System-side harmonic impedance estimation method and device thereof, storage medium, and electronic device

Abstract

The application relates to a system-side harmonic impedance estimation method and device thereof, a storage medium and an electronic device, which comprises the following steps: obtaining an h-order harmonic parameter equivalent circuit simplified according to power grid characteristics; h is a positive integer greater than or equal to 2; a system-side harmonic impedance estimation mathematical model is established according to the h-order harmonic parameter equivalent circuit; the amplitude and phase data of harmonic voltage and harmonic current at a PCC are obtained; the harmonic voltage amplitude data at the PCC are divided into several stable intervals; a variable coefficient regression model between harmonic voltage phasors and harmonic current phasors is established according to the amplitude and phase data of the harmonic voltage and the harmonic current in the several stable intervals; the optimal window width of the variable coefficient regression model kernel function is selected; and the system harmonic impedance is calculated according to the variable coefficient regression model and the optimal window width.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, in particular to a system side harmonic impedance estimation method and device thereof, a storage medium and an electronic device. BACKGROUND

[0002] With the large-scale grid connection of various power electronic devices, the degree of harmonic pollution of the power system tends to worsen. The injection of harmonics generated by a large number of nonlinear loads into the power grid can cause bus voltage waveform distortion, highlight the risk of power grid harmonic resonance, and affect the normal operation of electrical equipment, and even threaten the safe and stable operation of the power grid. Effective technical supervision and control of power grid harmonics or resonance problems is an important technical means to improve the operation level of power grid power quality and reduce economic losses of power grid power quality. Whether it is to carry out harmonic flow tracing, resonance risk analysis, harmonic responsibility division or harmonic / resonance suppression, the key lies in the accurate estimation of the system side harmonic impedance.

[0003] The current system harmonic impedance estimation method usually adopts a "non-intervention" method, including the fluctuation method, the linear regression method, and the independent component analysis method. The above methods are mostly based on the premise that the background harmonic parameters remain stable. However, in the new power system, changes in the operating state of the power grid, such as power grid operating mode adjustment, new energy station or capacitor switching, and power grid short-circuit fault, can cause sudden changes in background harmonic parameters, resulting in large errors in the estimation of system harmonic impedance, affecting the analysis conclusion of harmonics or resonance, and even affecting the harmonic or resonance treatment effect. Therefore, in the scenario of sudden changes in background harmonic parameters, how to track the time-varying law of background harmonic parameters and accurately estimate the system harmonic impedance is a key technical problem that needs to be solved for the technical supervision and control of power grid harmonics or resonance. SUMMARY

[0004] The purpose of the present application is to provide a system side harmonic impedance estimation method and device, a computer readable storage medium and an electronic device to solve the technical problems of accurate estimation and real-time tracking of system side harmonic impedance in the scenario of sudden changes in background harmonic parameters in the new power system.

[0005] To achieve the above-mentioned purpose, the embodiments of the present application provide a system side harmonic impedance estimation method, which comprises:

[0006] obtaining an hth harmonic parameter equivalent circuit simplified according to the characteristics of the power grid; h is a positive integer greater than or equal to 2;

[0007] establishing a system side harmonic impedance estimation mathematical model according to the hth harmonic parameter equivalent circuit;

[0008] obtaining the amplitude and phase data of the harmonic voltage and the harmonic current at the PCC;

[0009] The harmonic voltage amplitude data at the PCC is divided into several stable intervals;

[0010] According to the amplitude and phase data of the harmonic voltage and the harmonic current of the several stable intervals, a variable coefficient regression model between the harmonic voltage phasor and the harmonic current phasor is established;

[0011] An optimal window width of a kernel function of the variable coefficient regression model is selected;

[0012] The system harmonic impedance is calculated according to the variable coefficient regression model and the optimal window width.

[0013] Preferably, the h-th harmonic parameter equivalent circuit simplified according to the power grid characteristics comprises:

[0014] The background harmonic of the upper-level power grid is equivalent to a harmonic voltage source, and the nonlinear load of the current-level power grid is equivalent to a harmonic current source, and the conversion to the same voltage level obtains a Thevenin-Norton equivalent circuit model, that is, the h-th harmonic parameter equivalent circuit.

