Power distribution network interval state evaluation method considering measurement uncertainty, equipment and medium

Abstract

The invention discloses a power distribution network interval state evaluation method and device considering measurement uncertainty and a medium, and relates to the technical field of processing of the power distribution network interval state evaluation method considering the measurement uncertainty, and the method comprises the following steps: obtaining multi-source measurement data, and analyzing the difference of the multi-source measurement data in the aspect of sampling characteristics; processing the difference of the source measurement data in the aspect of sampling characteristics according to a multi-source measurement fusion strategy to obtain fused multi-source measurement data; combining and fusing multi-source measurement data to construct an interval state estimation model, and outputting a linearized interval measurement function by the model to obtain a state variable interval; and proposing an interval optimization algorithm to converge the state variable interval to obtain a target estimation interval.

Description

Technical Field

[0001] The present invention relates to the field of method technology, and more specifically, to a distribution network section state assessment method, device and medium taking into account measurement uncertainty. Background Art

[0002] Faced with the access of a large number of explosive, small-capacity, decentralized distributed power sources and the high integration of information and physical systems, the changes in the operating status of the distribution network are becoming increasingly complex and full of uncertainty and safety issues, which brings a series of problems to the safe and stable operation of the distribution network: First, the three types of measurement in the current distribution network, namely, distribution-level phasor measurement unit (D-PMU), supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI), will coexist for a long time.

[0003] In the above disclosed technical solutions, there are at least the following technical problems: the measurement data sets have certain differences in data accuracy, sampling period and form, resulting in high asynchrony and uncertainty of multi-source measurement data.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a distribution network section state assessment method, device and medium taking into account measurement uncertainty, and solve the problems raised in the above-mentioned background technology by processing multi-source measurement data.

[0006] To achieve the above object, the present invention provides the following technical solutions: A distribution network interval state assessment method considering measurement uncertainty comprises the following steps: acquiring multi-source measurement data, and analyzing the differences in sampling characteristics of the multi-source measurement data; processing the differences in sampling characteristics of the multi-source measurement data according to a multi-source measurement fusion strategy to obtain fused multi-source measurement data; constructing an interval state estimation model in combination with the fused multi-source measurement data, the model outputting a linearized interval measurement function to obtain a state variable interval; and proposing an interval optimization algorithm to converge the state variable interval to obtain a target estimation interval.

[0007] In a preferred embodiment, the analysis of the differences in sampling characteristics of the multi-source measurement data is performed by the following analysis method: presetting the measurement delay range and sampling period of the data acquisition and monitoring control system, receiving the first measurement data of the same data acquisition and monitoring control device within a given operating time period, recording the received measurement data in vector form, and obtaining a second measurement vector; using a distribution network synchronous phasor measurement device to screen and reorganize the second measurement vector to obtain a third measurement matrix; and respectively calculating the Pearson correlation coefficient of each row vector in the second measurement vector and the third measurement matrix.

[0008] In a preferred embodiment, the differences in sampling characteristics of the multi-source measurement data are processed according to the multi-source measurement fusion strategy, specifically: the measurement time stamp of the multi-source measurement data is uniformly processed; the multi-source measurement data after the unified time stamp is time-delay fused based on the AMI measurement device to obtain fused multi-source measurement data.

[0009] In a preferred embodiment, the method for acquiring the fused multi-source measurement data is as follows: using the state estimation time to read the distribution network synchronized phasor measurement device measurement from the distribution network synchronized phasor measurement device as the first measurement time; using the data acquisition and monitoring control system sampling value closest to the state estimation time to perform state estimation to obtain the second measurement time; processing the first measurement time and the second measurement time according to the AMI measurement device to obtain the fused multi-source measurement data.

[0010] In a preferred embodiment, the measurement time stamp of multi-source measurement data is uniformly processed, specifically: obtaining a preset measurement delay range and sampling period, calculating the measurement delay of the data acquisition and monitoring control system, and obtaining the sampling time stamp of the second measurement vector; assigning the sampling time stamp of the second measurement vector to the third measurement matrix.

[0011] In a preferred embodiment, the proposed interval optimization algorithm converges the state variable interval, including the following: setting a complete initial iteration interval, and presetting an approximate solution of the interval equation, solving the difference between the initial iteration interval and the approximate solution, and obtaining a difference interval; using the Krawczyk-Moore operator to iteratively converge the difference interval, and setting a convergence threshold, when the difference after convergence is less than the preset convergence threshold, the iteration is stopped, and the final iterative result is determined; if the difference after convergence is greater than the preset convergence threshold, an interval constraint propagation function is defined to further converge the difference interval; setting the difference after two consecutive iterations to be less than the preset convergence threshold as the iteration termination condition, the calculation is terminated immediately when the termination condition is reached, and the target estimation interval of the final state variable is output.

