Distribution network data fusion method and device considering measurement timing and nonlinear errors

Abstract

The present invention discloses a distribution network data fusion method and device taking into account measurement timing and nonlinear errors, belonging to the field of multi-source measurement data fusion of distribution network lines. Specifically, it includes: collecting PMU data, RTU data and AMI data; setting a time window after the time when the dispatching control center main station receives the PMU data, setting the time window to receive RTU data consistent with the PMU data time scale, and obtaining RTU data with PMU time scale characteristics; using the least squares method to achieve time section alignment of measurement data; using Lagrange interpolation to fill the measurement data after section alignment; after denoising the measurement data after data filling, constructing the distribution network state and measurement model; using the unscented Kalman filter algorithm to solve the state and measurement model, and realizing the fusion of multi-source measurement data of the distribution network. The present invention realizes fast data fusion of multi-source measurement data, and makes the fusion result more accurate by considering the actual communication delay and the combination of unscented Kalman filter.

Description

Technical Field

[0001] The present invention belongs to the field of multi-source measurement data fusion of distribution network lines, and in particular relates to a distribution network data fusion method and device taking into account measurement timing and nonlinear errors. Background Art

[0002] As the number of distributed power sources, power electronic transformers, flexible loads and their corresponding monitoring and measurement devices in the distribution network gradually increases, the difficulty of decision-making and control of the distribution network is increasing. In order to ensure the reliable operation of the distribution network, it is urgent to build a distribution automation system. DMS (Distributed Energy Management System) is one of the basic systems of the distribution automation system. In DMS, DSSE (Distribution System State Estimation) as a data filter is in a basic position, which provides reliable data for various advanced applications of DMS. Compared with the state estimation of the transmission network, the new distribution network multi-source measurement has many differences in sampling time scale, data structure and measurement accuracy, so the efficient fusion of multi-source measurement data has become an important prerequisite for the integration and application of multi-source information data in the distribution network.

[0003] The main issues that need to be addressed when integrating measurements from PMU (Phasor Management Unit), RTU (Remote Terminal Unit) and AMI (Advanced Metering Infrastructure) provided by SCADA (Supervisory Control and Data Acquisition) systems include:

[0004] 1) The measurement time sections are not synchronized, such as the existence of measurement delay, RTU data without time stamp, etc. If the time is not synchronized, data fusion will not only fail to achieve the purpose of data fusion, but also pollute the original measurement data;

[0005] 2) The fusion of multiple data sources is more complex and requires more computation, and the constraints on online analysis and computing time must also be considered.

[0006] The prior art document (CN109754013B) discloses a hybrid measurement fusion method for power systems based on unscented Kalman filtering. Its disadvantage is that time-scale alignment mainly relies on correlation analysis, which may be affected by factors such as data quality and computational complexity. When the data volume is large and complex, combining it with unscented Kalman filtering will reduce the accuracy of data fusion. Summary of the invention

[0007] In order to solve the deficiencies in the prior art, the present invention provides a distribution network data fusion method and device taking into account measurement timing and nonlinear errors. The study considers the nonlinear error introduced by calculating the Jacobian matrix and the problem of high-dimensionality being difficult to implement in the extended Kalman filtering process under the scenario of timing mismatch of PMU, RTU and AMI measurement data, and proposes a new distribution network multi-source measurement data fusion method, thereby providing more reliable data for the integration and application of distribution network multi-source information data such as distribution network state estimation.

[0008] The present invention adopts the following technical solution.

[0009] The first aspect of the present invention discloses a distribution network data fusion method and device taking into account measurement timing and nonlinear errors, comprising:

[0010] Step 1: Collect measurement data from PMU, SCADA system and AMI installed at different nodes of the distribution network system to obtain PMU data, RTU data and AMI data;

[0011] Step 2: The dispatch control center master station sets a time window after the time when it receives the PMU data obtained in step 1, sets the RTU data received within the time window to be consistent with the PMU data time stamp, and obtains PMU data, RTU data with PMU time stamp characteristics, and RTU data;

[0012] Step 3: Use the least square method to align the time sections of the PMU data obtained in step 2, the RTU data with the PMU time scale characteristics, and the AMI data to obtain the measurement data after section alignment;

[0013] Step 4: Use Lagrange interpolation to fill the measured data after the cross-section alignment obtained in step 3 to obtain the measured data after data filling;

[0014] Step 5: After denoising the measured data after data filling obtained in step 4, a distribution network state and measurement model is constructed;

[0015] Step 6: Use the unscented Kalman filter algorithm to solve the state and measurement model constructed in step 5 to achieve distribution network data fusion taking into account measurement timing and nonlinear errors.