[0015] Preferably, the system-side harmonic impedance estimation mathematical model is as follows:

[0016]

[0017] Wherein, Z S,h is the system-side h-th harmonic impedance, is the system-side h-th harmonic voltage phasor,

[0018] is the PCC-side h-th harmonic current phasor, is the PCC-side h-th harmonic voltage phasor.

[0019] Preferably, the harmonic voltage amplitude data at the PCC is divided into several stable intervals, comprising:

[0020] First, the number and rough position of the data mutation points in the harmonic voltage amplitude data at the PCC are found by using the wavelet transform modulus maximum value method, and then the positions of the data mutation points in the harmonic voltage amplitude data at the PCC are accurately positioned by combining the sliding difference maximum value method. If the positions positioned by the wavelet transform modulus maximum value method and the sliding difference maximum value method are too different, the transform scale D of the wavelet transform modulus maximum value method is changed, and the number and rough position of the data mutation points in the harmonic voltage amplitude data at the PCC are found again by using the wavelet transform modulus maximum value method. Until the positions positioned by the wavelet transform modulus maximum value method and the sliding difference maximum value method are approximately the same, the data mutation points are determined. Finally, the harmonic voltage amplitude data at the PCC is divided into several stable intervals according to the time corresponding to the data mutation points.

[0021] Preferably, the variable coefficient regression model between the harmonic voltage phasor and the harmonic current phasor is as follows:

[0022]

[0023] wherein t represents a sampling time, is the hth harmonic voltage phasor at the PCC at time t, is the hth harmonic current phasor at the PCC at time t, Z S,h is the hth harmonic impedance on the system side at time t, is the hth harmonic voltage phasor on the system side at time t.

[0024] Preferably, the optimal window width of the kernel function of the variable coefficient regression model is selected, comprising:

[0025] The data of several stable intervals are independently calculated, and the optimal window width of the kernel function of the variable coefficient regression model of each stable interval is obtained through adaptive particle swarm optimization.

[0026] Preferably, the system harmonic impedance is calculated according to the variable coefficient regression model and the optimal window width, comprising:

[0027] The system harmonic impedance is calculated according to the following formula;

[0028]

[0029]

[0030] wherein, is the regression coefficient matrix obtained according to the optimal window width, is the estimated value of the harmonic impedance on the system side.

[0031] Embodiments of the present application also provide a system harmonic impedance estimation device, comprising modules for implementing the system harmonic impedance estimation method as described above.

[0032] Embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the system harmonic impedance estimation method as described above.

[0033] Embodiments of the present application also provide an electronic device, comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, and the processor implements the system harmonic impedance estimation method as described above when executing the computer program.

[0034] The embodiment of the present application provides a system side harmonic impedance estimation method and device, computer readable storage medium and electronic equipment, which has the following beneficial effects.

[0035] (1) The PCC harmonic voltage amplitude or current amplitude data with a mutation can be divided into several sections with only small perturbations, so that the problem of background harmonic parameter mutation is solved.

[0036] (2) The PCC harmonic data is divided according to the wavelet transform and sliding difference maximum value method, and the selection of the parameters of the method is a purely objective way, avoiding the complexity of subjective selection of parameters.

[0037] (3) The optimal window width of the kernel function of the variable coefficient regression model is selected by combining the adaptive particle swarm optimization algorithm and the generalized cross-validation method, and the calculation error of the system harmonic impedance is further reduced.

[0038] (4) In the scene where the background harmonic parameters are time-varying and have mutations, the estimation performance and tracking ability are superior to those of the prior art.

[0039] Other features and advantages of the embodiments of the present application will be described in the following description. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0041] Figure 1 The flow chart of the system side harmonic impedance estimation method in the embodiment of the present application.

[0042] Figure 2 The simplified hth harmonic parameter equivalent circuit diagram in the embodiment of the present application.

[0043] Figure 3 The flow chart of the wavelet transform and sliding difference maximum value method data division in the embodiment of the present application.

[0044] Figure 4 The flow chart of selecting the optimal window width of the kernel function in the embodiment of the present application.

[0045] Figure 5 The PCC harmonic sample data in the embodiment of the present application.

[0046] Figure 6 The schematic diagram of the sliding difference maximum value method data division in the embodiment of the present application.

[0047] Figure 7 Fig. 1 is a diagram of estimation results of system harmonic impedance in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The detailed description of the drawings is intended as a description of the current preferred embodiments of the present application and is not intended to represent the only forms in which the present application can be practiced. It should be understood that the same or equivalent functions can be accomplished by different embodiments that are intended to be encompassed within the spirit and scope of the present application.

