Power line control method, device, equipment, medium and product

Abstract

The embodiment of the invention provides a power line control method and device, equipment, a medium and a product. The method comprises the following steps: acquiring a target traveling wave of a power line, and decomposing the target traveling wave by using SGMD to obtain a target sympathetic geometric component of the target traveling wave; and then, inputting the target sympathetic geometric component of the target traveling wave into a fault type determination model, and obtaining the fault type of the power line output by the fault type determination model, so as to control the power line according to the fault type of the power line. Wherein the parameters in the SGMD are optimized in advance through PSO, and the target symplectic geometric component is the symplectic geometric component, with fault feature information, in the symplectic geometric components of the target traveling wave. The method is used for achieving the effect of improving the accuracy of circuit control, and can effectively protect a power line.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and particularly relates to a power line control method, device, equipment, medium and product. BACKGROUND

[0002] With the continuous expansion of the power grid scale and the large-scale access of distributed new energy, the distribution network topology structure presents the characteristics of high complexity and dynamicity. The traveling wave signals excited by events such as short-circuit fault, grounding abnormality, lightning overvoltage, etc. show significant nonlinear oscillation and non-stationary transient characteristics, and the complex time-frequency characteristics thereof pose a severe challenge to traditional fault detection and protection technology.

[0003] At present, the existing technology mainly relies on simple traveling wave amplitude collection and static threshold triggering mechanism. Specifically, the traveling wave sensor captures discrete sampling points of the voltage / current signal in the fault transient window, and converts them into a digital sequence, and calculates the absolute amplitude extreme value in the digital sequence in real time. Then, the calculated absolute amplitude extreme value is compared with the preset fixed triggering threshold, if the absolute amplitude extreme value is greater than the preset fixed triggering threshold, it is determined that the power line has a fault, and a trip command is immediately sent to the magnetic control switch to drive the tripping mechanism to cut off the fault line; if the absolute amplitude extreme value is less than or equal to the preset fixed triggering threshold, it is considered that the power line has no fault, and the magnetic control switch maintains the closed state.

[0004] However, the existing technology has the problem of low accuracy in controlling the power line and cannot effectively protect the power line. SUMMARY

[0005] The embodiments of the present application provide a power line control method, device, equipment, medium and product to solve the problem of low accuracy in controlling the power line and the inability to effectively protect the power line.

[0006] In a first aspect, the embodiments of the present application provide a power line control method, comprising:

[0007] Collecting a target traveling wave of a power line;

[0008] Decomposing the target traveling wave by using an SGMD to obtain a target symplectic geometry component of the target traveling wave; wherein the parameters in the SGMD are pre-optimized by a PSO, and the target symplectic geometry component is a symplectic geometry component of the target traveling wave that has fault characteristic information.

[0009] inputting a target symplectic geometry component of the target traveling wave into a fault type determination model to obtain a fault type of the power line, the fault type determination model being trained in advance according to a target symplectic geometry component of each first sample traveling wave in a plurality of first sample traveling waves and a label corresponding to each first sample traveling wave, the label corresponding to each first sample traveling wave being used to indicate a fault type of the first sample traveling wave;

[0010] controlling the power line according to the fault type of the power line.

[0011] In a possible implementation, the parameters in the SGMD optimized by the PSO include at least one of the following: an embedding dimension, a time delay, and a number of reserved components.

[0012] In a possible implementation, the loss function of the PSO includes a mean square error subfunction, a signal-to-noise ratio subfunction, and an orthogonality subfunction.

[0013] In a possible implementation, the loss function is represented by formula (1):

[0014] J total = αJ MSE + βJ SNR + γJ OI (1)

[0015] wherein J total is the loss function, J MSE is the mean square error subfunction, J OI is the orthogonality subfunction, and α, β, and γ are all preset weights.

[0016] The mean square error subfunction is represented by formula (2):

[0017]

[0018] The orthogonality subfunction is represented by formula (3):

[0019]

[0020] The orthogonality subfunction can be represented by formula (4):

[0021]

[0022] wherein x(t) is an instantaneous value of a second sample traveling wave at a t th sampling point, is an instantaneous value of a target symplectic geometry component of the second sample traveling wave at the t th sampling point, N is a total number of sampling points of the second sample traveling wave, and C iC is the i-th symplectic geometric component of the second sample traveling wave j K is the total number of symplectic geometric components of the second sample traveling wave.

[0023] In a possible implementation, the method further includes:

[0024] acquiring the target traveling wave of the power line in real time at a T-joint of the power line;

[0025] acquiring the target traveling wave of the power line when the electromagnetic switch is open until the electromagnetic switch is re-closed; wherein the electromagnetic switch is opened when the traveling wave acquired in real time meets a preset condition, and the preset condition is used to determine whether the acquired traveling wave has abnormal fluctuation or mutation trend.

[0026] In a possible implementation, the fault type determination model is a CNN-LSTM model.

[0027] In a second aspect, an embodiment of the present application provides a power line control device, including:

[0028] an acquisition module, configured to acquire a target traveling wave of a power line;

[0029] a decomposition module, configured to decompose the target traveling wave by using an SGMD to obtain a target symplectic geometric component of the target traveling wave; wherein parameters in the SGMD are pre-optimized by a PSO, and the target symplectic geometric component is a symplectic geometric component of the target traveling wave that has fault feature information.

[0030] an input module, configured to input the target symplectic geometric component of the target traveling wave into a fault type determination model to obtain a fault type of the power line output by the fault type determination model, wherein the fault type determination model is pre-trained according to target symplectic geometric components of a plurality of first sample traveling waves and labels corresponding to each first sample traveling wave, and the label corresponding to each first sample traveling wave is used to indicate a fault type of the first sample traveling wave.

[0031] a control module, configured to control the power line according to the fault type of the power line.

[0032] In a possible implementation, the parameters in the SGMD that are optimized by the PSO include at least one of the following: embedding dimension, time delay, and number of reserved components.

