Feeder terminal overcurrent protection method and system based on second harmonic braking

Abstract

The invention discloses a feeder terminal overcurrent protection method and system based on second harmonic braking, and relates to the technical field of overcurrent protection. The method comprises the following steps: carrying out adaptive time-frequency decomposition on a current instantaneous sampling sequence collected by a feeder terminal to obtain a multi-scale frequency distribution map containing a fundamental component and harmonic waves of all orders; constructing a second harmonic coherent energy function, extracting the amplitude, frequency and instantaneous phase of the second harmonic, and forming a second harmonic feature vector; performing noise self-suppression filtering processing, and establishing a processing result; constructing a transient evolution trend of the second harmonic; forward prediction is carried out, and second harmonic prediction trajectories of N sampling periods are constructed; and executing mode comparison of the historical safe moving trajectory library, establishing an overcurrent risk index, and performing overcurrent protection management according to the overcurrent risk index. The technical problem that in the prior art, it is difficult for the feeder terminal to accurately recognize an overcurrent fault in a complex harmonic environment is solved, and the technical effect of improving the overcurrent protection accuracy of the feeder terminal is achieved.

Description

Technical Field

[0001] This invention relates to the field of overcurrent protection technology, and specifically to a method and system for overcurrent protection of feeder terminals based on second harmonic braking. Background Technology

[0002] With the continuous improvement of distribution automation, feeder terminals are playing an increasingly important role in fault detection, isolation, and sectional control of distribution networks. Overcurrent protection, as the most basic and critical protection function of feeder terminals, directly affects the reliability of power supply in the distribution network. However, in actual operating environments, distribution lines are often affected by factors such as electric arcs, nonlinear loads, and power electronic devices, resulting in significant harmonics and noise disturbances in the current signal. In particular, waveform distortion of the second harmonic component can interfere with traditional overcurrent protection criteria based on fundamental amplitude or fixed thresholds. Existing methods generally rely on static settings or simple filters for signal processing, which struggles to maintain accurate overcurrent fault identification under conditions of fluctuating harmonic proportions, unstable noise levels, and rapid fault evolution, leading to problems such as false tripping, failure to trip, or delayed action. Summary of the Invention

[0003] This application provides a method and system for overcurrent protection of feeder terminals based on second harmonic braking, which solves the technical problem in the prior art that feeder terminals are difficult to accurately identify overcurrent faults in complex harmonic environments.

[0004] The first aspect of this application provides a feeder terminal overcurrent protection method based on second harmonic braking, the method comprising: Adaptive time-frequency decomposition is performed on the instantaneous current sampling sequence collected by the feeder terminal to obtain a multi-scale frequency distribution map containing the fundamental component and harmonics of each order. Based on the multi-scale frequency distribution map, a coherent energy function for the second harmonic is constructed. The amplitude, frequency, and instantaneous phase of the second harmonic are extracted by maximizing the coherent energy path to form a second harmonic feature vector. The second harmonic feature vector is subjected to noise self-suppression filtering to establish the processing result. Using the processing result as input data, multi-dimensional gradient evolution analysis is performed based on the amplitude change rate, frequency drift gradient, and phase jump variable to construct the transient evolution trend of the second harmonic. Forward prediction is performed using the transient evolution trend to construct a second harmonic prediction trajectory for N sampling periods. Based on the processing result and the second harmonic prediction trajectory for N sampling periods, a pattern comparison with the historical safe operation trajectory database is performed to establish an overcurrent risk index. Overcurrent protection management is performed based on the overcurrent risk index.

[0005] A second aspect of this application provides a feeder terminal overcurrent protection system based on second harmonic braking, the system comprising: The system comprises the following modules: a time-frequency decomposition module, a feature extraction module, and an overcurrent protection management module. The time-frequency decomposition module performs adaptive time-frequency decomposition on the instantaneous current sampling sequence collected from the feeder terminal to obtain a multi-scale frequency distribution map containing the fundamental component and various harmonics. A feature extraction module constructs a coherent energy function for the second harmonic based on the multi-scale frequency distribution map. It extracts the amplitude, frequency, and instantaneous phase of the second harmonic by maximizing the coherent energy path, forming a second harmonic feature vector. A noise filtering module performs noise self-suppression filtering on the second harmonic feature vector to establish the processing result. An evolution analysis module uses the processing result as input data and performs multi-dimensional gradient evolution analysis based on the amplitude change rate, frequency drift gradient, and phase jump variable to construct the transient evolution trend of the second harmonic. A forward prediction module uses the transient evolution trend to perform forward prediction, constructing a second harmonic prediction trajectory for N sampling periods. An overcurrent protection management module compares the processing result and the second harmonic prediction trajectory for N sampling periods with patterns from a historical safe operation trajectory database to establish an overcurrent risk index. Overcurrent protection management is then performed based on the overcurrent risk index.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, adaptive time-frequency decomposition is performed on the instantaneous current sampling sequence collected from the feeder terminal to obtain a multi-scale frequency distribution map containing the fundamental component and harmonics. Next, based on the multi-scale frequency distribution map, a coherent energy function for the second harmonic is constructed. By maximizing the coherent energy path, the amplitude, frequency, and instantaneous phase of the second harmonic are extracted to form a second harmonic feature vector. Further, noise self-suppression filtering is applied to the second harmonic feature vector to establish the processing result. Then, using the processing result as input data, multi-dimensional gradient evolution analysis is performed based on the amplitude change rate, frequency drift gradient, and phase jump variable to construct the transient evolution trend of the second harmonic. This transient evolution trend is then used for forward prediction to construct a second harmonic prediction trajectory for N sampling periods. Finally, based on the processing result and the second harmonic prediction trajectory for N sampling periods, a pattern comparison with the historical safe operation trajectory database is performed to establish an overcurrent risk index. Overcurrent protection management is then implemented based on this overcurrent risk index. This invention solves the technical problem that feeder terminals are unable to accurately identify overcurrent faults in complex harmonic environments in existing technologies, and achieves the technical effect of improving the accuracy of overcurrent protection in feeder terminals. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1A schematic flowchart of a feeder terminal overcurrent protection method based on second harmonic braking provided in an embodiment of this application; Figure 2 A schematic diagram of the overcurrent protection system for feeder terminals based on second harmonic braking provided in this application embodiment.

