Overhead line fault diagnosis method, system and device based on space vector conversion and storage medium

Abstract

The invention discloses an overhead line fault diagnosis method, system and device based on space vector conversion and a storage medium, and relates to the technical field of intelligent power distribution, and the method comprises the steps: collecting the normal operation three-phase current data of an overhead power distribution line, and constructing a steady-state feature model; three-phase current data is collected in real time and converted into a space vector sequence, the Euclidean distance between a vector end point and a model center is calculated, and instantaneous offset is extracted. Integrating the offset in a sliding window, calculating a phase rotation rate, generating a multi-dimensional deviation tensor, inputting the multi-dimensional deviation tensor into a fusion function to obtain a misconstruction score, and normalizing the misconstruction score to obtain a misconstruction index; comparing the index with a threshold value, if the index exceeds the threshold value, determining that the disturbance is external transient disturbance, and adjusting the sampling frequency of the current sampling device; according to the method, interference can be effectively identified in a special electromagnetic environment, misjudgment of diagnosis is avoided, maloperation of a protection device is reduced, reclosing operation is guaranteed, and the fault diagnosis accuracy and robustness of a power distribution system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent power distribution, and in particular to an overhead line fault diagnosis method, system and device based on space vector conversion and a storage medium. BACKGROUND

[0002] In the field of intelligent power distribution, overhead line fault diagnosis is crucial. However, the operating environment of overhead lines is complex, and when the line is in a high-speed rail crossing area, a lightning-prone area, or near a high-power variable frequency device, etc. in a special electromagnetic environment, a series of problems affecting the accuracy of fault diagnosis will be encountered, and more effective solutions are urgently needed.

[0003] In the prior art, the overhead line fault diagnosis based on space vector conversion converts the fault signals such as multi-phase voltage and current into a three-dimensional space vector form, and diagnoses the fault by mathematically analyzing the dynamic characteristic changes in different coordinate systems. However, in a special electromagnetic environment, serious electromagnetic interference will be caused, the current signal will be abnormal, and the space vector will be misconfigured. Traditional filters are difficult to handle such interference, and are prone to misdiagnosis of the fault diagnosis system, triggering the misoperation of the protection device, and affecting the stability of the system and the safety of the equipment. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is to overcome the influence of electromagnetic interference in a special electromagnetic environment on overhead line fault diagnosis, to solve the problem that traditional filters are difficult to effectively handle interference and the fault diagnosis system is prone to misdiagnosis, and to improve the accuracy, robustness and anti-misoperation ability of fault diagnosis.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an overhead line fault diagnosis method based on space vector conversion, comprising:

[0008] Collecting three-phase current data of the overhead distribution line during normal operation to construct a steady-state characteristic model;

[0009] After converting the real-time collected three-phase current data into a space vector sequence, calculating the Euclidean distance between the endpoints of each vector and the center of the steady-state characteristic model, and extracting the space vector instantaneous deviation;

[0010] Based on the space vector instantaneous deviation, cumulative integration and phase rotation rate calculation are performed within a set sliding time window to generate a multi-dimensional deviation tensor containing amplitude and phase change information;

[0011] The multi-dimensional deviation tensor is input into a nonlinear fusion function, adaptive weights are given to the amplitude and phase components, and a spatial vector misconfiguration score is obtained; the misconfiguration score is normalized to obtain a spatial vector misconfiguration index;

[0012] The spatial vector misconfiguration index is compared with a set threshold value, and if the threshold value is exceeded, it is judged as an external transient disturbance, and the sampling frequency of the current sampling device is adjusted.

[0013] As a preferred scheme of the overhead line fault diagnosis method based on spatial vector conversion, wherein:

[0014] The three-phase current data of the overhead distribution line in normal operation is collected, and a steady-state feature model is constructed, including:

[0015] The three-phase current data of the overhead distribution line in a non-fault, non-disturbance state is collected; the collected three-phase current data is time-domain intercepted, and converted into spatial vectors in α, β coordinates through transformation to form a preliminary vector trajectory set; the geometric center coordinates and trajectory radius of the spatial vector trajectory are calculated based on the vector trajectory set to obtain a steady-state spatial vector model; the geometric center coordinates and trajectory radius are packaged as a reference vector matrix to constitute a steady-state feature model.

[0016] As a preferred scheme of the overhead line fault diagnosis method based on spatial vector conversion, wherein:

[0017] After the real-time collected three-phase current data is converted into a spatial vector sequence, the Euclidean distance between each vector endpoint and the center of the steady-state feature model is calculated, and the spatial vector instantaneous deviation is extracted, including:

[0018] The reference center point coordinates are extracted from the constructed steady-state feature model, the Euclidean distance between the spatial vector endpoint at each time and the center point is calculated; the instantaneous vector position is analyzed point by point using a sliding sampling window to obtain continuous deviation change information; the Euclidean distance is taken as the instantaneous deviation at the current time, and is recorded in the cache in time sequence.

[0019] As a preferred scheme of the overhead line fault diagnosis method based on spatial vector conversion, wherein:

[0020] Based on the spatial vector instantaneous deviation, the accumulation integral and phase rotation rate calculation are performed within a set sliding time window to generate a multi-dimensional deviation tensor containing amplitude and phase change information, including:

[0021] A fixed-length sliding time window is set, the window length is configured according to the system sampling frequency and the target recognition time delay, the spatial vector instantaneous offset at each time is sequentially stored in the window to form a time-ordered offset sequence data set, and a one-dimensional time feature vector is constructed; a cumulative integration operation is performed on the offset sequence; the phase change amount of the vector at each time in the sliding time window is calculated to derive the instantaneous angular velocity change, extract the dynamic feature, and construct another key dimension of the deviation tensor; and the offset cumulative integration sequence and the phase angular velocity change sequence are integrated into a two-dimensional deviation tensor.

[0022] The preferred technical scheme has the beneficial effects that: by performing the cumulative integration and phase rotation rate calculation in the sliding time window, the multi-dimensional deviation tensor is generated, the change information of the spatial vector in the amplitude and the phase can be comprehensively considered, the dynamic change of the line operation state can be more comprehensively reflected, and the accuracy of fault diagnosis is improved.

