more processors; a memory coupled to the processor and used to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors execute the power quality harmonic monitoring method as described in any one of the above items.

[0027] Beneficial effects of the present invention: The present invention utilizes the powerful computing power and adaptability of quantum neural networks to achieve high-precision recognition and separation of harmonic signals, which not only improves the accuracy and real-time performance of harmonic detection, but also maintains high detection performance in a noisy environment; in addition, by setting a dynamic threshold, the system can automatically adjust the detection standard according to the real-time power load changes, thereby maintaining higher sensitivity and accuracy, ensuring that harmonics can be effectively monitored under different working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0029] Figure 1 A schematic flow chart of a method for monitoring power quality harmonics according to a first embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the quantum error correction process according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0035] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0036] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] Example 1

[0038] Reference Figure 1-2, which is the first embodiment of the present invention, and provides a method for monitoring power quality harmonics, comprising:

[0039] S1: Collect electric energy data, including time series data of current and voltage.

[0040] Collect power data containing harmonics from the power grid or specific power system, including time series data of current and voltage at different frequencies.

[0041] S2: Preprocess the electric energy data.

[0042] Preprocessing includes filtering, adjusting the sampling rate, and normalization to prepare the power data for input into the quantum computing system.

[0043] Specifically, before collecting power data, an analog filter is used to filter out high-frequency noise to ensure that the instrument can accurately capture harmonic signals.

[0044] Furthermore, a digital signal processor is used to adjust the sampling rate of the collected power data in real time, and the power data is downsampled or upsampled as needed to ensure that key frequency information is not lost during the analysis process;

[0045] The signal amplitude range of the collected electric energy data is adjusted to the value range [0, 1].

[0046] S3: Perform pattern recognition and feature extraction on the preprocessed electric energy data to obtain first data.

[0047] In order to identify harmonic signals with high precision, this embodiment introduces a QNN model (Quantum Neural Network) to process large-scale data sets and simultaneously analyze harmonics of different frequency and amplitude combinations.

[0048] Among them, the QNN model includes an input layer, a hidden layer and an output layer; the input layer is composed of at least one quantum bit, and each quantum bit corresponds to a feature in the input data; hidden layer: the hidden layer is composed of three layers of quantum neural network (QNN), each layer contains a measurement gate, and these measurement gates can operate on the quantum bits of the input layer to achieve feature mapping and nonlinear transformation; the output layer is composed of a group of quantum bits, and each quantum bit corresponds to a feature in the output data.

[0049] First, the preprocessed electric energy data is quantum-encoded through the QNN model, that is, the preprocessed electric energy data is mapped to the state space of the quantum bit. Specifically, the preprocessed electric energy data is converted into binary form, and then each binary bit is mapped to a quantum bit.

[0050] Further, quantum gate operations are performed on the quantum bits of the input layer, and the relative phase and amplitude between the quantum bits are adjusted through the Hadamard gate;

[0051] Then, the quantum bits of the hidden layer are measured, and the information of the quantum state is extracted and analyzed through the measurement gate, and the information of the quantum state is mapped to the classical bits to obtain the first data.

[0052] Since quantum bits are easily affected by environmental noise and interference, this embodiment adopts a designed quantum error correction strategy to correct quantum states to ensure accuracy and reliability during quantum computing. The specific steps are as follows:

[0053] (1) Capturing quantum state evolution data, including the energy spectrum characteristics and coherence time of the quantum state;

[0054] (2) Analyze the evolution path and possible error types of the current quantum bit based on the quantum state evolution data.

[0055] The state vector of the quantum bit at the initial moment and the state vector after evolution are extracted from the quantum state evolution data, the state vector of each quantum bit is represented as a complex vector, the initial state and the evolved state of each quantum bit are encoded, and the encoded data is input into the Transformer network for training. After the training is completed, the possible evolution path of the quantum bit after a given initial state is predicted through the trained Transformer network, that is, the initial state vector is input into the trained Transformer network, and the predicted evolved state vector is output through the trained Transformer network.

[0056] Among them, possible error types include distortion, phase shift, interference, bit flipping, etc.

[0057] (3) Generate corresponding correction strategies and parameters based on the identified error type and the current characteristics of the quantum state;

[0058] Select an appropriate correction strategy based on the error type and the current quantum state, where the correction strategy includes adjusting the execution time of the quantum gate, increasing or decreasing the intensity of the pulse, changing the shape or frequency of the pulse, etc. Determine the specific parameters of each correction strategy, including adjusting the amplitude and duration of the pulse, or the correction factor introduced in the quantum gate operation.