[0033] In a possible implementation, the loss function of the PSO includes a mean square error sub-function, a signal-to-noise ratio sub-function, and an orthogonality sub-function.

[0034] In one possible implementation, the loss function is expressed by formula (1):

[0035] J total =αJ MSE +βJ SNR +γJ OI (1)

[0036] Among them, J total Let J be the loss function. MSE Let J be the mean square error sub-function. OI For the orthogonality sub-function, α, β, and γ are all pre-defined weights;

[0037] The mean square error sub-function is expressed by formula (2):

[0038]

[0039] The orthogonality sub-function is expressed by formula (3):

[0040]

[0041] The orthogonality sub-function can be expressed by formula (4):

[0042]

[0043] Where x(t) is the instantaneous value of the second sample traveling wave at the t-th sampling point. Let C be the instantaneous value of the target symplectic geometric component of the second sample traveling wave at the t-th sampling point, N be the total number of sampling points of the second sample traveling wave, and C be the instantaneous value of the target symplectic geometric component of the second sample traveling wave. i C is the i-th symplectic geometric component of the second sample traveling wave. j Let be the j-th symplectic geometric component of the second sample traveling wave, and K be the total number of symplectic geometric components of the second sample traveling wave.

[0044] In one possible implementation, the acquisition module is specifically used for:

[0045] Traveling waves are collected in real time at the T-junction of the power line;

[0046] When the electromagnetic switch is open, the target traveling wave of the power line is collected until the electromagnetic switch is closed again; wherein, the electromagnetic switch is opened when the traveling wave collected in real time meets a preset condition, the preset condition is used to determine whether there are abnormal fluctuations or abrupt changes in the collected traveling wave.

[0047] In one possible implementation, the fault type determination model is a CNN-LSTM model.

[0048] In a third aspect, an electronic device is provided, comprising: a memory, a processor;

[0049] The memory stores computer-executable instructions.

[0050] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect.

[0051] In a fourth aspect, a computer-readable storage medium is provided, and the computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the computer-executable instructions are used to implement the first aspect and / or various possible implementation manners of the first aspect.

[0052] In a fifth aspect, a computer program product is provided, and the computer program product comprises a computer program. When the computer program is executed by a processor, the computer program implements the first aspect and / or various possible implementation manners of the first aspect.

[0053] The power line control method, device, equipment, medium and product provided by the embodiments of the present application determine the fault type of the traveling wave input before the traveling wave input fault type determination model is determined. The SGMD is used to decompose the traveling wave through PSO parameter adjustment, and the sine geometric part with clear physical meaning is obtained. Then, the key component carrying the fault feature, i.e., the target sine geometric component, is screened out, so that the interference of the background noise in the traveling wave on the determination of the fault type is eliminated. Moreover, the PSO is used to dynamically optimize the SGMD parameters, which significantly improves the physical interpretability and fault feature extraction accuracy of the target sine geometric component obtained by decomposition, and lays a foundation for subsequent accurate determination of the fault type. Compared with the existing scheme of determining whether there is a fault through a fixed trigger threshold, the present scheme determines the fault type through the fault type determination model, can extract key time sequence features from complex and non-stationary fault waveforms (target waveforms), improves the accuracy of identifying whether there is a fault and the specific fault type, improves the accuracy of subsequent circuit control, and effectively protects the power line. BRIEF DESCRIPTION OF DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0055] Figure 1 A flowchart of the power line control method provided by the present application is shown.

[0056] Figure 2 A structure diagram of the CNN-LSTM model provided by the present application is shown.

[0057] Figure 3A structural diagram of a CNN network in a CNN-LSTM model provided by the present application is shown in the following figure.

[0058] Figure 4 A scene diagram of a power line control method provided by the present application is shown in the following figure.

[0059] Figure 5 A structural diagram of a power line control device provided by the present application is shown in the following figure.

[0060] Figure 6 A structural diagram of an electronic device provided by the present application is shown in the following figure.

[0061] The specific embodiments of the present application have been shown in the above figures, and will be described in more detail hereinafter. These figures and the written description are not intended to limit the scope of the present application concept in any way, but to illustrate the present application concept to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0062] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to indicate the same or similar components. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0063] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards, and provide corresponding operation portal for user to choose authorization or refusal.

[0064] First, the professional terms involved in the present application are explained:

[0065] Traveling wave: a transient electromagnetic wave generated when a power system (power transmission and distribution system) fails or is disturbed. The traveling wave detection device usually uses a Rogowski coil or a capacitive voltage transformer (CVT) to collect current and voltage signals respectively. In order to accurately capture the steep transient characteristics of the wave front, the sampling rate needs to reach MHz level (usually 1MHz to 10MHz or higher)

[0066] Symplectic Geometry Mode Decomposition (SGMD): A signal decomposition method based on the theory of symplectic geometry, suitable for processing non-linear and non-stationary signals (such as traveling wave signals in power systems). SGMD decomposes the original signal into a series of symplectic geometric components with physical meaning. Compared with traditional methods such as wavelet transform or variational mode decomposition, SGMD can more effectively preserve the dynamic characteristics of the signal and has lower decomposition error. By analyzing the symplectic geometric components obtained by decomposition, key features of the signal can be effectively extracted, such as frequency components (typical range: 0.1kHz-100kHz) and amplitude information of interest in traveling wave analysis.

[0067] Particle Swarm Optimization (PSO): A heuristic optimization algorithm based on swarm intelligence, which searches by simulating the social cooperation behavior of bird or fish swarms. PSO usually shows fast convergence speed in dealing with complex nonlinear optimization problems, and can dynamically adjust parameter values to approach the optimal solution.

[0068] Convolutional Neural Network (CNN): A deep neural network architecture specially designed for processing grid-like data such as images and time series signals. Its core automatically learns the local spatial features of input data such as image edges, textures or traveling wave front shapes through convolutional layers (English: Convolutional Layers).

