Plateau mountain power transmission line arc fault identification method, computer system and storage medium

Abstract

This application relates to the field of relay protection technology, and in particular to a method, computer system, and storage medium for identifying arc faults in transmission lines in high-altitude mountainous areas. By performing anti-aliasing filtering on the three-phase current signal, the phase compensation is adjusted based on the load current harmonic distortion characteristics to compensate for additional phase distortion caused by changes in load harmonic content. By dynamically calculating the load current reference value and the noise intensity reference value, and using a dynamic dual threshold generated based on the noise intensity reference value for over-limit detection, the application achieves the stripping and anti-interference screening of weak arc pulses under strong interference environments. By performing steady-state analysis on the effective pulse interval and performing proportional statistics and delay determination on the steady-state analysis results within multiple continuous time windows, the reliability of intermittent arc fault identification in high-altitude mountainous scenarios is improved. The aim is to solve the problem of how to identify the characteristics of intermittent arc faults in transmission lines in high-altitude mountainous scenarios.

Description

Technical Field

[0001] This application relates to the field of relay protection technology, and in particular to a method, computer system and storage medium for identifying arc faults in transmission lines in high-altitude mountainous areas. Background Technology

[0002] Arc faults are a serious and hidden type of fault in medium- and high-voltage transmission lines, and are particularly prevalent in areas with complex geographical and meteorological environments, such as plateaus and mountains. The harsh operating conditions of transmission lines in plateau and mountainous areas pose unique challenges to the reliable identification of arc faults: First, the variable climate along the line, large diurnal temperature variations, strong ultraviolet radiation, and frequent lightning strikes cause unstable electrical parameters and complex and intense electromagnetic background noise. Second, the line may pass through mining areas or connect to renewable energy power plants, resulting in high harmonic and interharmonic content and time-varying frequency spectra in the load, severely interfering with the accurate extraction of fault electrical quantities. Third, the long distances and large distributed capacitance of the transmission lines make the fault current signals weaker and more complex.

[0003] Currently, there has been some research on the detection of arc faults in transmission lines. However, when applied to the specific scenario of high-altitude mountainous areas, on the one hand, the conventional anti-aliasing filters used in the front-end signal processing stage, with their fixed amplitude and phase frequency characteristics, will distort or blur the key transient characteristics of the fault current. On the other hand, the traditional back-end criteria based on fixed thresholds and algorithms that rely on single time scale analysis are difficult to adapt to dynamically changing line loads and noise levels, and are prone to misjudgment or missed judgment under instantaneous interference such as lightning strikes.

[0004] In view of this, this application proposes an arc fault identification method for transmission lines in plateau and mountainous areas, which aims to accurately identify the intermittent arc fault characteristics of transmission lines through front-end adaptive phase compensation and back-end multi-timescale collaborative analysis. Summary of the Invention

[0005] The main purpose of this application is to provide a method for identifying arc faults in transmission lines in plateau and mountainous areas, aiming to solve the problem of how to identify the characteristics of intermittent arc faults in transmission lines in plateau and mountainous scenarios.

[0006] To achieve the above objectives, this application provides a method for identifying arc faults in high-altitude mountain transmission lines, the method comprising: S10, acquire the three-phase current signal of the transmission line, and perform anti-aliasing filtering on the three-phase current signal to obtain the filtered current signal. The phase compensation amount in the anti-aliasing filtering process is determined based on the load current harmonic distortion characteristics of the three-phase current signal. S20, extract the current characteristics of the filtered current signal, and calculate the load current reference value and noise intensity reference value based on the current characteristics; S30, perform over-limit detection based on the current characteristic quantity, the load current reference value and the noise intensity reference value to determine the effective over-limit pulse time sequence, wherein the over-limit detection process includes generating a first / second threshold based on the noise intensity reference value to identify the effective over-limit pulse time sequence; S40, based on the time interval between adjacent pulses in the effective over-limit pulse time sequence within multiple consecutive time windows, determine the target pulse in a steady state; S50, if the duration of the target pulse in the total continuous time window being greater than or equal to the preset action threshold exceeds the preset judgment delay, it is determined that there is an arc fault in the plateau mountain transmission line in the total continuous time window.

[0007] Optionally, an adaptive harmonic filter is used for anti-aliasing filtering, wherein the adaptive harmonic filter includes: The basic filtering unit is used to achieve anti-aliasing amplitude-frequency response; A phase compensation unit is used to determine the phase compensation amount based on the total harmonic distortion rate of the load current, so as to compensate for the phase distortion caused by the change in load harmonic content based on the phase compensation amount.

