Direct-current micro-grid dynamic state estimation method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of power system perception, in particular to a method and device for estimating the dynamic state of a direct current micro-grid, electronic equipment and a medium, and the method comprises the steps: obtaining target measurement data meeting a preset updating condition based on target pseudo measurement data; based on the target measurement data, determining a first estimation result of a pre-constructed DC micro-grid equivalent model; and obtaining a second estimation result of the target detailed switch model, determining a target compensation item according to the first estimation result and the second estimation result, and superposing the target compensation item to the first estimation result to obtain a final DC micro-grid dynamic state estimation result. Therefore, the problems that linearization errors exist and calculation complexity is high when direct current micro-grid dynamic state estimation is processed in the prior art, adaptability to non-Gaussian noise is limited, and accuracy and robustness of direct current micro-grid dynamic state estimation are reduced are solved.

Description

Technical Field

[0001] The present application relates to the field of power system sensing technology, and in particular to a method, device, electronic device and medium for estimating the dynamic state of a direct current microgrid. Background Art

[0002] Since the negative impedance characteristics of the constant power load in the existing DC microgrid will reduce the system damping, causing the DC bus voltage to oscillate or even collapse, and the current DC microgrid lacks sufficient voltage and current sensors, the actual system is not completely observable. In addition, the influence of noise also makes the existing measurement reliability insufficient, which in turn leads to a decrease in control performance. The increase in sensor sampling frequency makes it possible to monitor and track system state variables. Therefore, the use of DSE (dynamic state estimation) method to monitor the DC microgrid in real time and design appropriate stability performance control is the focus of current research.

[0003] However, the related technology has linearization errors and high computational complexity when processing the dynamic state estimation of DC microgrids, and has limited adaptability to non-Gaussian noise, resulting in insufficient accuracy of dynamic state estimation in noisy environments, reducing the accuracy and robustness of dynamic state estimation of DC microgrids, which needs to be urgently resolved. Summary of the invention

[0004] The present application provides a method, device, electronic device and medium for estimating the dynamic state of a DC microgrid, so as to solve the problems in the related art that there is linearization error and high computational complexity when processing the dynamic state estimation of a DC microgrid, and the adaptability to non-Gaussian noise is limited, resulting in insufficient accuracy of dynamic state estimation in a noisy environment, reducing the accuracy and robustness of dynamic state estimation of a DC microgrid, and the like.

[0005] The first aspect of the present application provides a method for estimating the dynamic state of a DC microgrid, comprising the following steps: based on target pseudo-measurement data, obtaining target measurement data that meets preset update conditions; based on the target measurement data, determining a first estimation result of a pre-constructed DC microgrid equivalent model; obtaining a second estimation result of a target detailed switch model, and determining a target compensation term based on the first estimation result and the second estimation result, and superimposing the target compensation term on the first estimation result to obtain a final DC microgrid dynamic state estimation result.

[0006] Optionally, in one embodiment of the present application, obtaining target measurement data that meets preset update conditions based on target pseudo-measurement data includes: collecting first measurement data and second measurement data that meet preset adjacent conditions in a DC microgrid system; generating the target pseudo-measurement data based on the first measurement data and the second measurement data; and adding the target pseudo-measurement data between the first measurement data and the second measurement data to obtain the target measurement data that meets the preset update conditions.

[0007] Optionally, in one embodiment of the present application, obtaining a second estimation result of the target detailed switch model and determining a target compensation item based on the first estimation result and the second estimation result include: determining the second estimation result of the target detailed switch model using the target measurement data; calculating a target difference between the first estimation result and the second estimation result to determine the target compensation item based on the target difference.

[0008] Optionally, in one embodiment of the present application, the calculation formula of the target pseudo measurement data is:

[0009]

[0010] Among them, z k-1 is the measured value at time k-1, z k is the measured value at time k, z i is the estimated value at the i-th moment.

[0011] The second aspect of the present application provides a device for estimating the dynamic state of a DC microgrid, including: an acquisition module, used to obtain target measurement data that meets preset update conditions based on target pseudo-measurement data; a determination module, used to determine a first estimation result of a pre-constructed DC microgrid equivalent model based on the target measurement data; an estimation module, used to obtain a second estimation result of a target detailed switch model, and determine a target compensation item based on the first estimation result and the second estimation result, and superimpose the target compensation item on the first estimation result to obtain a final DC microgrid dynamic state estimation result.