[0016] Preferably, in step 2, the RTU data and the PMU data are synchronized to be consistent in time stamp, which can be expressed by the following formula:

[0017] (1)

[0018] In the formula,

[0019] is the reference time for receiving PMU data,

[0020] , To set the minimum and maximum measurement delay,

[0021] The RTU data corresponds to The time range in which the moment data is received,

[0022] Expected value of the delay measured by the RTU.

[0023] Preferably, in step 3, the least square method is used to achieve time section alignment of multi-source measurement data of the distribution network, and a new expression is measured, which is expressed by the following formula:

[0024] (2)

[0025] In the formula,

[0026] For the The measured data after the cross section is aligned.

[0027] PMU data, RTU data with PMU time stamp characteristics and AMI data,

[0028] is the sampling period,

[0029] is the proportional coefficient of the sampling period,

[0030] is the noise value of the jth section during the data measurement process.

[0031] Preferably, in step 4, the time series data filling is implemented by using the Lagrange interpolation method, which is expressed by the following formula:

[0032] (3)

[0033] In the formula,

[0034] is the number of given value points,

[0035] is the measurement data item after the missing values ​​are aligned.

[0036] For the The measured data after the cross-section alignment of the non-missing items,

[0037] For the The measured data after the cross-section alignment of the non-missing items, ,

[0038] For the The time corresponding to the measured data after the cross-section alignment of the non-missing items,

[0039] is the interpolation basis function,

[0040] It is the measurement data after time series filling.

[0041] Preferably, in step 5, the noise reduction of the measured data after data filling is expressed by the formula:

[0042] (4)

[0043] In the formula,

[0044] is the noise-reduced measurement data at time point m,

[0045] is the measured data after data filling before noise reduction at time point m-1,

[0046] is the smoothed value of one step of the data at time point m,

[0047] is the secondary smoothing value of the data at time point m-1,

[0048] , are the trend smoothing parameters,

[0049] is the prediction step length.

[0050] Preferably, in step 5, the state and measurement equations are constructed and expressed as follows:

[0051] (5)

[0052] In the formula,

[0053] is the sample point set selected for the k-1th section,

[0054] is the state prediction value of the measurement data of the kth section,

[0055] is the predicted value of the measurement data of the kth section,

[0056] is the prediction function,

[0057] is the measurement function,

[0058] is the state process noise,

[0059] is the measurement error noise.

[0060] Preferably, in step 6, based on the unscented Kalman filter algorithm, a sample point set is selected according to the state and measurement equations, which can be expressed as follows:

[0061] (6)

[0062] In the formula,

[0063] is the scale parameter,

[0064] is the sampling parameter of the sample point, and its value range is [0, 1].

[0065] is the state dimension,

[0066] for The covariance matrix of the cross sections is

[0067] for The mean value of the cross section,

[0068] for The first section selected Sample points.

[0069] Preferably, according to The first section selected Sample points The sample point set , and obtain the state prediction value of the measured data of the kth section , and then calculate the predicted mean , expressed as follows:

[0070] (7)

[0071] In the formula,

[0072] for No. The mean weight of the state prediction value,

[0073] for No. The predicted value of a state.

[0074] Preferably, the measured value is predicted and the state is updated to solve the mean and the covariance matrix , and then construct a new set of sampling points , and then count the The measurement prediction value of each section is continuously updated to achieve data fusion.

[0075] A second aspect of the present invention provides a distribution network data fusion device taking into account measurement timing and nonlinear errors, which is used to run the distribution network data fusion method taking into account measurement timing and nonlinear errors, comprising:

[0076] Acquisition module: used to collect measurement data from PMU, SCADA system and AMI installed at different nodes of the distribution network system to obtain PMU data, RTU data and AMI data;

[0077] Time-stamp matching module: used to set a time window after the time when the main station of the scheduling control center receives the PMU data, set the RTU data received within the time window to be consistent with the PMU data time stamp, and obtain PMU data, RTU data with PMU time stamp characteristics, and RTU data;

[0078] Section alignment module: used to realize the time section alignment of PMU data, RTU data with PMU time scale characteristics and AMI data by using the least square method, and obtain the measurement data after section alignment;

[0079] Data denoising module: used to fill the measured data after the cross-section alignment by using Lagrange interpolation to obtain the measured data after data filling;

[0080] State and measurement model building module: used to build the distribution network state and measurement model after denoising the measurement data after data filling;

[0081] Solution module: It is used to solve the constructed state and measurement model using the unscented Kalman filter algorithm to realize the distribution network data fusion taking into account the measurement timing and nonlinear errors.