[0008] Optionally, the expression for the transfer function of the filter is:

[0009] In the formula, For the basic filtering unit, its coefficients { a k , b k According to the preset cutoff frequency f c Confirmed; e jϕ(ω) For phase compensation unit; ω Digital angular frequency, j The imaginary unit; Phase compensation amount The mathematical expression is:

[0010] In the formula, This is the system's fundamental angular frequency; γ is the Nyquist angular frequency; γ is the total harmonic distortion of the current load current; This refers to the set of harmonic orders that require phase compensation, determined based on the characteristics of typical harmonic sources in the line. For the first hThe compensation weighting coefficient for subharmonics; For the first h Frequency offset compensation for subharmonics; C This is the adjustment coefficient for the overall phase compensation intensity.

[0011] Optionally, in S20, extracting the current characteristics of the filtered current signal includes: The filtered current signal is synchronously discretely sampled to obtain a discrete time series. , , ; For discrete time series , , Perform a Clarke transformation to transform to a stationary orthogonal coordinate system. and :

[0012] based on and Calculate the spatial vector magnitude at the current moment. As a characteristic quantity of current: .

[0013] Optionally, in S20, calculating the load current reference value and the noise intensity reference value based on the current characteristic quantity includes: Select a spatial vector magnitude sequence of length N sampling points from the transmission line during its non-fault operation period. ; The average value of the space vector magnitude is calculated and used as the reference value for the load current. :

[0014] Calculate the standard deviation of the spatial vector magnitude, and use it as the noise intensity benchmark value σ: .

[0015] Optionally, in S30, exceeding the limit is detected based on the current characteristic, the load current reference value, and the noise intensity reference value to determine the effective exceeding limit pulse time sequence, including: The product of the noise intensity reference value and the preset short-time window sensitivity coefficient is used as the first threshold, and the product of the noise intensity reference value and the preset strong interference suppression coefficient is used as the second threshold. Calculate the instantaneous deviation between the current characteristic quantity at the current sampling moment and the load current reference value, and take the absolute value; If the absolute value of the instantaneous deviation is greater than the first threshold and less than the second threshold, the pulse corresponding to each current sampling time is determined as a valid over-limit pulse; The effective over-limit pulses at each sampling time determined within the preset acquisition time window constitute the effective over-limit pulse time sequence.

[0016] Optionally, S40 includes: Determine the time interval between each adjacent pulse in the effective over-limit pulse time sequence to form a pulse interval sequence; The coefficient of variation of the pulse interval sequence is determined based on the ratio of the standard deviation to the mean of the pulse interval sequence. The target pulse interval with a coefficient of variation less than a preset stability threshold is determined as the target pulse in a steady state.

[0017] Optionally, steps S30, S40 and S50 are executed in the first time window, the second time window and the third time window, respectively, wherein the first time window is smaller than the second time window and the third time window.

[0018] In addition, to achieve the above objectives, this application also provides a computer system, the computer system comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, it implements the steps of the arc fault identification method for high-altitude mountain transmission lines as described in any of the preceding claims.

[0019] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0020] This application has at least the following beneficial effects: 1. By performing anti-aliasing filtering on the three-phase current signal, the phase compensation amount is dynamically adjusted based on the harmonic distortion characteristics of the load current to compensate for the additional phase distortion caused by changes in the load harmonic content. 2. By dynamically calculating the load current reference value and the noise intensity reference value, and generating a dynamic dual threshold based on the noise intensity reference value, over-limit detection is performed to achieve the stripping and anti-interference screening of weak arc pulses in a strong interference environment. 3. By performing steady-state analysis on the effective pulse interval and conducting proportional statistics and delay determination on the steady-state analysis results within multiple continuous time windows, misjudgments caused by random interference under a single time scale are avoided, thereby improving the reliability of intermittent arc fault identification in plateau and mountainous scenarios. Attached Figure Description

[0021] Figure 1This is a flowchart illustrating the arc fault identification method for high-altitude mountain transmission lines according to an embodiment of this application. Figure 2 This is a diagram showing the detection results of a short time window under strong interference in an embodiment of this application; Figure 3 This is a diagram showing the short-time window detection results under arc faults according to an embodiment of this application; Figure 4 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0022] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] To better understand the above technical solutions, exemplary embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of this disclosure to those skilled in the art.