[0009] Figure labeling: Time-frequency decomposition module 11, Feature extraction module 12, Noise filtering module 13, Evolution analysis module 14, Forward prediction module 15, Overcurrent protection management module 16. Detailed Implementation

[0010] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0011] Example 1, as Figure 1 As shown, this application provides a feeder terminal overcurrent protection method based on second harmonic braking, wherein the method includes: Adaptive time-frequency decomposition is performed on the instantaneous current sampling sequence collected by the feeder terminal to obtain a multi-scale frequency distribution spectrum containing the fundamental component and harmonics.

[0012] The feeder terminal continuously acquires instantaneous sampling sequences of the operating current at a set sampling frequency. Using these instantaneous sampling sequences as input data, adaptive time-frequency analysis is performed on multiple preset time and frequency scales. During the analysis, the fundamental component and harmonic components of the signal are naturally separated on the time-frequency plane by iteratively estimating the instantaneous energy, instantaneous frequency, and local phase at different scales. The frequency responses at each scale are arranged in chronological order, and the response energy is normalized to form a multi-scale energy distribution covering the fundamental frequency and its integer multiples of harmonic frequencies. Finally, the distribution is visualized with time as the horizontal axis and frequency as the vertical axis, resulting in a multi-scale frequency distribution map characterizing the transient changes of the current signal at different harmonic orders. This multi-scale frequency distribution map fully reflects the instantaneous changes of the current signal at different harmonic components, providing a fundamental time-frequency characterization for subsequent extraction of the second harmonic amplitude, frequency, and phase.

[0013] Based on the multi-scale frequency distribution map, a coherent energy function for the second harmonic is constructed. The amplitude, frequency, and instantaneous phase of the second harmonic are extracted by maximizing the coherent energy path, forming a second harmonic feature vector.

[0014] First, a target frequency band centered at twice the fundamental frequency is selected from the obtained multi-scale frequency distribution map. The time-frequency energy of each frequency grid point within the target frequency band is normalized, and the energy values ​​are smoothed within a preset time sliding window to reduce local fluctuations caused by transient noise. Then, the local phase difference between frequencies is calculated based on the normalized energy distribution, and a cross-frequency coherence index is constructed by combining phase consistency. The local frequency band energy and cross-frequency coherence are then combined using a weighted logarithm to form a coherent energy function for measuring the intensity and stability of the second harmonic. To obtain the most probable instantaneous frequency evolution trajectory of the second harmonic, a point-by-point cumulative analysis of the coherent energy function is performed on the time axis. A local gain function for the time series is constructed based on preset frequency smoothing constraints, and the frequency path maximizing the cumulative gain is obtained using dynamic programming. The corresponding energy amplitude, instantaneous frequency, and phase information are read along this optimal path to obtain the complete second harmonic amplitude sequence, frequency sequence, and instantaneous phase sequence. These three are then arranged and combined in time to form the second harmonic characteristic vector for subsequent steady-state and transient analyses.

[0015] Furthermore, based on the aforementioned multi-scale frequency distribution spectrum, a second-harmonic coherent energy function is constructed, including: Based on the multi-scale frequency distribution map, energy normalization is performed on the frequency grid points at each time step, and time smoothing is performed within a preset time window. Local frequency band energy is constructed for the second harmonic target band centered at twice the fundamental frequency. Based on the local frequency band energy, a cross-frequency coherence metric is defined, and the local frequency band energy and the cross-frequency coherence metric are synthesized into a second harmonic coherence energy function based on a weighted logarithm. The second harmonic coherence energy function and the frequency smoothing penalty are used to construct a time series local revenue function, and the frequency path that maximizes the cumulative revenue is solved by dynamic programming in the time dimension. The amplitude, frequency, and instantaneous phase of the second harmonic are extracted by maximizing the coherence energy path to form a second harmonic feature vector.