[0082] The beneficial effect of the present invention is that, compared with the prior art, the present invention is based on the distribution network multi-source data fusion strategy of the PMU time scale, proposes to adopt the time alignment and data filling method, utilizes the characteristics of actual communication delay, and by setting a time window and assuming that the measurement delay has a fixed expected value, the time point when the PMU data and the RTU data are most likely to match can be found, which is closer to the actual situation, because it is based on the actual characteristics of network communication for operation, and does not require complex statistical calculations, reduces the uncertainty of synchronization, removes data noise, and obtains accurate distribution network data. Then, in view of the nonlinear error introduced by the calculation of the Jacobian matrix in the extended Kalman filtering process and the problem that high dimensions are difficult to achieve, a distribution network multi-source measurement fusion method based on the unscented Kalman filtering algorithm is proposed, which realizes fast data fusion in scenarios where the fusion of multi-source data sources is more complex and the amount of calculation is larger, and the fusion result is more accurate by considering the combination of actual communication delay and unscented Kalman filtering. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic diagram of the process of the present invention;

[0084] Figure 2 This is a configuration diagram of the IEEE33 node system measurement device used in the present invention;

[0085] Figure 3 This is a schematic diagram of the structure of a multi-source measurement data processing device for a distribution network considering the measurement timing problem of the present invention;

[0086] Figure 4 This is a diagram showing a comparison of relative errors of node voltage fusion according to an embodiment of the present invention;

[0087] Figure 5 This is a diagram showing the comparison results of the relative errors of node power fusion according to an embodiment of the present invention;

[0088] Figure 6 This is a comparison diagram of fusion efficiency results of embodiments of the present invention. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0090] Embodiment 1 of the present invention proposes a distribution network data fusion method taking into account measurement timing and nonlinear errors, such as Figure 1 As shown, including:

[0091] Step 1: Collect measurement data from PMU, SCADA system and AMI installed at different nodes of the distribution network system to obtain PMU data, RTU data and AMI data;

[0092] Based on the difference analysis of multi-source measurement data of the distribution network, PMU, SCADA system and AMI are installed at different nodes of the distribution network system to measure and accumulate measurement data of multiple time sections. The measurement data obtained includes PMU data, RTU data and AMI data.

[0093] The RTU data is provided by the SCADA system.

[0094] The differences of multi-source measurement devices are comprehensively considered. The differences of multi-source measurement devices include the impact of different measurement devices on the establishment of simulated distribution network systems and the impact of different measurability of different measurement data types collected by different measurement devices on data fusion effects. The characteristics of measurement data collected by different measurement devices include measurement speed, configuration density, observability and synchronization in actual situations; the data types collected by different devices include frequency, voltage amplitude, voltage phase angle and current amplitude. The specific differences are analyzed in the following table:

[0095] Table 1 Comparison of PMU, RTU and AMI data characteristics

[0096]

[0097] Table 2 Comparison of PMU, RTU and AMI measurement data types

[0098]

[0099] From Table 1 and Table 2, it can be seen that only by effectively integrating the three types of data from PMU, RTU and AMI in the distribution network can the observability of the distribution network measurement data be met and the measurement redundancy be improved as much as possible.

[0100] Step 2: The dispatch control center master station sets a time window after the time when it receives the PMU data obtained in step 1, sets the RTU data received within the time window to be consistent with the PMU data time stamp, and obtains PMU data, RTU data with PMU time stamp characteristics, and RTU data;

[0101] The RTU data and PMU data time stamps are synchronized and expressed as follows:

[0102] (1)

[0103] In the formula,

[0104] The reference time for receiving PMU data;

[0105] , Measure the delay for the minimum and maximum settings;

[0106] The RTU data corresponds to The time range in which the moment data is received;

[0107] Expected value of the delay measured by the RTU.

[0108] If the RTU data is received within the TW time window, it is considered that the RTU data is consistent with the PMU data time stamp.

