Power grid monitoring method and device based on distribution network deduction algorithm, and storage medium

Abstract

The invention provides a power grid monitoring method and device based on a distribution network deduction algorithm, and a storage medium, and belongs to the technical field of power grid monitoring. The monitoring method comprises the following steps: acquiring electrical parameter data reflecting the running state of the power distribution network; performing quantum coding and characterization on the electrical parameter data to obtain quantum characterization data; based on the quantum representation data, quantum evolution simulation is carried out by using a quantum neural network, the current state of the power distribution network is determined, and the state of the power distribution network at the next time step is predicted; and when the predicted state of the power distribution network at the next time step shows that the power distribution network has potential danger, obtaining an optimal power distribution network operation control strategy according to a preset quantum annealing model and an energy function. Through the quantum evolution simulation and quantum annealing technology, the operation condition of the power grid is effectively monitored, the fault point is positioned, and the intelligent level and reliability of power distribution network monitoring are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring, and in particular to a power grid monitoring method, device and storage medium based on a distribution network deduction algorithm. Background Art

[0002] The distribution network simulation algorithm is an algorithm that simulates the operation of the distribution network under different working conditions based on data such as the power grid topology, equipment parameters and operating status through mathematical models and calculation rules. It is used to predict possible faults in the power grid, as well as changes in power flow distribution after a fault occurs.

[0003] The current distribution network deduction algorithm often uses power flow calculation algorithm and fault analysis algorithm. However, there are still certain defects in the power flow calculation algorithm and fault analysis algorithm for power grid monitoring: the traditional power flow calculation algorithm and fault analysis algorithm are usually based on the deterministic representation of real numbers in data representation. This representation method may not be able to fully capture the potential uncertainty for the complex and changeable power grid operation environment. Traditional power flow calculation and fault analysis algorithms mainly focus on the calculation and analysis of power grid status. For distribution network optimization problems, such as the optimal configuration of reactive compensation equipment and the optimal distribution of loads, other optimization algorithms need to be combined to solve them. This separate solution method may result in low computational efficiency, and may fall into a local optimal solution during the optimization process due to the complexity of the objective function and the coordination problems between algorithms. Summary of the invention

[0004] The present invention provides a power grid monitoring method, device and storage medium based on a distribution network deduction algorithm, which are used to solve the problems that the prior art cannot cope with a changeable power grid environment, has low efficiency and poor reliability.

[0005] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a power grid monitoring method based on a distribution network deduction algorithm, characterized in that the monitoring method includes: obtaining electrical parameter data reflecting the operating status of the distribution network; performing quantum encoding and characterization on the electrical parameter data to obtain quantum characterization data; based on the quantum characterization data, and using a quantum neural network to perform quantum evolution simulation, determine the current state of the distribution network, and predict the state of the distribution network in the next time step; when the predicted state of the distribution network in the next time step shows that the distribution network is potentially dangerous, obtain the optimal distribution network operation control strategy according to a preset quantum annealing model and energy function.

[0006] Optionally, the quantum encoding and characterization of the electrical parameter data includes: constructing a corresponding quantum state based on the electrical parameter data; constructing a distribution network quantum graph state based on the topological structure of the distribution network and the quantum state of the electrical parameter data, and extracting quantum topological features; and forming a characterization based on the quantum state of the electrical parameter data and the quantum topological features.

[0007] Optionally, constructing a quantum state according to each of the electrical parameter data includes: determining a value range of the electrical parameter data and dividing it into intervals; selecting the number of quantum bits according to the number of divided intervals; and constructing the quantum state of the electrical parameter data according to a preset quantum state construction formula.

[0008] Optionally, the quantum state construction formula is represented by the following formula: in, represents the quantum state of the electrical parameter data, is the number of quantum bits, Indicates that The first state, The electrical parameters are in The probability amplitude of a coding interval.

[0009] Optionally, based on the quantum characterization data, quantum evolution simulation is performed using a quantum neural network to determine the current state of the distribution network and predict the state of the distribution network in the next time step, including: determining the current state of each node in the quantum graph of the distribution network according to the probability amplitude change of the quantum state of the electrical parameter data and a preset threshold to determine the current state of the distribution network; taking the quantum characterization data as input, outputting the distribution network quantum state changes in subsequent time steps through quantum gate operations to predict the distribution network state in the next time step.