[0016] Specifically, at each sampling moment, the energy values ​​of all frequency grid points at the corresponding moment are read from the multi-scale frequency distribution map. These energy values ​​are normalized to eliminate the influence of overall energy level changes over different time periods. Subsequently, within a preset time sliding window, the normalized energy is smoothed in the time direction to maintain the continuity of the energy sequence in the time dimension. A target frequency band is selected centered at twice the fundamental frequency, and the energy of the frequency grid points in this band is aggregated to construct a local frequency band energy that can characterize the local energy intensity of the second harmonic. Based on the local frequency band energy, the cross-frequency coherence metric is calculated according to the instantaneous phase difference of different frequency grid points. The local frequency band energy and the cross-frequency coherence metric are combined in a weighted logarithmic manner to enhance the harmonic components with high coherence and stable energy, making the second harmonic more prominent in the combined result, thus forming the second harmonic coherent energy function. Using the coherent energy function of the second harmonic, a local time-series revenue function with a frequency smoothing penalty term is constructed at each time step. By limiting the frequency jump amplitude between adjacent time steps on the frequency axis, the constructed revenue is made to better conform to the actual harmonic evolution law. A dynamic programming method is used in the time dimension to accumulate the local revenue, finding the frequency path that maximizes the accumulated revenue, thus obtaining the frequency sequence that best matches the time evolution trend of the second harmonic. After determining this optimal coherent energy path, the energy amplitude, instantaneous frequency, and phase information at corresponding time steps are sequentially read along this path to form the amplitude sequence, frequency sequence, and phase sequence of the second harmonic. These three are then combined in chronological order to construct the second harmonic feature vector.

[0017] Furthermore, the cross-frequency coherence metric is defined based on the local frequency band energy, including: Within the second harmonic target band, neighborhood energy aggregation is performed for each frequency grid point to form a second harmonic energy envelope; a phase consistency index is calculated for the second harmonic energy envelope, and a cross-frequency coherence metric is constructed by the instantaneous phase difference between frequency grid points.

[0018] Within the selected second harmonic target band, for each frequency grid point, the local frequency band energy corresponding to that frequency grid point at the current time is read, and multiple neighboring frequency grid points are selected to form a frequency neighborhood. The energy values ​​within this frequency neighborhood are weighted and aggregated to enhance the local structure of the energy distribution within the target frequency band, thereby forming a second harmonic energy envelope that reflects the degree of concentration of second harmonic energy. Within the same second harmonic target band, the corresponding instantaneous phase information is extracted from the constructed second harmonic energy envelope. By calculating the instantaneous phase difference between different frequency grid points, the phase consistency of the energy envelope in the frequency dimension is evaluated, and this phase consistency is used as a measure of cross-frequency coherence. When multiple frequency grid points maintain strong phase consistency, their cross-frequency coherence metric is higher, thus effectively distinguishing stable second harmonic components from incoherent energy caused by noise and transient disturbances, making the subsequently constructed second harmonic coherent energy function more selective and reliable.

[0019] The second harmonic eigenvector is subjected to noise self-suppression filtering to establish the processing result.

[0020] Furthermore, the second harmonic eigenvector is subjected to noise self-suppression filtering to establish the processing result, including: Within a sliding time window, the local amplitude fluctuation, frequency continuity index, and phase change stability are calculated separately. The calculation results are weighted and fused to output a local time-frequency consistency index. Based on the local time-frequency consistency index, filter coefficients are adaptively generated, noise self-suppression filtering is performed, and the processing result is established.

[0021] Within a set sliding time window, local variation characteristics are calculated for the second harmonic amplitude, frequency, and phase sequences, respectively. The amplitude local volatility measures the relative fluctuation of the amplitude within the window, the frequency continuity index reflects the smoothness of frequency evolution, and the phase change stability describes whether there are abrupt changes in phase over time. These three indices are weighted and fused according to preset weights to obtain a local time-frequency consistency index, which comprehensively characterizes the time-frequency stability of the second harmonic. A lower index indicates stronger noise interference within the window, while a higher index indicates more stable second harmonic characteristics. After obtaining the local time-frequency consistency index, filter coefficients are adaptively generated based on the index value, allowing the filter strength to be dynamically adjusted according to the noise level in different time periods. Subsequently, these filter coefficients are used to perform noise self-suppression filtering on the second harmonic amplitude, frequency, and phase sequences. By suppressing abrupt changes and discontinuities, the sequences exhibit a smooth and physically reasonable evolution trend in the time dimension, thus establishing the filtered processing result.

[0022] Using the processing results as input data, multidimensional gradient evolution analysis is performed based on amplitude change rate, frequency drift gradient, and phase jump variable to construct the transient evolution trend of the second harmonic.

[0023] Furthermore, based on the amplitude change rate, frequency drift gradient, and phase jump variable, multidimensional gradient evolution analysis is performed to construct the transient evolution trend of the second harmonic, including: The processing results are used as input data and fed into the multi-gradient evolution analysis channel. Through the three-dimensional gradient extraction layer in the multi-gradient evolution analysis channel, time differences are calculated based on amplitude, frequency, and phase, respectively, and amplitude change rate features, frequency drift gradient features, and phase jump variable features are extracted to construct a three-dimensional gradient vector field. The three-dimensional gradient vector field is synchronized to the coupling strength calculation layer, and the second harmonic stability tensor is constructed by calculating the cross-correlation features of the three-dimensional gradient vector field. The computational layer is activated, and a transient evolution driving force function is constructed based on the second harmonic stability tensor and the three-dimensional gradient vector field. Time integration is then performed to output the transient evolution trend of the second harmonic.