[0012] Optionally, in one embodiment of the present application, the acquisition module includes: an acquisition unit, used to acquire first measurement data and second measurement data that meet preset adjacent conditions in the DC microgrid system; a generation unit, used to generate the target pseudo-measurement data based on the first measurement data and the second measurement data; an acquisition unit, used to add the target pseudo-measurement data between the first measurement data and the second measurement data to obtain the target measurement data that meets the preset update conditions.

[0013] Optionally, in one embodiment of the present application, the estimation module includes: a first determination unit, used to determine a second estimation result of the target detailed switch model using the target measurement data; and a second determination unit, used to calculate a target difference between the first estimation result and the second estimation result to determine the target compensation item based on the target difference.

[0014] Optionally, in one embodiment of the present application, the calculation formula of the target pseudo measurement data is:

[0015]

[0016] Among them, z k-1 is the measured value at time k-1, z k is the measured value at time k, z i is the estimated value at the i-th moment.

[0017] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for estimating the dynamic state of a DC microgrid as described in the above embodiment.

[0018] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above method for estimating the dynamic state of a DC microgrid.

[0019] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above method for estimating the dynamic state of a DC microgrid.

[0020] The embodiment of the present application can obtain target measurement data that meets the update conditions based on the target pseudo-measurement data, and then determine the first estimation result of the pre-constructed DC microgrid equivalent model. Then, the target compensation term is determined according to the first estimation result and the second estimation result of the acquired target detailed switch model, and the target compensation term is superimposed on the first estimation result to obtain the final DC microgrid dynamic state estimation result, which effectively improves the accuracy and robustness of the DC microgrid dynamic state estimation. Thus, the problems in the related art that there is a linearization error and high computational complexity when processing the DC microgrid dynamic state estimation, and the adaptability to non-Gaussian noise is limited, which reduces the accuracy and robustness of the DC microgrid dynamic state estimation.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flow chart of a method for estimating the dynamic state of a DC microgrid provided according to an embodiment of the present application;

[0024] Figure 2 A schematic diagram of generating pseudo measurement data by interpolation method according to a specific embodiment of the present application;

[0025] Figure 3 A schematic diagram of a photovoltaic and energy storage independent DC microgrid topology according to a specific embodiment of the present application;

[0026] Figure 4 A schematic diagram of an equivalent model of a photovoltaic and energy storage independent DC microgrid according to a specific embodiment of the present application;

[0027] Figure 5 A schematic diagram comparing modeling results of different models in a specific embodiment of the present application;

[0028] Figure 6 A schematic diagram of the effect of model accuracy on dynamic state estimation according to a specific embodiment of the present application;

[0029] Figure 7 This is a schematic diagram of the DC bus control effect under complex noise according to a specific embodiment of the present application;

[0030] Figure 8 A schematic diagram of a DC bus control effect when the load changes according to a specific embodiment of the present application;

[0031] Fig. 9 A schematic diagram of the structure of a device for estimating the dynamic state of a DC microgrid provided according to an embodiment of the present application;

[0032] Fig.10 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0033] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0034] The following describes the method, device, electronic device and medium of the DC microgrid dynamic state estimation of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the related technology mentioned in the background technology center mentioned above has linearization error and high computational complexity when processing the dynamic state estimation of the DC microgrid, and has limited adaptability to non-Gaussian noise, which reduces the accuracy and robustness of the dynamic state estimation of the DC microgrid, the present application provides a method for dynamic state estimation of the DC microgrid, in which the target measurement data that meets the update condition can be obtained based on the target pseudo-measurement data, and then the first estimation result of the pre-constructed DC microgrid equivalent model is determined, and then the target compensation term is determined according to the first estimation result and the second estimation result of the obtained target detailed switch model, and the target compensation term is superimposed on the first estimation result to obtain the final DC microgrid dynamic state estimation result, which effectively improves the accuracy and robustness of the DC microgrid dynamic state estimation. As a result, the problem that the related technology has linearization error and high computational complexity when processing the dynamic state estimation of the DC microgrid, and has limited adaptability to non-Gaussian noise, which reduces the accuracy and robustness of the dynamic state estimation of the DC microgrid, is solved.