[0069] Long Short-Term Memory (LSTM): A special recurrent neural network (RNN) designed to solve the long-term dependence learning difficulty and gradient vanishing / explosion problem of standard RNN.

[0070] Next, the application scenarios involved in this application are explained:

[0071] In the smart grid power distribution system, the efficient and stable operation of the power network and the rapid fault handling capability are the core foundation to ensure the reliability of power supply. With the continuous expansion of the power grid scale and the large-scale access of distributed new energy, the topology structure of the distribution network presents the characteristics of high complexity and dynamic. Under this background, the traveling wave signals excited by short-circuit fault, grounding abnormality, lightning overvoltage and other events show significant nonlinear oscillation and non-stationary transient characteristics, and the complex time-frequency characteristics pose a severe challenge to traditional fault detection and protection technology. The traveling wave fault location technology has become a key means to improve the fault judgment accuracy of the main distribution network with its sub-millisecond response speed and high sensitivity advantage, and is widely used in the fast protection system of transmission lines and distribution networks.

[0072] However, the current traveling wave technology and magnetic control switch still have obvious limitations in cooperation. Specifically, the existing scheme relies on simple traveling wave amplitude collection and static threshold triggering mechanism. The traveling wave sensor (such as a Rogowski coil) captures discrete sampling points of the voltage / current signal in the fault transient window and converts them into a digital sequence. The absolute amplitude extreme value in the digital sequence is calculated in real time. Then, the calculated absolute amplitude extreme value is compared with the preset fixed trigger threshold. If the absolute amplitude extreme value is greater than the preset fixed trigger threshold, it is determined that the power line has a fault, and a trip command is sent to the magnetic control switch to drive its disconnecting mechanism to disconnect the fault line. If the absolute amplitude extreme value is less than or equal to the preset fixed trigger threshold, it is considered that the power line does not have a fault, and the magnetic control switch remains closed.

[0073] However, the existing technology can only determine whether there is a fault in the power line, but cannot determine the specific fault type, and thus cannot perform corresponding processing on the power line according to the fault type. That is, the existing technology has the problem of low accuracy in controlling the power line and cannot effectively protect the power line.

[0074] Based on this, the technical concept of the present application is as follows: a fault type determination model can be pre-trained, which can determine the corresponding fault type according to the traveling wave. At the same time, in order to ensure the accuracy of the fault type determination process and thus improve the accuracy of subsequent control of the power line according to the fault type, it is considered that the traveling wave can be decomposed by SGMD with PSO parameter tuning before being input into the fault type determination model, to obtain a symplectic geometric part with clear physical meaning, and then to select a key component carrying fault characteristics, i.e. a target symplectic geometric component, so as to eliminate the interference of background noise in the traveling wave on the determination of the fault type. And by dynamically optimizing the SGMD parameters through PSO, the physical interpretability and fault feature extraction accuracy of the target symplectic geometric component obtained by decomposition are significantly improved, laying a foundation for subsequent accurate determination of the fault type.

[0075] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes can not be described again in some examples. The embodiments of the present application will be described below with reference to the drawings.

[0076] Figure 1 The flowchart of the power line control method provided by the present application is shown as Figure 1 The method can be implemented by the following steps:

[0077] S11, collecting a target traveling wave of the power line.

[0078] In this step, a traveling wave triggered collection module can be deployed in the power line to capture and record high-frequency transient traveling wave signals generated by the power line during a fault in real time. The traveling wave triggered collection module integrates a microsecond-level response current transformer and a high-speed analog-to-digital conversion unit, which can capture and synchronously record transient current waveforms of each phase in the initial stage of the fault, achieving millisecond-level waveform interception and data caching. Specifically, the current transformer can collect voltage / current transient signals excited by the fault of the power line in real time, and the high-speed analog-to-digital conversion unit converts the collected voltage / current transient signals into digital traveling wave sequences (hereinafter referred to as traveling waves) as the basis for subsequent fault feature analysis.

[0079] Optionally, the traveling wave triggered collection module can be deployed at the T junction of the power line. The T junction refers to the connection point where a main power line and another branch line form a "T" shape, i.e., the branch line is perpendicular or approximately perpendicular to the main line at a certain position, similar to the letter "T".

[0080] Optionally, the traveling wave triggered collection module can also be deployed at other positions of the power line, and the embodiments of the present application do not specifically limit the installation position of the traveling wave triggered collection module in the power line.

[0081] In one possible implementation, the traveling wave triggered collection module can be used to collect the traveling wave, and the collected traveling wave is determined as the target traveling wave.

[0082] In one possible implementation, the traveling wave can be collected in real time at the T junction of the power line, and the target traveling wave of the power line can be collected until the electromagnetic switch is re-closed when the electromagnetic switch is opened.

[0083] The electromagnetic switch is opened when the real-time collected traveling wave meets the preset condition, and the preset condition is used to determine whether the collected traveling wave has abnormal fluctuations or mutation trends.

[0084] The preset condition is used to determine whether the collected traveling wave has abnormal fluctuations or mutation trends.

[0085] For example, the preset condition can be at least one of the following:

[0086] The amplitude change rate of adjacent sampling points is greater than the amplitude mutation rate threshold, the energy proportion of the preset frequency band (for example, 2-100 kHz) is greater than the preset increase rate, and the traveling wave front gradient is greater than the preset gradient.

[0087] Specifically, in this implementation, to improve the synchronization of the power system response and the accuracy of the action judgment, an electromagnetic switch linkage control unit is integrated in the traveling wave triggered acquisition module. Based on the magnetic control triggering principle, the electromagnetic switch linkage control unit monitors the real-time collected traveling wave. When the traveling wave meets the preset condition, the electromagnetic switch is triggered to automatically open, thereby realizing the primary response of the distribution network fault protection. When the electromagnetic switch is detected to be opened, the traveling wave continuously collected from the current time is determined as the target traveling wave until the electromagnetic switch is closed again, at which time the fault has been solved.