[0010] Optionally, obtaining the optimal distribution network operation control strategy according to the preset quantum annealing model and energy function includes: mapping key control variables in the distribution network into quantum bits, the key control variables including but not limited to the following parameters: the switching state of reactive compensation equipment and the transformer tap position; determining the minimum value of the energy function according to the quantum bits of the key control variables and the quantum annealing model to obtain the optimal distribution network operation control strategy.

[0011] Optionally, the energy function is expressed by the following formula: Among them, represents the energy loss of the distribution network, Indicates voltage deviation, represents the stability index of the distribution network, , , Represents the weight coefficient.

[0012] Optionally, after determining the current state of the distribution network and predicting the state of the distribution network in the next time step by performing quantum evolution simulation based on the quantum characterization data and using a quantum neural network, the monitoring method further includes: locating the fault node according to the quantum graph and quantum entanglement characteristics of the distribution network.

[0013] On the other hand, the present invention further provides a control device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned monitoring methods.

[0014] On the other hand, the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute any one of the above-mentioned monitoring methods.

[0015] The present invention provides a power grid monitoring method based on a distribution network deduction algorithm. By adopting a quantum evolution simulation method, each node in the distribution network can be monitored in real time, faulty nodes can be located, and the operating status of the distribution network can be predicted to prevent the occurrence of faults. Through technologies such as quantum annealing, the optimal solution can be quickly converged to effectively reduce energy consumption, improve voltage quality and grid stability. Through the above method, the present invention establishes a method for effectively monitoring and optimizing the distribution network, realizes a complete and continuously cyclically improved system from data collection to final monitoring and optimization adjustment, and effectively improves the intelligence level and reliability of distribution network monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 It is a flowchart of a power grid monitoring method based on a distribution network deduction algorithm provided by an embodiment of the present invention; Figure 2 It is a flowchart of another power grid monitoring method based on a distribution network deduction algorithm provided by an embodiment of the present invention; Figure 3It is a schematic diagram of a distribution network quantum graph of a power grid monitoring method based on a distribution network deduction algorithm provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0017] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0018] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0019] As mentioned above, with the continuous growth of electricity demand and the rise of the concept of energy Internet, the operation and management of distribution networks are facing unprecedented challenges and opportunities. Traditional distribution network technology has gradually exposed many limitations in dealing with the increasingly complex grid structure, diversified load demands, and strict requirements for high reliability and high efficiency operation. Therefore, a method that can effectively monitor the operation status of the grid is needed to locate grid faults and optimize operation.

[0020] To address this problem, the present invention provides a power grid monitoring method based on a distribution network deduction algorithm. By quantum encoding the electrical parameter data reflecting the operating status of the distribution network and performing quantum evolution simulation, the current state of the distribution network can be judged and the state of the distribution network in the next time step can be predicted. Through the quantum annealing model and energy function, the optimal distribution network operation control strategy can be obtained, thereby realizing effective monitoring of the power grid and optimization of the power grid operation system.

[0021] Combine the following Figure 1 and Figure 2 The present invention is described in detail.

[0022] Figure 1 It is a flow chart of a power grid monitoring method based on a distribution network deduction algorithm provided by an embodiment of the present invention.

[0023] like Figure 1 As shown, an embodiment of the invention provides a power grid monitoring method based on a distribution network deduction algorithm, the execution subject may be a controller, and the method includes the following steps: Step 101: Acquire electrical parameter data reflecting the operating status of the distribution network, wherein the electrical parameter data may include voltage, current, power, power factor, etc.; Step 102: quantum encoding and characterizing the electrical parameter data to obtain quantum characterization data; Step 103: Based on the quantum characterization data, a quantum evolution simulation is performed using a quantum neural network to determine the current state of the distribution network and predict the state of the distribution network at the next time step; Step 104: When the predicted distribution network state for the next time step shows that the distribution network is potentially dangerous, an optimal distribution network operation control strategy is obtained according to a preset quantum annealing model and energy function.

[0024] The power grid monitoring method provided by the present invention is based on the superposition state and parallel computing capabilities of quantum bits. In the data acquisition and quantum characterization, quantum evolution and prediction stages, this technology can simultaneously process the quantum states of multiple electrical parameters, greatly improving the data processing efficiency; it can also monitor the operating status of each node in the distribution network in real time through quantum evolution simulation, and predict the state of the distribution network at the next time, effectively improving the timeliness and accuracy of distribution network fault diagnosis. And through the quantum annealing model and energy function, the quantum annealing parameters and strategies can be continuously adjusted, and it can quickly converge to the optimal solution, effectively reducing energy loss, improving voltage quality and enhancing system stability.