[0035] Specifically, Figure 1 A flowchart of a method for estimating the dynamic state of a DC microgrid provided in an embodiment of the present application.

[0036] like Figure 1 As shown, the method for estimating the dynamic state of a DC microgrid includes the following steps:

[0037] In step S101 , target measurement data satisfying a preset update condition is obtained based on target pseudo measurement data.

[0038] It can be understood that in constructing the discretized DC microgrid state space model, the state equation and measurement equation of the corresponding mathematical model can be expressed as:

[0039] x k+1 =f(x k ,u k )+ω k

[0040] z k+1 =h(x k+1 ,u k+1 )+v k+1

[0041] Among them, x k is the state variable at time k, u k is the control variable at time k, ω k and v k+1 represent process noise and measurement noise respectively.

[0042] For the measurement noise v k+1 , is the true measurement value z k+1 The difference between the measured value and the measurement equation h(x) is that since the measured value is selected to be equal to the DC bus voltage in the state variable in the measurement equation, there is no approximation and simplification error in the model, and the measurement error mainly depends on the size of the measurement noise.

[0043] For process noise ω k , is the true value of the state variable x k The difference between the differential equation f(x) reflects the accuracy of the model and the dynamic change process. Therefore, the error mainly comes from the modeling error caused by the simplification of some components when modeling the original differential equation, the discretization error caused by the differentiation process, and the error caused by inaccurate model parameters.

[0044] Therefore, in order to address the discretization error caused by the conversion from differential equations to difference equations, the embodiment of the present application can add pseudo-measurement data between two adjacent measurement data by means of linear interpolation, which will be specifically explained in the following steps, so as to obtain target measurement data that meets the update conditions, thereby shortening the sampling step and effectively improving the feasibility of dynamic state estimation of the DC microgrid.

[0045] It should be noted that the preset update condition is a condition of adding pseudo measurement data to the original measurement data.

[0046] Among them, in one embodiment of the present application, based on the target pseudo-measurement data, target measurement data that meets the preset update condition is obtained, including: collecting first measurement data and second measurement data that meet the preset adjacent condition in the DC microgrid system; generating target pseudo-measurement data based on the first measurement data and the second measurement data; adding the target pseudo-measurement data between the first measurement data and the second measurement data to obtain the target measurement data that meets the preset update condition.

[0047] In the embodiment of the present application, the preset adjacent condition is a condition that two collected measurement data are continuous.

[0048] In the actual implementation process, the process noise mainly includes modeling error, discretization error and parameter error. Among them, the discretization error refers to the error caused by using the numerical approximate integration method instead of the definite integral operation in the process of discretizing the state equation to obtain the differential equation. Therefore, the discretization error is related to the sampling step, the discretization method used, and the characteristics of the differential state equation; among them, the characteristics of the state equation are known and cannot be changed. The discretization method generally uses the Euler method. Therefore, the process noise can be reduced here by shortening the sampling step.

[0049] Due to the limitation of the sensor sampling frequency, which can generally only reach 50-60Hz, in order to further shorten the sampling step, it is necessary to increase the corresponding pseudo-measurement data. In theory, the higher the accuracy of the pseudo-measurement data generated, the better the improvement of the estimation effect. However, increasing the interpolation factor r will improve the estimation accuracy more than the optimization of the interpolation algorithm. Therefore, in order to reduce the calculation time, the linear interpolation method is used in the embodiment of the present application to obtain the pseudo-measurement data.

[0050] Among them, the embodiment of the present application can add pseudo measurement data between two adjacent measurement data by linear interpolation, for example, Figure 2 As shown, by interpolation, according to the measured value z at time k-1 k-1 and the measured value z at time k k To estimate the null value at the i-th moment, the estimated value at the i-th moment, that is, the calculation formula of the target pseudo-measurement data is:

[0051]

[0052] Among them, z k-1 is the measured value at time k-1, z k is the measured value at time k, z i is the estimated value at the i-th moment.

[0053] In this way, pseudo measurement data can be determined and added between two adjacent measurement data to obtain target measurement data that meets the update conditions, which effectively shortens the sampling step and improves the accuracy of algorithm estimation.

[0054] In step S102, a first estimation result of a pre-constructed DC microgrid equivalent model is determined based on target measurement data.