[0024] First Embodiment Reference Figure 1 This embodiment provides a method for identifying arc faults in transmission lines in high-altitude mountainous areas. The method includes the following steps: S10, acquire the three-phase current signal of the transmission line, and perform anti-aliasing filtering on the three-phase current signal to obtain the filtered current signal. The phase compensation amount in the anti-aliasing filtering process is determined based on the load current harmonic distortion characteristics of the three-phase current signal. In this step, compared to conventional anti-aliasing filters with fixed parameters, which are prone to nonlinear distortion of key transient phase information in the fault current, resulting in feature ambiguity, this embodiment drives the phase compensation mechanism inside the filter to adaptively adjust by real-time monitoring of the harmonic distortion characteristics of the load current. This enables the filter to dynamically track and cancel the additional phase distortion caused by changes in the harmonic environment, thereby preserving the transient waveform characteristics at the moment of arc fault occurrence.

[0025] S20, extract the current characteristics of the filtered current signal, and calculate the load current reference value and noise intensity reference value based on the current characteristics; In this step, the current characteristic quantity is a single scalar characteristic obtained after fusing multi-phase current information. One of the purposes of mentioning this quantity is to eliminate the influence of three-phase imbalance or sampling channel differences.

[0026] In this embodiment, the load current reference value and the noise intensity reference value are not preset fixed constants, but statistical quantities calculated in real time based on the electrical quantities in the current characteristic quantity sequence during the current normal operation period. This dynamic reference generation mechanism enables the system to adapt to changes in line load fluctuations and environmental electromagnetic noise levels, thereby improving its adaptability under different operating conditions.

[0027] S30, perform over-limit detection based on the current characteristic quantity, the load current reference value and the noise intensity reference value to determine the effective over-limit pulse time sequence, wherein the over-limit detection process includes generating a first / second threshold based on the noise intensity reference value to identify the effective over-limit pulse time sequence; In this embodiment, the limit-crossing detection employs a dynamic dual threshold generated based on a noise intensity benchmark to distinguish between valid limit-crossing pulses and transient strong interference pulses. Specifically, the first threshold serves as a sensitivity threshold, used to filter out possible fault pulses from background noise; the second threshold serves as a strong interference suppression threshold, used to identify and eliminate transient interference with extremely high amplitude but not fault-related characteristics caused by phenomena such as lightning-induced overvoltages that are prone to occur in high-altitude mountainous environments. The sequence of limit-crossing pulse times obtained after filtering by the first and second thresholds is taken as the valid limit-crossing pulse time sequence.

[0028] This embodiment innovatively proposes to utilize the difference in amplitude statistical characteristics between arc fault pulses and strong interference pulses to distinguish fault types under strong interference background.

[0029] S40, based on the time interval between adjacent pulses in the effective over-limit pulse time sequence within multiple consecutive time windows, determine the target pulse in a steady state; Furthermore, considering that an essential characteristic of arc faults is the repetitive arcing phenomenon driven by power frequency voltage, the electrical signal exhibits a relatively stable periodicity in the pulse occurrence time interval, while the time distribution of random interference pulses is disordered. Therefore, in this embodiment, the effective over-limit pulse time sequence is statistically analyzed at intervals to quantify its stability. If the pulse interval shows good stability, it indicates that the currently detected signal is more likely to originate from a genuine arc fault rather than random noise, belonging to an arc fault in a steady state.

[0030] S50, if the duration of the target pulse in the total continuous time window being greater than or equal to the preset action threshold exceeds the preset judgment delay, it is determined that there is an arc fault in the plateau mountain transmission line in the total continuous time window.

[0031] After identifying the target pulse in a steady state, the system statistically analyzes the proportion of target pulses identified as being in a steady state across multiple consecutive time windows and combines this with preset delay conditions for comprehensive confirmation. Only when the stability characteristics continuously meet the specific proportion requirements over a longer period of time will the arc fault identification result be finally output.

[0032] It should be noted that a proportion greater than or equal to the preset action threshold means that the fault feature is statistically dominant, and a duration exceeding the preset judgment delay means that its state is continuous in time. This effectively filters out transient interference events that appear to be periodic but cannot be sustained within a short period of time, thus ensuring the credibility of the final fault identification result.

[0033] Further and optionally, in this embodiment, steps S30, S40 and S50 are executed in the first time window, the second time window and the third time window, respectively, wherein the first time window is smaller than the second time window and the third time window.

[0034] In some alternative implementations, step S40 is performed in multiple consecutive second time windows within a third time window.

[0035] For example, the first time window can be 40ms, the second time window can be 2s, and the third time window can be 6s.