[0024] Specifically, the processing results are used as a continuous time series input. The second harmonic amplitude, frequency, and phase sequences are mapped point-by-point along the time axis and processed uniformly in the multi-gradient evolution analysis channel. Subsequently, within the multi-gradient evolution analysis channel, time difference is performed on the amplitude, frequency, and phase respectively to obtain the rate of change of amplitude at adjacent time points, the drift gradient of frequency at adjacent time points, and the jump variable of phase at adjacent time points. This forms a three-dimensional gradient vector containing three gradient components at each time point, and a three-dimensional gradient vector field evolving along the time dimension is constructed based on this. This three-dimensional gradient vector field is kept synchronized in time, and the cross-correlation relationship between the components in the vector field is calculated to reflect the coupling strength of amplitude, frequency, and phase in the transient state. Based on the cross-correlation results, a stability tensor is constructed to describe the dynamic stability of the second harmonic. Based on the joint changes of the stability tensor and the three-dimensional gradient vector field, a transient evolution driving force function is defined to characterize the evolution direction and amplitude of the second harmonic under transient conditions. The driving force function is integrated and accumulated along the time axis to obtain the transient evolution trend of the second harmonic under disturbance. The transient evolution trend can reflect the continuous change process of the second harmonic before and after the fault occurs.

[0025] When constructing the transient evolution trend of the second harmonic, the time difference of the amplitude, frequency, and phase sequences after noise self-suppression filtering is calculated to obtain the gradient of the amplitude change rate. Frequency drift gradient Phase jump variable The three together form a three-dimensional gradient vector. .

[0026] The time-varying changes and partial derivatives of the three-dimensional gradient vectors with respect to amplitude, frequency, and phase are calculated respectively, and the real-time coupling relationship between gradients is constructed based on this. The second harmonic stability tensor can be expressed as: ,in, , , , which is the dynamic coupling coefficient, used to characterize the real-time coupling strength between three-dimensional gradients in the time dimension; This represents the direct change of the three-dimensional gradient vector over time. , , These represent the partial derivatives of the three-dimensional gradient vector with respect to amplitude, frequency, and phase, respectively. By superimposing these four terms, the continuous variation of the second harmonic in the time dimension and the mutual coupling between the characteristic dimensions can be considered simultaneously, thus obtaining the time-evolving second harmonic stability tensor. .

[0027] Based on this, the three-dimensional gradient vector and the stability tensor are fused according to the tensor product rule to obtain the driving force function used to characterize the direction and amplitude of the transient change of the second harmonic, and its expression is: , where the symbol It represents the tensor product of the three-dimensional gradient vector and the stability tensor in the corresponding dimension, and is used to synthesize the coupling structure of the instantaneous change of the three-dimensional gradient and the stability tensor.

[0028] The transient evolution trend is used for forward prediction to construct a second harmonic prediction trajectory for N sampling periods.

[0029] After obtaining the transient evolution trend of the second harmonic, the amplitude change direction, frequency drift direction, and phase change rate at the current moment are used as the prediction starting point. Based on the evolution rate and driving force reflected in the transient evolution trend, the amplitude of the second harmonic over several future sampling moments is determined. Subsequently, using the amplitude, frequency, and phase at the current moment as initial values, the amplitude increment, frequency shift, and phase accumulation at each future sampling moment are calculated recursively according to the direction and intensity of the transient evolution trend, and these are sequentially superimposed to form a continuous prediction sequence. By repeating the above recursive process sequentially on the time axis, a second harmonic predicted amplitude sequence, predicted frequency sequence, and predicted phase sequence covering N sampling periods are obtained. The combination of these three sequences constitutes the predicted trajectory of the second harmonic. The obtained N-sampling-period predicted trajectory can reflect the expected evolution state of the second harmonic in the short term.

[0030] Based on the processing results and the second harmonic prediction trajectory of N sampling periods, a pattern comparison is performed with the historical safe operation trajectory library to establish an overcurrent risk index, and overcurrent protection management is carried out based on the overcurrent risk index.

[0031] The amplitude, frequency, and phase sequences of the second harmonic (HH) after noise self-suppression filtering are jointly analyzed with the predicted HH trajectory for N sampling periods obtained from forward prediction. The comprehensive evolutionary offset of the HH characteristics at the current moment and in the short future period is calculated to characterize the degree of difference between the HH and the normal operating state. Subsequently, this comprehensive evolutionary offset is mapped to a HH situation fingerprint and compared one by one with various typical safety situations stored in the historical safe operation trajectory database. The corresponding local matching score is obtained based on the matching degree of three dimensions: amplitude offset, frequency stability, and phase consistency. Combining the matching scores of each dimension, an overcurrent risk index is generated to quantify the safety of the current operating state through deviation evaluation. When the overcurrent risk index rises and exceeds the set risk threshold, the protection system responds to potential overcurrent risks in advance by adjusting protection sensitivity, adaptively modifying protection settings, or directly triggering protection actions. When the overcurrent risk index is in a gradually changing range, braking adjustment is performed and its changing trend is continuously monitored. When the overcurrent risk index is in a safe range, the normal sensitivity of the protection device is maintained.