[0055] It can be understood that the embodiment of the present application can input the target measurement data into a pre-built DC microgrid equivalent model, and use the unscented Kalman filter algorithm to obtain a first estimation result, that is, a preliminary estimation value of the unscented Kalman algorithm, which effectively improves the accuracy of the dynamic state estimation of the DC microgrid.

[0056] In the embodiment of the present application, the DC microgrid can be modeled first, where Figure 3As shown in the figure, it is a DC microgrid topology diagram, which is mainly composed of photovoltaic array, energy storage battery, DC / DC (direct current-direct current) converter, constant power load and DC / AC (direct current-alternating current) converter. Among them, the photovoltaic array cascade Boost converter achieves maximum power output through the MPPT (Maximum PowerPoint Tracking) algorithm, and MPPT adopts the perturbation observation method; the energy storage battery controls the DC bus voltage through the bidirectional DC / DC circuit, and the DC bus is connected to the AC grid through the VSC (Voltage Source Converter).

[0057] Next, when mathematically modeling the DC microgrid, it is generally chosen to replace each micro-source part with equivalent, thereby reducing the dimension of the state variable. The equivalent model is as follows: Figure 4 As shown, according to Figure 4 It can be seen that the microgrid model can be obtained using KVL (Kirchhoff's Voltage Law) and KCL (Kirchhoff's Current Law), that is:

[0058]

[0059] Among them, v dc is the equivalent DC voltage, r s , L s , C s are the equivalent resistance, inductance and capacitance of the power supply side, r1, L1, C1 are the resistance, inductance and capacitance of the filter circuit, P1 is the constant power load size, i es is the equivalent current on the energy storage side, u cs is the equivalent capacitor voltage on the power supply side, u c1 is the capacitor voltage of the filter circuit, i L1 is the capacitor current of the filter circuit.

[0060] According to the above differential equation, the differential state equation can be further obtained by Euler method:

[0061]

[0062] Among them, X, H, A, B, U, and D are all matrices.

[0063] Among them, the matrices are:

[0064] X=[i Ls u Cs i L1 u C1 ] T ,U=[v dci es ] T

[0065] H=[1 / i Ls 1 / u Cs 1 / i L1 1 / u C1 ] T

[0066]

[0067] Furthermore, the unscented Kalman filter algorithm in the embodiment of the present application mainly includes the following parts:

[0068] 1) Time update step: Use unscented transformation to estimate the value of the state variable at time k and the covariance matrix P k The Sigma point set, sampling point and weight expressions are obtained as follows:

[0069]

[0070] in, is the estimated value of the state variable at time k; n is the dimension of the state variable; is the Sigma point set; λ is the scaling factor; α is the UT transformation coefficient; k is the default coefficient, generally k=3-n; β is the regularization coefficient, generally β=2; for the weight ω, the superscript m represents the mean, the superscript c represents the covariance, and the subscript i represents the i-th sampling point.

[0071] 2) State prediction step: Calculate the mean and covariance matrix of the one-step prediction value of the Sigma point set. The specific expression is:

[0072]

[0073] in, is the new Sigma point set, is the mean of the one-step prediction values, P k+1|k is the covariance matrix of the one-step forecast, Q k is the process noise covariance matrix.

[0074] 3) Measurement update step: based on the one-step prediction value Generate new Sigma point set by unscented transformation Then substitute the measurement equation to get the new predicted measurement value, and further get the error covariance matrix and the cross covariance matrix

[0075]

[0076] in, is the prior estimated mean of the system observations, R is the observation noise covariance matrix, is the error covariance matrix, is the cross covariance matrix.

[0077] 4) State update step: Calculate the Kalman gain at time k+1 and update the state estimate of the system at the next moment and covariance P k+1 :

[0078]

[0079] In step S103, a second estimation result of the target detailed switch model is obtained, and a target compensation term is determined according to the first estimation result and the second estimation result, and the target compensation term is superimposed on the first estimation result to obtain a final DC microgrid dynamic state estimation result.

