A power transmission and distribution line grounding state recognition method, device, equipment and medium

Abstract

The application discloses a power transmission and distribution line grounding state recognition method, device, equipment and medium, and belongs to the field of power transmission and distribution line state detection. The method is: collecting the feedback voltage signal of the to-be-tested line when a non-power frequency current signal is injected; calculating a spectrum distortion weight index reflecting the ratio of induced harmonic to injected main wave amplitude value according to the spectrum data of the feedback voltage signal; performing multi-scale decomposition on the feedback voltage signal to obtain energy data corresponding to each decomposition scale, and calculating wavelet energy spectrum entropy according to the energy data; calculating the similarity between the feature vector constituted by the spectrum distortion weight index and the wavelet energy spectrum entropy and a plurality of pre-constructed state vectors; wherein the state vector is constructed through electromagnetic transient simulation, and each state vector corresponds to a grounding state; and taking the grounding state corresponding to the first state vector with the largest similarity as the grounding state of the to-be-tested line. The application can improve the accuracy of grounding state recognition of the to-be-tested line.

Description

Technical Field

[0001] This application relates to the field of power transmission and distribution line condition detection, and in particular to a method, device, equipment and medium for identifying the grounding condition of power transmission and distribution lines. Background Technology

[0002] During the operation and maintenance of power transmission and distribution lines, grounding switches or temporary grounding wires are typically installed at both ends of the line to ensure the safety of personnel and equipment. If power is restored without promptly removing the grounding devices after maintenance, short-circuit faults can easily occur, causing equipment damage or even personal injury accidents. Therefore, accurately identifying whether power transmission and distribution lines are grounded is a crucial technical means to ensure the safe and stable operation of the power grid and prevent operational errors.

[0003] In existing technologies, impedance measurement methods based on signal injection are commonly used to determine the grounding status of transmission and distribution lines. This involves injecting a specific frequency signal into the line and calculating the line impedance based on the feedback signal to determine whether the line is grounded. However, such methods typically rely on a single electrical parameter as a criterion, making it difficult to distinguish between different types of grounding states. Furthermore, in complex electromagnetic environments, they are susceptible to induced voltage and high-frequency interference, resulting in poor stability of the detection results. In addition, existing methods are mostly based on steady-state characteristics and fail to effectively utilize transient information in the signal, leading to limited ability to identify complex grounding conditions. Therefore, existing technologies suffer from low accuracy in identifying the grounding status of transmission and distribution lines. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for identifying the grounding status of power transmission and distribution lines, which can solve the problem of low accuracy in identifying the grounding status of power transmission and distribution lines in the prior art.

[0005] Some embodiments of this application provide a method for identifying the grounding status of power transmission and distribution lines, including: A non-power frequency current signal of a preset frequency is injected into the circuit under test, and the feedback voltage signal of the circuit under test under the non-power frequency current signal is collected. The feedback voltage signal is converted from the time domain to the frequency domain to obtain spectrum data, and the spectrum distortion weighting index, which reflects the ratio of the amplitude of the induced harmonics to the injected main wave, is calculated based on the spectrum data. The feedback voltage signal is decomposed into multiple scales to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale. The spectral distortion weighting index and each of the wavelet energy spectrum entropies are used to construct a feature vector, and the similarity between the feature vector and the multiple pre-constructed state vectors is calculated respectively; wherein, the state vector is constructed through electromagnetic transient simulation, and each state vector corresponds to a grounding state; The first state vector with the highest similarity is determined, and the grounding state corresponding to the first state vector is taken as the grounding state of the line under test.

[0006] Compared with existing technologies, the above embodiments have the following beneficial effects: This application injects a non-power frequency current signal into the line under test and collects the feedback voltage signal. Based on this, frequency domain features and time-frequency domain features are extracted respectively. Among them, by calculating the spectral distortion weight index to reflect the amplitude relationship between the induced harmonics and the injected main wave, the nonlinear distortion characteristics of the line under the action of induced voltage can be characterized. At the same time, by performing multi-scale decomposition on the feedback voltage signal and calculating the wavelet energy spectrum entropy, the discreteness of the signal energy distribution can be characterized, thereby reflecting the stability characteristics of the grounding contact state. Furthermore, the above-mentioned spectral distortion weight index and wavelet energy spectrum entropy are used to construct a feature vector, and similarity matching is performed with the state vectors corresponding to various grounding states constructed through electromagnetic transient simulation. This transforms the identification process from a single parameter criterion to a multi-dimensional feature matching criterion, thereby improving the accuracy of grounding state identification of transmission and distribution lines under complex electromagnetic environments and various grounding conditions.

[0007] Further, the step of converting the feedback voltage signal from the time domain to the frequency domain to obtain spectral data, and calculating the spectral distortion weighting index reflecting the ratio of the induced harmonics to the injected main wave amplitude based on the spectral data, includes: Perform a Fast Fourier Transform on the feedback voltage signal to obtain the spectrum data; Extract the first amplitude of a number of preset harmonic components and the second amplitude of the injected main wave component from the spectrum data; Calculate the square root of the sum of the squares of all the first amplitudes to obtain the third amplitude, and use the ratio of the third amplitude to the second amplitude as the spectral distortion weighting index.

[0008] Compared with the prior art, the above embodiments have the following beneficial effects: by performing a fast Fourier transform on the feedback voltage signal to obtain spectrum data, and extracting the amplitude of the induced harmonic component and the injected main wave component based on the spectrum data, an accurate frequency domain basis is provided for the calculation of the spectrum distortion weight index, which enables the effective characterization of the harmonic distortion characteristics generated by the line under the action of induced voltage, thereby improving the distinguishability between different grounding states.

[0009] Further, the multi-scale decomposition of the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale includes: By performing discrete wavelet transform on the feedback voltage signal, sub-signals corresponding to each of the decomposition scales are obtained; Calculate the energy data corresponding to the decomposition scale based on each of the sub-signals; Calculate the energy percentage corresponding to each of the aforementioned decomposition scales based on the energy data. The wavelet energy spectrum entropy is calculated based on the energy percentages described above.