[0049] One embodiment of the present application provides a system-side harmonic impedance estimation method, comprising the following steps:

[0050] Step S1, obtaining an hth harmonic parameter equivalent circuit simplified according to power grid characteristics; h is a positive integer greater than or equal to 2;

[0051] Step S2, establishing a system-side harmonic impedance estimation mathematical model according to the hth harmonic parameter equivalent circuit;

[0052] Step S3, obtaining amplitude and phase data of harmonic voltage and harmonic current at the PCC;

[0053] Step S4, dividing harmonic voltage amplitude data at the PCC into several stable intervals;

[0054] Step S5, establishing a variable coefficient regression model between harmonic voltage phasors and harmonic current phasors according to amplitude and phase data of harmonic voltage and harmonic current in the several stable intervals;

[0055] Step S6, selecting an optimal window width of a kernel function of the variable coefficient regression model;

[0056] Step S7, calculating system harmonic impedance according to the variable coefficient regression model and the optimal window width.

[0057] Further, the step S1 comprises:

[0058] Equivalent the background harmonics of the upper power grid to a harmonic voltage source, and equivalent the nonlinear load of the current power grid to a harmonic current source, and convert to the same voltage level to obtain a Thevenin-Norton equivalent circuit model, i.e., the hth harmonic parameter equivalent circuit, as shown in Figure 2 .

[0059] Figure 2 In the formula: is a system-side hth harmonic voltage phasor; Z S,h is a system hth harmonic impedance;

[0060] is a hth harmonic voltage phasor at the PCC; is the hth harmonic current phasor at PCC; is the hth harmonic current phasor at PCC; C,h is the hth harmonic impedance at PCC; h is the harmonic order, h = 2, 3, 4, ….

[0061] Further, the system-side harmonic impedance estimation mathematical model is as follows:

[0062]

[0063] wherein, Z S,h is the system-side hth harmonic impedance, is the system-side background hth harmonic voltage phasor, is the hth harmonic current phasor at PCC, is the hth harmonic voltage phasor at PCC.

[0064] Specifically, the harmonic voltage phasor and the harmonic current phasor at PCC are the results of superposition of both system-side and user-side harmonic sources, and according to the relationship between the harmonic sources on both sides in the model, the following circuit equation can be obtained:

[0065]

[0066] The system impedance is related to the short-circuit capacity at PCC, and for a typical industrial nonlinear user, the harmonic impedance on the user side and the system side often presents a much greater relationship, i.e. |Z C,h | >> |Z S,h |, so the above formula can be simplified as:

[0067]

[0068] Further, we get: Therefore, under the precondition of satisfying |Z C,h | >> |Z S,h |, if it is assumed that Z S,h and are constant during the analysis period, there is a linear relationship between the harmonic voltage and the harmonic current at PCC, and Z S,h can be obtained by regression method. However, when this assumption is not met, there is a large error in obtaining Z S,h by traditional regression method, and the variable coefficient regression method is used to obtain the system harmonic impedance, which regards Z S,h and as functions of time, has greater flexibility, and can obtain more accurate estimation results.

[0069] Further, the step S3 comprises:

[0070] Specifically, seamless sampling technology is used to sample the time-domain signals of the voltage and current at the PCC point, and a 256-point Fourier transform is performed on the voltage and current signals at the PCC point to obtain the voltage phasor of the h-th harmonic. Current phasor of the h-th harmonic The formula for calculating the 256-point Fourier transform of the discrete sampled signals of voltage and current at points A and B is as follows:

[0071]

[0072] Where N is the number of sampling points, n is the index of the sampling point, u(n) is the voltage time-domain signal sampled at the PCC point, i(n) is the current time-domain signal sampled at the PCC point, and U PCC,h I represents the amplitude of the h-th harmonic voltage at point PCC. PCC,h Let θ be the amplitude of the h-th harmonic current at point PCC. U,h Let θ be the phase angle of the h-th harmonic voltage at point PCC. I,h Let be the phase angle of the h-th harmonic current at point PCC. The sliding time window width is always selected as 256 sampling points, and the calculation interval is 78.125 microseconds for each sampling point, to obtain the amplitude and phase data of each harmonic of the time domain signal of the voltage and current at point PCC.