[0080] It can be understood that the embodiment of the present application can obtain the second estimation result of the target detailed switch model. For example, in order to improve the accuracy of DC bus voltage estimation, the embodiment of the present application introduces a detailed switch model, determines the second estimation result of the detailed switch model through target measurement data, and determines the target compensation item according to the first estimation result and the second estimation result, that is, the solution results of the detailed switch model and the equivalent model are taken as input, and the difference between the two is taken as output. The output result can be considered as a compensation item for ripple based on the equivalent model, and the compensation item is superimposed on the first estimation result to obtain the final DC microgrid dynamic state estimation result, thereby reducing the process noise caused by equivalent alternative modeling and effectively improving the accuracy and robustness of the DC microgrid dynamic state estimation.

[0081] Optionally, in one embodiment of the present application, a second estimation result of the target detailed switch model is obtained, and a target compensation item is determined based on the first estimation result and the second estimation result, including: using target measurement data to determine the second estimation result of the target detailed switch model; calculating a target difference between the first estimation result and the second estimation result to determine the target compensation item based on the target difference.

[0082] As a possible implementation method, the equivalent model makes equivalent substitutions for some micro-sources, so the model accuracy is somewhat different from the theoretical results. In the mathematical modeling of microgrids, a more accurate switch model can be established by considering the on and off states of the converter.

[0083] like Figure 5The figure shows the comparison results of different mathematical models and time domain simulation models. It can be seen that the equivalent model cannot describe the DC voltage ripple due to the simplification of the converter part; the switch model can describe the voltage ripple more accurately, but the complex dynamic equation will make the state matrix large in size, and the nonlinear and discontinuous characteristics will easily lead to divergence of the estimation results.

[0084] In addition, the purpose of the Kalman algorithm is to filter out noise so that the filtering result is as consistent as possible with the model. Figure 6 As shown in the figure, when an equivalent model is used for dynamic state estimation, since the filtering result is close to the true value of the equivalent model, the error between the estimated result and the actual true value mainly comes from the gap between the average value and the ripple, that is, the difference between the blue curve and the green curve in the figure, which corresponds to the modeling error in the process noise. At this time, the negative effect of the process noise may be greater than the filtering effect, resulting in poor estimation results.

[0085] Secondly, in order to improve the accuracy of DC bus voltage estimation, the embodiment of the present application considers introducing a detailed switch model. Due to its discontinuous and nonlinear characteristics, it cannot be directly applied to the differential equation, but its model accuracy is significantly higher than that of the equivalent model. Therefore, the solution results of the detailed switch model and the equivalent model are used as input, and the difference between the two is used as output. The output result can be considered as a compensation term for the ripple based on the equivalent model, and then it is superimposed on the preliminary estimation result of the unscented Kalman filter solved by the equivalent model, thereby improving the model accuracy and reducing the process noise caused by the equivalent substitution modeling.

[0086] For example, in order to verify the effectiveness of the present invention, Figure 3 Taking the DC microgrid shown in the figure as an example, the filtering performance and tracking performance of ICUKF (Interpolated Compensation Unscented Kalman Filter) and ICEKF (Interpolated Compensation Extended Kalman Filter) are verified. Among them, Table 1 is the photovoltaic, energy storage and network parameter table in the simulation, and the specific Table 1 is as follows:

[0087] Table 1

[0088]

[0089] In actual microgrids, due to the complexity of the operating environment and working conditions, the noise variance often increases or the mean is non-zero. In order to verify the robustness of the estimation algorithm, the following measurement noise models are set up.

[0090] ① Gaussian noise:

[0091]

[0092] Where s is the scale parameter; μ is the average value of the random quantity s. When μ = 0, the measurement noise is Gaussian white noise; when μ ≠ 0, the measurement noise is Gaussian biased noise.

[0093] ② Laplace noise:

[0094] r L =ms·sgn(U1)ln(1-|U1|)

[0095] Among them, m is the mean value, s is the scale parameter, and U1 is a random quantity that obeys a uniform distribution within the sampling interval.

[0096] ③ Uniformly distributed noise

[0097]

[0098] Where b and a are the maximum and minimum values ​​of the noise respectively.