[0088] In this implementation, the part of the traveling wave with the richest fault characteristics can be accurately captured from the real-time collected traveling wave according to the opening and closing state of the electromagnetic switch, and the part of the traveling wave caused by non-fault disturbance is filtered, thereby reducing the subsequent data processing amount and improving the efficiency of subsequent data processing.

[0089] Optionally, in this implementation, the target traveling wave and the description information can also be packaged and transmitted back to the backend server through a high-speed industrial Ethernet or wireless transmission channel, thereby providing a time alignment basis for subsequent data analysis. The description information includes the time sequence label, the sampling point sequence, the phase information, the current / voltage channel identifier, and the fault event flag of the target traveling wave.

[0090] S12, decompose the target traveling wave using SGMD to obtain a target symplectic geometric component of the target traveling wave.

[0091] In this step, considering that the target traveling wave itself has redundant noise, which will affect the accuracy of subsequent determination of the fault type. Therefore, the target traveling wave can be decomposed using SGMD to obtain a series of symplectic geometric components with physical meaning, and the target symplectic geometric component with fault characteristic information is determined from the symplectic geometric components, so as to subsequently determine the fault type based on the target symplectic geometric component and improve the accuracy of the determination.

[0092] Among them, the target symplectic geometric component is a symplectic geometric component with fault characteristic information in the symplectic geometric components of the target traveling wave.

[0093] Specifically, the target traveling wave is represented by x = [x1, x2, …, x n ] and the target symplectic geometric component is represented by x = [x1, x2, …, x nis the instantaneous value of the nth sampling point in the target traveling wave. By using time series topology equivalence method, one-dimensional time series (x = [x1, x2, …, xn]) can be mapped to high-dimensional space, i.e. embedding time series into a high-dimensional space, and then constructing trajectory matrix X for further analysis and decomposition. n ]) can be mapped to high-dimensional space, i.e. embedding time series into a high-dimensional space, and then constructing trajectory matrix X for further analysis and decomposition.

[0094] The trajectory matrix can be calculated by the following formula (5):

[0095]

[0096] Where d represents the embedding dimension, which determines the dimension of each row vector; τ represents the time delay; determines the time interval between adjacent elements in X.

[0097] Next, the autocorrelation analysis of the trajectory matrix X is performed, and the covariance matrix A is calculated:

[0098] The covariance matrix A can be calculated by the following formula (6):

[0099] A = X T X (6)

[0100] Where X T is the transpose of the trajectory matrix X. The covariance matrix A is a dxd matrix, which reflects the correlation between the dimensions of the trajectory matrix. Where d is the number of retained components, which determines the number of extracted geometric components.

[0101] Further, the covariance matrix A is decomposed by the eigenvalue decomposition to extract the eigenvectors.

[0102] The eigenvectors can be calculated by the following formula (7):

[0103] AQ i = λ i Q i (7)

[0104] Where λ i is the ith eigenvalue, and Q i is the ith element of the eigenvector. Through this step, the characteristic mode of the target traveling wave is decoupled and represented in the form of a symplectic manifold.

[0105] Then, the eigenvector Q i and the single-component matrix Z are used to construct the coefficient matrix.

[0106] The coefficient matrix can be calculated by the following formula (8):

[0107] Z i = Q iW i (8)

[0108] wherein Z i is the i-th element of the single-component matrix, which is the output of the target traveling wave after SGMD decomposition. The single-component matrix is a specific representation of the geometric mode of the target traveling wave, and contains the main information of the target traveling wave in component decomposition.

[0109] Next, finally, the single-component matrix is subjected to symplectic geometric transformation to generate symplectic geometric components.

[0110] wherein the symplectic geometric component can be calculated by the following formula (9):

[0111]

[0112] wherein, is the i-th symplectic geometric component.

[0113] By decomposing the target traveling wave by SGMD, the consistency of the target traveling wave in geometric structure is ensured, so that the symplectic geometric component after decomposition can effectively retain the geometric characteristics of the target traveling wave.

[0114] wherein the parameters in the SGMD are pre-optimized by PSO. The parameters in the SGMD optimized by PSO include at least one of the following: embedding dimension, time delay, and number of retained components. It should be understood that the process and principles of optimizing the parameters in the SGMD by PSO will be specifically explained in subsequent embodiments, and will not be repeated here.

[0115] In one possible implementation, according to the transient pulse intensity (such as the wave front steepness characteristic) and the characteristic frequency band energy dominance (such as the energy concentration degree of lightning traveling wave in the 0.1-1MHz frequency band) of the symplectic geometric component obtained by decomposition, candidate components significantly different from noise oscillation can be screened out; then, the similarity of the waveform form is verified to the preset fault mode (such as double exponential lightning pulse, arc high frequency oscillation wave), and finally the component that simultaneously satisfies the pulse steepness, the characteristic frequency band energy and the high waveform matching degree is extracted as the target symplectic geometric component.

[0116] For example, assuming that the target traveling wave generated by the ground fault is decomposed by SGMD, only the specific symplectic geometric component simultaneously presents a low-frequency slow waveform mode (reflecting high resistance grounding characteristics), a 1-5kHz frequency band energy ratio exceeding 90% (different from power frequency noise), and a high degree of coincidence with the ground fault template waveform, the symplectic geometric component is determined as the target symplectic geometric component carrying fault characteristic information.

[0117] In another possible implementation, the relevant staff can pre-configure the number of target symplectic geometric components according to experience. After the symplectic geometric components are decomposed, the symplectic geometric component corresponding to the number is determined as the target symplectic geometric component.

[0118] S13, input the target symplectic geometric component of the target traveling wave into the fault type determination model, and obtain the fault type of the power line output by the fault type determination model.