[0073] When there are abrupt changes in background harmonic parameters, the estimated value of the system harmonic impedance near the abrupt change point will deviate from the actual value. Therefore, data partitioning is required before estimating the system harmonic impedance. Because the wavelet transform modulus maxima method can detect abrupt change points that deviate from the actual value when the data fluctuation amplitude is large or the wavelet function and transform scale are not appropriately selected, this application proposes a sliding difference maxima method combined with wavelet transform to accurately locate abrupt change points. The process is as follows: Figure 3 As shown, see reference Figure 3 Furthermore, step S4 includes:

[0074] First, the wavelet transform modulus maxima method is used to find the number and approximate location of data abrupt changes in the harmonic voltage amplitude data at the PCC. Then, the slip difference maxima method is used to precisely locate the location of the data abrupt changes in the harmonic voltage amplitude data at the PCC. If the locations located by the wavelet transform modulus maxima method and the slip difference maxima method differ too much, the transform scale D of the wavelet transform modulus maxima method is changed (initially set to 10, then gradually decreased by 1). The wavelet transform modulus maxima method is used again to find the number and approximate location of data abrupt changes in the harmonic voltage amplitude data at the PCC, until the locations located by the wavelet transform modulus maxima method and the slip difference maxima method are approximately the same, at which point the data abrupt changes are determined. Finally, based on the time corresponding to these data abrupt changes, the harmonic voltage amplitude data at the PCC is divided into several stable intervals.

[0075] The principle of the sliding difference maximum method is as follows: first, the window width w of the sliding window and the sliding difference maximum threshold s are set t Second, the w is taken as the sliding window width to slide point by point, the sum s of the absolute values of the difference between the last w / 2 data and the first w / 2 data in each sliding window is calculated, and the image of s changing with the sliding number i (i = 0, 1,..., n-w) is obtained after traversing all the data, as shown in the following formula:

[0076]

[0077] where j represents the sampling time, j = 1, 2,..., n.

[0078] Finally, the maximum value of s is obtained, the maximum value points are arranged in descending order according to the corresponding maximum values and recorded as S, and w / 2+1 is added to each data in S to record the position of the detected mutation point by the sliding difference maximum method; the number of the mutation points detected by the wavelet transform modulus maximum method is k, and the rough position is index_pre, the first k values of S are recorded as index_deal and compared with index_pre, if the difference is too large, the transform scale D is reduced, and the wavelet transform modulus maximum method is used again until the positions detected by the two methods are approximately the same, and the index_deal is the actual detected mutation time.

[0079] Further, the variable coefficient regression model between the harmonic voltage phasor and the harmonic current phasor is as follows:

[0080]

[0081] where t represents the sampling time, is the first derivative of the hth harmonic voltage phasor at the PCC at time t, is the first derivative of the hth harmonic current phasor at the PCC at time t, and Z S,h (t) is the hth harmonic impedance on the system side at time t, is the hth harmonic voltage phasor on the system side at time t.

[0082] If the background harmonic parameters Z S,h and are regarded as functions changing with time t, the following variable coefficient regression model can be established:

[0083]

[0084] The variable coefficient regression method requires that Z S,h (t) and have a first-order continuous derivative with respect to the sampling time t, that is, Z S,h (t) and The change curve is flat and smooth. In the actual power grid, short-circuit fault, large-capacity power supply, load or capacitor switching will make Z S,h (t) and mutation, at which time the method is not applicable. Therefore, the PCC point harmonic voltage amplitude data needs to be screened first, divided into several relatively stable intervals, and ensure that Z S,h (t) and There is no mutation point.

[0085] Let the regression coefficient matrix corresponding to the sampling time t be For the n groups of harmonic voltage and harmonic current sampling values measured at the PCC, to obtain the regression coefficient matrix corresponding to the sampling time t0, a group of is found to make the following formula minimum.

[0086]

[0087] In the formula, K H (t) is a weighted function, and K H (t) = K(t / H) / H; H is the window width of the kernel function; K(t) is a Gaussian kernel function, and its formula is as follows.