[0012] A device for a distribution network interval state assessment method considering measurement uncertainty comprises a data acquisition module, a data processing module, a state estimation module and a result output module; the data acquisition module is used to acquire multi-source measurement data and analyze the differences in sampling characteristics of the multi-source measurement data; the data processing module is used to process the differences in sampling characteristics of the multi-source measurement data according to a multi-source measurement fusion strategy to obtain fused multi-source measurement data; the state estimation module is used to construct an interval state estimation model in combination with the fused multi-source measurement data, the model outputs a linearized interval measurement function to obtain a state variable interval; the result output module is used to propose an interval optimization algorithm to converge the state variable interval to obtain a target estimation interval.

[0013] An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a distribution network section state assessment method taking into account measurement uncertainty as described in any one of claims 1 to 7.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a distribution network interval state assessment method that takes measurement uncertainty into account.

[0015] The technical effects and advantages of the distribution network interval state assessment method, device and medium considering measurement uncertainty of the present invention are as follows: 1. Aiming at the problem that the measurement values ​​in multi-source measurement data sets often lack synchronization and differ in data accuracy and form, which leads to reduced accuracy and computational efficiency of state estimation, the present invention proposes a corresponding time delay fusion strategy to achieve synchronization of measurement data at the sampling time.

[0016] 2. The present invention aims to solve the problem that there are large differences in the data forms of the original mixed measurement set, which leads to increased complexity and calculation time of the measurement function, thereby reducing the efficiency and accuracy of state estimation calculations. By performing measurement transformation through interval operations and linearizing the interval measurement function, the formal unification of multi-source measurement data is achieved, thereby reducing the complexity of the model.

[0017] 3. Aiming at the problems of initial iteration interval dependence and interval expansion in different degrees in traditional interval optimization algorithms, the present invention proposes an interval optimization algorithm with low dependence on the initial iteration interval, high convergence and computational efficiency, and combines the advantages of the interval constraint propagation algorithm and the Krawczyk-Moore algorithm to realize the solution of the interval state estimation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a flow chart of a distribution network interval state assessment method considering measurement uncertainty according to the present invention; Figure 2 This is a functional module diagram of a distribution network section state assessment device taking into account measurement uncertainty according to the present invention; Figure 3 The present invention is a schematic diagram of the structure of an electronic device for implementing a distribution network section state assessment method taking measurement uncertainty into account.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, Figure 1 A flow chart of a distribution network section state assessment method considering measurement uncertainty of the present invention is provided. In this embodiment, the distribution network section state assessment method considering measurement uncertainty includes: S1, acquiring multi-source measurement data, and analyzing differences in sampling characteristics of the multi-source measurement data; The multi-source measurement data is acquired through a data acquisition and monitoring control system. The data acquisition and monitoring control system is a real-time data acquisition, monitoring and control system widely used in the industrial field. It monitors and controls the industrial process by collecting and processing data from different sensors and equipment.

[0022] Since the timestamps of the multi-source measurement data acquired through the data acquisition and monitoring control system are not aligned, it is necessary to align the timestamps of the multi-source measurement data, and then analyze the differences in sampling characteristics of the multi-source measurement data.

[0023] The analyzing the differences in sampling characteristics of the multi-source measurement data comprises the following steps: Preset a measurement delay range and a sampling period of the data acquisition and monitoring control system, receive first measurement data of the same data acquisition and monitoring control device within a given operation time period, record the received measurement data in a vector form, and obtain a second measurement vector; Using a distribution network synchronous phasor measurement device to screen and reorganize the second measurement vector to obtain a third measurement matrix; The Pearson correlation coefficients of the second measurement vector and each row vector in the third measurement matrix are calculated respectively.

[0024] The second measurement vector is in the following form:

[0025] Where: The total number of data received by the same data acquisition and monitoring control device, is the second measurement vector.

[0026] The third measurement matrix is ​​expressed as follows:

[0027] Where: The third measurement matrix reconstructed for the synchronous phasor measurement device of the distribution network, is the first row vector in the third measurement matrix, n d is the total number of rows of vectors in the third measurement matrix, is the first vector of the first row in the third measurement matrix.

[0028] The Pearson correlation coefficient of each row vector in the second measurement vector and the third measurement matrix is ​​calculated respectively, and the calculation process is as follows:

[0029] Where: is the Pearson correlation coefficient between the second measurement vector and the j-th row vector in the third measurement matrix, Cov is the covariance between the second measurement vector and the j-th row vector in the third measurement matrix, is the standard deviation of the second measurement vector, is the standard deviation of the j-th row vector in the third measurement matrix, is the mean.