[0059] (4) According to the actual evolution of the quantum state, adjust the generated correction strategy and parameters to optimize the correction effect.

[0060] Implement the adjusted correction strategy and run multiple quantum operations to observe whether the correction effect is as expected. If it is not as expected, further adjust the corrected parameters until the expected correction effect is achieved.

[0061] Preferably, the quantum error correction strategy of the present application can dynamically adjust the correction strategy and parameters according to the real-time monitored quantum state evolution data to minimize error accumulation and quantum bit distortion.

[0062] S4: Calculate the energy and intensity of each harmonic component according to the first data.

[0063] (1) Calculate the energy of each harmonic component based on the first data:

[0064] According to the harmonic frequency information of the first data, the interaction term is introduced and the coupling between different harmonic frequencies is considered, that is, a system with two harmonic frequencies is considered. and A quantum harmonic oscillator system, and there is a coupling term between them , construct the Hamiltonian corresponding to n harmonic components , each harmonic component corresponds to an item in the Hamiltonian, which is expressed as the product of the generation and annihilation operators of the corresponding harmonic component, as follows:

[0065]

[0066] In the formula, is the reduced Planck constant, defined as =h / 2π, h is Planck's constant; is the natural frequency of the harmonic oscillator corresponding to the nth harmonic component, is the natural frequency of the harmonic oscillator corresponding to the n+th harmonic component; is the generation operator (also called the raising operator) corresponding to the nth harmonic component, is the generation operator (also called the raising operator) corresponding to the n+1th harmonic component, and Used to describe the state of adding a quantum (vibration energy quantum) to the nth or n+1th oscillator; is the annihilation operator corresponding to the nth harmonic component, is the annihilation operator corresponding to the n+1th harmonic component, and Used to describe the removal of a quantum from the state of the 1st or n+1th oscillator; is the coupling strength, which represents the interaction strength between two harmonic oscillators;

[0067] The Hamiltonian To diagonalize, specifically, in order to find the energy eigenvalues ​​and eigenstates of the system, it is necessary to diagonalize the Hamiltonian. This application introduces a new generation and annihilation operator defined by linear combination. , , , To simplify this process, the operator , satisfies the same commutation relation as the original operator, and , There is a linear relationship:

[0068]

[0069]

[0070] According to the operator , Get the inverse operator , The expression is:

[0071]

[0072]

[0073] Further, , , and They are:

[0074]

[0075]

[0076]

[0077]

[0078] Rearrange the original Hamiltonian, ignoring the constant term since it does not affect the dynamical behavior of the Hamiltonian ,get:

[0079]

[0080] Diagonalize the Hamiltonian and add zero-point energy:

[0081] ,

[0082] According to the diagonalization result Solve for the energy corresponding to the nth harmonic component ;

[0083] .

[0084] (2) Calculate the intensity of each harmonic component based on the first data:

[0085] Get the amplitude of each harmonic component from the first data , the amplitude of each harmonic component It can be obtained by Fourier transform;

[0086] According to the amplitude Calculate the strength of harmonic components :

[0087] .

[0088] S5: Set the energy and intensity thresholds of each harmonic component.

[0089] Since harmonics of different frequencies may have different effects on the system, the present application sets different energy thresholds for harmonics of different frequencies to more accurately control and evaluate harmonic problems.

[0090] Set the energy threshold of each harmonic component:

[0091] Set the energy threshold of each harmonic component according to the total harmonic distortion of the harmonic component and the learning rate:

[0092]

[0093] In the formula, is the energy threshold of the nth harmonic component in the i-th iteration, is the energy threshold of the nth harmonic component in the i+1th iteration, is the learning rate of the energy threshold, is the effective value of the fundamental voltage or current, used as the basic energy reference, is the signal-to-noise ratio of the nth harmonic component, is the nonlinear index specific to the harmonic component, is the total harmonic distortion of the nth harmonic component, is the desired total harmonic distortion target.

[0094] Preferably, the energy threshold set in the present application is adjusted as the total harmonic distortion of the harmonic changes, and the influence of the effective value of the fundamental voltage or current and the signal-to-noise ratio on the energy threshold is adjusted through the nonlinear index Adjustments are made to ensure that the energy threshold effectively assesses the actual contribution of the harmonic content.