[0023] As an optimal scheme of the overhead line fault diagnosis method based on the spatial vector conversion, the optimal scheme has the beneficial effects that:

[0024] The multi-dimensional deviation tensor is input into the nonlinear fusion function, adaptive weights are given to the amplitude and phase components, the spatial vector misconfiguration score is obtained, the misconfiguration score is normalized to obtain the spatial vector misconfiguration index, and the spatial vector misconfiguration index includes:

[0025] Each row of the feature vector is read in time sequence according to the constructed deviation tensor to construct an input stream, the amplitude change feature and the phase rate change feature in the tensor are extracted, dynamic adjustment weight coefficients are given to the two features, the weighted features are formed, the weighted features are input into the nonlinear fusion function for combination calculation, the spatial vector misconfiguration score at the current time is output, and the spatial vector misconfiguration score and the time label are bound to form a continuous scoring sequence.

[0026] The preferred technical scheme has the beneficial effects that: by giving adaptive weights to the amplitude and phase components, and calculating the spatial vector misconfiguration score by using the nonlinear fusion function, the influence of the amplitude and phase changes on the line operation state can be more reasonably integrated, the accuracy of the misconfiguration score is improved, and more reliable basis is provided for subsequent judgment.

[0027] As an optimal scheme of the overhead line fault diagnosis method based on the spatial vector conversion, the optimal scheme has the beneficial effects that:

[0028] The multi-dimensional deviation tensor is input into the nonlinear fusion function, adaptive weights are given to the amplitude and phase components, the spatial vector misconfiguration score is obtained, the misconfiguration score is normalized to obtain the spatial vector misconfiguration index, and the spatial vector misconfiguration index includes:

[0029] A dynamic sampling window is established to record a space vector misconfiguration score sequence in the current period, and the maximum value and the minimum value in the space vector misconfiguration score sequence are extracted as normalization boundaries; the current misconfiguration score is linearly interpolated and compressed according to the maximum value and the minimum value, a normalized space vector misconfiguration index is generated, and the normalized space vector misconfiguration index is bound with a time stamp to form a time sequence.

[0030] As a preferred scheme of the overhead line fault diagnosis method based on space vector conversion, wherein:

[0031] The comparison of the space vector misconfiguration index with the set threshold value, if the threshold value is exceeded, the external transient disturbance is judged, and the sampling frequency of the current sampling device is adjusted, including:

[0032] Based on the space vector misconfiguration index and the preset reference threshold value, a disturbance response intensity factor is calculated by a nonlinear logarithmic mapping function; a frequency gain factor is calculated according to the disturbance response intensity factor; a new sampling frequency is calculated according to the frequency gain factor, and a control command is issued to the sampling device.

[0033] The beneficial effects of the preferred technical scheme are: by setting the threshold value mechanism, when the space vector misconfiguration index exceeds the threshold value, the sampling frequency of the current sampling device is dynamically adjusted, the real change trend of the current signal in the transient process can be tracked more finely, the influence of high-frequency pulse interference on space vector modeling is reduced, and more stable and real data basis is provided for subsequent fault location, strategy execution and protection control of the system.

[0034] In a second aspect, the present application provides an overhead line fault diagnosis system based on space vector conversion, comprising:

[0035] A steady-state model construction module is used to collect three-phase current data of the overhead distribution line in normal operation, and to construct a steady-state characteristic model.

[0036] An offset extraction module is used to convert the real-time collected three-phase current data into a space vector sequence, calculate the Euclidean distance between each vector endpoint and the center of the steady-state characteristic model, and extract the space vector instantaneous offset.

[0037] A deviation tensor generation module is used to generate a multi-dimensional deviation tensor containing amplitude and phase change information based on the space vector instantaneous offset, and to perform cumulative integration and phase rotation rate calculation within a set sliding time window.

[0038] A misconfiguration index calculation module is used to input the multi-dimensional deviation tensor into a nonlinear fusion function, give adaptive weights to the amplitude and phase components, obtain a space vector misconfiguration score, and perform normalization processing on the misconfiguration score to obtain a space vector misconfiguration index.

[0039] The disturbance judgment and adjustment module is used for comparing the space vector misconfiguration index with a set threshold value, and if the threshold value is exceeded, it is judged that an external transient disturbance occurs, and the sampling frequency of the current sampling device is adjusted.

[0040] In a third aspect, the present application provides a computer device, comprising:

[0041] a memory and a processor;

[0042] The memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, which realize the steps of the overhead line fault diagnosis method based on space vector conversion.

[0043] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, which realize the steps of the overhead line fault diagnosis method based on space vector conversion when executed by a processor.

[0044] The present application has the following beneficial effects: the present application constructs a steady-state space vector reference model, combines real-time offset and phase characteristics to construct a multi-dimensional tensor, generates a misconfiguration index through nonlinear fusion, and guides adaptive adjustment of the sampling frequency; in special electromagnetic environments such as high-speed rail crossing areas, lightning-prone areas, or near high-power variable frequency equipment, the present application can effectively identify high-frequency, non-periodic electromagnetic interference, avoid the problem of space vector misconfiguration caused by the fact that traditional filters cannot handle such interference, prevent the diagnostic system from misjudging interference as a fault, reduce the issuance of incorrect isolation control instructions of the protection device, avoid reclosing failure, protect the stability of circuit breakers, equipment insulation, and power distribution network structure, and improve the accuracy and robustness of fault diagnosis of the power distribution system in complex electromagnetic environments, and ensure stable operation of the overhead line. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0046] Figure 1 It is a whole flow chart of the overhead line fault diagnosis method based on space vector conversion provided by the present application. DETAILED DESCRIPTION

[0047] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0048] Embodiment 1, refer to Figure 1 For the first embodiment of the present application, the embodiment provides an overhead line fault diagnosis method based on space vector conversion, comprising:

[0049] S1: collecting three-phase current data of the overhead distribution line in normal operation, and constructing a steady-state feature model;

[0050] S2: after converting the real-time collected three-phase current data into a space vector sequence, calculating the Euclidean distance between each vector endpoint and the center of the steady-state feature model, and extracting the space vector instantaneous deviation;

[0051] S3: based on the space vector instantaneous deviation, performing cumulative integration and phase rotation rate calculation within a set sliding time window, and generating a multi-dimensional deviation tensor containing amplitude and phase change information;

[0052] S4: inputting the multi-dimensional deviation tensor into a nonlinear fusion function, giving adaptive weights to the amplitude and phase components, and obtaining a space vector misconfiguration score; normalizing the misconfiguration score to obtain a space vector misconfiguration index;

[0053] S5: comparing the space vector misconfiguration index with a set threshold value, if the threshold value is exceeded, judging as external transient disturbance, and adjusting the sampling frequency of the current sampling device.