[0030] The Pearson correlation coefficient is a statistic that measures the degree of linear correlation between two variables, and its value ranges from -1 to 1. Values ​​close to 1 indicate a strong positive correlation, values ​​close to -1 indicate a strong negative correlation, and values ​​close to 0 indicate almost no linear relationship.

[0031] Calculating the Pearson correlation coefficient between the second measurement vector and each row vector in the third measurement matrix can help understand the linear relationship between these vectors. Calculating the correlation coefficient between a certain time point of the second measurement vector and each row of the third measurement matrix representing different time points can reveal the correlation between the data at these time points, thereby providing a basis for further analysis.

[0032] The distribution network synchronized phasor measurement device is a measurement device used in distribution networks. It is designed to provide dynamic operating condition information of the power grid, support more efficient multi-party interaction, and effectively improve the measurability, observability and controllability of the power grid.

[0033] S2, unifying the measurement time scale of the multi-source measurement data according to the multi-source measurement fusion strategy, performing time delay fusion on the multi-source measurement data after the unified time scale based on the AMI measurement device to obtain fused multi-source measurement data; Unifying the measurement time scale of the multi-source measurement data includes the following steps: Obtaining a preset measurement delay range and sampling period, calculating the measurement delay of the data acquisition and monitoring control system, and obtaining a sampling time scale of the second measurement vector; The sampling time scale of the second measurement vector is assigned to the third measurement matrix.

[0034] The measurement time scale of the multi-source measurement data is unified, and the calculation formula is as follows:

[0035] Where: is the measurement delay of the second measurement vector, The measurement delay range of the preset data acquisition and monitoring control system is is the sampling period of the preset distribution network synchronous phasor measurement device. is the sampling time scale of the first row vector in the second measurement vector, J is the number of the row vector with the largest Pearson correlation coefficient between the second measurement vector and the third measurement matrix, is the receiving time of the first measurement data.

[0036] The above method can realize the alignment of the measurement of the data acquisition and monitoring control system without time stamp and the measurement of the distribution network synchronous phasor measurement device with precise time stamp, and calculate the measurement delay of the data acquisition and monitoring control system.

[0037] The multi-source measurement data after unified time scale is subjected to time delay fusion based on the AMI measurement device to obtain fused multi-source measurement data.

[0038] The method for obtaining the fused multi-source measurement data is as follows: Using the state estimation time, reading the distribution network synchronized phasor measurement device measurement from the distribution network synchronized phasor measurement device to obtain a first measurement time; The state estimation is performed by using the data acquisition and monitoring control system sampling value closest to the state estimation time to obtain the second measurement time; The first measurement time and the second measurement time are processed according to the AMI measurement device to obtain fused multi-source measurement data.

[0039]

[0040] Where: In order to integrate multi-source measurement data, is the first measurement moment, It is the last acquisition moment before the second measurement moment of the data acquisition and monitoring control system. is the previous first measurement moment, is the multi-source measurement data at the second measurement time, is the last acquisition moment after the unified time scale, is the time window width, is the pseudo-measurement prediction value at the first measurement moment.

[0041] For AMI measurement, its measurement accuracy is relatively high, but compared with other real-time measurements, its sampling period is as long as minutes. In order to ensure the accuracy of hybrid measurement, a time window is set before the state estimation time, the size of which is the pseudo-measurement prediction period. If the AMI measurement is delivered within this time window, the measurement data is used; if the AMI measurement arrival time is outside the time window, the pseudo-measurement prediction data is used.

[0042] S3, based on the interval number model, an interval state estimation model integrating multi-source measurement data is constructed, and interval operations are used to perform measurement transformation to obtain a linearized interval measurement function, thereby obtaining a state variable interval; The interval number model is a mathematical model used to deal with uncertainty and fuzziness. It describes the possible range of data by expressing variables as intervals. For example, the interval [a, b] indicates that the possible value of the variable is between a and b. This model is widely used in optimization, decision analysis, and control systems to deal with data uncertainty and variability in practical applications.

[0043] The interval state estimation model is a model that performs state estimation in the presence of uncertainty and error. It uses interval numbers to represent the possible range of system states rather than a single precise value. In this way, the interval state estimation model can handle and reflect measurement errors, system disturbances, and model uncertainties.