[0010] Compared with the prior art, the above embodiments have the following beneficial effects: by performing discrete wavelet transform on the feedback voltage signal and obtaining the energy data corresponding to each decomposition scale, the energy ratio is calculated on this basis and the wavelet energy spectrum entropy is further calculated, thereby enabling the discreteness of the signal energy distribution from the time and frequency domain perspective, so as to reflect the signal fluctuation characteristics caused by the change of grounding state, and thus improve the ability to identify complex grounding conditions.

[0011] Further, the calculation of the wavelet energy spectrum entropy based on each of the energy proportions includes: Calculate the logarithm of each energy percentage and multiply the logarithm by the corresponding energy percentage to obtain the product of each energy percentage; The wavelet energy spectrum entropy is obtained by summing the products of all the energy percentages and taking the opposite number.

[0012] Compared with the prior art, the above embodiments have the following beneficial effects: by performing logarithmic operations on each energy proportion and multiplying it with the corresponding energy proportion, and then accumulating all the product results and taking the opposite number, the wavelet energy spectrum entropy is calculated, which enables the quantitative characterization of the discreteness of signal energy based on the probability distribution of energy proportion, thereby improving the ability to characterize the differences in signal complexity under different grounding states and helping to improve the accuracy of grounding state identification.

[0013] Furthermore, the state vector includes: a line suspension state vector, a grounding switch closed state vector, and a temporary grounding wire connection state vector; the state vector is constructed through electromagnetic transient simulation and includes: Obtain the electrical parameters of the circuit under test, and initialize the pre-constructed electromagnetic transient simulation model based on the electrical parameters; the electromagnetic transient simulation model includes: circuit breaker, grounding switch, grounding disconnect switch and grounding resistor; By setting the circuit breaker to open and not engaging the grounding switch in the electromagnetic transient simulation model, a first voltage signal is obtained, and the line floating state vector is extracted from the first voltage signal. By closing the grounding switch and setting the grounding resistance to a preset low resistance value in the electromagnetic transient simulation model, a second voltage signal is obtained, and the grounding switch closing state vector is extracted from the second voltage signal. By introducing a time-varying resistor and a preset arc model into the electromagnetic transient simulation model, a third voltage signal is obtained, and the temporary grounding wire connection state vector is extracted from the third voltage signal.

[0014] Compared with the prior art, the above embodiments have the following beneficial effects: by constructing an electromagnetic transient simulation model and modeling the line suspension state, the grounding switch closed state, and the temporary grounding wire connection state respectively, the corresponding feature parameters of each state are obtained and the state vector is constructed, thereby forming a feature fingerprint corresponding to different grounding mechanisms, so that matching and judgment can be made based on multiple typical working conditions when performing state identification, thereby improving the reliability and accuracy of the identification results.

[0015] Further, calculating the similarity between the feature vector and a plurality of pre-constructed state vectors includes: Collect on-site interference data of the line under test, and determine the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy based on the on-site interference data; Calculate the weighted Euclidean distance between the feature vector and each of the state vectors based on the weight coefficients. The reciprocal of the weighted Euclidean distance is used as the similarity.

[0016] Compared with the prior art, the above embodiments have the following beneficial effects: by calculating the similarity between the feature vector and each state vector, and by comprehensively evaluating based on multiple feature parameters, the errors caused by relying on a single feature for judgment are avoided, so that the differences between different grounding states can be reflected more comprehensively, thereby improving the stability of the grounding state identification results.

[0017] Further, determining the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectral entropy based on the on-site interference data includes: Based on the on-site interference data, determine the interference index characterizing the intensity of high-frequency interference and the induction index characterizing the intensity of induced voltage. The interference index is compared with a preset interference threshold. When the interference index is greater than the preset interference threshold, the weight coefficient corresponding to the wavelet energy spectrum entropy is reduced based on the first preset rule. The sensing index is compared with a preset sensing threshold. When the sensing index is less than the preset sensing threshold, the weight coefficient corresponding to the spectrum distortion weight index is increased. When the interference index is not greater than the preset interference threshold and the sensing index is not less than the preset sensing threshold, the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy are set to preset weighting values.

[0018] Compared with the prior art, the above embodiments have the following beneficial effects: by adaptively adjusting the weight coefficients corresponding to the spectral distortion weight index and the wavelet energy spectrum entropy according to the interference data, the influence of susceptible features is reduced when the high-frequency interference is strong, and the role of key features is enhanced when the induced signal is weak. Thus, the feature contribution can be dynamically optimized according to the actual electromagnetic environment, thereby improving the adaptability and accuracy of grounding status identification in complex environments.

[0019] Another embodiment of this application provides a grounding status identification device for power transmission and distribution lines, including: a data acquisition module, a first feature extraction module, a second feature extraction module, a feature comparison module, and an identification module; The acquisition module is used to inject a non-power frequency current signal of a preset frequency into the circuit under test, and to acquire the feedback voltage signal of the circuit under test under the non-power frequency current signal. The first feature extraction module is used to convert the feedback voltage signal from the time domain to the frequency domain to obtain spectrum data, and calculate the spectrum distortion weighting index reflecting the ratio of the amplitude of the induced harmonics to the injected main wave based on the spectrum data. The second feature extraction module is used to perform multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale. The feature comparison module is used to construct a feature vector by combining the spectral distortion weight index with each wavelet energy spectrum entropy, and to calculate the similarity between the feature vector and a plurality of pre-constructed state vectors; wherein, the state vector is constructed through electromagnetic transient simulation, and each state vector corresponds to a grounding state; The identification module is used to determine the first state vector with the highest similarity, and to take the grounding state corresponding to the first state vector as the grounding state of the line under test.

[0020] Another embodiment of this application also provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the power transmission and distribution line grounding status identification method of this application.