[0025] Please refer to Figure 2 Example, preferably, in step 101, after obtaining the electrical parameter data reflecting the operating status of the distribution network, the monitoring method also includes: preprocessing the electrical parameter data to remove noise and abnormal values ​​in the electrical parameter data; using digital filtering technology to smooth the electrical parameter data to reduce the interference of measurement noise.

[0026] In the distribution network operating environment, the collection of electrical parameter data will be interfered by various factors and generate noise. For example, electromagnetic interference is one of the common sources of noise. Noise will cause deviations in electrical parameter data and affect the accurate judgment of the distribution network operating status. For example, the operating status of the distribution network may be mistakenly judged as abnormal, or the real potential fault may not be discovered in time. Smoothing can effectively reduce the interference of measurement noise, and digital filtering technology is a method of changing the signal spectrum by performing mathematical operations on discrete digital signals, which can effectively remove noise. This helps to more accurately construct the quantum state of electrical parameter data in the subsequent quantum encoding and characterization links.

[0027] Preferably, in step 102, the quantum encoding and characterization of the electrical parameter data includes: constructing a corresponding quantum state according to the electrical parameter data; constructing a distribution network quantum graph state according to the topological structure of the distribution network and the quantum state of the electrical parameter data, and extracting quantum topological features; and forming a characterization according to the quantum state of the electrical parameter data and the quantum topological features.

[0028] The distribution network has a complex topological structure, consisting of multiple nodes (such as substations, busbars, line connection points, etc.) and branches (lines). After constructing the quantum state of the electrical parameter data of each node, these quantum states are associated according to the topological structure of the distribution network to construct the quantum graph of the distribution network. For example, the distribution network can be regarded as a graph, where the nodes represent the nodes of each electrical equipment in the distribution network, and the edges represent the connection lines between the nodes. By analyzing the mutual relationship and entanglement characteristics between the quantum states of each node in the quantum graph of the distribution network, the quantum topological features can be extracted, and the extracted quantum topological features are combined to form quantum representation data that can comprehensively and accurately characterize the operating status of the distribution network. The voltage and current quantum states of each node and the quantum entanglement degree and coherence between nodes are combined into quantum representation data. This quantum representation data can be used as input for subsequent quantum evolution simulation and other links, providing a rich information basis for accurately judging the current state of the distribution network and predicting the future state, so that the monitoring system can deeply understand the operation of the distribution network from a quantum perspective, thereby more effectively monitoring the power grid.

[0029] Preferably, constructing a corresponding quantum state according to each of the electrical parameter data includes: determining a value range of the electrical parameter data and dividing it into intervals; selecting the number of quantum bits according to the number of divided intervals; and constructing the quantum state of the electrical parameter data according to a preset quantum state construction formula.

[0030] Further preferably, the quantum state construction formula can be expressed by the following formula: (1) in, represents the quantum state of the electrical parameter data, is the number of quantum bits, is the number of intervals, , Indicates that The first state, The electrical parameters are in the The probability amplitude of the coding interval.

[0031] Taking the voltage of electrical parameters as an example, first determine the normal value range of the voltage in the distribution network, such as [0.95pu, 1.05pu]; then divide the range into several intervals, assuming that it is divided into 5 intervals, at this time, select 3 quantum bits (because 2³>5). Using formula (1), when the voltage value is in the second coding interval, the corresponding probability amplitude is calculated. , thereby determining the quantum state of the voltage parameter.

[0032] Preferably, in step 103, based on the quantum characterization data, quantum evolution simulation is performed using a quantum neural network to determine the current state of the distribution network and predict the state of the distribution network at the next time step, including: determining the current state of each node in the quantum graph of the distribution network according to the probability amplitude change of the quantum state of the electrical parameter data and a preset threshold value to determine the current state of the distribution network; using the quantum characterization data as input, outputting the distribution network quantum state changes in subsequent time steps through quantum gate operations to predict the distribution network state at the next time step.