[0119] In this step, the fault type determination model can be obtained, so that the target symplectic geometric component of the target traveling wave is input into the fault type determination model, and the corresponding fault type is determined by using the fault type determination model.

[0120] It should be understood that the fault type determination model is pre-trained according to the target symplectic geometric component of each first sample traveling wave in a plurality of first sample traveling waves and the label corresponding to each first sample traveling wave, and the label corresponding to each first sample traveling wave is used to indicate the fault type of the first sample traveling wave.

[0121] For example, the fault type can be single-phase grounding, two-phase short circuit, three-phase short circuit, metallic grounding, high resistance grounding, etc.

[0122] Optionally, the fault prediction model can be a CNN-LSTM model. It should be understood that the structure and principle of the CNN-LSTM model can be explained and described by the following embodiments, which will not be repeated here.

[0123] S14, control the power line according to the fault type of the power line.

[0124] In this step, after the fault type of the power line is determined, the corresponding control can be performed on the power line according to the fault type, so as to ensure the accuracy of the control.

[0125] In a possible implementation, the mapping relationship between the fault type and the control strategy can be pre-configured. After the fault type of the power line is determined, the control strategy corresponding to the fault type can be found from the mapping relationship, and the control strategy is executed to realize the control of the power line.

[0126] For example, the above mapping relationship can be at least one of the following (the format of the mapping relationship is fault type: control strategy):

[0127] 1, lightning overvoltage: the magnetic control switch is tripped within 150 ms, and is automatically reclosed after 300 ms of insulation recovery.

[0128] 2, permanent metal short circuit: drive the magnetic control switch to trip at an extremely fast speed of ≤20 ms, then lock the reclosing, and simultaneously push the fault positioning coordinates to the dispatch center

[0129] 3. Intermittent arc ground: Start the vacuum arc chamber to suppress reignition, then adjust the zero sequence protection setting value, and activate fault recording.

[0130] 4. High resistance ground: Enable inverse time protection characteristics and delay trip to prevent misoperation, and start island detection for feeder automation.

[0131] 5. Capacitor switching disturbance: Shield protection action and record event log.

[0132] The power line control method provided by the embodiments of the present application acquires a target traveling wave of a power line, decomposes the target traveling wave by using an SGMD to obtain a target symplectic geometric component of the target traveling wave. Then, the target symplectic geometric component of the target traveling wave is input into a fault type determination model to obtain a fault type of the power line output by the fault type determination model, so as to control the power line according to the fault type of the power line. The parameters in the SGMD are pre-optimized by PSO, and the target symplectic geometric component is a symplectic geometric component in the symplectic geometric components of the target traveling wave that contains fault characteristic information. The fault type determination model is obtained by pre-training according to the target symplectic geometric components of each first sample traveling wave in a plurality of first sample traveling waves and labels corresponding to each first sample traveling wave. The label corresponding to each first sample traveling wave is used to indicate the fault type of the first sample traveling wave. In the technical solution, before the traveling wave is input into the fault type determination model, the SGMD with parameters optimized by PSO is used to decompose the traveling wave to obtain symplectic geometric components with clear physical meaning, and then the key components carrying fault characteristics, i.e., the target symplectic geometric components, are screened out, so as to eliminate the interference of background noise in the traveling wave on the process of determining the fault type. Moreover, the parameters of the SGMD are dynamically optimized by PSO, which significantly improves the physical interpretability and fault characteristic extraction accuracy of the target symplectic geometric components obtained by decomposition, and lays a foundation for subsequent accurate determination of the fault type. Compared with the existing scheme of determining whether a fault exists by using a fixed trigger threshold, the present scheme determines the fault type by using a fault type determination model, can extract key time sequence features from complex and non-stationary fault waveforms (target waveforms), improve the accuracy of identifying whether a fault exists and the specific fault type, and improve the accuracy of subsequent circuit control, so as to effectively protect the power line.

[0133] Next, the implementation process of pre-optimizing the parameters of the SGMD by using PSO is specifically described.

[0134] In the application process of SGMD, the first step is to construct the trajectory matrix of the signal, and this step depends on two key parameters: embedding dimension and time delay. The embedding dimension determines the number of columns of the trajectory matrix, that is, the degree of expansion of the topological structure of the signal in high-dimensional space; and the time delay controls the time interval considered when constructing the trajectory. These two parameters not only affect the shape of the covariance matrix, but also have a direct impact on the subsequent symplectic geometric similarity transformation and the effect of modal decomposition. If the embedding dimension is too small, the dynamic structure of the target traveling wave may not be fully expanded; if it is too large, redundant dimensions may be introduced, resulting in a decrease in feature extraction accuracy. In addition, in the modal selection stage, SGMD needs to determine the number of retained components (also known as the number of retained feature modes) to achieve the retention and reconstruction of the main signal components. Therefore, the embedding dimension, time delay, and the number of retained components are the core objectives of PSO optimization.

[0135] In order to effectively optimize the above parameters by PSO, a suitable loss function must be constructed to guide the search direction. The mean square error between the original signal (second sample traveling wave) and the reconstructed signal (target symplectic geometric component of the second sample traveling wave) can be used as the optimization objective, and a weighted combination of multiple indicators such as signal-to-noise ratio, reconstruction error, and orthogonality between decomposition components can be considered to form the loss function, in order to comprehensively evaluate the effectiveness of the decomposition result.

[0136] That is, the loss function of PSO includes a mean square error sub-function, a signal-to-noise ratio sub-function, and an orthogonality sub-function.

[0137] The loss function is represented by formula (1):

[0138] J total = αJ MSE + βJ SNR + γJ OI (1)

[0139] Where J total is the loss function, J MSE is the mean square error sub-function, J OI is the orthogonality sub-function, α, β, γ are pre-set weights, and α + β + γ = 1.