[0036] It is understandable that if the conditions in S50 are not met, it is determined that there is no arcing fault in the total continuous time window of the plateau mountain transmission line.

[0037] For example, the preset action threshold ranges from 0.6 to 0.8; the determination delay ranges from 1 to 5 power frequency cycles.

[0038] In the technical solution provided in this embodiment, the phase compensation amount of the three-phase current signal is dynamically adjusted based on the harmonic distortion characteristics of the load current by performing anti-aliasing filtering, so that the filter can track and compensate for the additional phase distortion caused by the change of load harmonic content in real time. By dynamically calculating the load current reference value and the noise intensity reference value, and using dynamic dual thresholds generated based on the noise intensity reference value for over-limit detection in the first time window, the weak arc pulses can be stripped and screened in a strong interference environment. By performing steady-state analysis on the effective pulse interval and performing proportional statistics and delay judgment on the steady-state analysis results in multiple continuous time windows, misjudgment caused by random interference under a single time scale is avoided, thereby improving the reliability of intermittent arc fault identification in plateau mountain scenarios.

[0039] Second Embodiment Based on any embodiment, in this embodiment, a harmonic adaptive filter is used for anti-aliasing filtering, and the harmonic adaptive filter includes: The basic filtering unit is used to achieve anti-aliasing amplitude-frequency response; A phase compensation unit is used to determine the phase compensation amount based on the total harmonic distortion rate of the load current, so as to compensate for the phase distortion caused by the change in load harmonic content based on the phase compensation amount.

[0040] In this embodiment, the basic filtering unit can be a finite-length unit impulse response (FIR) filter or an infinite-length unit impulse response (IIR) filter with linear or zero-phase characteristics. Its main task is to cut off high-frequency noise and aliasing components and ensure the purity of the signal spectrum.

[0041] The phase compensation unit is used to introduce a frequency-dependent nonlinear phase factor to dynamically correct the signal phase within a specific frequency band. It is important to emphasize that this unit does not alter the signal's amplitude spectrum; rather, it performs inverse compensation for the nonlinear phase shift generated in the basic filtering unit or the system transmission path, thereby restoring the waveform shape of the fault transient signal.

[0042] Furthermore, and optionally, the expression for the filter's transfer function is:

[0043] In the formula, For the basic filtering unit, its coefficients { a k , b k According to the preset cutoff frequency f c Confirmed; e jϕ(ω) For phase compensation unit; ω Digital angular frequency, j It is the imaginary unit.

[0044] Phase compensation amount The mathematical expression is:

[0045] In the formula, This is the system's fundamental angular frequency; γ is the Nyquist angular frequency; γ is the total harmonic distortion of the current load current; This refers to the set of harmonic orders that require phase compensation, determined based on the characteristics of typical harmonic sources in the line. For the first h The compensation weighting coefficient for subharmonics; For the first h Frequency offset compensation for subharmonics; C This is the adjustment coefficient for the overall phase compensation intensity.

[0046] It should be noted that in the phase compensation amount In the mathematical expression, the entire formula constructs a nonlinear mapping relationship with the total harmonic distortion rate as the core variable. When the harmonic content of the line load increases, the total harmonic distortion rate increases, driving a corresponding increase, thereby automatically enhancing the correction force for phase distortion. The summation term in the numerator reflects the weighted accumulation of the distortion energy of each characteristic harmonic, while the radical term in the denominator introduces a frequency normalization factor, ensuring that the phase compensation amount is close to the Nyquist frequency. It can converge smoothly and prevent numerical singularities at high frequencies.

[0047] Third Embodiment Based on any embodiment, in this embodiment, in S20, extracting the current feature quantity of the filtered current signal includes: S21, synchronously discretely sample the filtered current signal to obtain a discrete time sequence. , , ; S22, for discrete time series , , Perform a Clarke transformation to transform to a stationary orthogonal coordinate system. and :

[0048] S23, based on and Calculate the spatial vector magnitude at the current moment. As a characteristic quantity of current: .

[0049] Further and optionally, in S20, calculating the load current reference value and the noise intensity reference value based on the current characteristic quantity includes: S24, Select a spatial vector magnitude sequence of length N sampling points for the transmission line during its non-fault operation period. ; S25, Calculate the average value of the space vector magnitude as the load current reference value. :

[0050] S26, Calculate the standard deviation of the spatial vector magnitude as the noise intensity reference value σ: .