[0021] Another embodiment of this application also provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the power transmission and distribution line grounding status identification method of this application. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for identifying the grounding status of power transmission and distribution lines provided in some embodiments of this application; Figure 2 Here are comparison diagrams of the time-domain waveforms of the feedback voltage under three grounding states provided in some embodiments of this application; Figure 3 Comparison of spectral characteristics and distortion index under three grounding states provided in some embodiments of this application; Figure 4 Comparison diagrams of wavelet entropy verification of ground knife and ground wire states provided in some embodiments of this application; Figure 5 This is a clustering distribution diagram of the three states provided in some embodiments of this application in a two-dimensional feature space; Figure 6 This is a confusion matrix diagram of the decision results provided in some embodiments of this application; Figure 7 This is a schematic diagram of the structure of a power transmission and distribution line grounding status identification device provided in some embodiments of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0031] In existing technologies, impedance measurement methods based on signal injection are commonly used to determine the grounding status of transmission and distribution lines. This involves injecting a specific frequency signal into the line and calculating the line impedance based on the feedback signal to determine whether the line is grounded. However, such methods typically rely on a single electrical parameter as a criterion, making it difficult to distinguish between different types of grounding conditions. Furthermore, in complex electromagnetic environments, they are susceptible to induced voltage and high-frequency interference, resulting in poor stability of the detection results. In addition, existing methods are mostly based on steady-state characteristics and fail to effectively utilize transient information in the signal, leading to limited ability to identify complex grounding conditions.

[0032] Please refer to Figure 1 To address the problem of low accuracy in identifying the grounding status of power transmission and distribution lines in existing technologies, this application provides a method for identifying the grounding status of power transmission and distribution lines, comprising the following steps S101 to S105: S101: Inject a non-power frequency current signal of a preset frequency into the circuit under test, and collect the feedback voltage signal of the circuit under test under the non-power frequency current signal.

[0033] Preferably, in some embodiments of this application, the step of injecting a non-power frequency current signal of a preset frequency into the circuit under test and collecting the feedback voltage signal of the circuit under test under the non-power frequency current signal includes: first confirming that the circuit under test is in a power-off state, and then starting to actively detect each circuit under test. When detecting each circuit under test, the frequency converter module is controlled to inject a frequency of [missing information - likely a specific frequency] into the circuit under test. Non-power frequency current signals, in this embodiment 220Hz is preferred to avoid interference from power frequency and its harmonics. A high-precision sampling module is used synchronously to sample at the specified frequency. The feedback voltage signal from the secondary side of the voltage transformer is acquired, and a segment with a length of [length missing] is extracted. Discrete voltage sequences ,in This represents the sequence number of the sampling point, with a value ranging from 0 to... This sequence will serve as the basis for subsequent digital signal processing.

[0034] S102: Convert the feedback voltage signal from the time domain to the frequency domain to obtain spectrum data, and calculate the spectrum distortion weighting index reflecting the ratio of the amplitude of the induced harmonics to the injected main wave based on the spectrum data.

[0035] Furthermore, in some embodiments of this application, the step of converting the feedback voltage signal from the time domain to the frequency domain to obtain spectral data, and calculating the spectral distortion weighting index reflecting the ratio of the induced harmonics to the injected main wave amplitude based on the spectral data, includes: Perform a Fast Fourier Transform on the feedback voltage signal to obtain the spectrum data; Extract the first amplitude of a number of preset harmonic components and the second amplitude of the injected main wave component from the spectrum data; Calculate the square root of the sum of the squares of all the first amplitudes to obtain the third amplitude, and use the ratio of the third amplitude to the second amplitude as the spectral distortion weighting index.

[0036] Preferably, in some embodiments of this application, performing a fast Fourier transform on the feedback voltage signal to obtain the spectrum data includes: For length of sampling sequence Perform a Fast Fourier Transform to convert a time-domain signal into a frequency-domain complex sequence. The spectrum data is obtained, and the calculation formula is as follows: ; in, For the first Voltage values ​​at each sampling time. Represents the first in the frequency domain Amplitude and phase information of spectral lines; Pi; It is an ordinal number; Natural numbers .

[0037] Preferably, in some embodiments of this application, the calculation process of the spectral distortion weighting index is as follows: After acquiring the spectrum data, in order to quantify the degree of interference of the induced voltage on the injected signal and thus identify the floating state, the system calculates the spectrum distortion weighting index. This step aims to separate the ratio of induced harmonics to the injected main wave from the spectrum, and its calculation expression is as follows: ; in, It is the fundamental frequency of the power frequency (usually 50Hz). Preset number of times; The frequency of the actively injected non-power frequency current signal; This indicates the amplitude of the 3rd or 5th induced harmonic (i.e., the induced harmonic component of the preset number). The larger the value, the higher the degree to which the voltage transformer core has entered the nonlinear magnetic saturation region. The amplitude of the injected signal component (i.e., the injected main wave component).

[0038] This application obtains spectral data by performing a fast Fourier transform on the feedback voltage signal, and extracts the amplitude of the induced harmonic components and the injected main wave components based on the spectral data, thereby providing an accurate frequency domain basis for the calculation of the spectral distortion weighting index. This enables the effective characterization of the harmonic distortion characteristics generated by the line under the action of induced voltage, thereby improving the distinguishability between different grounding states.

[0039] S103: Perform multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale.

[0040] Furthermore, in some embodiments of this application, the step of performing multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale includes: By performing discrete wavelet transform on the feedback voltage signal, sub-signals corresponding to each of the decomposition scales are obtained; Calculate the energy data corresponding to the decomposition scale based on each of the sub-signals; Calculate the energy percentage corresponding to each of the aforementioned decomposition scales based on the energy data. The wavelet energy spectrum entropy is calculated based on the energy percentages described above.

[0041] Preferably, in some embodiments of this application, the step of obtaining sub-signals corresponding to each decomposition scale by performing discrete wavelet transform on the feedback voltage signal includes: performing discrete wavelet transform processing on the feedback voltage signal to represent the original signal as a discrete time series. The db4 wavelet from the Daubechies wavelet basis was selected as the mother wavelet function to perform multi-scale decomposition on the original signal. Layered wavelet decomposition yields low-frequency approximation coefficients and high-frequency detail coefficients corresponding to each decomposition scale. These two coefficients together constitute the sub-signal. The number of decomposition layers is specified in the original text. The settings are based on actual analysis requirements; in this embodiment, we take... .