[0033] Quantum Neural Network (QNN) is a computing model that combines the concepts of quantum computing and neural networks. Quantum gates are the basic operating units in quantum computing, similar to logic gates in traditional digital circuits. For example, common quantum gates include Hadamard gates and CNOT gates. After the quantum representation data is input into QNN, a series of quantum gate operations are performed to output the quantum state changes of the distribution network in the subsequent time step. For example, if the distribution network state in the next 10 minutes is to be predicted, QNN will perform evolutionary calculations on the current quantum representation data based on its internal quantum gate combination and parameters. Taking the voltage quantum state as an example, after the quantum gate operation, the probability amplitude of the voltage quantum state will change. By analyzing these changed probability amplitudes, the predicted values ​​of the voltage of each node in the next 10 minutes can be obtained. In order to determine the current state of each node in the topological structure, a threshold value of the probability amplitude change needs to be set. This threshold can be obtained based on the actual operation experience, historical data or theoretical analysis of the distribution network. According to the set threshold, when the change in the probability amplitude of the electrical parameter data quantum state of a node exceeds the threshold in the current time step, it is judged that the state of the node has changed. For example, if the threshold is 0.1, and the voltage quantum state probability amplitude of node A changes by more than 0.1 within the current minute, it is considered that the voltage state of node A has changed significantly. By judging the state of all nodes, it is possible to more sensitively capture small changes in the state of the distribution network and improve the accuracy of fault warning.

[0034] Preferably, after step 103, the monitoring method further comprises: locating the faulty node according to the quantum graph and quantum entanglement characteristics of the distribution network.

[0035] In a normally operating distribution network, the quantum entanglement between nodes is in a relatively stable state. This entanglement reflects the intrinsic connection between nodes formed by line connections and electrical parameter interactions. When a fault occurs in the distribution network, the quantum entanglement characteristics between the quantum states of the nodes around the fault point will change significantly, so that the fault point can be located. Comparing the prediction results of quantum evolution simulation with the actual collected data is a key step in evaluating the performance of the monitoring method. For example, for voltage prediction, the node voltage prediction value obtained by quantum evolution simulation is compared with the actual measured voltage value at each time step. This comparison can intuitively reflect the accuracy of the prediction so as to adjust the monitoring strategy.

[0036] Preferably, in step 104, the optimal distribution network operation control strategy is obtained according to the preset quantum annealing model and energy function, including: mapping the key control variables in the distribution network into quantum bits, the key control variables including: the switching state of the reactive compensation equipment and the transformer tap position; according to the quantum bits of the key control variables and the quantum annealing model, determining the minimum value of the energy function to obtain the optimal distribution network operation control strategy. The energy function is represented by the following formula: (2) Among them, Indicates the energy loss of the distribution network, which mainly includes line loss and transformer loss, etc. Indicates voltage deviation, that is, the difference between the actual voltage and the rated voltage. represents the stability index of the distribution network, , , Represents weight coefficients, which are used to balance the importance of different indicators in the energy function.

[0037] Quantum annealing is an optimization algorithm based on the principles of quantum physics. It simulates the behavior of quantum systems during the annealing process to find the optimal solution to the problem. In this embodiment, the quantum annealing model maps the operating state and control variables of the distribution network to the Hamiltonian of a quantum system. The Hamiltonian is a quantum operator used to describe the energy of the system. In this model, the system energy is associated with the energy function of the distribution network. By evolving in the quantum state space, the system will gradually transition from an initial state to a low-energy state, and finally converge to the state with the lowest energy. The control variable combination corresponding to this state is the optimal distribution network operation control strategy, and the distribution network can be optimized through this control strategy.

[0038] Figure 3 It is a schematic diagram of a distribution network quantum graph of a power grid monitoring method based on a distribution network deduction algorithm provided by another embodiment of the present invention.

[0039] like Figure 3 As shown in the example: For example, a simple distribution network has three nodes, namely node A, node B and node C, and their voltage quantum states are , and , then the quantum graph state of the distribution network can be represented as a graph structure containing these three quantum states, and the edges can determine the corresponding quantum correlation relationship based on the connection relationship and electrical parameters of the lines (such as line impedance Z).

[0040] The distribution network quantum graph integrates the quantum states of electrical parameters of each node, and fuses the distribution network topology with quantum information, so that the presentation of the distribution network operation status is no longer limited to isolated parameters, but is accurately and comprehensively described from the perspective of the entire network, providing a rich data basis for subsequent analysis. And when a fault occurs, based on the changes in characteristics such as the quantum entanglement degree between nodes in the quantum graph, the faulty node or area can be quickly and accurately located. Compared with traditional fault location methods, the fault location can be determined in a shorter time, effectively reducing the time and cost of fault detection, and improving the reliability and operation and maintenance efficiency of the distribution network.

[0041] The present invention provides a power grid monitoring method based on a distribution network deduction algorithm, which is based on quantum evolution simulation of quantum neural networks and quantum gate operations, fully considers the quantum interaction of distribution network topology, and has a dynamic simulation capability that is stronger than traditional methods, effectively predicting the state changes of distribution networks under different working conditions. The monitoring method can proactively detect the potential fault risks of the distribution network by predicting quantum state changes, take measures when abnormal trends first appear and no faults are formed, and obtain a fault prevention strategy by combining a quantum annealing model with an energy function, thereby achieving effective monitoring of the distribution network and optimizing the distribution network operation system.