[0099] A simulation model of a DC microgrid is established in MATLAB / Simulink, and the operation results are regarded as ideal true values. Gaussian white noise, Gaussian biased noise, Laplace noise, and uniformly distributed noise are superimposed on the true values ​​as measured values. Assuming that the sensor acquisition frequency is 60Hz, Table 2 is a comparison table of evaluation indicators of control effects of different algorithms under complex noise. The parameter settings of several noises used in the example are shown in Table 2:

[0100] Table 2

[0101]

[0102] Assume that the estimated result at the kth moment is The true value of the measurement is z k , the reference voltage of the energy storage module control part is u ref , establish the quantitative evaluation index of DC microgrid dynamic state estimation: where M e 、M ae 、M mse Used to evaluate the filtering effect of the algorithm, I sse ,I ae ,I mse Used to evaluate the tracking effect of the controller:

[0103]

[0104] Among them, M e 、M ae 、M mse Both are quantitative evaluation indicators for evaluating the filtering effect of the algorithm. sse,I ae ,I mse Both are quantitative evaluation indicators for evaluating the tracking effect of the controller.

[0105] Figure 7 (a)-(d) in the figure respectively represent the DC bus control effect of Gaussian white noise, Gaussian biased noise, Laplace noise, and uniformly distributed noise. It can be seen that the actual operation result of the DC bus voltage under the influence of noise will deviate from the reference voltage. The more serious the noise, the worse the control effect. Especially when there is biased noise in the system, the actual operation result will have a significant deviation from the target voltage. By adding the Kalman filtering algorithm proposed in this paper, the estimation result can be as close to the ideal state as possible, so that the controller can maintain good tracking performance when facing different measurement noises, thereby improving the stability of the system.

[0106] To further demonstrate the estimation accuracy and robustness of the proposed algorithm, Table 3 gives a comparison of the evaluation indicators of the above situation under different noises. Table 3 is a comparison table of evaluation indicators of filtering effects of different algorithms under complex noises, as follows:

[0107] Table 3

[0108]

[0109] Among them, KF in Table 3 is (Kalman Filter).

[0110] Depend on Figure 8 It can be seen that when facing different noises, ICUKF and ICEKF have been significantly improved compared with no filtering. For the DC bus voltage, under Gaussian white noise, ICUKF has improved the filtering accuracy by 92.87% compared with no filtering; under Gaussian biased noise, ICUKF has improved the filtering accuracy by 97.69% compared with no filtering; under Laplace noise, ICUKF has improved the filtering accuracy by 92.49% compared with no filtering; under uniformly distributed noise, ICUKF has improved the filtering accuracy by 93.46% compared with no filtering. In all simulations, ICUKF has better filtering effect than ICEKF, which is because the UKF algorithm has higher linearization accuracy than the EKF algorithm.

[0111] The control effects of different algorithms are shown in Table 4, where Table 4 is a comparison table of evaluation indicators of control effects of different algorithms under complex noise. The specific Table 4 is as follows:

[0112] Table 4

[0113]

[0114]

[0115] Indicator I in Table 4 sse Essentially, it represents the change in the measurement of tracking error, I sse The lower it is, the better the controller can track the expected value. sse The higher the value, the lower the controller's ability to track the expected value. Therefore, it can be found that without considering the filtering algorithm, the more serious the noise, the lower the robustness of the controller, especially under the influence of biased noise, the control result of the actual voltage will deviate seriously from the reference voltage. By considering the ICUKF algorithm proposed in this paper, the measured data can filter out the noise on the controller input side, thereby achieving better tracking performance in complex noise systems.

[0116] In general, data with measurement errors greater than ±3σ are considered bad data. In the power system, there are about 2% bad data. Under steady-state conditions, the measurement errors at different times are increased by 5%, 10% and 50%, respectively. Table 5 shows the relative error comparison of the estimated values ​​of the above algorithms. Table 5 is a comparison table of evaluation indicators of filtering effects of different algorithms under bad data. The specific table 5 is as follows:

[0117] Table 5

[0118]

[0119] As shown in Table 5, when the system has a 5% error, ICEKF and ICUKF can reduce the relative error to the original 4.07% and 4.12%; when the system has a 10% error, ICEKF and ICUKF can reduce the relative error to the original 1.82% and 1.96%; when the system has a 30% error, ICEKF and ICUKF can reduce the relative error to the original 20.86% and 0.12%. Therefore, when there is bad data in the system measurement and the error is small, there is no obvious difference in the estimation results of the UKF (Unscented Kalman Filter) and EKF (Extended Kalman Filter) algorithms; when the measurement error is large, the estimated value obtained by the UKF algorithm considering compensation is closer to the true value than the EKF algorithm, and the relative error is smaller.