[0042] Preferably, in some embodiments of this application, the step of calculating the energy data corresponding to the decomposition scale based on each of the sub-signals includes: calculating the energy data of the first sub-signal using the following formula. High-frequency detail factor energy ,Will As energy data at the corresponding decomposition scale: ; In the formula, For the first The first layer under the decomposition Each wavelet detail coefficient reflects the degree of abrupt changes in the signal within that frequency band.

[0043] Preferably, in some embodiments of this application, in order to transform complex waveform details into a single quantization index, wavelet energy spectral entropy is calculated. This allows us to use the principle of information entropy to characterize the degree of disorder in signal energy distribution, and to distinguish between steady-state ground wires (low entropy) and dynamic ground wires (high entropy).

[0044] First, calculate the number using the following formula. probability density of layer energy data ,Will As the energy percentage at the corresponding decomposition scale: ; in, For the first Layer energy data.

[0045] This application performs discrete wavelet transform on the feedback voltage signal and obtains the energy data corresponding to each decomposition scale. Based on this, it calculates the energy ratio and further calculates the wavelet energy spectrum entropy, thereby characterizing the discreteness of the signal energy distribution from the time and frequency domain perspective. This enables it to reflect the signal fluctuation characteristics caused by changes in grounding state, thereby improving the ability to identify complex grounding conditions.

[0046] Furthermore, in some embodiments of this application, the step of calculating the wavelet energy spectrum entropy based on each of the energy proportions includes: Calculate the logarithm of each energy percentage and multiply the logarithm by the corresponding energy percentage to obtain the product of each energy percentage; The wavelet energy spectrum entropy is obtained by summing the products of all the energy percentages and taking the opposite number.

[0047] Preferably, in some embodiments of this application, the formula for calculating the wavelet energy spectral entropy is: ; Among them, if A large value indicates that the signal energy is dispersed across multiple high-frequency bands, suggesting the existence of non-periodic contact arcing.

[0048] This application calculates wavelet energy spectrum entropy by performing logarithmic operations on each energy percentage and multiplying it by the corresponding energy percentage, then summing all the product results and taking the opposite number. This enables the quantitative characterization of the discreteness of signal energy based on the probability distribution of energy percentage, thereby improving the ability to characterize the differences in signal complexity under different grounding states and improving the accuracy of grounding state identification.

[0049] S104: Construct a feature vector by combining the spectral distortion weighting index with each of the wavelet energy spectrum entropies, and calculate the similarity between the feature vector and the multiple pre-constructed state vectors respectively; wherein, the state vector is constructed through electromagnetic transient simulation, and each state vector corresponds to a grounding state.

[0050] Furthermore, in some embodiments of this application, the state vector includes: a line suspension state vector, a grounding switch closure state vector, and a temporary grounding wire connection state vector; the state vector is constructed through electromagnetic transient simulation and includes: Obtain the electrical parameters of the circuit under test, and initialize the pre-constructed electromagnetic transient simulation model based on the electrical parameters; the electromagnetic transient simulation model includes: circuit breaker, grounding switch, grounding disconnect switch and grounding resistor; By setting the circuit breaker to open and not engaging the grounding switch in the electromagnetic transient simulation model, a first voltage signal is obtained, and the line floating state vector is extracted from the first voltage signal. By closing the grounding switch and setting the grounding resistance to a preset low resistance value in the electromagnetic transient simulation model, a second voltage signal is obtained, and the grounding switch closing state vector is extracted from the second voltage signal. By introducing a time-varying resistor and a preset arc model into the electromagnetic transient simulation model, a third voltage signal is obtained, and the temporary grounding wire connection state vector is extracted from the third voltage signal.

[0051] Preferably, in some embodiments of this application, the process of obtaining the state vector includes the following steps S1041 to S1044: S1041: First, the basic physical model (i.e., electromagnetic transient simulation model) of the power transmission and distribution network line is constructed using PSCAD / EMTDC electromagnetic transient simulation software. Based on the actual voltage level and line length of the line under test, the resistance, inductance, and distributed capacitance to ground parameters of the line in the electromagnetic transient simulation model are precisely set, as shown in Table 1. Simultaneously, a nonlinear excitation characteristic model of the voltage transformer is constructed, thereby introducing the power frequency induced voltage source and high-frequency interference source of the adjacent energized line into the basic physical model to simulate the real electromagnetic environment. Table 1 S1042: Next, based on the basic physical model constructed above, the circuit breaker is set to the open position, and no grounding switch or grounding wire is connected to simulate the line being suspended. Under this condition, due to the line-to-ground capacitance... A small induced voltage from a nearby line forces the voltage transformer core to operate in the nonlinear magnetic saturation region. The secondary voltage waveform exhibits a sharp distortion, with the third and fifth harmonic amplitudes significantly higher than the injected signal components. The system acquires the first voltage signal under this condition and extracts the frequency domain feature vector from it, defining it as the line floating state vector. And store it in the database.

[0052] S1043: Subsequently, in the basic physical model constructed above, the line-side grounding switch is closed, and the grounding resistance parameter is set to 0.01Ω to 0.1Ω to simulate a stable metallic connection formed by mechanical contacts. Under this condition, the circuit exhibits extremely low impedance characteristics, and the feedback voltage after injecting a characteristic frequency signal presents a smooth standard sine wave with no obvious high-frequency noise interference. The system acquires the second voltage signal under this condition and extracts the pure spectrum and stable time-domain characteristics of this state from the second voltage signal, defining them as the grounding switch closing state vector. And store it in the database.

[0053] S1044: Finally, to simulate the physical characteristics of manually attached temporary grounding wires, a Cassie arc model and a time-varying resistor series branch were introduced into the constructed basic physical model. The contact resistance was set to fluctuate randomly between 10Ω and 500Ω, and a microsecond-level intermittent arc was introduced to simulate the micro-motion contact phenomenon caused by the oxide layer or wind vibration of the hook. The system acquired the third voltage signal under this condition and extracted the micro-motion energy characteristics and wavelet energy spectrum entropy under this condition from the third voltage signal, defining them as the temporary grounding wire attachment state vector. And store it in the database.

[0054] This application constructs an electromagnetic transient simulation model and models the line in the suspended state, the grounding switch closed state, and the temporary grounding wire connected state respectively. It obtains the corresponding feature parameters for each state and constructs a state vector, thereby forming a feature fingerprint corresponding to different grounding mechanisms. This enables matching and judgment based on multiple typical working conditions when performing state identification, thereby improving the reliability and accuracy of the identification results.