[0140] The mean square error sub-function is represented by formula (2):

[0141]

[0142] The orthogonality sub-function is represented by formula (3):

[0143]

[0144] The orthogonality sub-function can be represented by formula (4):

[0145]

[0146] wherein x(t) is an instantaneous value of the second sample traveling wave at a t-th sampling point, is an instantaneous value of a target symplectic geometry component of the second sample traveling wave at the t-th sampling point, N is a total number of sampling points of the second sample traveling wave, C i is an i-th symplectic geometry component of the second sample traveling wave, C j is a j-th symplectic geometry component of the second sample traveling wave, and K is a total number of symplectic geometry components of the second sample traveling wave.

[0147] wherein the mean square error minimization can guarantee high-fidelity reconstruction of the decomposition component to the original traveling wave, the signal-to-noise ratio maximization can suppress the pollution of the noise component to the fault feature (such as the microsecond wave front), and the orthogonality constraint can ensure that different fault modes are decoupled to independent components, avoiding the modal aliasing to cause feature confusion. This multi-index collaborative optimization makes the SGMD adaptively match the complex transient characteristics of the traveling wave, for example, in the concurrent scene of lightning and high-resistance grounding, the PSO driven parameter adjustment can accurately separate the high-frequency pulse component (lightning feature) and the low-frequency decay component (grounding feature), to provide a target component input with clear physical meaning and strong feature separability for the fault classification model. Compared with the existing artificial configuration of SGMD parameters, the present method can effectively improve the accuracy of parameter configuration, and further improve the effectiveness of the SGMD in decomposing the target traveling wave.

[0148] The PSO initializes a plurality of particle groups, each particle representing a parameter combination, and searches for a parameter combination that optimizes the target function in the search space by constantly updating the speed and position of the particle. In each iteration, the PSO algorithm records the individual historical optimal value of the particle and the global optimal value of the population, and guides the particle to approach the optimal solution, so as to gradually obtain a set of SGMD parameters that can achieve the best decomposition effect.

[0149] In each iteration, the speed of the particle can be represented by the following formula (10):

[0150]

[0151] The position of the particle can be represented by the following formula (11):

[0152]

[0153] wherein, is the position of the particle i at t time, is the position of the particle i at t+1 time, is the speed of the particle i at t time, is the velocity of particle i at time t+1, is a preset inertia weight used to balance the global and local search ability, c1 and c2 are both preset learning factors, usually taking values in [1.5, 2.5], r1 and r2 are random numbers in [0, 1], pBest i is the historical optimal solution of particle i, gBest i is the global optimal value of particle i.

[0154] Next, taking the CNN-LSTM model as an example, the training process and structure of the CNN-LSTM model are explained.

[0155] Firstly, a refined power distribution network fault simulation model can be constructed. Through the power distribution network fault simulation model, corresponding simulation is carried out for various fault types (single-phase grounding, two-phase short circuit, three-phase short circuit, metallic grounding, high resistance grounding, etc.), to obtain the first sample traveling wave and the label of each first sample traveling wave, i.e. the corresponding fault type. Then, the SGMD adjusted by PSO is used to decompose each first sample traveling wave to obtain the target symplectic geometric component of each first sample traveling wave.

[0156] At the same time, the first sample traveling wave can also be collected in the actual sample power line, and the fault type corresponding to the first sample traveling wave is determined manually. Then, the SGMD adjusted by PSO is used to decompose each first sample traveling wave to obtain the target symplectic geometric component of each first sample traveling wave.

[0157] Next, all the target symplectic geometric components of each first sample traveling wave obtained above are divided into a training set, a validation set and a test set according to a certain proportion, to ensure the comprehensiveness and balance of data distribution. Then, the original CNN-LSTM model is trained using the training set. Among them, CNN is responsible for extracting the local mode features of the waveform in the time-frequency domain, and the LSTM network is used to establish the time dependence and dynamic evolution law between modes, so as to construct a complete time-space joint feature expression. In the training process, the original CNN-LSTM model is optimized based on the cross-entropy loss function, uses Adam for back propagation update, and dynamically adjusts the learning rate, early termination and other strategies on the validation set to prevent overfitting, and finally obtains the trained fault type determination model. The fault type determination model can accept the target symplectic geometric component of the target traveling wave decomposed by SGMD, and identify the fault type.

[0158] It should be understood that the first sample traveling wave and the second sample traveling wave can be the same data or different data, which can be determined according to the actual situation, and will not be repeated here.

[0159] Figure 2 A structural diagram of the CNN-LSTM model provided in the present application is shown in FIG. 1. As shown in FIG. 1, the CNN-LSTM model includes a CNN network and an LSTM network, and the output of the CNN network is connected to the input of the LSTM network. Figure 2

[0160] The CNN-LSTM model combines the time series modeling capability of LSTM and the spatial feature extraction capability of CNN. LSTM is a special recurrent neural network that solves the gradient disappearance problem of traditional RNN by introducing memory cells and three gating mechanisms, thereby effectively capturing the long-term dependence of time series data. CNN extracts spatial features of high-dimensional data through convolutional layers, pooling layers, and fully connected layers, and is good at processing local patterns. Convolutional neural networks are good at extracting local features from input data, especially when processing images and time series data.