[0032] Furthermore, based on the processing results and the second harmonic prediction trajectory of N sampling periods, a pattern comparison is performed with the historical safe operation trajectory database to establish an overcurrent risk index, including: Based on the processing results and the second harmonic prediction trajectory for N sampling periods, the comprehensive evolutionary offset of amplitude, frequency, and phase is calculated to construct a second harmonic situation fingerprint vector. The second harmonic situation fingerprint vector is input into the hierarchical scene index unit of the historical safe operation trajectory database. The three-dimensional local matching score is calculated through the amplitude evolution layer, frequency stability layer, and phase consistency layer to form a hierarchical scene matching vector. Based on the hierarchical scene matching vector, the harmonic deviation degree evaluation is performed, and the overcurrent risk index is output.

[0033] Based on the second harmonic amplitude, frequency, and phase sequences processed by noise self-suppression filtering, and the predicted trajectories of N sampling periods obtained through forward prediction, the amplitude shift, frequency drift, and phase deviation within the time period are calculated. These three are then combined in chronological order to form a second harmonic situation fingerprint vector that simultaneously characterizes second harmonic energy changes, frequency stability, and phase consistency. This situation fingerprint vector is then compared with typical safety situations recorded in the historical safe operation trajectory database, performing matching at different levels of scenarios. Specifically, the similarity of amplitude change trends is calculated at the amplitude evolution layer, the stability score of frequency deviation is calculated at the frequency stability layer, and the synchronicity score of phase over time is calculated at the phase consistency layer, thus forming a hierarchical scenario matching vector containing three local matching scores. Finally, based on the deviation of the three dimensions in the hierarchical scenario matching vector, the difference between the current operating state and the historical safety state is determined through a comprehensive evaluation method. This difference is quantified as an overcurrent risk index to reflect the potential overcurrent risk level of the current feeder under second harmonic characteristics.

[0034] Furthermore, overcurrent protection management based on the aforementioned overcurrent risk index includes: Based on the processing results and the second harmonic prediction trajectory of N sampling periods, the stability tensor change amplitude of the second harmonic is extracted, and a second harmonic braking weight coefficient is constructed according to the extraction results. An adaptive protection threshold curve is configured using the second harmonic braking weight coefficient. The adaptive protection threshold curve enters the sensitive region when the overcurrent risk index exceeds the first threshold, so that the transient characteristics quickly drive the protection response. The adaptive protection threshold curve enters the buffer zone when the overcurrent risk index is between the first threshold and the second threshold, and braking adjustment is performed. The adaptive protection threshold curve enters the steady-state region when the overcurrent risk index is below the second threshold, and the protection device is kept at the normal sensitivity. Overcurrent protection management is performed according to the configured adaptive protection threshold curve.

[0035] Based on the processing results and the predicted second harmonic trajectory over N sampling periods, the joint evolution of the predicted amplitude change, frequency drift, and phase shift in the time dimension is analyzed. The variation amplitude of the second harmonic stability tensor within consecutive sampling periods is calculated to characterize the strength of the second harmonic's evolution from a stable state to a disturbed state. A second harmonic braking weight coefficient is constructed based on this variation amplitude, enabling the braking weight to be dynamically adjusted according to changes in second harmonic stability. Subsequently, the braking weight coefficient is combined with conventional overcurrent setting logic to configure an adaptive protection threshold curve that varies with the risk level, allowing the protection system to possess different response strategies at different risk stages. When the overcurrent risk index exceeds the first threshold, the adaptive protection threshold curve enters the sensitive zone, enabling the protection system to respond quickly to the transient characteristics of the second harmonic and trigger protection actions in a timely manner. When the overcurrent risk index is between the first and second thresholds, the protection threshold curve enters the buffer zone, adjusting the protection action by increasing the braking weight or suppressing transient offsets to prevent false tripping due to transient disturbances. When the overcurrent risk index is below the second threshold, the adaptive protection threshold curve enters the steady-state zone, maintaining the protection device within the normal sensitivity range and ensuring stability during normal operation. Based on the configured adaptive protection threshold curve, the overcurrent condition of the feeder is ultimately protected and managed, achieving adaptive control of overcurrent risk under different operating conditions.

[0036] Furthermore, overcurrent protection management based on the overcurrent risk index also includes: Within the calculation period of the overcurrent risk index, the local variance and rising slope of the risk index are statistically analyzed using a sliding window; based on the statistical results, a gradual increase trend characteristic is identified, and a gradual change early warning is triggered based on the gradual increase trend characteristic, and early warning issuance management is implemented.

[0037] A sliding time window is set within the continuous calculation period of the overcurrent risk index. Statistical analysis is performed on the risk index at each moment within the window to calculate the local variance of the risk index within that window, reflecting the intensity of risk index fluctuations. The upward slope of the risk index over time is also calculated to characterize whether the risk level shows a continuous upward trend. If the local variance is small and the upward slope is significantly positive, it indicates that although the risk index has not experienced a drastic jump, it is steadily and slowly increasing, which is identified as a gradual upward trend. Based on this gradual upward trend characteristic, a gradual change early warning mechanism is triggered in advance, providing risk alerts before the protection system enters a high-sensitivity state. This allows maintenance personnel or upper-level control systems to promptly monitor the line status and execute corresponding early warning management, thereby enhancing the system's response capability to gradually accumulating risks.