[0109] Step 3: Use the least square method to align the time sections of the PMU data obtained in step 2, the RTU data with the PMU time scale characteristics, and the AMI data to obtain the measurement data after section alignment;

[0110] After dealing with the time scale problem, the least square method is used to achieve the time section alignment of the multi-source measurement data of the distribution network, and the new expression is measured, which is expressed as the following formula:

[0111] (2)

[0112] In the formula,

[0113] For the The measured data after the cross section is aligned.

[0114] PMU data, RTU data with PMU time stamp characteristics and AMI data,

[0115] is the sampling period,

[0116] is the proportional coefficient of the sampling period,

[0117] is the noise value of the jth section during the data measurement process.

[0118] Step 4: Use Lagrange interpolation to fill the measured data after the cross-section alignment obtained in step 3 to obtain the measured data after data filling;

[0119] After aligning the time sections, the method to synchronize the sampling period is to use the Lagrange interpolation method to achieve time series data filling:

[0120] (3)

[0121] In the formula,

[0122] is the number of given value points,

[0123] is the measurement data item after the missing values ​​are aligned.

[0124] For the The measured data after the cross-section alignment of the non-missing items,

[0125] For the The measured data after the cross-section alignment of the non-missing items, ,

[0126] For the The time corresponding to the measured data after the cross-section alignment of the non-missing items,

[0127] is the interpolation basis function,

[0128] It is the measurement data after time series filling.

[0129] Step 5: After denoising the measured data after data filling obtained in step 4, a distribution network state and measurement model is constructed;

[0130] Further noise reduction of time series data based on time series data filling can be expressed as:

[0131] (4)

[0132] In the formula,

[0133] is the noise-reduced measurement data at time point m,

[0134] is the measured data after data filling before noise reduction at time point m-1,

[0135] is the smoothed value of one step of the data at time point m,

[0136] is the secondary smoothing value of the data at time point m-1,

[0137] , are the trend smoothing parameters,

[0138] is the prediction step length.

[0139] Through the above processing, the denoised time series data is obtained, providing a basis for data fusion.

[0140] The above steps can solve the problems of measurement delay and timing asynchrony of multi-source measurement data, and provide high-quality and high-precision fused data for data fusion.

[0141] The state and measurement equations are constructed as:

[0142] (5)

[0143] In the formula,

[0144] is the sample point set selected for the k-1th section,

[0145] is the predicted value of the measurement data of the kth section,

[0146] is the predicted value of the measurement data of the kth section,

[0147] is the prediction function,

[0148] is the measurement function,

[0149] is the state process noise,

[0150] is the measurement error noise.

[0151] Step 6: Use the unscented Kalman filter algorithm to solve the state and measurement model constructed in step 5 to achieve distribution network data fusion taking into account measurement timing and nonlinear errors.

[0152] Based on the unscented Kalman filter algorithm, the sample point set is selected according to the state and measurement equations:

[0153] (6)

[0154] In the formula,

[0155] is the ratio parameter used to control the sample points and the mean The distance between k-1 sections,

[0156] is the sampling parameter of the sample point, and its value range is [0, 1].

[0157] is the state dimension,

[0158] for The covariance matrix of the cross sections is

[0159] for The mean value of the cross section,

[0160] for The first section selected Sample points.

[0161] according to The first section selected Sample points The sample point set , and obtain the state prediction value of the measured data of the kth section , and then calculate the predicted mean , expressed as follows:

[0162] (7)

[0163] In the formula,

[0164] for No. The mean weight of the state prediction value,

[0165] for No. The predicted value of a state.

[0166] Predict measured values ​​and update status to solve for mean and the covariance matrix , and then construct a new set of sampling points , and then count the The measurement prediction value of each section is continuously updated to achieve data fusion.

[0167] The beneficial effect of the present invention is that, compared with the prior art, the present invention is based on the distribution network multi-source data fusion strategy of the PMU time scale, proposes to adopt the time alignment and data filling method, utilizes the characteristics of actual communication delay, and by setting a time window and assuming that the measurement delay has a fixed expected value, the time point when the PMU data and the RTU data are most likely to match can be found, which is closer to the actual situation, because it is based on the actual characteristics of network communication for operation, and does not require complex statistical calculations, reduces the uncertainty of synchronization, removes data noise, and obtains accurate distribution network data. Then, in view of the nonlinear error introduced by the calculation of the Jacobian matrix in the extended Kalman filtering process and the problem that high dimensions are difficult to achieve, a distribution network multi-source measurement fusion method based on the unscented Kalman filtering algorithm is proposed, which realizes fast data fusion in scenarios where the fusion of multi-source data sources is more complex and the amount of calculation is larger, and the fusion result is more accurate by considering the combination of actual communication delay and unscented Kalman filtering.