[0055] Furthermore, in some embodiments of this application, calculating the similarity between the feature vector and a plurality of pre-constructed state vectors includes: Collect on-site interference data of the line under test, and determine the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy based on the on-site interference data; Calculate the weighted Euclidean distance between the feature vector and each of the state vectors based on the weight coefficients. The reciprocal of the weighted Euclidean distance is used as the similarity.

[0056] Preferably, in some embodiments of this application, the formula for calculating the weighted Euclidean distance is: based on the feature vector obtained in real time. Mapped to a preset feature space, state matching is performed using the following formula. Three types of state vectors in the database Weighted Euclidean distance To determine the current state: ; In the formula, These represent three state vectors: suspended, ground knife closed, and ground wire connected. For the first Frequency domain features in a state vector; For the first Temporal features in state vectors; These are weighting coefficients that are adaptively adjusted based on the level of on-site interference. It is understandable that... The larger the value, the lower the similarity.

[0057] This application calculates the similarity between the feature vector and each state vector, and performs a comprehensive evaluation based on multiple feature parameters. This avoids the error caused by relying on a single feature for judgment, and allows the differences between different grounding states to be reflected more comprehensively, thereby improving the stability of the grounding state identification results.

[0058] Furthermore, in some embodiments of this application, determining the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy based on the on-site interference data includes: Based on the on-site interference data, determine the interference index characterizing the intensity of high-frequency interference and the induction index characterizing the intensity of induced voltage. The interference index is compared with a preset interference threshold. When the interference index is greater than the preset interference threshold, the weight coefficient corresponding to the wavelet energy spectrum entropy is reduced based on the first preset rule. The sensing index is compared with a preset sensing threshold. When the sensing index is less than the preset sensing threshold, the weight coefficient corresponding to the spectrum distortion weight index is increased. When the interference index is not greater than the preset interference threshold and the sensing index is not less than the preset sensing threshold, the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy are set to preset weighting values.

[0059] Further, in some embodiments of this application, the step of determining the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy based on the on-site interference data specifically includes the following process: collecting environmental interference data during line operation and extracting the amplitude information of high-frequency components and the amplitude information of power frequency induced voltage from it; wherein, the amplitude of the high-frequency components is used to characterize the high-frequency interference intensity, and the amplitude of the power frequency induced voltage is used to characterize the induced voltage intensity. An interference index is calculated based on the amplitude of the high-frequency components, specifically using the ratio of signal energy in the high-frequency band to the total signal energy as the interference index; simultaneously, an induction index is calculated based on the amplitude of the power frequency induced voltage, for example, using the effective value of the power frequency voltage or its normalized result as the induction index. The interference index is compared with a preset interference threshold; when the interference index is greater than the preset interference threshold, the weighting coefficient corresponding to the wavelet energy spectrum entropy is reduced. For example, The influence of high-frequency interference on the feature calculation results is reduced proportionally or multiplied by an adjustment factor less than 1. The sensing index is compared with a preset sensing threshold; when the sensing index is less than the preset threshold, the weighting coefficient corresponding to the spectral distortion weighting index is increased. For example, The adjustment factor is increased proportionally or multiplied by a factor greater than 1 to enhance the role of harmonic distortion features in the identification process. When the interference index is not greater than a preset interference threshold and the induction index is not less than a preset induction threshold, the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy are set to preset weight values. In a specific example, the preset weight values ​​can be two weighting coefficients of 0.5 and 0.5 respectively.

[0060] This application adaptively adjusts the weighting coefficients corresponding to the spectral distortion weighting index and wavelet energy spectrum entropy based on interference data. This reduces the influence of susceptible features when high-frequency interference is strong and enhances the role of key features when the induced signal is weak. As a result, the feature contribution can be dynamically optimized according to the actual electromagnetic environment, thereby improving the adaptability and accuracy of grounding status identification in complex environments.

[0061] S105: Determine the first state vector with the highest similarity, and take the grounding state corresponding to the first state vector as the grounding state of the line under test.

[0062] Specifically, in some embodiments of this application, after calculating the three weighted Euclidean distances... ,like The minimum value indicates the line is ungrounded (not connected), indicating a safety warning; if... The minimum value indicates the ground blade is closed, indicating safety; if If the minimum value is reached, it is determined that the ground wire is not properly connected, and a prompt is made to remove the temporary ground wire. Finally, the device outputs the determination result through the display screen or communication interface.

[0063] In summary, the grounding status identification method for power transmission and distribution lines provided in this application has the following advantages compared to existing technologies: This application injects a non-power frequency current signal into the line under test and collects the feedback voltage signal. Based on this, frequency domain features and time-frequency domain features are extracted. Specifically, by calculating the spectral distortion weighting index to reflect the amplitude relationship between induced harmonics and the injected main wave, the nonlinear distortion characteristics of the line under induced voltage can be characterized. Simultaneously, by performing multi-scale decomposition on the feedback voltage signal and calculating the wavelet energy spectrum entropy, the dispersion of the signal energy distribution can be characterized, thereby reflecting the stability characteristics of the grounding contact state. Furthermore, the spectral distortion weighting index and the wavelet energy spectrum entropy are used to construct a feature vector, which is then matched with the state vectors corresponding to various grounding states constructed through electromagnetic transient simulation. This transforms the identification process from a single-parameter criterion to a multi-dimensional feature matching criterion, thereby improving the accuracy of power transmission and distribution line grounding status identification under complex electromagnetic environments and various grounding conditions.

[0064] To further illustrate the effectiveness of the power transmission and distribution line grounding status identification method provided in the embodiments of this application, the following will be combined with... Figures 2 to 6To provide further explanation.

[0065] This embodiment constructs transmission and distribution line models covering different voltage levels of 10kV, 110kV and 220kV based on the PSCAD / EMTDC simulation platform, and conducts differentiated parameter settings and batch data tests for three working conditions: line suspension, grounding switch closure and temporary grounding wire connection.