[0088]

[0089] Further, the following is obtained:

[0090] W(t0) = Diag(K H (1-t0), K H (2-t0), …, K H (n-t0))

[0091] Combining the following two formulas:

[0092]

[0093]

[0094] The following can be obtained:

[0095]

[0096] Finally, the system harmonic impedance estimation value is obtained

[0097]

[0098] Further, the optimal window width of the variable coefficient regression model kernel function is selected, including:

[0099] The data of several stable intervals are independently calculated, and the optimal window width of the kernel function of the variable coefficient regression model is obtained by the adaptive particle swarm optimization.

[0100] Specifically, for the variable coefficient regression model, the size of the kernel function window width controls the smoothness of the weighting function. If the value is too large, the regression curve will be too smooth, resulting in insufficient fitting, and if the value is too small, it will cause overfitting of the regression curve. In view of the above problems, the particle swarm optimization algorithm is combined with the generalized cross-validation method to find the optimal window width of the kernel function, and the process is as shown in Figure 4

[0101] Specifically, when the window width is H, the regression coefficient matrix corresponding to the sampling time t0 can be calculated by the variable coefficient regression method, and the fitted value of the dependent variable can be calculated by the following formula:

[0102]

[0103] In the formula, e t0 is a 1×n-dimensional row matrix whose t0th element is 1 and the other elements are 0.

[0104] Combining the following two formulas:

[0105] T(t0)=(X T (t0)*W(t0)*X(t0)) -1 *X T (t0)*W(t0)

[0106]

[0107] The fitted value of the dependent variable at each time can be obtained as:

[0108]

[0109] The statistical quantity GCV(H) of the generalized cross-validation method is defined as:

[0110]

[0111] The window width of the kernel function is taken as the independent variable of the particle swarm optimization algorithm, and the minimum value of the cross-validation statistical quantity is taken as the objective function of the particle swarm optimization algorithm. The optimal window width is selected by iteration, thereby improving the estimation accuracy and tracking ability of the system harmonic impedance.

[0112] Further, the calculation of the system harmonic impedance according to the variable coefficient regression model and the optimal window width comprises:

[0113] The system harmonic impedance is calculated according to the following formula:

[0114] ​

[0115]

[0116] in, The regression coefficient matrix is ​​obtained based on the optimal window width. This is the estimated value of the system-side harmonic impedance;

[0117] Based on the above formula, it can be seen that by combining the variable coefficient regression method, the regression coefficient matrix corresponding to each sampling time can be obtained, and the estimated value of the system harmonic impedance at each time can be obtained.

[0118] To better illustrate the embodiments of this application, a specific embodiment is described in detail below:

[0119] Taking the 5th harmonic as an example, the specific parameters are shown in the table below.

[0120]

[0121] Where Mean is the average value of the set parameters, and Sin and Randn are the sinusoidal fluctuation and random disturbance applied to Mean, respectively.

[0122] Sampling was performed every 0.02 seconds, with the simulation time set to 18 seconds, resulting in 900 sets of sample data. To simulate the changes in background harmonic parameters, Z was set... S,5 The change occurs in the [6s, 12s] time. The change occurs between [3s, 15s], where the amplitudes of the PCC harmonic voltage and current are as follows: Figure 5 (A) and Figure 5 As shown in (B), the correlation analysis is as follows: Figure 5 As shown in (C). Figure 5 (C) It can be seen that there is a certain relationship between the harmonic voltage and harmonic current at PCC, and they can be roughly divided into three states, corresponding to different operating scenarios, i.e., different Z values. S,5 and

[0123] Depend on Figure 5 (A) Figure 5 (B) It can be seen that the location of harmonic voltage abrupt change points at PCC is easier to distinguish than the location of harmonic current abrupt change points throughout the entire sampling time. Therefore, it is necessary to detect the abrupt change points of harmonic voltage amplitude data at PCC. The wavelet function is "DB2", the transform scale after iteration is 3, and the sliding window width is 25 sample points. The results are as follows: Figure 6 As shown.

[0124] The data mutation point positions obtained by different algorithms are shown in the following table. It can be seen that the wavelet transform and sliding difference maximum value method proposed in the embodiment of the application can more accurately locate the position of the harmonic voltage amplitude data mutation point at the PCC, and the wavelet transform modulus maximum value method is more accurate than the wavelet transform and sliding difference maximum value method. Figure 6 The positions corresponding to the four maximum points.