[0051] It should be noted that, due to the complex and variable electromagnetic environment of transmission lines in high-altitude and mountainous environments, the background noise levels vary greatly during thunderstorms, windy days, or under heavy load conditions. Using a fixed threshold can easily lead to false tripping or failure to trip. Therefore, this embodiment uses the standard deviation as the noise benchmark, so that the subsequent discrimination threshold can rise as the environmental noise increases and fall as the noise decreases, thereby achieving adaptive anti-interference.

[0052] Fourth embodiment Based on any embodiment, in this embodiment, in S30, exceeding the limit is detected based on the current characteristic quantity, the load current reference value, and the noise intensity reference value to determine the effective exceeding limit pulse time sequence, including: S31, the product of the noise intensity reference value and the preset short window sensitivity coefficient is used as the first threshold, and the product of the noise intensity reference value and the preset strong interference suppression coefficient is used as the second threshold. S32, calculate the instantaneous deviation between the current characteristic quantity and the load current reference value at the current sampling time and take the absolute value; S33, if the absolute value of the instantaneous deviation is greater than the first threshold and less than the second threshold, the pulse corresponding to each current sampling time is determined as a valid over-limit pulse; S34, the effective over-limit pulses determined at each sampling time within the preset acquisition time window are used to form the effective over-limit pulse time sequence.

[0053] In this embodiment, the dynamic over-limit detection process first calculates the instantaneous deviation between the current characteristic quantity at the current sampling moment and the load current reference value. Subsequently, based on the noise intensity reference value and a preset short-time window sensitivity coefficient, a first threshold, also referred to as the dynamic discrimination threshold in this embodiment, is calculated; simultaneously, based on the noise intensity reference value and a preset strong interference suppression coefficient, a second threshold, also referred to as the strong interference threshold in this embodiment, is calculated.

[0054] It should be noted that both the first and second thresholds are obtained by multiplying the noise intensity benchmark value, so that the detection threshold can fluctuate in real time with the fluctuation of the ambient electromagnetic noise level: when the background noise increases, the two thresholds automatically rise to prevent false triggering; when the background noise decreases, the thresholds decrease to ensure sensitivity.

[0055] The above steps are explained below using formulas: In length T short Within a short-time analysis window of 1 to 5 power frequency cycles, the spatial vector magnitude I corresponding to sampling time n is used. m [n] serves as the current current characteristic quantity, used to quickly capture sudden changes in current amplitude; Calculate the current current characteristic quantity I m[n] and the load current reference value I load Instantaneous deviation D[n]:

[0056] Based on the noise intensity reference value σ and the preset short-time window sensitivity coefficient K1, the dynamic discrimination threshold θs is calculated:

[0057] The short-time window sensitivity coefficient K1 is tuned based on the noise statistical characteristics of transmission lines in plateau and mountainous areas, with a value range of 3.0 to 5.0; Based on the noise intensity reference value σ and the preset strong interference suppression coefficient K2, the strong interference discrimination threshold θ is calculated. h :

[0058] The strong interference suppression system is tuned based on the strong interference statistical characteristics of the K2 plateau mountain transmission line, with a value range of 8.0 to 15.0. The absolute value of the instantaneous deviation |D[n]| is compared with the dynamic discrimination threshold θs and the strong interference discrimination threshold θ. h Perform real-time comparison, if θs <∣D[n]∣<θ h If the pulse occurs at a certain time, it is determined to be a valid over-limit pulse, and the time t when the pulse occurs is recorded. k If |D[n]|>θ h If the pulse is strong and transient, it is considered a transient interference pulse and is not marked as a valid over-limit pulse.

[0059] Furthermore, and optionally, to eliminate repeated counting caused by transient interference, a pulse debouncing time T is set. d In T d Only the first pulse marker is retained within the time window. Specifically, due to the possibility of high-frequency oscillations or tailing phenomena in arc reignition or interference signals, a single physical event may manifest as multiple adjacent limit-crossing points in the sampling sequence. Therefore, by introducing a pulse debouncing time, the system is forced to respond to only the first trigger signal within this time window, ignoring subsequent cascading triggers.

[0060] Fifth embodiment Based on any embodiment, in this embodiment, S40 includes: S41, determine the time interval between each adjacent pulse in the effective over-limit pulse time sequence to form a pulse interval sequence; S42, determine the coefficient of variation of the pulse interval sequence based on the ratio of the standard deviation to the mean of the pulse interval sequence; S43, the target pulse interval with a coefficient of variation less than a preset stability threshold is determined as a target pulse in a steady state.