[0161] Figure 3 A structural diagram of the CNN network in the CNN-LSTM model provided in the present application is shown in FIG. 2. As shown in FIG. 2, the CNN network includes, in sequence, a convolutional layer 1, an activation function 1, a convolutional layer 2, an activation function 1, a convolutional layer 2, an activation function 1, a pooling layer, and a flattening layer (English: Flatten). CNN can effectively extract and compress high-dimensional features of input data through convolutional layers, activation functions, and pooling layers. These high-dimensional features represent the ability to capture local patterns in target traveling waves, such as short-term trends and changes in time series. Then, the high-dimensional features are flattened into one-dimensional vectors by the flattening layer for classification or regression tasks to determine the fault type. Figure 3

[0162] Figure 4 A scenario diagram of the power line control method provided in the present application is shown in FIG. 3. As shown in FIG. 3, in this scenario, when a fault occurs in the power line, the traveling wave trigger acquisition module synchronously records the high-frequency traveling wave during the fault period, and triggers the magnetic control switch to open, which is used for auxiliary control and data positioning. From the opening of the magnetic control switch to the re-closing of the magnetic control switch, the acquired high-frequency traveling wave is determined as the target traveling wave and is uploaded to the backend server in real time. Figure 4

[0163] Among them, the backend server is deployed with the SGMD adjusted by PSO to perform multi-scale decomposition on the target traveling wave, eliminating most of the noise parameters in the target traveling wave, so as to effectively retain the key dynamic features under different time scales. Moreover, the SGMD is pre-adjusted by PSO to improve the overall recognition accuracy.

[0164] ​​​Further, the backend server is also deployed with a fault type determination model, which can be a CNN-LSTM model. The CNN network is used to extract spatial local features, and the LSTM network is used to model the timing dynamic changes. The CNN-LSTM model introduces the distribution network fault traveling wave simulation data in the training process to expand the sample coverage, and through the three-stage division of the training set, the validation set and the test set, the generalization ability and accuracy of the model are evaluated.

[0165] It should be understood that the specific implementation details and principles of the embodiments have been described in detail in the above embodiments, and will not be repeated here.

[0166] In summary, the power line control scheme provided by the present application has the following technical effects:

[0167] 1. Fast response and high-fidelity acquisition of fault signals are realized:

[0168] The traveling wave trigger acquisition module with microsecond-level response is adopted, and the magnetic control electromagnetic switch is connected in parallel, which not only guarantees the fast protection control function of the power line, but also provides a clear time anchor point for the fault waveform data (target traveling wave), effectively solving the problems of inaccurate data positioning and delayed response in traditional acquisition systems.

[0169] 2. The expression ability of non-stationary waveform features is improved:

[0170] The SGMD algorithm is introduced to segmentally and adaptively decompose the multi-modal traveling wave signal (target traveling wave), which can significantly reduce the modal aliasing phenomenon, enhance the extraction ability of weak disturbance features, and improve the accuracy and robustness of target traveling wave processing.

[0171] 3. Self-optimization of SGMD is realized, and the generalization ability is improved

[0172] The PSO is used to globally optimize the SGMD parameters, effectively avoiding the limitations of relying on artificial experience to set parameters, and enhancing the adaptive ability of SGMD in different fault scenarios.

[0173] 4. A spatial-temporal fusion fault type determination model is constructed

[0174] The CNN-LSTM model takes into account the spatial distribution features and timing dynamic information of the waveform signal (target traveling wave), which can effectively analyze the timing evolution process of the fault while maintaining the recognition accuracy of the structural features, thereby significantly improving the fault classification accuracy and type subdivision ability.

[0175] 5. The application ability of the system in real engineering environment is enhanced

[0176] The hybrid training system of "simulation data + field collected data" is adopted to effectively overcome the overfitting problem of the fault type determination model to pure ideal waveforms, and the robustness and practicality of the fault type determination model in the actual power grid environment are improved in the face of complex factors such as interference and noise.

[0177] Figure 5 A schematic structural diagram of a power line control device provided in the present application is shown in FIG. 1, and the power line control device 50 provided in the present embodiment includes: Figure 5

[0178] The acquisition module 51 is configured to acquire a target traveling wave of the power line.

[0179] The decomposition module 52 is configured to decompose the target traveling wave by using the SGMD to obtain a target symplectic geometry component of the target traveling wave; wherein the parameters in the SGMD are pre-optimized by the PSO, and the target symplectic geometry component is a symplectic geometry component in the symplectic geometry components of the target traveling wave that contains fault characteristic information.

[0180] The input module 53 is configured to input the target symplectic geometry component of the target traveling wave into the fault type determination model to obtain a fault type of the power line output by the fault type determination model, and the fault type determination model is pre-trained according to the target symplectic geometry component of each first sample traveling wave in a plurality of first sample traveling waves and a label corresponding to each first sample traveling wave, and the label corresponding to each first sample traveling wave is used to indicate the fault type of the first sample traveling wave.

[0181] The control module 54 is configured to control the power line according to the fault type of the power line.

[0182] In a possible implementation, the parameters in the SGMD that are optimized by the PSO include at least one of the following: embedding dimension, time delay, and number of reserved components.

[0183] In a possible implementation, the loss function of the PSO includes a mean square error sub-function, a signal-to-noise ratio sub-function, and an orthogonality sub-function.

[0184] In a possible implementation, the loss function is represented by formula (1):

[0185] J total = αJ MSE + βJ SNR + γJ OI (1)

[0186] wherein J total is the loss function, J MSE is the mean square error sub-function, J OI is the orthogonality sub-function, and α, β, and γ are pre-set weights. ​

[0187] The mean square error sub-function is represented by formula (2):

[0188]

[0189] The orthogonality sub-function is represented by formula (3):

[0190]

[0191] The orthogonality sub-function can be represented by formula (4):

[0192]

[0193] Wherein, x(t) is the instantaneous value of the second sample traveling wave at the tth sampling point, is the instantaneous value of the target symplectic geometry component of the second sample traveling wave at the tth sampling point, N is the total sampling point number of the second sample traveling wave, C i is the i th symplectic geometry component of the second sample traveling wave, C j is the j th symplectic geometry component of the second sample traveling wave, and K is the total number of symplectic geometry components of the second sample traveling wave.

[0194] In a possible implementation, the acquisition module 51 is specifically configured to:

[0195] acquiring the traveling wave of the power line in real time at a T joint of the power line;

[0196] acquiring the target traveling wave of the power line when the electromagnetic switch is open until the electromagnetic switch is re-closed; wherein the electromagnetic switch is opened when the traveling wave acquired in real time meets a preset condition, and the preset condition is used to determine whether the acquired traveling wave has abnormal fluctuation or mutation trend.