[0120] Assume that the constant power load is switched at 3.5s, the power increases from 10kW to 20kW, and the simulated system runs abnormally to observe the tracking performance of the filtering algorithm.

[0121] Figure 8The control effects of different algorithms when the system undergoes a sudden change under the influence of biased noise are given. It can be found that the transient process of the system will have obvious fluctuations due to the influence of noise. The proposed method can track the DC bus under the condition of constant power load sudden change, and can recover to steady state faster than the former, thereby improving the stability of the system.

[0122] In addition, dynamic state estimation requires that the numerical solution speed of each prediction and correction step must be faster than the scanning speed of the PMU (Phasor Measurement Unit). For the proposed ICEKF and ICUFK algorithms, the PMU acquisition frequency is set to 60Hz, and the total sampling is 180 times. The calculation time is 0.2025s and 1.1517s respectively, and the single calculation time is 0.0011 and 0.0064s, which is less than the single data sampling period of 0.0167s. Even if the algorithm needs to consider the compensation mechanism, the calculation time of the proposed algorithm can meet the real-time requirements of state estimation.

[0123] According to the method for estimating the dynamic state of a DC microgrid proposed in the embodiment of the present application, the target measurement data that meets the update conditions can be obtained based on the target pseudo-measurement data, and then the first estimation result of the pre-constructed DC microgrid equivalent model can be determined. Then, the target compensation term is determined according to the first estimation result and the second estimation result of the acquired target detailed switch model, and the target compensation term is superimposed on the first estimation result to obtain the final DC microgrid dynamic state estimation result, which effectively improves the accuracy and robustness of the DC microgrid dynamic state estimation. As a result, the problems in the related art that there is a linearization error and high computational complexity when processing the DC microgrid dynamic state estimation, and the adaptability to non-Gaussian noise is limited, which reduces the accuracy and robustness of the DC microgrid dynamic state estimation.

[0124] Next, a device for estimating the dynamic state of a DC microgrid proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0125] Fig. 9 It is a block diagram of a device for estimating the dynamic state of a DC microgrid according to an embodiment of the present application.

[0126] like Fig. 9 As shown, the device 10 for estimating the dynamic state of a DC microgrid includes: an acquisition module 100 , a determination module 200 and an estimation module 300 .

[0127] Specifically, the acquisition module 100 is used to obtain target measurement data that meets a preset update condition based on the target pseudo measurement data.

[0128] The determination module 200 is used to determine a first estimation result of a pre-constructed DC microgrid equivalent model based on target measurement data.

[0129] The estimation module 300 is used to obtain a second estimation result of the target detailed switch model, determine a target compensation item according to the first estimation result and the second estimation result, and superimpose the target compensation item on the first estimation result to obtain a final DC microgrid dynamic state estimation result.

[0130] Optionally, in one embodiment of the present application, the acquisition module 100 includes: a collection unit, a generation unit and an acquisition unit.

[0131] The acquisition unit is used to acquire first measurement data and second measurement data that meet preset adjacent conditions in the DC microgrid system.

[0132] The generating unit is configured to generate target pseudo measurement data based on the first measurement data and the second measurement data.

[0133] The acquisition unit is used to add the target pseudo measurement data between the first measurement data and the second measurement data to obtain the target measurement data that meets the preset update condition.

[0134] Optionally, in one embodiment of the present application, the estimation module 300 includes: a first determination unit and a second determination unit.

[0135] The first determining unit is used to determine a second estimation result of the target detailed switch model using the target measurement data.

[0136] The second determining unit is used to calculate a target difference between the first estimation result and the second estimation result to determine a target compensation item according to the target difference.

[0137] Optionally, in one embodiment of the present application, the calculation formula of the target pseudo measurement data is:

[0138]

[0139] Among them, z k-1 is the measured value at time k-1, z k is the measured value at time k, z i is the estimated value at the i-th moment.

[0140] It should be noted that the aforementioned explanation of the embodiment of the method for estimating the dynamic state of a DC microgrid is also applicable to the device for estimating the dynamic state of a DC microgrid of this embodiment, and will not be repeated here.