[0054] It should be noted that through steps S1-S5, starting from constructing a steady-state feature model, key features are gradually extracted, misconfiguration indexes are calculated, and finally effective judgment of external transient disturbance and adaptive adjustment of sampling frequency are realized, which can significantly improve the accuracy of overhead line fault diagnosis and the anti-interference ability of the system in special electromagnetic environment, and ensure the stable and reliable operation of the power distribution system.

[0055] Embodiment 2, refer to Figure 1 For an embodiment of the present application, based on the previous embodiment, an overhead line fault diagnosis method based on space vector conversion is provided, comprising:

[0056] In the present embodiment, the step S1 of collecting three-phase current data of the overhead distribution line in normal operation and constructing a steady-state feature model comprises:

[0057] The three-phase current data of the overhead distribution line in a non-fault and non-disturbance state is collected, time domain interception is performed, and the Clarke transformation is used to convert the spatial vector in the α, β coordinates;

[0058] In another possible implementation, the three-phase current data of the overhead distribution line in a non-fault and non-disturbance state can also be collected multiple times within a specific time period. For example, the three-phase current data is collected at the same time period for multiple consecutive days during the daily low power consumption period when the line load is relatively stable and less likely to be disturbed, and then the collected data is screened and preprocessed to remove possible outliers, thereby obtaining more accurate three-phase current data in a non-fault and non-disturbance state.

[0059] In another possible implementation, the three-phase current data in a non-fault and non-disturbance state can also be combined with online monitoring and historical data. Real-time three-phase current data is collected by using online monitoring equipment, and the online collected data is compared and verified with reference to historical data of a known non-fault and non-disturbance time period. If the online collected data is consistent with the historical data within a certain error range, it is considered that the data collected is in a normal state, thereby improving the accuracy of data collection.

[0060] The geometric center coordinates and the trajectory radius of the spatial vector trajectory in multiple cycles are calculated to obtain a steady-state spatial vector model;

[0061] The vector trajectory is subjected to a moving average process to remove abnormal points and improve the stability of the model;

[0062] The geometric center coordinates and the trajectory radius are encapsulated as a reference vector matrix.

[0063] Specifically, stable operation data of the overhead distribution line in a non-fault and non-disturbance state is collected, and complete three-phase current sampling data in a continuous cycle is extracted as an original input signal. The three-phase current signals are marked as I a , I b , and I c , and the sampling frequency needs to meet the requirements of signal complete reconstruction. In this embodiment, a high-precision data point of not less than 128 points per phase per cycle is preferably set to ensure that the details of the current waveform are fully captured. The sampling process avoids disturbance periods such as power grid fluctuations, load switching, and switch operation, to ensure the purity and representativeness of the data and ensure that the reference model reflects the true steady-state working condition.

[0064] The collected three-phase current sampling data is subjected to time domain interception according to a fixed time step, and the Clarke transformation is used to convert the three-phase current into a spatial vector expression form in the α, β coordinates. Further, the specific transformation formula is as follows:

[0065] Let the transformation matrix T be a regular Clarke transformation matrix, input the original three-phase current into the T matrix, and output I α and I β Two components, constituting a current space vector on a two-dimensional plane. Under ideal steady-state symmetric operating conditions, the space vector makes a circular motion on the αβ plane, forming a closed stable trajectory path. To ensure the accuracy of the reference trajectory modeling, the transformed results of at least multiple complete power frequency cycles should be intercepted and arranged in time sequence to form a preliminary vector trajectory set.

[0066] Based on the above-obtained stable space vector trajectory set, the quantitative calculation of the geometric center and the trajectory radius is carried out. The geometric center of the space vector is obtained by calculating the arithmetic mean of all αβ vector endpoint coordinates, denoted as the vector set center point (X c ,Y c ); the trajectory radius is calculated by the Euclidean distance from all vector endpoints to the center point, and then the mean value fitting and standard deviation analysis are carried out on the distance set, and finally the average radius is taken as the nominal radius value of the steady-state vector trajectory.

[0067] It should be noted that, in order to enhance the stability and robustness of the model, extreme fluctuation points can be removed during the calculation process, and the sliding average technique can be used to smooth the calculation results. Through this step, a mathematical geometric model representing the current behavior pattern of the line under normal operating conditions can be effectively obtained.

[0068] The obtained geometric center coordinates and trajectory radius value are combined to form a steady-state characteristic model, and are packaged as the core parameters of the reference vector matrix for subsequent use in the abnormal detection process.

[0069] It should be noted that the steady-state characteristic model can be used for deviation comparison analysis between real-time space vector and reference trajectory, so as to identify non-steady-state disturbances in the running process. In order to improve the adaptability of the model, a model updating strategy can be set, for example, periodically refreshing the characteristic model under the condition of no fault disturbance, so as to reflect the fine adjustment of the steady-state trajectory caused by seasonal load variation or network structure adjustment. The characteristic model as the basis of the entire space vector misconfiguration diagnosis process provides a stable reference standard and reference baseline for subsequent instantaneous deviation extraction, multi-dimensional tensor construction and misconfiguration index output.

[0070] In another possible implementation, constructing a steady-state characteristic model can also perform frequency domain analysis on the collected three-phase current data, convert the time domain data to the frequency domain using Fourier transform, extract the characteristic parameters of the main frequency components such as amplitude and phase, and then construct a steady-state characteristic model based on these frequency domain features. For example, analyze the main frequency components of the three-phase current under normal operation and their corresponding amplitude and phase ranges, combine these parameters into a feature vector set, and use it as the steady-state characteristic model.