[0125] Method Mutation point position True value 151，301，601，751 Wavelet transform modulus maxima method 153，301，598，750 Wavelet transform and sliding difference maxima method 151，301，601，751

[0126] The robust regression method is used as method 1, the dominant fluctuation method is used as method 2, the sliding window method is used to track the time-varying characteristics of the system harmonic impedance, the window width is 25 sample points, and the interpolation function is used to supplement the missing data. The improved particle swarm variable coefficient regression method proposed in the embodiment of the application is used as method 3, and three data screening methods are added for comparison and analysis.

[0127] Figure 7 (A), Figure 7 (B) are respectively the comparison of the estimated system harmonic impedance results of different methods with or without data screening. It can be seen that under the premise of no data screening, the estimated values of the three methods will suddenly increase or decrease at the positions of the system harmonic impedance and the background harmonic voltage mutation. After data screening based on the wavelet transform sliding difference maximum value method proposed in the embodiment of the application, the estimation error at the data mutation point is eliminated, so that the estimated values of the three methods are obviously closer to the true value. Among the six estimation methods with or without data screening, the estimation curve of the improved particle swarm variable coefficient regression method with data screening proposed in the embodiment of the application is closest to the true value, which verifies the effectiveness and superiority of the method proposed in the embodiment of the application.

[0128] Further, the calculation error of the system harmonic parameters of different algorithms is compared, as shown in the following table. RMSE represents the root mean square error, and MAPE represents the average absolute percentage error. It can be seen that under the above simulation conditions, the method of pre-screening data has smaller error than the method without data screening, which again reflects the necessity and effectiveness of data screening. The error indexes of the improved particle swarm variable coefficient regression method with data screening proposed in the embodiment of the application are far less than those of other methods, which indicates that the proposed method is superior to other algorithms in terms of estimation accuracy, and verifies the accuracy and superiority of the method proposed in the embodiment of the application.

[0129]

[0130] The embodiment of the application also provides a system side harmonic impedance estimation device, which comprises a module for implementing the system side harmonic impedance estimation method as described above.

[0131] It should be noted that the system-side harmonic impedance estimation device of the embodiment corresponds to the system-side harmonic impedance estimation method of the above-mentioned embodiment, and thus the system-side harmonic impedance estimation device of the above-mentioned embodiment can refer to the content of the system-side harmonic impedance estimation method of the above-mentioned embodiment for the parts not described in detail, that is, the specific step content described in the system-side harmonic impedance estimation method of the above-mentioned embodiment can be understood as the function that the system-side harmonic impedance estimation device of the above-mentioned embodiment can achieve, and thus the above-mentioned parts will not be described here.

[0132] Another embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the system-side harmonic impedance estimation method according to the above-mentioned embodiment.

[0133] Specifically, the computer readable storage medium can include any entity or recording medium that can carry the computer program instructions, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, and the like.

[0134] Another embodiment of the present application provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor implements the system-side harmonic impedance estimation method according to the above-mentioned embodiment when executing the computer program.

[0135] The electronic device can also include a bus that connects different components (including the memory and the processor). The memory can include a computer readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The memory can also include at least one program product having a set (for example, at least one) of program modules configured to perform the functions of the embodiments of the present application. The electronic device can also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device, and / or with any devices (such as a network card) that enable the electronic device to communicate with one or more other computing devices. Such communication can be carried out through an input / output (I / O) interface, and the electronic device can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter.

[0136] In summary, the various embodiments of the present application respectively provide a system-side harmonic impedance estimation method and device, a computer readable storage medium and an electronic device, which have the following advantages:

[0137] (1) Data volume control: The cyclic storage module and event triggering mechanism are adopted to record only the data of key events, avoiding the recording of excessive redundant data; in this way, the data volume can be effectively controlled, the storage demand can be reduced, and the storage cost can be lowered;

[0138] (2) High precision and accuracy: By adopting high-resolution periodic sampling, the details of voltage and current waveforms can be captured, and transient events can be analyzed more accurately; by using interval period comparison triggering, fault overvoltage events can be more accurately identified and recorded, and the accuracy and reliability of fault detection can be improved;

[0139] (3) Real-time monitoring and response: The intelligent event triggering mechanism is equipped to monitor the running state of the power system in real time, and to collect relevant data immediately when a key event occurs; in this way, potential problems can be responded to quickly, the fault diagnosis time can be reduced, and the reliability of the system can be improved;

[0140] (4) Flexibility and scalability: The scheme can be flexibly adjusted and extended according to actual needs; the threshold and parameters can be adjusted according to the requirements of specific applications to adapt to different power system environments; at the same time, the storage capacity can be increased as needed to meet the needs of more data storage.