[0066] The specific model differences are as follows: 1) For the suspended line condition: The circuit breaker and disconnector are set to the open position. To simulate the worst interference environment, a nearby line is set to operate at full load, and an induced voltage is applied to the line under test through the inter-line coupling capacitor. In this model, the voltage transformer is set as a nonlinear inductor model, and its excitation curve includes a saturation inflection point to reproduce the physical phenomenon of saturation caused by induced voltage.

[0067] 2) For the grounding switch closure condition: A grounding switch assembly is installed at the end of the line under test. Considering that the grounding switch is driven by a mechanical linkage, the contact pressure is large and the contact surface is stable, it is equivalent to a constant pure resistance in the model, with the resistance value set to 0.05Ω to simulate an ideal metallic grounding state.

[0068] 3) For temporary grounding wire connection scenarios: Considering factors such as hook oxidation, insufficient clamping force, and vibration from light winds associated with manually connected grounding wires, the model abandons constant resistance and instead employs a series combination of time-varying resistance and the Cassie micro-arc model. The contact resistance is set as a time-varying signal that fluctuates randomly between 20Ω and 200Ω, and the Cassie arc module is used to simulate intermittent discharge at the microscale of the contact point, thereby generating non-stationary high-frequency micro-motion characteristics.

[0069] refer to Figure 2 The feedback voltage signals (i.e., the first voltage signal, the second voltage signal, and the third voltage signal) on the secondary side of the voltage transformer under three operating conditions are used to obtain a comparison diagram of the time-domain waveforms of the feedback voltage under the three different operating conditions by extracting data in a 0.1-second time window.

[0070] Depend on Figure 2 The voltage waveform corresponding to the floating state of the intermediate line shows that due to excessively high induced voltage causing saturation of the voltage transformer core, the waveform is severely distorted, exhibiting significant non-sinusoidal "peaked wave" characteristics, and the amplitude fluctuates greatly. Meanwhile, the voltage waveform corresponding to the floating state of the intermediate line is severely distorted, exhibiting significant non-sinusoidal "peaked wave" characteristics, and the amplitude fluctuates greatly. Figure 2 The voltage waveform corresponding to the closed state of the grounding switch is a very pure standard sine wave, indicating that the circuit connection is stable and free from interference. Figure 2 Although the voltage waveform corresponding to the neutral-to-ground connection state has an overall waveform profile close to a sine wave, irregular fine spikes are superimposed near the peaks and zero crossings.

[0071] Furthermore, to accurately quantify the nonlinear distortion characteristics caused by magnetic saturation of the induced voltage and digitally distinguish it from normal sinusoidal signals, this embodiment further performs frequency domain spectrum analysis on the acquired voltage signal, such as... Figure 3 As shown. From Figure 3 It can be seen that the higher harmonic characteristics in the suspended state have a calculated spectral distortion index. With a value as high as 0.82, it can be clearly identified by the algorithm. However, compared to... Figure 3 The frequency spectra of the grounding switch state and the ground wire state show that the two states, grounding switch closed and ground wire connected, exhibit a high degree of similarity in the frequency domain.

[0072] To further distinguish between the two states of ground switch closure and ground wire connection, this embodiment introduces wavelet transform technology for time-frequency domain analysis, and the results are as follows: Figure 4 As shown in the grounding switch state, when the grounding switch is closed, due to the tight and stable contact of the mechanical contacts, the corresponding voltage signal exhibits high stability in the time-frequency domain. Its wavelet high-frequency detail coefficients fluctuate slightly near zero throughout the entire sampling period, without any obvious abrupt changes. Based on the wavelet energy spectrum entropy calculation formula in the above embodiment, its wavelet energy spectrum entropy... Only 0.12 (low entropy). Conversely, when the local wire is connected, Figure 4 The ground state exhibits significant clustered high-frequency pulses, and the signal energy distribution in the time and frequency domain becomes extremely discrete. The calculated... The entropy increases to 0.58 (high entropy). Therefore, the numerical difference between the two in wavelet energy spectrum entropy is significant, thus effectively distinguishing the grounding state of the grounding switch closure and the artificial ground wire.

[0073] To further verify the robustness and generalization ability of the proposed method under massive data and random interference, this embodiment generates 1272 sets of random test samples covering different voltage levels, different line lengths, and different contact resistances based on the Monte Carlo method, and maps them to a two-dimensional feature space, such as... Figure 5 As shown. The horizontal axis represents the spectral distortion index. The vertical axis represents the wavelet energy spectrum entropy. Despite the large sample size and significant random noise, the 1272 data points were still clearly and automatically clustered into three linearly separable independent regions. Region I, the suspended region, is distributed in... The high-distortion region is completely isolated from grounding states; the height of the grounding knife region in region II converges to near the origin. The samples exhibit extremely strong steady-state characteristics; Region III, containing approximately 40% of the samples, overlaps with the ground scimitar samples in the frequency domain. It consistently remained above 0.5, forming independent clusters.

[0074] Furthermore, in this embodiment of the application, the state recognition results of the above 1272 groups of samples are judged by calculating the weighted Euclidean distance and outputting the recognition and classification results according to the minimum distance principle, such as... Figure 6 As shown, in 1272 independent tests, the overall identification accuracy of the method proposed in this application reached 100%. This result demonstrates that the method proposed in this application has extremely high robustness and can accurately identify the grounding status of power transmission and distribution lines.

[0075] like Figure 7 As shown, based on the above-described method embodiments, this application provides a grounding status identification device for power transmission and distribution lines, including: a data acquisition module 201, a first feature extraction module 202, a second feature extraction module 203, a feature comparison module 204, and an identification module 205; The acquisition module 201 is used to inject a non-power frequency current signal of a preset frequency into the circuit under test, and to acquire the feedback voltage signal of the circuit under test under the non-power frequency current signal. The first feature extraction module 202 is used to convert the feedback voltage signal from the time domain to the frequency domain to obtain spectrum data, and calculate the spectrum distortion weight index reflecting the ratio of the amplitude of the induced harmonics to the injected main wave based on the spectrum data. The second feature extraction module 203 is used to perform multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale; The feature comparison module 204 is used to construct a feature vector by combining the spectral distortion weight index and each wavelet energy spectrum entropy, and to calculate the similarity between the feature vector and a plurality of pre-constructed state vectors respectively; wherein, the state vector is constructed by electromagnetic transient simulation, and each state vector corresponds to a grounding state. The identification module 205 is used to determine the first state vector with the highest similarity, and to take the grounding state corresponding to the first state vector as the grounding state of the line under test.