[0071] In another possible implementation, the construction of the steady-state feature model can also employ a clustering algorithm in machine learning: after converting the collected three-phase current data into a suitable feature vector, the data is divided into different clusters using a clustering algorithm (such as K-means clustering), a cluster representing a normal operating state is selected, the center vector of the cluster and the distribution range of the cluster are calculated, and the information is encapsulated as the steady-state feature model.

[0072] In the above step S2, after converting the real-time collected three-phase current data into a spatial vector sequence, the Euclidean distance between each vector endpoint and the center of the steady-state feature model is calculated, and the spatial vector instantaneous deviation is extracted, including:

[0073] Collecting three-phase current data under the current operating state and synchronously inputting the buffer structure with high time resolution;

[0074] Performing synchronous spatial vector transformation on the three-phase current data to generate a spatial vector sequence in a two-dimensional coordinate system;

[0075] Calculating the Euclidean distance between the endpoint of each spatial vector and the center of the steady-state feature model as the deviation;

[0076] Taking the Euclidean distance as the instantaneous deviation at the current time and recording it in the cache in chronological order.

[0077] Specifically, three-phase current data of the overhead distribution line under the current operating state is collected, and the sampling process requires real-time and high time resolution. It is recommended that the sampling rate be no less than 128 points per cycle to ensure effective capture of small disturbances in the current waveform. The sampled data should be strictly synchronized to ensure that the three-phase currents are compared and processed under the same time reference, avoiding phase distortion caused by sampling delay. The original three-phase current signal obtained in this step is temporarily stored in the buffer structure for subsequent spatial vector transformation. In order to ensure the effectiveness of the data, the sampling process should be carried out as much as possible under the condition of no transient events such as mechanical operation, power grid switching or lightning disturbance, to ensure that the sampled data reflects the current real operating state.

[0078] Synchronous space vector transformation is performed on the collected three-phase current data to map the time-domain current signals to a two-dimensional coordinate system. Through the transformation, the three-phase current is converted into a vector form that can represent its relative relationship on a plane. Each space vector at a time point represents the spatial composition of the three-phase current at that time point, with both direction and amplitude attributes. In the implementation process, to enhance the synchronization and response efficiency, the transformation process is performed in parallel on a pipeline that samples and processes simultaneously, thereby ensuring that each set of current data can be quickly converted into a space vector and participate in the judgment after sampling. The endpoints of all converted space vectors form a real-time updated vector trajectory sequence in the two-dimensional space, which can be regarded as the spatial mapping state of the current operating characteristics.

[0079] For the space vector generated at each time point, the reference center point coordinates are extracted from the constructed steady-state feature model as the fixed reference benchmark for the deviation measurement. The distance between the endpoint of each space vector and the center point is taken as the geometric measure of the current deviation from the steady-state trajectory. The distance here does not depend on any specific direction or component, but directly measures whether the vector endpoint deviates from the preset stable trajectory.

[0080] It should be noted that the deviation amount can effectively reflect the non-physical drift of the current vector under the action of external interference, such as sudden change in direction and dramatic change in amplitude. Considering that the space vector trajectory may present irregular jumps due to non-steady-state interference, the system analyzes each instantaneous vector position point by point using a sliding sampling window to obtain time-continuous deviation change information, thereby enhancing the sensitivity and continuity of transient state recognition.

[0081] The distance between each vector point and the center of the steady-state model is recorded as the instantaneous deviation at the current time point and stored in the data buffer according to the timestamp sequence, providing basic data support for subsequent cumulative integration, multi-dimensional feature extraction, and dynamic discrimination.

[0082] It should be noted that the extraction process of the instantaneous deviation has a very high time accuracy and can reflect the spatial response performance of subtle disturbances in the current signal in real time, which is a key pre-indicator for judging whether a misconfiguration occurs. To enhance the accuracy of the data, the system can set a dynamic threshold mechanism to automatically exclude low-amplitude or occasional random error points, avoiding their misjudgment as abnormal events in the subsequent process.

[0083] In another possible implementation, the extraction of the space vector instantaneous deviation can also calculate the tangential and normal deviation components of the space vector endpoint relative to the center of the steady-state feature model. First, the direction of motion of the space vector at a time point is determined, and then the line connecting the vector endpoint and the center point is decomposed into two components along the motion direction (tangential) and perpendicular to the motion direction (normal), and the sizes of the two components are calculated as two-dimensional information of the instantaneous deviation, which can more meticulously describe the deviation of the space vector.

[0084] In another possible implementation, the extraction of the space vector instantaneous deviation can also adopt the method of weighted Euclidean distance. According to the reliability or importance of the three-phase current data at different times, different weights are given to the space vector endpoints at each time, and these weight factors are considered when calculating the Euclidean distance, so as to obtain the weighted Euclidean distance as the instantaneous deviation, so as to more reasonably reflect the deviation degree of the space vector.

[0085] In the embodiment, the step S3 of generating the multi-dimensional deviation tensor containing the amplitude and phase change information based on the space vector instantaneous deviation in the set sliding time window includes:

[0086] A sliding time window of a fixed length is set, and the space vector instantaneous deviations at consecutive times are recorded to construct a time sequence.

[0087] Cumulative integration is performed on the time sequence to extract the aggregation feature of the deviation intensity in the time dimension.

[0088] The space vector phase angle velocity at the corresponding time point is calculated to extract the phase rotation dynamic feature.

[0089] The deviation integral sequence and the phase rate sequence are combined to generate a two-dimensional structure of the deviation tensor.

[0090] Specifically, a sliding time window of a fixed length is set, and the window length is configured according to the system sampling frequency and the target recognition time delay. In the embodiment, the preferred value range is 2 to 5 times of the total amount of data of the power frequency period, so as to maintain the response sensitivity while taking into account the integrity of feature accumulation. In the time window, the space vector instantaneous deviation calculated at each time is sequentially stored by the system to form a time-ordered deviation sequence data set. The sequence reflects the evolution trajectory of the space vector deviation trend in the selected time interval, and is used to reveal whether the disturbance behavior has the time persistence, accumulation and directionality, and is particularly suitable for capturing the residual effect of the non-periodic short-time interference at the vector level. The entire deviation sequence takes the time axis as the reference, and constitutes a one-dimensional time feature vector, which becomes the first dimension of the multi-dimensional tensor construction.