[0042] An embodiment of the present invention further provides a control device, which may include: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned monitoring method.

[0043] An embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute the above-mentioned monitoring method.

[0044] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0045] In addition, the terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0046] It should be understood that in the embodiment of the present invention, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0047] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0050] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0051] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0052] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that the present invention can be implemented in hardware, firmware, or a combination thereof. When implemented using software, the above functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a computer. Taking this as an example but not limited to: a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition. Any connection can be appropriately a computer-readable medium. For example, if the software is transmitted from a website, server or other remote source using a coaxial cable, optical fiber cable, twisted pair, digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwave, then the coaxial cable, optical fiber cable, twisted pair, DSL or wireless technologies such as infrared, wireless and microwave are included in the fixing of the medium. As used in the present invention, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blue-ray disc, wherein disk usually reproduces data magnetically, while disc uses laser to reproduce data optically. The above combination should also be included in the scope of protection of computer-readable media.

[0053] In short, the above is only a preferred embodiment of the technical solution of the present invention, and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A power grid monitoring method based on a distribution network deduction algorithm, characterized in that: The monitoring method comprises: Obtain electrical parameter data reflecting the operating status of the distribution network; Performing quantum encoding and characterization on the electrical parameter data to obtain quantum characterization data; Based on the quantum characterization data, and using a quantum neural network to perform quantum evolution simulation, determine the current state of the distribution network and predict the state of the distribution network in the next time step; When the predicted distribution network state for the next time step shows that the distribution network is potentially dangerous, the optimal distribution network operation control strategy is obtained according to the preset quantum annealing model and energy function.

2. The monitoring method according to claim 1, characterized in that: The quantum encoding and characterization of the electrical parameter data includes: Constructing a corresponding quantum state according to the electrical parameter data; According to the topological structure of the distribution network and the quantum state of the electrical parameter data, a distribution network quantum graph is constructed, and quantum topological features are extracted; and A representation is formed based on the quantum state of the electrical parameter data and the quantum topological characteristics.

3. The monitoring method according to claim 2, characterized in that: The constructing a corresponding quantum state according to the electrical parameter data comprises: Determine the value range of the electrical parameter data and divide it into intervals; Selecting the number of quantum bits according to the number of the divided intervals; The quantum state of the electrical parameter data is constructed according to a preset quantum state construction formula.

4. The monitoring method according to claim 3, characterized in that: The quantum state construction formula is represented by the following formula: in, represents the quantum state of the electrical parameter data, is the number of quantum bits, Indicates that The first state, The electrical parameters are in The probability amplitude of a coding interval.

5. The monitoring method according to claim 2, characterized in that: The method of performing quantum evolution simulation based on the quantum characterization data and using a quantum neural network to determine the current state of the distribution network and predict the state of the distribution network at the next time step includes: Determine the current state of each node in the distribution network quantum graph state according to the probability amplitude change of the quantum state of the electrical parameter data and a preset threshold value to determine the current state of the distribution network; The quantum characterization data is used as input, and the distribution network quantum state changes of subsequent time steps are output through quantum gate operations to predict the distribution network state of the next time step.

6. The monitoring method according to claim 1, characterized in that: The method of obtaining the optimal distribution network operation control strategy according to the preset quantum annealing model and energy function includes: Mapping key control variables in the distribution network into quantum bits, wherein the key control variables include the switching state of reactive compensation equipment and the position of transformer taps; According to the quantum bits of the key control variables and the quantum annealing model, the minimum value of the energy function is determined to obtain an optimal distribution network operation control strategy.

7. The monitoring method according to claim 6, characterized in that: The energy function is expressed by the following formula: Among them, represents the energy loss of the distribution network, Indicates voltage deviation, represents the stability index of the distribution network, , , Represents the weight coefficient.

8. The monitoring method according to claim 2, characterized in that: After the quantum evolution simulation is performed based on the quantum characterization data and using a quantum neural network to determine the current state of the distribution network and predict the state of the distribution network at the next time step, the monitoring method further includes: The faulty node is located according to the quantum graph and quantum entanglement characteristics of the distribution network.

9. A control device, characterized in that: The control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the monitoring method according to any one of claims 1 to 8.

10. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions, which enable the machine to execute the monitoring method according to any one of claims 1-8.

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