[0061] In this embodiment, the coefficient of variation is used as a normalized dispersion index, one purpose of which is to eliminate the influence of the mean pulse interval on the evaluation of dispersion. Since AC arc faults are periodic reignition phenomena that occur under power frequency voltage, the pulse intervals they generate should exhibit high stability, i.e., a small coefficient of variation. In contrast, pulses generated by random noise or intermittent load disturbances are disordered in time distribution, i.e., a large coefficient of variation. Therefore, by comparing the coefficient of variation with a preset stability threshold, if the coefficient of variation is less than the stability threshold, the target pulse interval is determined to be stable.

[0062] The above steps are explained below using formulas: In length T medium Within a mid-time analysis window spanning tens to hundreds of power frequency cycles, the effective over-limit pulse time sequence output by the short-time window over-limit detection step is extracted. , where M is the total number of pulses within the window; Based on the valid over-limit pulse time sequence, the time interval between adjacent pulses is calculated to form a pulse interval sequence. ,in:

[0063] The statistical dispersion index of the pulse interval sequence is the coefficient of variation. The formula for its calculation is:

[0064] In the formula, μ Δt σ is the mean of the pulse interval sequence. Δt Its standard deviation; The coefficient of variation CV Δt Compare with the preset stability threshold η: If If < η, then determine the current time window T. medium The pulse interval within the pulse is stable; In this example, the stability threshold η is set based on the periodic characteristics of typical arc faults, and the value range is generally from 0.1 to 0.3.

[0065] Verification Example 1 Based on the first to fifth embodiments, a fault recording and traveling wave ranging system installed on a 110kV transmission line in a plateau region was used to collect a segment of normal operating current data containing multiple lightning-induced interference processes. The sampling frequency was set to 10kHz. The stability of this actual recorded three-phase current sequence under strong interference conditions was verified using the data as input. The specific steps are as follows: (1) After the acquired three-phase current raw sequence is processed by the harmonic adaptive nonlinear phase compensation filter, synchronous sampling and coordinate transformation are performed to extract the spatial vector magnitude sequence as the current feature I. m [n]. The calculated baseline value is: Load current baseline value I. load = 518.6A, noise intensity reference value σ = 18.7A.

[0066] (2) Set the length as T short A sliding analysis window of 40 ms is used to achieve real-time tracking of current changes. At each sampling time n, the current space vector magnitude I is calculated. m [n] and the load current reference value I load instantaneous deviation ; With K1 set to 4.0 and K2 set to 10.0, two dynamic discrimination thresholds are generated based on the aforementioned noise benchmark: Dynamic discrimination threshold, strong interference discrimination threshold ; Based on the comparison between the absolute value of the instantaneous deviation |D[n]| and the two thresholds, a hierarchical determination is performed: when |D[n]| > θ h When θs < |D[n] | < θ, it is judged as a strong instantaneous interference and filtered out; only when θs < |D[n] | < θ h When the time is right, the current moment will be marked as a valid over-limit pulse; In the data analysis of this embodiment, there are a small number of large-amplitude pulses among the 100,000 sampling points, all of which satisfy |D[n]|>θ h These were directly filtered out. In the remaining time periods, only a small number of pulses with amplitudes between 80A and 160A exceeded the limit, failing to meet the valid pulse condition. After this step, no valid pulses exceeding the limit were output during the entire 10-second analysis period, and the pulse sequence was empty.

[0067] (3) Since the effective pulse sequence output in step (2) is empty, it is possible to output any pulse of length T. medium Within a sliding time analysis window of 2 seconds, no pulse occurrence time sequence suitable for statistical analysis could be extracted. Based on this, the system determined that the pulse interval stability index for all time windows was "no stable sequence," and output the stability flag R. j =0.

[0068] (4) In a length of T long Within a long-term analysis window of 6.0 s, the stability results of continuous intermediate time windows were statistically analyzed. Since all intermediate time windows output R... j All values ​​are 0, and the calculated proportion of stable results K stableThe value is always 0. This value is always less than the action threshold P_set = 0.70, therefore the comprehensive fault judgment condition is never met.

[0069] Reference Figure 2 The short-window detection results under strong interference, as shown, demonstrate that the method of this invention operates stably throughout the entire 10-second analysis period. Although the waveform data includes lightning-induced interference, these interferences are effectively identified and isolated from the core fault analysis process through the adaptive filtering and short-window dynamic dual-threshold identification mechanism. Ultimately, the system continuously outputs a "no-arc fault" judgment, which is completely consistent with the actual operating state of the line.