[0091] Cumulative integration is performed on the deviation sequence recorded in the above sliding window to capture the aggregation trend of the disturbance energy in the time dimension.

[0092] It should be noted that by time integrating the offset at each time, not only the expression ability of weak disturbance can be enhanced, but also the influence of incidental fluctuation and system noise can be effectively filtered out, and the dominant direction and overall offset trend of the interference signal are highlighted. The integral result represents the overall offset degree of the space vector relative to the steady-state model within the set time interval, which is an important indicator for judging whether the disturbance is persistent or not. In order to improve the stability of the data, the system can also introduce a recursive sliding algorithm for smoothing processing to avoid feature distortion caused by short-term peak fluctuations.

[0093] In another possible implementation, the time-ordered offset sequence dataset can also be formed by interpolation. When the sampling interval of the instantaneous offset of the space vector is not uniform, the offset data is processed by an interpolation algorithm (such as linear interpolation, spline interpolation, etc.) to fill it up as a data sequence with uniform time interval, thereby forming a time-ordered offset sequence dataset. For example, for offset data with different sampling intervals, linear interpolation is used to insert appropriate values between adjacent two sampling points, so that the data sequence has uniform time interval.

[0094] In another possible implementation, the time-ordered offset sequence dataset can also be grouped and sorted according to the characteristics of the data. First, the instantaneous offset data of the space vector is extracted, such as calculating the change rate and fluctuation amplitude of each offset, and then the data is divided into different groups according to these characteristics, and then sorted in time order within each group, and finally all the data of the groups are connected in time order to form a time-ordered offset sequence dataset. This way can better handle complex data situations.

[0095] In order to further extract the dynamic behavior of the space vector in the phase direction, the phase change of the vector at each time in the sliding time window is calculated, and the corresponding instantaneous angular velocity change is derived.

[0096] Further, by calculating the angular increment change of the vector between two consecutive time points, the phase rotation speed at that time can be obtained in combination with the time difference, and then the fluctuation trend, turning frequency and abnormal acceleration and other dynamic characteristics can be extracted from the angular velocity change sequence of all vectors in the whole window. Such information can accurately depict the rotation behavior of the space vector under disturbed conditions, and further identify nonlinear phase abnormal phenomena caused by interference sources such as variable frequency equipment, electromagnetic induction or transient impact. The phase dynamic sequence as another key dimension and the aforementioned offset intensity sequence together constitute the core feature dimension of the deviation tensor.

[0097] The offset accumulation integral sequence and the phase angle velocity change sequence are combined to form a two-dimensional deviation tensor with clear structure and time sequence characteristics. Each row of the tensor corresponds to a time point, and the column vector is composed of the spatial offset integral value at the current time and the instantaneous phase rate, which represents the amplitude disturbance and phase change of the system at each micro time period. The deviation tensor not only has a high information density, but is also naturally suitable for subsequent nonlinear fusion function processing, and can maintain the stability and sensitivity of feature recognition in a complex disturbance environment. In order to adapt to different application scenarios, the structure of the deviation tensor can be flexibly expanded, such as adding high-order statistics, time derivatives or frequency domain features, to enhance the adaptability of the model to multi-source disturbance signals. The tensor, as an intermediate calculation result in the fault diagnosis algorithm of the application, constitutes a key bridge connecting the original current data and the final misconfiguration score, and provides a high-dimensional and quantifiable input basis for subsequent decision-making.

[0098] In the embodiment, the multi-dimensional deviation tensor is input into the nonlinear fusion function in step S4, adaptive weights are assigned to the amplitude and phase components, and a spatial vector misconfiguration score is obtained; the misconfiguration score is normalized to obtain a spatial vector misconfiguration index, which includes:

[0099] The amplitude change and the phase rate change are respectively assigned adaptive weights to form weighted features;

[0100] The weighted features are input into the nonlinear fusion function, and a spatial vector misconfiguration score at the current time is output;

[0101] The misconfiguration score is bound with the time label to form a continuous score sequence for subsequent control judgment.

[0102] A dynamic sampling window is established to record the spatial vector misconfiguration score sequence in the current period;

[0103] The maximum and minimum values in the spatial vector misconfiguration score sequence are extracted as the normalization boundary;

[0104] The maximum and minimum values are used to perform linear interpolation compression on the current misconfiguration score to generate a normalized spatial vector misconfiguration index;

[0105] The spatial vector misconfiguration index is bound with the time stamp to form a time sequence.

[0106] Specifically, each data unit in the constructed deviation tensor corresponds to the spatial vector offset integral value at a certain time point and the corresponding phase angle velocity change value, forming a two-dimensional structure matrix. According to the time sequence, each row of feature vectors in the tensor is read to construct an input stream based on time as the sequence. This process ensures the continuity of information input and the time semantics are not destroyed, which is the basic premise of fusion function processing. At the same time, in order to prevent extreme outliers in the tensor from causing deviation to the final fusion result, the original tensor data is normalized or segmented scaled in this stage, and the dimension of the feature is unified, which establishes a consistent data basis for subsequent weight allocation and fusion calculation.

[0107] The amplitude change feature representing the degree of spatial vector offset and the phase rate change feature describing the intensity of phase disturbance are extracted respectively, and according to the current system operating condition, adaptive rule or historical experience weight model, the two key features are respectively given dynamically adjustable weight coefficients. The weight allocation is not fixed proportion setting, but is calculated in combination with the reliability index of the current operating state, disturbance persistence, frequency characteristics and device importance. For example, in the case of frequent phase fluctuations and small amplitude changes, the system will automatically enhance the weight of the phase feature to improve the sensitivity to phase abnormal dominant disturbance. Conversely, when the amplitude changes suddenly but the phase changes smoothly, the weight proportion of the amplitude feature is automatically increased. The adaptive weighting mechanism can realize the response optimization of different disturbance types through online adjustment, and improve the adaptability and discrimination ability of the fusion process.