[0141] According to the device for estimating the dynamic state of a DC microgrid proposed in the embodiment of the present application, the target measurement data that meets the update conditions can be obtained based on the target pseudo-measurement data, and then the first estimation result of the pre-constructed DC microgrid equivalent model can be determined. Then, the target compensation term is determined according to the first estimation result and the second estimation result of the acquired target detailed switch model, and the target compensation term is superimposed on the first estimation result to obtain the final DC microgrid dynamic state estimation result, which effectively improves the accuracy and robustness of the DC microgrid dynamic state estimation. As a result, the problems in the related art that there is a linearization error and high computational complexity when processing the DC microgrid dynamic state estimation, and the adaptability to non-Gaussian noise is limited, which reduces the accuracy and robustness of the DC microgrid dynamic state estimation.

[0142] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0143] A memory 1001 , a processor 1002 , and a computer program stored in the memory 1001 and executable on the processor 1002 .

[0144] When the processor 1002 executes the program, the method for estimating the dynamic state of the DC microgrid provided in the above embodiment is implemented.

[0145] Furthermore, the electronic device further comprises:

[0146] The communication interface 1003 is used for communication between the memory 1001 and the processor 1002 .

[0147] The memory 1001 is used to store computer programs that can be executed on the processor 1002 .

[0148] The memory 1001 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0149] If the memory 1001, the processor 1002 and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001 and the processor 1002 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0150] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can communicate with each other through an internal interface.

[0151] The processor 1002 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0152] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the method for estimating the dynamic state of a DC microgrid as described above is implemented.

[0153] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above method for estimating the dynamic state of a DC microgrid.

[0154] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0155] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0156] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0157] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0158] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0159] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0160] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0161] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for estimating the dynamic state of a DC microgrid, characterized in that: The following steps are involved: Based on the target pseudo-measurement data, obtaining target measurement data that meets a preset update condition; Determining a first estimation result of a pre-constructed DC microgrid equivalent model based on the target measurement data; A second estimation result of the target detailed switch model is obtained, and a target compensation term is determined according to the first estimation result and the second estimation result, and the target compensation term is superimposed on the first estimation result to obtain a final DC microgrid dynamic state estimation result.

2. The method according to claim 1, characterized in that The step of obtaining target measurement data satisfying a preset update condition based on the target pseudo measurement data includes: Collecting first measurement data and second measurement data that meet preset adjacent conditions in the DC microgrid system; generating the target pseudo measurement data based on the first measurement data and the second measurement data; The target pseudo measurement data is added between the first measurement data and the second measurement data to obtain the target measurement data that meets the preset update condition.

3. The method according to claim 1, characterized in that The obtaining a second estimation result of the target detailed switch model, and determining a target compensation item according to the first estimation result and the second estimation result, comprises: Determining a second estimation result of the target detailed switch model using the target measurement data; A target difference between the first estimation result and the second estimation result is calculated to determine the target compensation term according to the target difference.

4. The method according to claim 1, characterized in that: The calculation formula of the target pseudo measurement data is: Among them, z k-1 is the measured value at time k-1, z k is the measured value at time k, z i is the estimated value at the i-th moment.

5. A device for estimating the dynamic state of a DC microgrid, characterized in that: include: An acquisition module, used for acquiring target measurement data satisfying a preset update condition based on the target pseudo measurement data; A determination module, configured to determine a first estimation result of a pre-constructed DC microgrid equivalent model based on the target measurement data; An estimation module is used to obtain a second estimation result of the target detailed switch model, determine a target compensation item according to the first estimation result and the second estimation result, and superimpose the target compensation item on the first estimation result to obtain a final DC microgrid dynamic state estimation result.

6. The device according to claim 5, characterized in that The acquisition module comprises: A collection unit, used for collecting first measurement data and second measurement data satisfying a preset adjacent condition in a DC microgrid system; a generating unit, configured to generate the target pseudo measurement data based on the first measurement data and the second measurement data; An acquiring unit is used to add the target pseudo measurement data between the first measurement data and the second measurement data to obtain the target measurement data that meets the preset update condition.

7. The device according to claim 5, characterized in that The estimation module comprises: A first determining unit, configured to determine a second estimation result of the target detailed switch model using the target measurement data; The second determining unit is used to calculate a target difference between the first estimation result and the second estimation result to determine the target compensation item according to the target difference.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for estimating the dynamic state of a DC microgrid as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for estimating the dynamic state of a DC microgrid as described in any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method for estimating the dynamic state of a DC microgrid as described in any one of claims 1 to 4.

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