[0070] Verification Example 2 Based on the first to fifth embodiments, a fault recording and traveling wave ranging system installed on a 110kV transmission line in a plateau region was used to collect current data containing an intermittent arc fault process via high-resistance grounding. The sampling frequency was set to 10kHz. Using this actual recorded three-phase current sequence as input, the complete identification process of the arc fault was verified according to the method proposed in this invention. The specific steps are as follows: (1) After the acquired three-phase current raw sequence is processed by the harmonic adaptive nonlinear phase compensation filter, synchronous sampling and coordinate transformation are performed to extract the spatial vector magnitude sequence I. m [n]. The calculated baseline value is: Load current baseline value I. load =512.4A, noise intensity reference value σ=16.3A.

[0071] (2) Perform short-window over-limit detection procedure. First, set the length to T. short A sliding analysis window of 40 ms is used to achieve real-time tracking of current changes. At each sampling time n, the current space vector magnitude I is calculated. m [n] and the load current reference value I load instantaneous deviation ; With K1 set to 4.0 and K2 set to 10.0, two dynamic discrimination thresholds are generated based on the aforementioned noise benchmark: Dynamic discrimination threshold, strong interference discrimination threshold ; Based on the comparison between the absolute value of the instantaneous deviation |D[n]| and the two thresholds, a hierarchical determination is performed: when |D[n]| > θ h When θs < |D[n] | < θ, it is judged as a strong instantaneous interference and filtered out; only when θs < |D[n] | < θ h When the time is right, the current moment will be marked as a valid over-limit pulse; (3) Set T mediumA 2s intermediate time analysis window is used, sliding at 1.0-second intervals, to perform continuity analysis on the effective pulse time sequence output in step (2). Pulse times within each intermediate time window are extracted, and the adjacent interval Δt is calculated. i and its coefficient of variation CV Δt During the fault duration, the CV was calculated using multiple consecutive sliding time windows. Δt The values ​​all remained stable within the range of 0.05 to 0.12, significantly lower than the preset stability threshold η = 0.25. Therefore, this series of consecutive intermediate time windows were all determined to be "pulse interval stable," and their respective output stability flags R... j =1.

[0072] (4) Set K set It is 0.7, T delay It is 0.08s, at T long A long-term analysis window of 6 seconds can sequentially cover the discrimination results of the above five consecutive sliding time windows. In the statistical time window, R... j The proportion of stable outcomes K = 1 stable The value is 1. According to the decision logic requirements: K stable >K set The state must continue for more than the preset judgment delay T. delay .

[0073] Reference Figure 3 The results of short-time window detection under arc faults are shown. After the fault characteristics stabilize and appear, R in the medium-time window... j The proportion of stable outcomes K = 1 stable The value continuously exceeds the action threshold K set Furthermore, this state persists for far longer than the set delay time T. delay If the comprehensive judgment conditions are met, the final judgment result of "confirmation of arc fault" is output, which is completely consistent with the actual operating status of the line.

[0074] As one implementation scheme, Figure 4 This is a schematic diagram of the hardware operating environment of the computer system involved in the embodiments of this application.

[0075] like Figure 4As shown, the computer system may include: a processor 1001, such as a CPU; a memory 1005; a user interface 1003; a network interface 1004; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0076] Those skilled in the art will understand that Figure 4 The computer system architecture shown does not constitute a limitation on the computer system and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0077] like Figure 4 As shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and computer programs. The operating system is a program that manages and controls the hardware and software resources of the computer system, as well as the operation of the computer programs and other software or programs.

[0078] exist Figure 4 In the computer system shown, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal; the network interface 1004 is mainly used to communicate with the backend server; and the processor 1001 can be used to call the computer program stored in the memory 1005.

[0079] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a computer program stored in the memory and executable on the processor, wherein: When the processor 1001 calls the computer program stored in the memory 1005, it implements each step of the arc fault identification method for high-altitude mountain transmission lines as described in the above embodiment.

[0080] Furthermore, those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in a computer system to implement the process steps of the embodiments of the above methods.

[0081] Therefore, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the various steps of the arc fault identification method for high-altitude mountain transmission lines as described in the above embodiments.