[0044] The interval state estimation model is defined as follows:

[0045] Where: X is the uncertainty set of state variables of the interval state estimation model, is the set of mixed quantity measurements, To measure the lower boundary, is the upper boundary of the quantity measurement, x is the state variable vector, is the dimension of state variables, is the nonlinear measurement function vector, and Z is the measurement uncertainty set.

[0046] The mathematical expression of the measurement uncertainty set is:

[0047] Where: is the measurement vector, is the dimension of the measurement vector, is the rth quantity measurement, is the lower boundary of the rth quantity measurement, is the upper boundary of the rth quantity measurement.

[0048] Each variable is expressed in the form of interval numbers, and interval operations are used to perform measurement transformation to obtain a linearized interval measurement function, which is shown as follows:

[0049] Where: is a quantity measurement vector in the form of interval numbers, is the linearized interval measurement function, is the state variable vector.

[0050] The state variable interval, the acquisition process is as follows:

[0051] Where: N is the obtained state variable interval, is the measurement weight matrix, is the identity matrix, is the transition interval vector.

[0052] S4, an interval optimization algorithm is proposed to converge the state variable interval and obtain the target estimation interval.

[0053] The proposed interval optimization algorithm converges the state variable interval, and the steps are as follows: Set a complete initial iteration interval, preset the approximate solution of the interval equation, solve the difference between the iteration interval and the approximate solution, and obtain the difference interval; Using the Krawczyk-Moore operator to iterate and converge the difference interval, and setting a convergence threshold, if the difference after convergence is less than the preset convergence threshold, stopping the iteration and determining the final iterative result; If the difference after convergence is greater than the preset convergence threshold, then an interval constraint propagation function is defined to further converge the difference interval; The iteration termination condition is set when the difference between two consecutive iterations is less than the preset convergence threshold. When the termination condition is reached, the calculation is terminated immediately and the estimated interval of the final state variable is output.

[0054] The interval constraint propagation function is defined as follows:

[0055] Where: To set a complete initial iteration interval, is the difference interval obtained in the pth iteration.

[0056] The interval constraint propagation function is used to solve the interval constraints in the constraint satisfaction problem (CSP). It propagates the constraint information to narrow the value range of the variable and ensure that the variable satisfies the given constraint conditions.

[0057] like Figure 2 As shown, it is a functional module diagram of a multi-source measurement data processing device. According to the implemented functions, the distribution network section state assessment device considering measurement uncertainty may include xx module. The module described in the present invention may also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of the distribution network section state assessment device considering measurement uncertainty and can complete fixed functions, which are stored in the memory of the multi-source measurement data processing device.

[0058] In this embodiment, the functions of each module / unit are as follows: Data acquisition module: acquiring multi-source measurement data and analyzing the differences in sampling characteristics of the multi-source measurement data; Data processing module: processing the differences in sampling characteristics of the source measurement data according to the multi-source measurement fusion strategy to obtain fused multi-source measurement data; State estimation module: constructing an interval state estimation model in combination with the fused multi-source measurement data, the model outputs a linearized interval measurement function to obtain a state variable interval; Result output module: An interval optimization algorithm is proposed to converge the state variable interval and obtain the target estimation interval.

[0059] In detail, the modules described in the distribution network section state assessment device considering measurement uncertainty described in the embodiment of the present invention adopt the same technical means as the distribution network section state assessment method considering measurement uncertainty described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0060] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing the distribution network section state assessment method taking measurement uncertainty into account according to the present invention.

[0061] The electronic device may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, such as a distribution network section state assessment model generation program that takes measurement uncertainty into account.

[0062] Among them, the processor is the control core (Control Unit) of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory and calling data stored in the memory.

[0063] The memory includes at least one type of readable storage medium, and in some embodiments, the memory may be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may be used to store not only application software installed in the electronic device but also various data.

[0064] The communication bus is configured to realize connection and communication between the memory and at least one processor.

[0065] The communication interface is used for communication between the above electronic device and other devices, including a network interface and a user interface.

[0066] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0067] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0068] The distribution network section state assessment model generation program considering measurement uncertainty stored in the memory of the electronic device is a combination of multiple instructions. When running in the processor, the steps in the above-mentioned distribution network section state assessment method considering measurement uncertainty can be implemented.

[0069] Specifically, the specific implementation system of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0070] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement the steps in the above-mentioned distribution network section state assessment method considering measurement uncertainty.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0072] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0073] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0074] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0075] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0076] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A distribution network interval state assessment method considering measurement uncertainty, characterized in that: The steps include: Acquiring multi-source measurement data, and analyzing differences in sampling characteristics of the multi-source measurement data; Processing the differences in sampling characteristics of the multi-source measurement data according to a multi-source measurement fusion strategy to obtain fused multi-source measurement data; An interval state estimation model is constructed by combining and fusing multi-source measurement data. The model outputs a linearized interval measurement function to obtain the state variable interval. An interval optimization algorithm is proposed to converge the state variable interval and obtain the target estimation interval.