[0076] Further, in some embodiments of this application, the first feature extraction module 202 includes: a first transformation unit, an extraction unit, and a first calculation unit; the first feature extraction module 202 is used to convert the feedback voltage signal from the time domain to the frequency domain to obtain spectrum data, and calculate a spectrum distortion weighting index reflecting the ratio of induced harmonics to injected main wave amplitude based on the spectrum data, including: The first transformation unit is used to perform a fast Fourier transform on the feedback voltage signal to obtain the spectrum data; The extraction unit is used to extract the first amplitude of a number of preset harmonic components and the second amplitude of the injected main wave component from the spectrum data. The first calculation unit is used to calculate the square root of the sum of the squares of all the first amplitudes to obtain the third amplitude, and to use the ratio of the third amplitude to the second amplitude as the spectral distortion weight index.

[0077] Further, in some embodiments of this application, the second feature extraction module 203 includes: a second transformation unit, a second calculation unit, a third calculation unit, and a fourth calculation unit; the second feature extraction module 203 is used to perform multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale, including: The second transformation unit is used to obtain sub-signals corresponding to each of the decomposition scales by performing discrete wavelet transform on the feedback voltage signal; The second calculation unit is used to calculate energy data corresponding to the decomposition scale based on each of the sub-signals; The third calculation unit is used to calculate the energy percentage corresponding to each decomposition scale based on the energy data. The fourth calculation unit is used to calculate the wavelet energy spectrum entropy based on the respective energy proportions.

[0078] Furthermore, in some embodiments of this application, the fourth calculation unit is used to calculate the wavelet energy spectrum entropy based on each of the energy proportions, including: Calculate the logarithm of each energy percentage and multiply the logarithm by the corresponding energy percentage to obtain the product of each energy percentage; The wavelet energy spectrum entropy is obtained by summing the products of all the energy percentages and taking the opposite number.

[0079] Furthermore, in some embodiments of this application, the state vector includes: a line suspension state vector, a grounding switch closure state vector, and a temporary grounding wire connection state vector; the state vector is constructed through electromagnetic transient simulation and includes: Obtain the electrical parameters of the circuit under test, and initialize the pre-constructed electromagnetic transient simulation model based on the electrical parameters; the electromagnetic transient simulation model includes: circuit breaker, grounding switch, grounding disconnect switch and grounding resistor; By setting the circuit breaker to open and not engaging the grounding switch in the electromagnetic transient simulation model, a first voltage signal is obtained, and the line floating state vector is extracted from the first voltage signal. By closing the grounding switch and setting the grounding resistance to a preset low resistance value in the electromagnetic transient simulation model, a second voltage signal is obtained, and the grounding switch closing state vector is extracted from the second voltage signal. By introducing a time-varying resistor and a preset arc model into the electromagnetic transient simulation model, a third voltage signal is obtained, and the temporary grounding wire connection state vector is extracted from the third voltage signal.

[0080] Further, in some embodiments of this application, the feature comparison module 204 includes: a collection unit, a fifth calculation unit, and a sixth calculation unit; the feature comparison module 204 is used to calculate the similarity between the feature vector and a plurality of pre-constructed state vectors, including: The acquisition unit is used to acquire the field interference data of the line under test, and determine the weight coefficients corresponding to the spectral distortion weight index and the wavelet energy spectrum entropy based on the field interference data. The fifth calculation unit is used to calculate the weighted Euclidean distance between the feature vector and the state vector based on each of the weight coefficients. The sixth calculation unit is used to take the reciprocal of the weighted Euclidean distance as the similarity.

[0081] Furthermore, in some embodiments of this application, the acquisition unit is used to determine the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy respectively based on the on-site interference data, including: Based on the on-site interference data, determine the interference index characterizing the intensity of high-frequency interference and the induction index characterizing the intensity of induced voltage. The interference index is compared with a preset interference threshold. When the interference index is greater than the preset interference threshold, the weight coefficient corresponding to the wavelet energy spectrum entropy is reduced based on the first preset rule. The sensing index is compared with a preset sensing threshold. When the sensing index is less than the preset sensing threshold, the weight coefficient corresponding to the spectrum distortion weight index is increased. When the interference index is not greater than the preset interference threshold and the sensing index is not less than the preset sensing threshold, the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy are set to preset weighting values.

[0082] In summary, the grounding status identification device for power transmission and distribution lines provided in this application has the following advantages compared to the prior art: This application injects a non-power frequency current signal into the line under test and collects the feedback voltage signal. Based on this, frequency domain features and time-frequency domain features are extracted. Specifically, by calculating the spectral distortion weight index to reflect the amplitude relationship between induced harmonics and the injected main wave, the nonlinear distortion characteristics of the line under induced voltage can be characterized. Simultaneously, by performing multi-scale decomposition on the feedback voltage signal and calculating the wavelet energy spectrum entropy, the dispersion of the signal energy distribution is characterized, thereby reflecting the stability characteristics of the grounding contact state. Furthermore, the spectral distortion weight index and the wavelet energy spectrum entropy are used to construct a feature vector, which is then matched with the state vectors corresponding to various grounding states constructed through electromagnetic transient simulation. This transforms the identification process from a single-parameter criterion to a multi-dimensional feature matching criterion, thereby improving the accuracy of power transmission and distribution line grounding status identification under complex electromagnetic environments and various grounding conditions.

[0083] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the method for identifying the grounding status of power transmission and distribution lines provided by any of the above-described method embodiments of this application.

[0084] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0085] Based on the above embodiments of the power transmission and distribution line grounding status identification method, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power transmission and distribution line grounding status identification method of any embodiment of this application.

[0086] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0087] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0088] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0089] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power transmission and distribution line grounding status identification method described in any of the above-described method embodiments of this application.