[0197] In a possible implementation, the fault type determination model is a CNN-LSTM model.

[0198] It should be understood that the acquisition module 51 can be the above-mentioned traveling wave triggered acquisition module 51.

[0199] The power line control device provided in the embodiment can execute the method provided in the above-mentioned method embodiment, and has similar implementation principles and technical effects, which will not be described here in detail.

[0200] Figure 6 The structure schematic diagram of the electronic device provided in the present application is shown in FIG. 1. Figure 6 As shown in FIG. 1, the electronic device 60 provided in the embodiment includes at least one processor 601 and a memory 602. Optionally, the electronic device 60 further includes a communication component 603. The processor 601, the memory 602 and the communication component 603 are connected through a bus 604.

[0201] In the implementation process, the at least one processor 601 executes the computer execution instructions stored in the memory 602, so that the at least one processor 601 executes the above-mentioned method.

[0202] The specific implementation process of the processor 601 can refer to the method embodiments described above, which have similar implementation principles and technical effects, and will not be described here in detail.

[0203] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in combination with the application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0204] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0205] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0206] The present application also provides a computer program product, comprising a computer program, which is executed by a processor to implement the above-mentioned method.

[0207] The present application also provides a computer readable storage medium, which stores computer execution instructions, and when the processor executes the computer execution instructions, the above-mentioned method is implemented.

[0208] The aforementioned readable storage medium can be realized by any type of volatile or nonvolatile storage devices or a combination thereof, such as a static random-access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic storage, a flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0209] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0210] The division of units is only a logical functional division, and in actual implementation, there can be another division manner. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0211] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiment.

[0212] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0213] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0214] It can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The aforementioned program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0215] Finally, it should be noted that: those skilled in the art will easily derive other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application that follow the general principles of the present application and include known or customary technical means in the art that are not disclosed in the present application, and is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is only limited by the appended claims.

Claims

1. A power line control method characterized by, The method comprises the following steps: Collecting a target traveling wave of a power line; Decomposing the target traveling wave by using a symplectic geometry modal decomposition (SGMD) to obtain a target symplectic geometry component of the target traveling wave; wherein parameters in the SGMD are pre-optimized by a particle swarm optimization (PSO) algorithm, and the target symplectic geometry component is a symplectic geometry component of the target traveling wave that contains fault feature information; Inputting the target symplectic geometry component of the target traveling wave into a fault type determination model to obtain a fault type of the power line output by the fault type determination model, wherein the fault type determination model is trained in advance according to target symplectic geometry components of a plurality of first sample traveling waves and labels corresponding to each first sample traveling wave, and each label is used to indicate a fault type of the corresponding first sample traveling wave; Controlling the power line according to the fault type of the power line.

2. The method of claim 1, wherein, The parameters in the SGMD optimized by the PSO include at least one of the following: embedding dimension, time delay, and number of retained components.

3. The method according to claim 1 or 2, characterized in that, The loss function of the PSO includes a mean square error sub-function, a signal-to-noise ratio sub-function, and an orthogonality sub-function.

4. The method of claim 3, wherein, The loss function is represented by formula (1): J total = αJ MSE + βJ SNR + γJ OI (1) wherein J total is the loss function, J MSE is the mean square error sub-function, J OI is the orthogonality sub-function, and α, β, γ are all preset weights. The mean square error sub-function is represented by formula (2): The orthogonality sub-function is represented by formula (3): The orthogonality sub-function can be represented by formula (4): wherein x(t) is the instantaneous value of the second sample traveling wave at the tth sampling point, is the instantaneous value of the target symplectic geometric component of the second sample traveling wave at the tth sampling point, N is the total number of sampling points of the second sample traveling wave, i is the i th symplectic geometric component of the second sample traveling wave, j is the j th symplectic geometric component of the second sample traveling wave, K is the total number of symplectic geometric components of the second sample traveling wave.

5. The method according to claim 1 or 2, characterized in that, The target traveling wave of the power line is collected by: Real-time collection of traveling waves at a T-junction of the power line; When an electromagnetic switch is opened, the target traveling wave of the power line is collected until the electromagnetic switch is re-closed; wherein the electromagnetic switch is opened when the real-time collected traveling wave meets a preset condition, and the preset condition is used to determine whether the collected traveling wave has abnormal fluctuations or mutation trends.

6. The method of claim 1 or 2, wherein, The fault type determination model is a convolutional neural network (CNN)-long short-term memory (LSTM) model.

7. A power line control device, characterized by comprising: The method comprises the following steps: A collection module is configured to collect a target traveling wave of a power line; A decomposition module is configured to decompose the target traveling wave by using a symplectic geometry modal decomposition (SGMD) to obtain a target symplectic geometry component of the target traveling wave; wherein parameters in the SGMD are pre-optimized by a particle swarm optimization (PSO) algorithm, and the target symplectic geometry component is a symplectic geometry component of the target traveling wave that contains fault feature information; An input module is configured to input the target symplectic geometry component of the target traveling wave into a fault type determination model to obtain a fault type of the power line output by the fault type determination model, wherein the fault type determination model is trained in advance according to target symplectic geometry components of a plurality of first sample traveling waves and labels corresponding to each first sample traveling wave, and each label is used to indicate a fault type of the corresponding first sample traveling wave; A control module is configured to control the power line according to the fault type of the power line.

8. An electronic device, comprising: The method comprises the following steps: A memory and a processor; The memory stores computer execution instructions; The processor executes computer-executable instructions stored in the memory such that the processor performs the method of any of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium has stored therein computer-executable instructions that, when executed by a processor, perform the method of any of claims 1-6.

10. A computer program product, characterised in that, A computer program that, when executed by a processor, performs the method of any of claims 1-6.

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