[0108] The two feature values subjected to the above weighting process are input into a nonlinear fusion function for combination calculation. The nonlinear fusion function used can be a function model with high-order excitation structure, such as hyperbolic tangent function, exponential mapping function or trained and optimized neural network fusion, etc. The purpose of the function model is to enhance the response ability of feature combination to disturbance behavior through nonlinear mapping, especially in the case of uneven disturbance degree and nonlinear feature variation, which can effectively suppress the misjudgment deviation caused by single feature dominance.

[0109] Further, the fusion function generates a fusion output value, i.e. a spatial vector misconstruction score, according to the relative position, gradient change rate and historical evolution trend of the input feature value. The score is a numerical representation of the current system disturbance state, and its size is positively correlated with the severity of the spatial vector trajectory deviation from the steady-state model.

[0110] The spatial vector misconstruction score calculated at each time is cached as a single output indicator and bound with a time tag to form a continuous misconstruction score sequence for subsequent threshold judgment and dynamic control logic invocation.

[0111] It should be noted that the misconfiguration score, as a feature expression form with clear structure, single numerical value and low calculation cost, not only greatly reduces the calculation complexity of multi-dimensional tensor in the subsequent judgment process, but also enables the system to perform real-time monitoring and dynamic discrimination at a higher frequency.

[0112] Subsequently, minimum and maximum normalization processing is performed on the space vector misconfiguration score: a dynamic sampling window of the space vector misconfiguration score is established, and the misconfiguration score sequence output in the current operating period is recorded continuously to form a raw score dataset with a time identifier. The score sequence is derived from the real-time score value output by the previous fusion function and represents the misconfiguration degree of the current system space vector state at different time points. The length of the sliding sampling window needs to be determined in combination with the system response time delay, disturbance duration and control period, and it is recommended to cover 5 to 10 power frequency periods to ensure that the upper and lower bounds of the score with statistical representativeness can be extracted.

[0113] It should be noted that this window mechanism ensures that the intensity of short-term disturbances can be reflected in the normalization process, and the normalization ratio will not be imbalanced due to extreme single-point values.

[0114] The current misconfiguration score sequence is analyzed in real time in the sliding window, and the minimum score value and the maximum score value in the time interval are extracted as the upper and lower boundaries of the current normalization processing. The acquisition of the boundary should exclude error points and accidental extreme value interference to improve the stability of the normalization result. To enhance the adaptability of the system to sudden disturbances, a dynamic boundary adjustment mechanism can also be introduced: in the case of strong disturbance persistence or high score change rate, the maximum and minimum values are dynamically corrected by weighted historical average score or statistical filter, so as to avoid distortion of the normalization result caused by single severe fluctuation. The boundary extraction process provides a standard scale basis for subsequent compression mapping.

[0115] The space vector misconfiguration score value calculated at the current time is normalized and compressed according to the extracted maximum and minimum boundaries, i.e. the original score is compressed to the closed interval [0, 1] by linear interpolation. The mapping process preserves the relative position and trend information of the original score, while eliminating the difference between absolute values, so that the misconfiguration risk level from different operating states or different line nodes can be uniformly represented. The space vector misconfiguration index, as the normalized output result, has high universality and high recognition, and can be directly compared with the set risk threshold to determine whether the intervention response condition is met.

[0116] The normalized space vector misconfiguration index is bound with the corresponding timestamp to form a continuous time sequence of standardized misconfiguration evaluation results; in order to improve the stability and control accuracy of the misconfiguration index, an exponentially weighted moving average or wavelet filtering method can also be used to release the sudden index fluctuation, to prevent unnecessary misjudgment or control instructions triggered by a single mutation point. The misconfiguration index, as a standardized quantitative index for space vector disturbance judgment, can directly participate in key decision-making processes such as fault identification, reclosing control, and adaptive protection device configuration, to achieve real-time, accurate, and robust responses to space vector abnormal behavior in complex electromagnetic disturbance environments.

[0117] In the above step S5, the space vector misconfiguration index is compared with the set threshold value, and if it exceeds the threshold value, it is judged as an external transient disturbance, and the sampling frequency of the current sampling device is adjusted, including:

[0118] The space vector misconfiguration index is compared with the set threshold value, and if it exceeds the threshold value, it is judged that the current three-phase current data has been affected by external transient electromagnetic disturbance, and the sampling frequency of the current current sampling device is immediately adjusted to high frequency state, and the current three-phase current data is sampled by increasing the time resolution to compensate for the abnormal change of space vector caused by transient disturbance in fine granularity.

[0119] It should be noted that the role of this step is to realize intelligent identification and dynamic response control of space vector abnormal behavior to deal with the non-physical data abnormality problem that may occur on overhead distribution lines under external transient electromagnetic disturbance. By continuously monitoring the normalized space vector misconfiguration index, the system can real-time perceive whether the running state of the space vector deviates from its steady-state reference trajectory. When the misconfiguration index exceeds the preset threshold value, it indicates that the current three-phase current data has been affected by short-time non-steady-state interference possibly caused by lightning, high-frequency current conversion equipment, electromagnetic induction, etc. If not handled, such interference can easily lead to misconfiguration of the space vector model, which is then misidentified as a short circuit or ground fault by the diagnosis system, triggering misoperation, mis-isolation and other serious consequences. Therefore, this step sets a threshold mechanism to immediately adjust the working mode of the current sampling device after triggering, switching the sampling frequency from the normal state to the high frequency state, making the sampling period shorter and the time resolution higher to track the real change trend of the current signal in the transient process in fine granularity. This can significantly reduce the impact of high-frequency pulse interference on space vector modeling and suppress the non-linear distortion caused by data mutation, thereby achieving rapid suppression of electromagnetic disturbance and precise control of misconfiguration risk, providing a more stable and real data basis for subsequent fault location, strategy execution and protection control of the system.