[0095] Furthermore, the intensity threshold of each harmonic component is set:

[0096] The intensity threshold of each harmonic component is set according to the total harmonic distortion of the harmonic component and the learning rate:

[0097]

[0098] Where: is the intensity threshold of the nth harmonic component in the i+1th iteration, is the intensity threshold of the nth harmonic component in the i-th iteration, is the learning rate of the intensity threshold, is the total harmonic distortion of the nth harmonic component, is the desired total harmonic distortion target, is the maximum allowable total harmonic distortion, It is the influencing factor of the system load and is used to adjust the change of the intensity threshold.

[0099] Preferably, the intensity threshold set in the present application is adjusted as the deviation of the total harmonic distortion relative to the target and the change of the system load, and the learning rate The rate at which the intensity threshold is updated is controlled to ensure that the system remains within the maximum allowed total harmonic distortion while achieving the desired harmonic distortion target.

[0100] S6: If the energy or intensity of the harmonic component exceeds the set threshold, an alarm is triggered to notify the operator or system to make corrections.

[0101] Example 2

[0102] This embodiment is different from the first embodiment in that it provides a power quality harmonic monitoring device, including steps for implementing the power quality harmonic monitoring method of any of the above embodiments, and the power quality harmonic monitoring device includes:

[0103] A collection module is configured to collect electric energy data, including time series data of current and voltage;

[0104] A data processing module is configured to perform pre-processing on the electric energy data;

[0105] A feature extraction module is configured to perform pattern recognition and feature extraction on the preprocessed electric energy data to obtain first data;

[0106] A calculation module, configured to calculate the energy and intensity of each harmonic component according to the first data;

[0107] A threshold setting module, configured to set energy and intensity thresholds of each harmonic component;

[0108] The monitoring and early warning module is configured to determine whether the energy or intensity of the harmonic component exceeds a set threshold. If so, an alarm is triggered to notify the operator or the system to make corrections.

[0109] Example 3

[0110] This embodiment provides an electronic device, comprising: one or more processors; a memory coupled to the processor and used to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the power quality harmonic monitoring method as described above.

[0111] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned method for automatically confirming the comprehensive order of the dispatch operation ticket. The memory is used to store various types of data to support the operation of the terminal device, and these data may include, for example, instructions for any application or method used to operate on the terminal device, and data related to the application. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0112] The electronic device can be implemented by one or more application specific integrated circuits (Application Specific1n Integrated Circuit, ASC), digital signal processors (Digital Signal Processor, DSP), digital signal processing devices (Digital Signal Processing Device, DSPD), programmable logic devices (Programmable Logic Device, PLD), field programmable gate arrays (Field Programmable Gate Array, FPGA), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the power quality harmonic monitoring method described in any of the above embodiments, and achieve the same technical effect as the above method.

[0113] It should be appreciated that embodiments of the present invention may be implemented or enforced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method may be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in an assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed ASIC for this purpose.

[0114] In addition, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0115] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or part thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques described in the present invention, the present invention also includes the computer itself. The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on a display.

[0116] As used in this application, the terms "component", "module", "system", etc. are intended to refer to a computer-related entity, which can be hardware, firmware, a combination of hardware and software, software, or software in operation. For example, a component can be, but is not limited to: a process running on a processor, a processor, an object, an executable file, a thread in execution, a program, and / or a computer. As an example, an application running on a computing device and the computing device can both be components. One or more components can exist in a process and / or thread in execution, and a component can be located in a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures thereon. These components can communicate in a local and / or remote process manner, such as according to a signal having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, and / or interacts with other systems in a signal manner through a network such as the Internet).