[0038] Furthermore, when the feeder terminal is in an early warning state, an adaptive sampling update command is activated to perform adaptive enhancement management of the sampling frequency.

[0039] After the overcurrent risk index triggers a gradual change warning or other warning conditions are met, the feeder terminal's operating status is marked as a warning state. In this state, based on the risk increase trend and the rate of change of the second harmonic characteristics, an adaptive sampling update command is automatically initiated to dynamically adjust the current sampling frequency. When there is a significant trend in the amplitude, frequency, or phase of the second harmonic, the sampling frequency is increased and the sampling interval is shortened, enabling the feeder terminal to capture subtle changes in the current signal with higher time-domain resolution, thereby enhancing its ability to detect transient disturbances and early overcurrent characteristics. When the risk index falls or the signal change tends to stabilize, the sampling frequency can be gradually restored to the normal sampling frequency to reduce unnecessary computation and communication burden.

[0040] In summary, the embodiments of this application have at least the following technical effects: First, adaptive time-frequency decomposition is performed on the instantaneous current sampling sequence collected from the feeder terminal to obtain a multi-scale frequency distribution map containing the fundamental component and harmonics. Next, based on the multi-scale frequency distribution map, a coherent energy function for the second harmonic is constructed. By maximizing the coherent energy path, the amplitude, frequency, and instantaneous phase of the second harmonic are extracted to form a second harmonic feature vector. Further, noise self-suppression filtering is applied to the second harmonic feature vector to establish the processing result. Then, using the processing result as input data, multi-dimensional gradient evolution analysis is performed based on the amplitude change rate, frequency drift gradient, and phase jump variable to construct the transient evolution trend of the second harmonic. This transient evolution trend is then used for forward prediction to construct a second harmonic prediction trajectory for N sampling periods. Finally, based on the processing result and the second harmonic prediction trajectory for N sampling periods, a pattern comparison with the historical safe operation trajectory database is performed to establish an overcurrent risk index. Overcurrent protection management is then implemented based on this overcurrent risk index. This invention solves the technical problem that feeder terminals are unable to accurately identify overcurrent faults in complex harmonic environments in existing technologies, and achieves the technical effect of improving the accuracy of overcurrent protection in feeder terminals.

[0041] Example 2 is based on the same inventive concept as the feeder terminal overcurrent protection method based on second harmonic braking in the previous examples, such as... Figure 2 As shown, this application provides a feeder terminal overcurrent protection system based on second harmonic braking, wherein the system includes: Time-frequency decomposition module 11: Performs adaptive time-frequency decomposition on the instantaneous current sampling sequence collected by the feeder terminal to obtain a multi-scale frequency distribution map containing the fundamental component and harmonics of each order; Feature extraction module 12: Based on the multi-scale frequency distribution map, constructs a second harmonic coherent energy function, extracts the amplitude, frequency, and instantaneous phase of the second harmonic by maximizing the coherent energy path, and forms a second harmonic feature vector; Noise filtering module 13: Performs noise self-suppression filtering on the second harmonic feature vector and establishes the processing result; Evolution analysis module 14 Using the processing results as input data, multidimensional gradient evolution analysis is performed based on amplitude change rate, frequency drift gradient, and phase jump variable to construct the transient evolution trend of the second harmonic; Forward prediction module 15: uses the transient evolution trend to perform forward prediction and constructs a second harmonic prediction trajectory for N sampling periods; Overcurrent protection management module 16: performs pattern comparison with the historical safe operation trajectory library based on the processing results and the second harmonic prediction trajectory for N sampling periods, establishes an overcurrent risk index, and performs overcurrent protection management based on the overcurrent risk index.

[0042] Furthermore, the feature extraction module 12 is used to perform the following method: Based on the multi-scale frequency distribution map, energy normalization is performed on the frequency grid points at each time step, and time smoothing is performed within a preset time window. Local frequency band energy is constructed for the second harmonic target band centered at twice the fundamental frequency. Based on the local frequency band energy, a cross-frequency coherence metric is defined, and the local frequency band energy and the cross-frequency coherence metric are synthesized into a second harmonic coherence energy function based on a weighted logarithm. The second harmonic coherence energy function and the frequency smoothing penalty are used to construct a time series local revenue function, and the frequency path that maximizes the cumulative revenue is solved by dynamic programming in the time dimension. The amplitude, frequency, and instantaneous phase of the second harmonic are extracted by maximizing the coherence energy path to form a second harmonic feature vector.

[0043] Furthermore, the feature extraction module 12 is used to perform the following method: Within the second harmonic target band, neighborhood energy aggregation is performed for each frequency grid point to form a second harmonic energy envelope; a phase consistency index is calculated for the second harmonic energy envelope, and a cross-frequency coherence metric is constructed by the instantaneous phase difference between frequency grid points.

[0044] Furthermore, the evolutionary analysis module 14 is used to perform the following methods: The processing results are used as input data and fed into the multi-gradient evolution analysis channel. Through the three-dimensional gradient extraction layer in the multi-gradient evolution analysis channel, time differences are calculated based on amplitude, frequency, and phase, respectively, to extract amplitude change rate characteristics, frequency drift gradient characteristics, and phase jump variable characteristics, thus constructing a three-dimensional gradient vector field. This three-dimensional gradient vector field is synchronized to the coupling strength calculation layer, and the cross-correlation characteristics of the three-dimensional gradient vector field are calculated to construct a second harmonic stability tensor. The calculation layer is activated, and based on the second harmonic stability tensor and the three-dimensional gradient vector field, a transient evolution driving force function is constructed and integrated over time to output the transient evolution trend of the second harmonic.