[0120] Specifically, based on the space vector misconfiguration index and the preset space vector misconfiguration index reference threshold, a disturbance response intensity factor is calculated through a nonlinear logarithmic mapping function, which is used to measure the response intensity of the current system deviating from the steady-state model, and the calculation expression is as follows:

[0121]

[0122] In the formula, is the disturbance response intensity factor, is the final calculated disturbance response coefficient, is a dimensionless positive real number, and dynamically maps the disturbance severity of the current system at time point t; is the space vector misconfiguration index, is the space vector misconfiguration index calculated at time point t, is a normalized value after normalization, which is derived from the space vector misconfiguration score obtained by fusing the amplitude change and the phase rate change, and the value range is [0, 1], the closer to 1, the more serious the space vector formed by the current deviates from the steady-state model trajectory; the closer to 0, the higher the consistency of the space vector with the normal trajectory, and the system runs stably; is the space vector misconfiguration index reference threshold, which is a preset reference threshold of the space vector misconfiguration index, used to represent whether the misconfiguration degree reaches the critical point of the system sensitive response, used to determine the amplification rate and sensitivity interval of the function, the smaller, the same SVMI will produce greater , so that the system is more sensitive to small disturbances, otherwise, if tends to be large, the system is more tolerant to slight disturbances and only responds when the misconfiguration index increases significantly; is a nonlinear logarithmic mapping function.

[0123] It should be noted that the core role of the nonlinear logarithmic mapping function is to convert the space vector misconfiguration index (SVMI) into a disturbance response intensity factor with more engineering significance and controllable response, in order to drive the subsequent dynamic adjustment of the sampling frequency. Since the space vector misconfiguration index is a normalized value, although it can reflect the deviation degree, its numerical distribution under different disturbance levels is relatively dense, and if it is directly linearly mapped, it may lead to overreaction of the system to slight disturbances and insufficient adjustment to severe disturbances. By introducing a logarithmic function with a dynamic base for nonlinear mapping, when the disturbance is small, the output response grows rapidly, improving the sensitivity of the system to weak misconfiguration signs; when the disturbance tends to be large, the output tends to be flat, suppressing the response overshoot and ensuring the stability of the system adjustment. The nonlinear logarithmic mapping function not only realizes the response curve of "sensitive start-stable enhancement", but also enhances the adaptive ability of the function by introducing an adjustable base parameter (related to the misconfiguration threshold), which is the key mathematical basis for realizing the high robustness, high resolution and gradual control mechanism of the present application.

[0124] According to the acquired disturbance response intensity factor , the frequency gain factor at the current time point is calculated, and the calculation expression is as follows:

[0125]

[0126] In the formula, is the frequency gain factor, is a dimensionless proportion factor, used to determine the multiple of the current basic sampling frequency that should be increased, the value range is: , used to represent the conversion amplitude of the disturbance response intensity on the sampling frequency, the stronger the disturbance, the larger the value, indicating that the sampling frequency needs to be increased by a larger amplitude; is the maximum sampling frequency, which is the maximum sampling frequency allowed by the system hardware and communication bandwidth, is the basic sampling frequency, which is the default sampling frequency of the system under the condition of no disturbance or low disturbance, is the natural base.

[0127] According to the frequency gain factor , the new sampling frequency is calculated and the control command is issued to the sampling device to perform the sampling frequency adjustment operation, and the calculation expression is as follows:

[0128]

[0129] In the formula, is the updated sampling frequency, indicating the final current sampling frequency command of the system at the current time, is the real sampling frequency actually executed by the current sampling device; the control signal granularity, that is, the current signal is divided into how many sampling points; the higher indicates that the system is more sensitive to disturbance, the sampling is more intensive, and the modeling is more fine.

[0130] At this frequency, the sampling device will acquire three-phase current data with higher time resolution, so that the current space vector construction is more fine-grained and dynamic changes are more visible, thereby effectively tracking the nonlinear changes caused by external disturbance and reducing the problem of false construction due to data jump.

[0131] It should be noted that the role of this step is to realize the closed-loop intervention from "false construction index determination" to "high-frequency sampling response" by dynamically controlling the sampling rate, and improve the robustness and stability of the space vector model in the interference environment.

[0132] Embodiment 3, the above is a schematic scheme of the overhead line fault diagnosis method based on space vector conversion of the present embodiment. It should be noted that the technical scheme of the overhead line fault diagnosis system based on space vector conversion belongs to the same concept as the technical scheme of the overhead line fault diagnosis method based on space vector conversion, and the details of the technical scheme of the overhead line fault diagnosis system based on space vector conversion in the present embodiment are not described in detail, which can be referred to the description of the technical scheme of the overhead line fault diagnosis method based on space vector conversion.

[0133] The present embodiment also provides an overhead line fault diagnosis system based on space vector conversion, comprising:

[0134] A steady-state model construction module is configured to collect three-phase current data when the overhead distribution line is in normal operation, and construct a steady-state characteristic model.

[0135] An offset extraction module is configured to convert the real-time collected three-phase current data into a space vector sequence, calculate the Euclidean distance between each vector endpoint and the center of the steady-state characteristic model, and extract a space vector instantaneous offset.

[0136] A deviation tensor generation module is configured to, based on the space vector instantaneous offset, perform cumulative integration and phase rotation rate calculation within a set sliding time window, and generate a multi-dimensional deviation tensor containing amplitude and phase change information.

[0137] A misconfiguration index calculation module is configured to input the multi-dimensional deviation tensor into a nonlinear fusion function, assign adaptive weights to the amplitude and phase components, and obtain a space vector misconfiguration score; and perform normalization processing on the misconfiguration score to obtain a space vector misconfiguration index.

[0138] A disturbance judgment and adjustment module is configured to compare the space vector misconfiguration index with a set threshold value, and if the threshold value is exceeded, it is judged as an external transient disturbance, and the sampling frequency of the current sampling device is adjusted.

[0139] The present embodiment also provides an electronic device suitable for the case of the overhead line fault diagnosis method based on space vector conversion, comprising:

[0140] A memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the overhead line fault diagnosis method based on space vector conversion as proposed in the above embodiment.

[0141] The present embodiment also provides a storage medium having a computer program stored thereon, which is executed by a processor to implement the overhead line fault diagnosis method based on space vector conversion as proposed in the above embodiment.