[0168] Embodiment 2 of the present invention proposes a distribution network data fusion device taking into account measurement timing and nonlinear errors, which is used to run a distribution network data fusion method taking into account measurement timing and nonlinear errors in embodiment 1, such as Figure 3 As shown, including:

[0169] Acquisition module: used to collect measurement data from PMU, SCADA system and AMI installed at different nodes of the distribution network system to obtain PMU data, RTU data and AMI data;

[0170] Time-stamp matching module: used to set a time window after the time when the main station of the scheduling control center receives the PMU data, set the RTU data received within the time window to be consistent with the PMU data time stamp, and obtain PMU data, RTU data with PMU time stamp characteristics, and RTU data;

[0171] Section alignment module: used to realize the time section alignment of PMU data, RTU data with PMU time scale characteristics and AMI data by using the least square method, and obtain the measurement data after section alignment;

[0172] Data denoising module: used to fill the measured data after the cross-section alignment by using Lagrange interpolation to obtain the measured data after data filling;

[0173] State and measurement model building module: used to build the distribution network state and measurement model after denoising the measurement data after data filling;

[0174] Solution module: It is used to solve the constructed state and measurement model using the unscented Kalman filter algorithm to realize the distribution network data fusion taking into account the measurement timing and nonlinear errors.

[0175] This embodiment 3 implements the method of the present invention through the IEEE33 node simulation system, such as Figure 2 As shown, the simulation system assumes the following conditions:

[0176] (1) The PMU data sampling period is 1 second. A random error with a standard deviation of 0.02 is superimposed on the true value of the data. The error obeys the normal distribution. No measurement delay is set. The configuration nodes are 1, 5, 8, 11, 14, 19, 21, 23, 27, and 33 nodes.

[0177] (2) The RTU data sampling period is 10 seconds, superimposed with the deviation caused by the measurement delay and the normal distribution random measurement error with a standard deviation of 0.05, and full node configuration;

[0178] (3) The AMI data sampling period is 1 minute. A random error with a standard deviation of 0.01 is superimposed on the true value of the data. The error follows a normal distribution. No measurement delay is set. The configuration nodes are 1, 2, 7, 18, 26, and 33.

[0179] like Figure 4 , Figure 5 and Figure 6 As shown in the figure, the rationality of the distribution network multi-source measurement fusion method based on the unscented Kalman filter algorithm is verified by comparing the fusion methods in terms of node voltage fusion relative error, node frequency fusion relative error and fusion efficiency. Method 1 is a method without setting measurement delay and without using the unscented Kalman filter algorithm, and Method 2 is a method with setting measurement delay and using the unscented Kalman filter algorithm.

[0180] The beneficial effect of the present invention lies in that, compared with the prior art, the present invention is based on the distribution network multi-source data fusion strategy of the PMU time scale, proposes to adopt time alignment and data filling methods, removes data noise, and obtains accurate distribution network data, and then proposes a distribution network multi-source measurement fusion method based on the unscented Kalman filter algorithm to address the nonlinear errors introduced by the calculation of the Jacobian matrix in the extended Kalman filter process and the problem that high dimensions are difficult to implement.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution network data fusion method taking into account measurement timing and nonlinear errors, characterized in that: include: Step 1: Collect measurement data from PMU, SCADA system and AMI installed at different nodes of the distribution network system to obtain PMU data, RTU data and AMI data; Step 2: The dispatch control center master station sets a time window after the time when it receives the PMU data obtained in step 1, sets the RTU data received within the time window to be consistent with the PMU data time stamp, and obtains PMU data, RTU data with PMU time stamp characteristics, and RTU data; Step 3: Use the least square method to align the time sections of the PMU data obtained in step 2, the RTU data with the PMU time scale characteristics, and the AMI data to obtain the measurement data after section alignment; In step 3, the least square method is used to achieve the time section alignment of the multi-source measurement data of the distribution network, and the new expression is measured, which is expressed by the following formula: (2) In the formula, For the The measured data after the cross section is aligned. PMU data, RTU data with PMU time stamp characteristics and AMI data, is the sampling period, is the proportional coefficient of the sampling period, is the noise value of the jth section during the data measurement process; Step 4: Use Lagrange interpolation to fill the measured data after the cross-section alignment obtained in step 3 to obtain the measured data after data filling; In step 4, the Lagrange interpolation method is used to fill in the time series data, which is expressed as the following formula: (3) In the formula, is the number of given value points, is the measurement data item after the missing values ​​are aligned. For the The measured data after the cross section alignment of the non-missing items, For the The measured data after the cross section alignment of the non-missing items, , For the The time corresponding to the measured data after the cross-section alignment of the non-missing items, is the interpolation basis function, It is the measurement data after time series filling; Step 5: After denoising the measured data after data filling obtained in step 4, a distribution network state and measurement model is constructed; Step 6: Use the unscented Kalman filter algorithm to solve the state and measurement model constructed in step 5 to achieve distribution network data fusion taking into account measurement timing and nonlinear errors.