[0045] Furthermore, the overcurrent protection management module 16 is used to perform the following method: Based on the processing results and the second harmonic prediction trajectory for N sampling periods, the comprehensive evolutionary offset of amplitude, frequency, and phase is calculated to construct a second harmonic situation fingerprint vector. The second harmonic situation fingerprint vector is input into the hierarchical scene index unit of the historical safe operation trajectory database. The three-dimensional local matching score is calculated through the amplitude evolution layer, frequency stability layer, and phase consistency layer to form a hierarchical scene matching vector. Based on the hierarchical scene matching vector, the harmonic deviation degree evaluation is performed, and the overcurrent risk index is output.

[0046] Furthermore, the overcurrent protection management module 16 is used to perform the following method: Based on the processing results and the second harmonic prediction trajectory of N sampling periods, the stability tensor change amplitude of the second harmonic is extracted, and a second harmonic braking weight coefficient is constructed according to the extraction results. An adaptive protection threshold curve is configured using the second harmonic braking weight coefficient. The adaptive protection threshold curve enters the sensitive region when the overcurrent risk index exceeds the first threshold, so that the transient characteristics quickly drive the protection response. The adaptive protection threshold curve enters the buffer zone when the overcurrent risk index is between the first threshold and the second threshold, and braking adjustment is performed. The adaptive protection threshold curve enters the steady-state region when the overcurrent risk index is below the second threshold, and the protection device is kept at the normal sensitivity. Overcurrent protection management is performed according to the configured adaptive protection threshold curve.

[0047] Furthermore, the noise filtering module 13 is used to perform the following method: Within a sliding time window, the local amplitude fluctuation, frequency continuity index, and phase change stability are calculated separately. The calculation results are weighted and fused to output a local time-frequency consistency index. Based on the local time-frequency consistency index, filter coefficients are adaptively generated, noise self-suppression filtering is performed, and the processing result is established.

[0048] Furthermore, the overcurrent protection management module 16 is used to perform the following method: Within the calculation period of the overcurrent risk index, the local variance and rising slope of the risk index are statistically analyzed using a sliding window; based on the statistical results, a gradual increase trend characteristic is identified, and a gradual change early warning is triggered based on the gradual increase trend characteristic, and early warning issuance management is implemented.

[0049] Furthermore, the overcurrent protection management module 16 is used to perform the following method: When the feeder terminal is in an early warning state, the adaptive sampling update command is activated to perform adaptive enhancement management of the sampling frequency.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method of overcurrent protection for a feeder terminal based on second harmonic braking, characterized by, The method comprises: Adaptive time-frequency decomposition is performed on the current transient sample sequence collected by the feeder terminal to obtain a multi-scale frequency distribution atlas containing fundamental wave components and each order of harmonics; Based on the multi-scale frequency distribution atlas, a second harmonic coherent energy function is constructed, the amplitude, frequency and instantaneous phase of the second harmonic are extracted by maximizing the coherent energy path, and a second harmonic feature vector is formed; Noise self-suppression filtering processing is performed on the second harmonic feature vector to establish a processing result; The processing result is taken as input data, multi-dimensional gradient evolution analysis is performed based on the amplitude variation rate, frequency drift gradient and phase jump variable, and the transient evolution trend of the second harmonic is constructed; Forward prediction is performed using the transient evolution trend to construct a second harmonic prediction trajectory of N sampling periods; Mode comparison is performed on the processing result and the second harmonic prediction trajectory of N sampling periods based on the historical safe operation trajectory library to establish an overcurrent risk index, and overcurrent protection management is performed based on the overcurrent risk index.

2. The second harmonic braking based feeder terminal overcurrent protection method of claim 1, wherein, Based on the multi-scale frequency distribution atlas, a second harmonic coherent energy function is constructed, including: Based on the multi-scale frequency distribution atlas, the energy of each frequency bin is normalized at each time and time smoothing processing is performed within a preset time window, and a local frequency band energy is constructed for a second harmonic target band centered on twice the fundamental wave; The local frequency band energy and the cross-frequency coherence measure are combined into a second harmonic coherent energy function based on a weighted logarithmic form; The second harmonic coherent energy function and the frequency smoothing penalty are used to construct a time series local return function, and the frequency path that maximizes the cumulative return is solved in the time dimension by dynamic programming; The amplitude, frequency and instantaneous phase of the second harmonic are extracted by maximizing the coherent energy path to form a second harmonic feature vector.

3. The second harmonic braking based feeder terminal overcurrent protection method of claim 2, wherein, Based on the local frequency band energy, a cross-frequency coherence measure is defined, including: Within the second harmonic target band, the neighborhood energy of each frequency bin is aggregated to form a second harmonic energy envelope; The phase consistency index of the second harmonic energy envelope is calculated, and the cross-frequency coherence measure is constructed through the instantaneous phase difference between the frequency bins.