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring power quality harmonics, characterized in that: include: Collect electric energy data, including time series data of current and voltage; Preprocessing the electric energy data; Performing pattern recognition and feature extraction on the preprocessed electric energy data to obtain first data; Calculate the energy and intensity of each harmonic component according to the first data; Set energy and intensity thresholds for each harmonic component; If the energy or intensity of the harmonic component exceeds the set threshold, an alarm is triggered to notify the operator or system to make corrections; Wherein, obtaining the first data includes: The QNN model is used to process large-scale data sets and simultaneously analyze harmonics of different frequency and amplitude combinations; the QNN model includes an input layer, a hidden layer, and an output layer; the input layer is composed of at least one quantum bit, each of which corresponds to a feature in the input data; the hidden layer is composed of three layers of quantum neural networks, each of which contains a measurement gate, and the quantum bits of the input layer are operated through the measurement gate to achieve feature mapping and nonlinear transformation; the output layer is composed of a group of quantum bits, each of which corresponds to a feature in the output data; The preprocessed electric energy data is quantum-encoded through the QNN model, that is, the preprocessed electric energy data is mapped to the state space of the quantum bit. Specifically, the preprocessed electric energy data is converted into binary form, and each binary bit is mapped to a quantum bit. Perform quantum gate operations on the qubits in the input layer and adjust the relative phase and amplitude between qubits through the Hadamard gate; Performing a measurement operation on the quantum bits of the hidden layer, extracting and analyzing information of the quantum state through a measurement gate, mapping the information of the quantum state to classical bits, and obtaining the first data; The calculation of the energy of each harmonic component includes: According to the harmonic frequency information of the first data, the Hamiltonian corresponding to the n harmonic components is constructed , each harmonic component corresponds to an item in the Hamiltonian, which is expressed as the product of the harmonic component annihilation operator corresponding to the harmonic component, as follows: , In the formula, is the reduced Planck constant, is the natural frequency of the harmonic oscillator corresponding to the nth harmonic component, is the natural frequency of the harmonic oscillator corresponding to the n+th harmonic component, is the generation operator corresponding to the nth harmonic component, is the generation operator corresponding to the n+1th harmonic component, is the annihilation operator corresponding to the nth harmonic component, is the annihilation operator corresponding to the n+1th harmonic component, is the coupling strength, which represents the interaction strength between two harmonic oscillators; The Hamiltonian Diagonalize: , In the formula, is the diagonalization result, , , , New creation and annihilation operators are used to simplify the calculation process; According to the diagonalization result Solve for the energy corresponding to the nth harmonic component ; ； Among them, calculating the intensity of each harmonic component includes: Get the amplitude of each harmonic component from the first data ; According to the amplitude Calculate the strength of harmonic components : 。 2. The power quality harmonic monitoring method according to claim 1, characterized in that: The preprocessing includes filtering, adjusting the sampling rate and normalization.

3. The power quality harmonic monitoring method according to claim 2, characterized in that: The energy threshold of each harmonic component is set as follows: Set the energy threshold of each harmonic component according to the total harmonic distortion of the harmonic component and the learning rate: , In the formula, is the energy threshold of the nth harmonic component in the i-th iteration, is the energy threshold of the nth harmonic component in the i+1th iteration, is the learning rate of the energy threshold, is the effective value of the fundamental voltage or current, is the signal-to-noise ratio of the nth harmonic component, is the nonlinear index specific to the harmonic component, is the total harmonic distortion of the nth harmonic component, is the desired total harmonic distortion target.

4. The power quality harmonic monitoring method according to claim 3, characterized in that: The step of setting the intensity threshold of each harmonic component includes: The intensity threshold of each harmonic component is set according to the total harmonic distortion of the harmonic component and the learning rate: , Where: is the intensity threshold of the nth harmonic component in the i+1th iteration, is the intensity threshold of the nth harmonic component in the i-th iteration, is the learning rate of the intensity threshold, is the total harmonic distortion of the nth harmonic component, is the desired total harmonic distortion target, is the maximum allowable total harmonic distortion, It is the influencing factor of the system load and is used to adjust the change of the intensity threshold.

5. The power quality harmonic monitoring method according to claim 4, characterized in that: Also includes: Capture quantum state evolution data, including energy spectrum characteristics and coherence time of quantum states; Analyzing the evolution path and possible error types of the current quantum bit according to the quantum state evolution data; Generate corresponding correction strategies and parameters based on the identified error types and current characteristics of the quantum state; According to the actual evolution of the quantum state, the generated correction strategies and parameters are adjusted to optimize the correction effect.

6. A power quality harmonic monitoring device, characterized in that: Used to implement the power quality harmonic monitoring method according to any one of claims 1 to 5, the power quality harmonic monitoring device comprises: A collection module is configured to collect electric energy data, including time series data of current and voltage; A data processing module, configured to perform pre-processing on the electric energy data; A feature extraction module is configured to perform pattern recognition and feature extraction on the preprocessed electric energy data to obtain first data; A calculation module, configured to calculate the energy and intensity of each harmonic component according to the first data; A threshold setting module, configured to set energy and intensity thresholds of each harmonic component; The monitoring and early warning module is configured to determine whether the energy or intensity of the harmonic component exceeds a set threshold. If so, an alarm is triggered to notify the operator or the system to make corrections.

7. An electronic device, comprising one or more processors; a memory coupled to the processor and used to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the power quality harmonic monitoring method as described in any one of claims 1-5.

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