[0082] The computer-readable storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0083] It should be noted that, since the storage medium provided in the embodiments of this application is the storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0084] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0086] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0088] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0089] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for identifying arc faults in high-altitude mountain transmission lines, characterized in that, The method includes the following steps: S10, acquire the three-phase current signal of the transmission line, and perform anti-aliasing filtering on the three-phase current signal to obtain the filtered current signal. The phase compensation amount in the anti-aliasing filtering process is determined based on the load current harmonic distortion characteristics of the three-phase current signal. S20, extract the current characteristics of the filtered current signal, and calculate the load current reference value and noise intensity reference value based on the current characteristics; S30, perform over-limit detection based on the current characteristic quantity, the load current reference value and the noise intensity reference value to determine the effective over-limit pulse time sequence, wherein the over-limit detection process includes generating a first / second threshold based on the noise intensity reference value to identify the effective over-limit pulse time sequence; S40, based on the time interval between adjacent pulses in the effective over-limit pulse time sequence within multiple consecutive time windows, determine the target pulse in a steady state; S50, if the duration of the target pulse in the total continuous time window being greater than or equal to the preset action threshold exceeds the preset judgment delay, it is determined that there is an arc fault in the plateau mountain transmission line in the total continuous time window.

2. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 1, characterized in that, Anti-aliasing filtering is performed using a harmonic adaptive filter, which includes: The basic filtering unit is used to achieve anti-aliasing amplitude-frequency response; A phase compensation unit is used to determine the phase compensation amount based on the total harmonic distortion rate of the load current, so as to compensate for the phase distortion caused by the change in load harmonic content based on the phase compensation amount.

3. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 2, characterized in that, The expression for the transfer function of the filter is: ； In the formula, For the basic filtering unit, its coefficients { a k , b k According to the preset cutoff frequency f c Confirmed; e jϕ(ω) For phase compensation unit; ω Digital angular frequency, j The imaginary unit; Phase compensation amount The mathematical expression is: ； In the formula, This is the system's fundamental angular frequency; γ is the Nyquist angular frequency; γ is the total harmonic distortion of the current load current; This refers to the set of harmonic orders that require phase compensation, determined based on the characteristics of typical harmonic sources in the line. For the first h The compensation weighting coefficient for subharmonics; For the first h Frequency offset compensation for subharmonics; C This is the adjustment coefficient for the overall phase compensation intensity.

4. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 1, characterized in that, In S20, the current characteristic quantities extracted from the filtered current signal include: The filtered current signal is synchronously discretely sampled to obtain a discrete time series. , , ; For discrete time series , , Perform a Clarke transformation to transform to a stationary orthogonal coordinate system. and : ； based on and Calculate the spatial vector magnitude at the current moment. As a characteristic quantity of current: 。 5. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 1 or 4, characterized in that, In S20, the calculation of the load current reference value and the noise intensity reference value based on the current characteristic quantity includes: Select a spatial vector magnitude sequence of length N sampling points from the transmission line during its non-fault operation period. ; The average value of the space vector magnitude is calculated and used as the reference value for the load current. : ； Calculate the standard deviation of the spatial vector magnitude, and use it as the noise intensity benchmark value σ: 。 6. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 1, characterized in that, In S30, over-limit detection is performed based on the current characteristic quantity, the load current reference value, and the noise intensity reference value to determine the effective over-limit pulse time sequence, including: The product of the noise intensity reference value and the preset short-time window sensitivity coefficient is used as the first threshold, and the product of the noise intensity reference value and the preset strong interference suppression coefficient is used as the second threshold. Calculate the instantaneous deviation between the current characteristic quantity at the current sampling moment and the load current reference value, and take the absolute value; If the absolute value of the instantaneous deviation is greater than the first threshold and less than the second threshold, the pulse corresponding to each current sampling time is determined as a valid over-limit pulse; The effective over-limit pulses at each sampling time determined within the preset acquisition time window constitute the effective over-limit pulse time sequence.

7. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 1, characterized in that, S40 includes: Determine the time interval between each adjacent pulse in the effective over-limit pulse time sequence to form a pulse interval sequence; The coefficient of variation of the pulse interval sequence is determined based on the ratio of the standard deviation to the mean of the pulse interval sequence. The target pulse interval with a coefficient of variation less than a preset stability threshold is determined as the target pulse in a steady state.

8. The method for identifying arc faults in high-altitude mountain transmission lines as described in claim 1, characterized in that, Steps S30, S40 and S50 are executed in the first time window, the second time window and the third time window, respectively, wherein the first time window is smaller than the second time window and the third time window.

9. A computer system, characterized in that, The computer system includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the method for identifying arc faults in high-altitude mountain transmission lines as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the arc fault identification method for high-altitude mountain transmission lines as described in any one of claims 1 to 8.

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