2. A distribution network data fusion method taking into account measurement timing and nonlinear errors according to claim 1, characterized in that: include: In step 2, the RTU data is synchronized with the PMU data time stamp, which can be expressed as follows: (1) In the formula, is the reference time for receiving PMU data, , To set the minimum and maximum measurement delay, The RTU data corresponds to The time range in which the moment data is received, Expected value of the delay measured by the RTU.

3. A distribution network data fusion method taking into account measurement timing and nonlinear errors according to claim 1, characterized in that: include: In step 5, the noise reduction of the measured data after data filling is expressed as: (4) In the formula, is the noise-reduced measurement data at time point m, is the measured data after data filling before noise reduction at time point m-1, is the smoothed value of one step of the data at time point m, is the secondary smoothing value of the data at time point m-1, , are the trend smoothing parameters, is the prediction step length.

4. A distribution network data fusion method taking into account measurement timing and nonlinear errors according to claim 1, characterized in that: include: In step 5, the state and measurement equations are constructed and expressed as follows: (5) In the formula, is the sample point set selected for the k-1th section, is the state prediction value of the measurement data of the kth section, is the predicted value of the measurement data of the kth section, is the prediction function, is the measurement function, is the state process noise, is the measurement error noise.

5. A distribution network data fusion method taking into account measurement timing and nonlinear errors according to claim 1, characterized in that: include: In step 6, based on the unscented Kalman filter algorithm, a sample point set is selected according to the state and measurement equations, which can be expressed as follows: (6) In the formula, is the scale parameter, is the sampling parameter of the sample point, and its value range is [0, 1]. is the state dimension, for The covariance matrix of the cross sections is for The mean value of the cross section, for The first section selected Sample points.

6. A distribution network data fusion method taking into account measurement timing and nonlinear errors according to claim 5, characterized in that: include: according to The first section selected Sample points The sample point set , and obtain the state prediction value of the measured data of the kth section , and then calculate the predicted mean , expressed as follows: (7) In the formula, for No. The mean weight of the state prediction value, for No. The predicted value of a state.

7. A distribution network data fusion method taking into account measurement timing and nonlinear errors according to claim 6, characterized in that: include: Predict measured values ​​and update status to solve for mean and the covariance matrix , and then construct a new set of sampling points , and then count the The measurement prediction value of each section is continuously updated to achieve data fusion.

8. A distribution network data fusion device taking into account measurement timing and nonlinear errors, used to run a distribution network data fusion method taking into account measurement timing and nonlinear errors as claimed in any one of claims 1 to 7, characterized in that: include: Acquisition module: used to collect measurement data from PMU, SCADA system and AMI installed at different nodes of the distribution network system to obtain PMU data, RTU data and AMI data; Time-stamp matching module: used to set a time window after the time when the main station of the scheduling control center receives the PMU data, set the time window so that the RTU data received is consistent with the PMU data time stamp, and obtain PMU data, RTU data with PMU time stamp characteristics, and RTU data; Section alignment module: used to realize the time section alignment of PMU data, RTU data with PMU time scale characteristics and AMI data by using the least square method, and obtain the measurement data after section alignment; Data denoising module: used to fill the measured data after the cross-section alignment by using Lagrange interpolation to obtain the measured data after data filling; State and measurement model building module: used to build the distribution network state and measurement model after denoising the measurement data after data filling; Solution module: It is used to solve the constructed state and measurement model using the unscented Kalman filter algorithm to realize the distribution network data fusion taking into account the measurement timing and nonlinear errors.

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