[0141] In summary, the embodiments of the present application can effectively solve the problem of excessive transient sampling data volume in modern power system monitoring devices and improve the monitoring and protection capability of the power system by controlling the data volume, improving the accuracy, realizing real-time monitoring, and being flexible and scalable.

[0142] The above has described the embodiments of the present application, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles, practical applications, or technical improvements in the market of the embodiments, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A system-side harmonic impedance estimation method, characterized by, The method comprises: obtaining a reduced model of the power grid based on the power grid characteristics h subharmonic parameter equivalent circuit; h is a positive integer greater than or equal to 2. According to the h The sub-harmonic parameter equivalent circuit establishes a mathematical model for estimating the system side harmonic impedance. obtaining the amplitude-phase data of the harmonic voltage and the harmonic current at the PCC; dividing the harmonic voltage amplitude data at the PCC into several stable intervals; establishing a variable coefficient regression model between the harmonic voltage phasor and the harmonic current phasor according to the amplitude-phase data of the harmonic voltage and the harmonic current of the several stable intervals; selecting the optimal window width of the kernel function of the variable coefficient regression model; calculating the system harmonic impedance according to the variable coefficient regression model and the optimal window width; wherein the dividing of the harmonic voltage amplitude data at the PCC into several stable intervals comprises: Firstly, the number and the rough position of the data mutation points in the harmonic voltage amplitude data at the PCC are found by using the wavelet transform modulus maxima method, and then the position of the data mutation points in the harmonic voltage amplitude data at the PCC is accurately located by combining the sliding difference maxima method; if the positions located by the wavelet transform modulus maxima method and the sliding difference maxima method are too different, the transform scale of the wavelet transform modulus maxima method is changed D , the number and the rough position of the data mutation points in the harmonic voltage amplitude data at the PCC are found again by using the wavelet transform modulus maxima method until the positions located by the wavelet transform modulus maxima method and the sliding difference maxima method are approximately the same, and then the data mutation points are determined; finally, the harmonic voltage amplitude data at the PCC is divided into several stable intervals according to the time corresponding to the data mutation points.

2. The method of claim 1, wherein, The acquisition is according to the grid characteristic simplification h The sub-harmonic parameter equivalent circuit comprises: The background harmonic of the superior power grid is equivalent to a harmonic voltage source, and the nonlinear load of the current power grid is equivalent to a harmonic current source. The equivalent circuit model of Thevenin-Norton is obtained by converting them to the same voltage level. h Sub-harmonic parameter equivalent circuit.

3. The method of claim 2, wherein, The mathematical model of the system-side harmonic impedance estimation is as follows: wherein is the system side h sub-harmonic impedance, is the system side h sub-harmonic voltage phasor, is the PCC side h sub-harmonic current phasor, is the PCC side h sub-harmonic voltage phasor.

4. The method of claim 3, wherein, The variable coefficient regression model between the harmonic voltage phasor and the harmonic current phasor is as follows: wherein, t denotes the sampling time instant, is t the voltage phasor at the PCC at time instant h the second harmonic voltage phasor, is t the current phasor at the PCC at time instant h the second harmonic current phasor, is t the system side impedance at time instant h the second harmonic impedance, is t the system side voltage phasor at time instant h the second harmonic voltage phasor.

5. The method of claim 4, wherein, The selecting of the optimal window width of the kernel function of the variable coefficient regression model comprises: independent calculation of the data of the several stable intervals, and optimization of the optimal window width of the kernel function of the variable coefficient regression model of each stable interval through the adaptive particle swarm algorithm.

6. The method of claim 5, wherein, The calculating of the system harmonic impedance according to the variable coefficient regression model and the optimal window width comprises: calculating the system harmonic impedance according to the following formula; wherein, is a regression coefficient matrix obtained from the optimal window width, is a system-side harmonic impedance estimate.

7. A system-side harmonic impedance estimation device, characterized by comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the system-side harmonic impedance estimation method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the system-side harmonic impedance estimation method according to any one of claims 1-6.

9. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the system-side harmonic impedance estimation method according to any one of claims 1-6. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the system-side harmonic impedance estimation method according to any one of claims 1-6.

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