[0090] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

Claims

1. A method for identifying the grounding status of power transmission and distribution lines, characterized in that, include: A non-power frequency current signal of a preset frequency is injected into the circuit under test, and the feedback voltage signal of the circuit under test under the non-power frequency current signal is collected. The feedback voltage signal is converted from the time domain to the frequency domain to obtain spectrum data, and the spectrum distortion weighting index, which reflects the ratio of the amplitude of the induced harmonics to the injected main wave, is calculated based on the spectrum data. The feedback voltage signal is decomposed into multiple scales to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale. The spectral distortion weighting index and each of the wavelet energy spectrum entropies are used to construct a feature vector, and the similarity between the feature vector and the multiple pre-constructed state vectors is calculated respectively; wherein, the state vector is constructed through electromagnetic transient simulation, and each state vector corresponds to a grounding state; The first state vector with the highest similarity is determined, and the grounding state corresponding to the first state vector is taken as the grounding state of the line under test.

2. The method for identifying the grounding status of power transmission and distribution lines as described in claim 1, characterized in that, The step of converting the feedback voltage signal from the time domain to the frequency domain to obtain spectral data, and calculating the spectral distortion weighting index reflecting the ratio of induced harmonics to injected main wave amplitude based on the spectral data, includes: Perform a Fast Fourier Transform on the feedback voltage signal to obtain the spectrum data; Extract the first amplitude of a number of preset harmonic components and the second amplitude of the injected main wave component from the spectrum data; Calculate the square root of the sum of the squares of all the first amplitudes to obtain the third amplitude, and use the ratio of the third amplitude to the second amplitude as the spectral distortion weighting index.

3. The method for identifying the grounding status of power transmission and distribution lines as described in claim 1, characterized in that, The step of performing multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale includes: By performing discrete wavelet transform on the feedback voltage signal, sub-signals corresponding to each of the decomposition scales are obtained; Calculate the energy data corresponding to the decomposition scale based on each of the sub-signals; Calculate the energy percentage corresponding to each of the aforementioned decomposition scales based on the energy data. The wavelet energy spectrum entropy is calculated based on the energy percentages described above.

4. The method for identifying the grounding status of power transmission and distribution lines as described in claim 3, characterized in that, The calculation of the wavelet energy spectrum entropy based on the respective energy proportions includes: Calculate the logarithm of each energy percentage and multiply the logarithm by the corresponding energy percentage to obtain the product of each energy percentage; The wavelet energy spectrum entropy is obtained by summing the products of all the energy percentages and taking the opposite number.

5. The method for identifying the grounding status of power transmission and distribution lines as described in claim 1, characterized in that, The state vectors include: line suspension state vector, grounding switch closed state vector, and temporary grounding wire connected state vector; the state vectors are constructed through electromagnetic transient simulation and include: Obtain the electrical parameters of the circuit under test, and initialize the pre-constructed electromagnetic transient simulation model based on the electrical parameters; the electromagnetic transient simulation model includes: circuit breaker, grounding switch, grounding disconnect switch and grounding resistor; By setting the circuit breaker to open and not engaging the grounding switch in the electromagnetic transient simulation model, a first voltage signal is obtained, and the line floating state vector is extracted from the first voltage signal. By closing the grounding switch and setting the grounding resistance to a preset low resistance value in the electromagnetic transient simulation model, a second voltage signal is obtained, and the grounding switch closing state vector is extracted from the second voltage signal. By introducing a time-varying resistor and a preset arc model into the electromagnetic transient simulation model, a third voltage signal is obtained, and the temporary grounding wire connection state vector is extracted from the third voltage signal.

6. The method for identifying the grounding status of power transmission and distribution lines as described in claim 1, characterized in that, The calculation of the similarity between the feature vector and multiple pre-constructed state vectors includes: Collect on-site interference data of the line under test, and determine the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy based on the on-site interference data; Calculate the weighted Euclidean distance between the feature vector and each of the state vectors based on the weight coefficients. The reciprocal of the weighted Euclidean distance is used as the similarity.

7. The method for identifying the grounding status of power transmission and distribution lines as described in claim 6, characterized in that, The step of determining the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectral entropy based on the on-site interference data includes: Based on the on-site interference data, determine the interference index characterizing the intensity of high-frequency interference and the induction index characterizing the intensity of induced voltage. The interference index is compared with a preset interference threshold. When the interference index is greater than the preset interference threshold, the weight coefficient corresponding to the wavelet energy spectrum entropy is reduced based on the first preset rule. The sensing index is compared with a preset sensing threshold. When the sensing index is less than the preset sensing threshold, the weight coefficient corresponding to the spectrum distortion weight index is increased based on the second preset rule. When the interference index is not greater than the preset interference threshold and the sensing index is not less than the preset sensing threshold, the weighting coefficients corresponding to the spectral distortion weighting index and the wavelet energy spectrum entropy are set to preset weighting values.

8. A grounding status identification device for power transmission and distribution lines, characterized in that, include: The system includes an acquisition module, a first feature extraction module, a second feature extraction module, a feature comparison module, and a recognition module. The acquisition module is used to inject a non-power frequency current signal of a preset frequency into the circuit under test, and to acquire the feedback voltage signal of the circuit under test under the non-power frequency current signal. The first feature extraction module is used to convert the feedback voltage signal from the time domain to the frequency domain to obtain spectrum data, and calculate the spectrum distortion weighting index reflecting the ratio of the amplitude of the induced harmonics to the injected main wave based on the spectrum data. The second feature extraction module is used to perform multi-scale decomposition on the feedback voltage signal to obtain the wavelet energy spectrum entropy corresponding to each decomposition scale. The feature comparison module is used to construct a feature vector by combining the spectral distortion weight index with each wavelet energy spectrum entropy, and to calculate the similarity between the feature vector and a plurality of pre-constructed state vectors; wherein, the state vector is constructed through electromagnetic transient simulation, and each state vector corresponds to a grounding state; The identification module is used to determine the first state vector with the highest similarity, and to take the grounding state corresponding to the first state vector as the grounding state of the line under test.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a method for identifying the grounding status of a power transmission and distribution line as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a method for identifying the grounding status of a power transmission and distribution line as described in any one of claims 1 to 7.

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