[0142] The storage medium provided in the embodiment belongs to the same inventive concept as the overhead line fault diagnosis method based on space vector conversion provided in the above embodiment, and the technical details not described in the embodiment can be seen from the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for overhead line fault diagnosis based on space vector transformation, characterized in that, The method comprises the following steps: Collecting three-phase current data of overhead distribution lines in normal operation to construct a steady-state characteristic model; Converting the real-time collected three-phase current data into a spatial vector sequence, calculating the Euclidean distance between the end point of each vector and the center of the steady-state characteristic model, and extracting the spatial vector instantaneous deviation; Based on the spatial vector instantaneous deviation, cumulative integration and phase rotation rate calculation are performed within a set sliding time window to generate a multi-dimensional deviation tensor containing amplitude and phase change information; Inputting the multi-dimensional deviation tensor into a nonlinear fusion function, giving adaptive weights to the amplitude and phase components, and obtaining a spatial vector misconfiguration score; Normalizing the misconfiguration score to obtain a spatial vector misconfiguration index; Comparing the spatial vector misconfiguration index with the set threshold value, if it exceeds the threshold value, it is judged as external transient disturbance, and the sampling frequency of the current sampling device is adjusted.

2. A space vector transformation based overhead line fault diagnostic method as claimed in claim 1, wherein, The method comprises the following steps: Collecting three-phase current data of overhead distribution lines in non-fault, non-disturbance state; performing time domain interception on the collected three-phase current data, and converting it into spatial vectors in α, β coordinates through transformation to form a preliminary vector trajectory set; calculating the geometric center coordinates and trajectory radius of the spatial vector trajectory based on the vector trajectory set to obtain a steady-state spatial vector model; and encapsulating the geometric center coordinates and trajectory radius into a reference vector matrix to constitute a steady-state characteristic model.

3. A space vector transformation based overhead line fault diagnostic method as claimed in claim 2, wherein, The method comprises the following steps: Extracting the reference center point coordinates from the constructed steady-state characteristic model, calculating the Euclidean distance between the end point of each spatial vector and the center point at each time; using a sliding sampling window to analyze the instantaneous vector position point by point, and obtaining continuous deviation change information; taking the Euclidean distance as the instantaneous deviation at the current time, and recording it in the cache in time sequence.

4. A space vector transformation based overhead line fault diagnostic method as claimed in claim 3, wherein, The method comprises the following steps: Setting a sliding time window of fixed length, configuring the window length according to the system sampling frequency and the target recognition time delay, sequentially storing the spatial vector instantaneous deviation at each time in the window to form a time-ordered deviation sequence data set, and constituting a one-dimensional time feature vector; performing cumulative integration operation on the deviation sequence; calculating the phase change amount of each time vector within the sliding time window, deriving the instantaneous angular velocity change, extracting the dynamic characteristics, and constituting another key dimension of the deviation tensor; integrating the cumulative integration sequence of the deviation and the phase angular velocity change sequence into a two-dimensional deviation tensor.

5. A space vector transformation based overhead line fault diagnostic method as claimed in claim 4, wherein, The method comprises the following steps: Inputting the multi-dimensional deviation tensor into a nonlinear fusion function, giving adaptive weights to the amplitude and phase components, and obtaining a spatial vector misconfiguration score; The method comprises the following steps: Normalizing the misconfiguration score to obtain a spatial vector misconfiguration index. The constructed deviation tensor is read in time sequence to construct an input stream of each row of feature vectors, and features of amplitude variation and phase rate variation in the tensor are extracted respectively, and dynamic adjustment weight coefficients are given to the features to form weighted features; the weighted features are input into a nonlinear fusion function for combined calculation, and a spatial vector misconstruction score at a current time is output, and the spatial vector misconstruction score is bound with a time label to form a continuous score sequence.

6. A space vector transformation based overhead line fault diagnostic method as claimed in claim 5, wherein, The multi-dimensional deviation tensor is input into a nonlinear fusion function, adaptive weights are given to amplitude and phase components, and a spatial vector misconstruction score is obtained; The misconstruction score is normalized to obtain a spatial vector misconstruction index, and the method further includes: A dynamic sampling window is established to record the spatial vector misconstruction score sequence in a current period, the maximum value and the minimum value in the spatial vector misconstruction score sequence are extracted as normalization boundaries, the current misconstruction score is linearly interpolated and compressed according to the maximum value and the minimum value, a normalized spatial vector misconstruction index is generated, and the normalized spatial vector misconstruction index is bound with a time stamp to form a time sequence.

7. A space vector transformation based overhead line fault diagnostic method as claimed in claim 6, wherein, The spatial vector misconstruction index is compared with a set threshold value, and if the threshold value is exceeded, it is judged as an external transient disturbance, and the sampling frequency of the current sampling device is adjusted, and the method further includes: A disturbance response intensity factor is calculated by a nonlinear logarithmic mapping function based on the spatial vector misconstruction index and a preset reference threshold value, a frequency gain factor is calculated according to the disturbance response intensity factor, a new sampling frequency is calculated according to the frequency gain factor, and a control command is issued to the sampling device.

8. An overhead line fault diagnostic system based on space vector transformation, applying the method according to any one of claims 1 to 7, characterized in that, The method includes: A steady-state model construction module is configured to collect three-phase current data when an overhead distribution line is in normal operation, and construct a steady-state feature model; An offset extraction module is configured to convert real-time collected three-phase current data into a spatial vector sequence, calculate the Euclidean distance between each vector endpoint and the center of the steady-state feature model, and extract spatial vector instantaneous offset; A deviation tensor generation module is configured to generate a multi-dimensional deviation tensor containing amplitude and phase variation information based on the spatial vector instantaneous offset, and perform cumulative integration and phase rotation rate calculation within a set sliding time window; A misconstruction index calculation module is configured to input the multi-dimensional deviation tensor into a nonlinear fusion function, give adaptive weights to amplitude and phase components, and obtain a spatial vector misconstruction score. The misconstruction score is normalized to obtain a spatial vector misconstruction index. A disturbance judgment and adjustment module is configured to compare the spatial vector misconstruction index with a set threshold value, and if the threshold value is exceeded, it is judged as an external transient disturbance, and the sampling frequency of the current sampling device is adjusted.

9. An electronic device, comprising: The method includes: a memory and a processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer executable instructions, when executed by the processor, implement the steps of the method of any one of claims 1 to 7.

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