2. The distribution network section state assessment method considering measurement uncertainty according to claim 1 is characterized in that: The analysis method for analyzing the differences in sampling characteristics of the multi-source measurement data is as follows: Preset a measurement delay range and a sampling period of the data acquisition and monitoring control system, receive first measurement data of the same data acquisition and monitoring control device within a given operation time period, record the received measurement data in a vector form, and obtain a second measurement vector; Using a distribution network synchronous phasor measurement device to screen and reorganize the second measurement vector to obtain a third measurement matrix; The Pearson correlation coefficients of the second measurement vector and each row vector in the third measurement matrix are calculated respectively.

3. The distribution network section state assessment method considering measurement uncertainty according to claim 2 is characterized in that: The processing of the differences in sampling characteristics of the multi-source measurement data according to the multi-source measurement fusion strategy is specifically as follows: Unified processing of measurement time scales of multi-source measurement data; The multi-source measurement data after unified time scale is subjected to time delay fusion based on the AMI measurement device to obtain fused multi-source measurement data.

4. The distribution network section state assessment method considering measurement uncertainty according to claim 3 is characterized in that: The method for obtaining the fused multi-source measurement data is as follows: Using the state estimation time, reading the distribution network synchronized phasor measurement device measurement from the distribution network synchronized phasor measurement device as a first measurement time; The state estimation is performed by using the data acquisition and monitoring control system sampling value closest to the state estimation time to obtain the second measurement time; The first measurement time and the second measurement time are processed according to the AMI measurement device to obtain fused multi-source measurement data.

5. The distribution network section state assessment method considering measurement uncertainty according to claim 4 is characterized in that: The unified processing of the measurement time scales of multi-source measurement data is specifically as follows: Obtaining a preset measurement delay range and sampling period, calculating the measurement delay of the data acquisition and monitoring control system, and obtaining a sampling time scale of the second measurement vector; The sampling time scale of the second measurement vector is assigned to the third measurement matrix.

6. The distribution network section state assessment method considering measurement uncertainty according to claim 5, characterized in that: The Pearson correlation coefficient of each row vector in the second measurement vector and the third measurement matrix is ​​calculated respectively, and the calculation process is as follows; Where: is the Pearson correlation coefficient between the second measurement vector and the j-th row vector in the third measurement matrix, Cov is the covariance between the second measurement vector and the j-th row vector in the third measurement matrix, is the standard deviation of the second measurement vector, is the standard deviation of the j-th row vector in the third measurement matrix, is the mean, is the i-th row vector in the second measurement vector, is the second measurement vector, is the total number of data received by the same data acquisition and monitoring control device, n d is the total number of rows of vectors in the third measurement matrix, is the i-th vector in the j-th row of the third measurement matrix.

7. The distribution network section state assessment method considering measurement uncertainty according to claim 6, characterized in that: The proposed interval optimization algorithm converges the state variable interval, including the following: Set a complete initial iteration interval, preset the approximate solution of the interval equation, solve the difference between the initial iteration interval and the approximate solution, and obtain the difference interval; Using the Krawczyk-Moore operator to iterate and converge the difference interval, and setting a convergence threshold, when the difference after convergence is less than the preset convergence threshold, stopping the iteration and determining the final iterative result; If the difference after convergence is greater than the preset convergence threshold, then an interval constraint propagation function is defined to further converge the difference interval; The iteration termination condition is set when the difference between two consecutive iterations is less than the preset convergence threshold. When the termination condition is reached, the calculation is terminated immediately and the target estimation interval of the final state variable is output.

8. A device using the distribution network section state assessment method considering measurement uncertainty as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a data processing module, a state estimation module and a result output module; Data acquisition module: used to acquire multi-source measurement data and analyze the differences in sampling characteristics of the multi-source measurement data; Data processing module: used for processing the differences in sampling characteristics of the source measurement data according to the multi-source measurement fusion strategy to obtain fused multi-source measurement data; State estimation module: used to construct an interval state estimation model in combination with the fused multi-source measurement data, the model outputs a linearized interval measurement function to obtain a state variable interval; Result output module: used to propose an interval optimization algorithm to converge the state variable interval and obtain the target estimation interval.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a distribution network section state assessment method taking into account measurement uncertainty as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating the state of a distribution network section taking into account measurement uncertainty as described in any one of claims 1 to 7 is implemented.

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