4. The second harmonic braking based feeder terminal overcurrent protection method of claim 1, wherein, Multi-dimensional gradient evolution analysis is performed based on the amplitude variation rate, frequency drift gradient and phase jump variable to construct the transient evolution trend of the second harmonic, including: The processing result is taken as input data and input into a multi-gradient evolution analysis channel; Through a three-dimensional gradient extraction layer in the multi-gradient evolution analysis channel, time difference is calculated based on the amplitude, frequency and phase respectively to extract the amplitude variation rate feature, frequency drift gradient feature and phase jump variable feature, and a three-dimensional gradient vector field is constructed; The three-dimensional gradient vector field is synchronized to a coupling strength calculation layer, a second harmonic stability tensor is constructed by calculating the cross-correlation characteristics of the three-dimensional gradient vector field; The calculation layer is activated, a transient evolution driving force function is constructed based on the second harmonic stability tensor and the three-dimensional gradient vector field, and time integration is performed to output the transient evolution trend of the second harmonic.

5. The second harmonic braking based feeder terminal overcurrent protection method of claim 1, wherein, According to the processing result, the mode comparison of the second harmonic prediction trajectory of N sampling periods is performed on the historical safe operation trajectory library to establish an overcurrent risk index, including: Based on the processing result, the second harmonic prediction trajectory of N sampling periods, the comprehensive evolution offset of amplitude, frequency, and phase is calculated to construct a second harmonic trend fingerprint vector; The second harmonic trend fingerprint vector is input into a hierarchical scene indexing unit of the historical safe operation trajectory library to calculate a three-dimensional local matching score through an amplitude evolution layer, a frequency stability layer, and a phase consistency layer to form a hierarchical scene matching vector; Based on the hierarchical scene matching vector, a harmonic deviation degree evaluation is performed to output an overcurrent risk index.

6. The second harmonic braking based feeder terminal overcurrent protection method of claim 5, wherein, According to the overcurrent risk index, overcurrent protection management is performed, including: Based on the processing result, the second harmonic prediction trajectory of N sampling periods, a stability tensor variation amplitude of the second harmonic is extracted, and a second harmonic braking weight coefficient is constructed according to the extraction result; The second harmonic braking weight coefficient is used to configure an adaptive protection threshold curve; When the overcurrent risk index exceeds a first threshold value to enter a sensitive zone, the adaptive protection threshold curve makes a transient feature quickly drive a protection response; When the overcurrent risk index is between the first threshold value and a second threshold value, the adaptive protection threshold curve enters a buffer zone to perform braking adjustment; When the overcurrent risk index is lower than the second threshold value, the adaptive protection threshold curve enters a steady state zone to keep a protection device at a regular sensitivity; According to the configured adaptive protection threshold curve, overcurrent protection management is performed.

7. The second harmonic braking based feeder terminal overcurrent protection method of claim 1, wherein, Noise self-suppression filtering processing is performed on the second harmonic feature vector to establish a processing result, including: The amplitude local fluctuation degree, the frequency continuity index, and the phase change smoothness are calculated in a sliding time window, respectively, the calculation results are weighted and fused, and a local time-frequency consistency index is output; According to the local time-frequency consistency index, a filtering coefficient is adaptively generated to perform noise self-suppression filtering processing and establish a processing result.

8. The second harmonic braking based feeder terminal overcurrent protection method of claim 1, wherein, According to the overcurrent risk index, overcurrent protection management is performed, and further including: In the calculation period of the overcurrent risk index, the local variance and the rising slope of the risk index are statistically calculated by using a sliding window; According to the statistical result, a slow rising trend feature is identified, a slow change warning is triggered according to the slow rising trend feature, and a warning report management is performed.

9. The second harmonic braking based feeder terminal overcurrent protection method of claim 8, wherein, When the feeder terminal is in a warning state, an adaptive sampling update instruction is activated to perform adaptive enhancement management of the sampling frequency.

10. A feeder terminal overcurrent protection system based on second harmonic braking, characterized by, A system for implementing the feeder terminal overcurrent protection method based on second harmonic braking according to any one of claims 1-9, the system comprising: A time-frequency decomposition module: performing adaptive time-frequency decomposition on a current instantaneous sampling sequence collected by a feeder terminal to obtain a multi-scale frequency distribution map containing a fundamental wave component and each order harmonic; A feature extraction module: based on the multi-scale frequency distribution map, a second harmonic coherent energy function is constructed, the amplitude, frequency, and instantaneous phase of the second harmonic are extracted through the maximum coherent energy path to form a second harmonic feature vector; A noise filtering processing module: performing noise self-suppression filtering processing on the second harmonic feature vector to establish a processing result; The evolution analysis module: taking the processing result as input data, performing multi-dimensional gradient evolution analysis based on the amplitude change rate, the frequency drift gradient and the phase jump amount, and constructing a transient evolution trend of the second harmonic; The forward prediction module: performing forward prediction by using the transient evolution trend, and constructing a second harmonic prediction trajectory of N sampling periods; The overcurrent protection management module: performing mode comparison of a historical safe operation trajectory library according to the processing result and the second harmonic prediction trajectory of N sampling periods, establishing an overcurrent risk index, and performing overcurrent protection management according to the overcurrent risk index.

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