Method and system for evaluating real-time state of power distribution network

Abstract

The invention provides a power distribution network real-time state evaluation method and system, and the method comprises the steps: obtaining an initial millisecond-level data set of a hub node and a hub branch, and obtaining a first second-level voltage value of a basic node and a first second-level current value of a basic branch; obtaining a second-second-level voltage value and a second-second-level current value through the initial millisecond-level data set, and further obtaining a stage voltage value and a stage current value; obtaining an optimal voltage value and an optimal voltage phase angle based on the stage current value and the stage voltage value, and obtaining a predicted voltage value and a predicted voltage phase angle in combination with the prediction model; and obtaining a predicted current value based on the predicted voltage value and the predicted voltage phase angle, and evaluating the real-time state of the power distribution network through the predicted current value, the predicted voltage phase angle and the predicted voltage value. By means of the mode, stable capturing of the static characteristics of the power distribution network is achieved, continuous tracking of the dynamic characteristics is achieved, and the real-time state of the power distribution network can be mastered more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for evaluating the real-time status of a distribution network. Background Art

[0002] A distribution network is a power grid that receives electricity from the transmission grid or regional power plants and distributes it locally or step-by-step according to voltage to various users through distribution facilities. It is composed of overhead lines, cables, towers, distribution transformers, disconnectors, VAR compensators, and other ancillary facilities, and plays a key role in distributing electricity within the power grid.

[0003] With rapid economic development, energy demand and electricity consumption are rapidly increasing, leading to increasing attention for renewable energy and distributed generation. Distributed energy resources are typically directly connected to distribution networks. With the integration of diverse loads, the operation and control of distribution networks are becoming increasingly complex and diverse, increasing the risks faced by distribution systems during operation.

[0004] Real-time state assessment is a state estimation that reflects the time scale. In distribution networks, real-time state assessment provides the management system with the ability to respond to operations and emergency events in real time, thereby supporting the reliability of the power grid and improving energy efficiency. Static state estimation models are widely used in real-time state assessment of distribution networks due to their simplicity, fast convergence speed, and lack of influence from historical measurements. However, with the increasing complexity of distribution networks and the increase in load-side fluctuations, the accuracy of the assessment results of static state estimation models gradually decreases, making it difficult to accurately predict the real-time state of the distribution network. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a real-time status evaluation method and system for a distribution network, aiming to solve the technical problem in the existing technology that as the complexity of the distribution network increases and the load side fluctuations increase, the accuracy of the evaluation results of the static state estimation model on the real-time status of the distribution network gradually decreases, making it difficult to accurately estimate the real-time status of the distribution network.

[0006] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a method for evaluating the real-time status of a distribution network, which is used to evaluate the real-time status of a distribution network. The distribution network includes a plurality of basic nodes and a plurality of basic branches, each of which is used to connect two of the basic nodes. The method for evaluating the real-time status of a distribution network includes the following steps:

[0007] Selecting a plurality of hub nodes from the plurality of basic nodes, selecting a plurality of hub branches corresponding to the hub nodes from the plurality of basic branches, obtaining initial millisecond-level data sets of the hub nodes and the hub branches, obtaining first-second-level voltage values ​​of the basic nodes and first-second-level current values ​​of the basic branches, and performing data processing on the initial millisecond-level data sets to obtain a stand-by millisecond-level data set;

[0008] Simplifying the standby millisecond-level data set, obtaining a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch based on the simplified standby millisecond-level data set, obtaining a stage voltage value corresponding to the basic node based on the first-second-level voltage value and the second-second-level voltage value, and obtaining a stage current value corresponding to the basic branch using the first-second-level current value and the second-second-level current value;

[0009] constructing an observation vector group based on the stage current value and the stage voltage value, obtaining an optimal voltage value and an optimal voltage phase angle corresponding to the base node through the observation vector group, and obtaining a predicted voltage value and a predicted voltage phase angle through a prediction model, the optimal voltage value and the optimal voltage phase angle;

[0010] A predicted current value is obtained based on the predicted voltage value and the predicted voltage phase angle, and a real-time state of the distribution network is evaluated through the predicted current value, the predicted voltage phase angle, and the predicted voltage value.

[0011] Furthermore, the initial millisecond-level dataset includes a plurality of type units, each of which includes a plurality of node sub-units, each of which includes initial sub-data in a plurality of time series. The step of processing the initial millisecond-level dataset to obtain a millisecond-level dataset to be used includes:

[0012] Obtaining a plurality of first median values ​​of the initial sub-data, obtaining a plurality of numerical differences between the initial sub-data and the first median value, and obtaining a plurality of second median values ​​of the numerical differences;

[0013] determining whether the initial sub-data is abnormal data based on the first median value and the second median value, and if the initial sub-data is abnormal data, removing the initial sub-data to obtain a plurality of stage sub-data;

[0014] determining whether there is a missing region between two adjacent stage sub-data based on the time series; if there is a missing region between the two adjacent stage sub-data, generating a filling value based on the two stage sub-data, and filling the missing region with the filling value to obtain a plurality of final sub-data;

[0015] Several of the final sub-data are combined into a final node sub-unit, several of the final node sub-units are combined into a final type unit, and several of the final type units are combined into a stand-by millisecond-level data set.

[0016] Furthermore, the judgment formula for abnormal data is:

[0017] |X i -X z1 |>T*θ*X z2 ,

[0018] Among them, X i represents the i-th initial sub-data, X z1 represents the first median, T represents the critical value, δ represents the standard deviation, X z2 represents the second median value;

[0019] The formula for obtaining the fill value is:

[0020]

[0021] Among them, Z(t m ) represents the filling value at the mth moment, t n represents the nth moment, t o represents the oth moment, t m represents the mth moment, and t o <t m <t n , A o represents the second-order derivative at the oth moment, A n represents the second-order derivative at the nth moment, Z(t o ) represents the stage sub-data at the oth moment, Z(t n ) represents the stage sub-data at the nth moment.

[0022] Furthermore, the standby millisecond-level data set includes a first data packet corresponding to the hub node and a second data packet corresponding to the hub branch, the first data packet and the second data packet respectively including a plurality of millisecond-level hub voltages and a plurality of millisecond-level hub currents, and the step of performing data simplification on the standby millisecond-level data set and obtaining a second-second-level voltage value corresponding to the basic node and a second-second-level current value corresponding to the basic branch based on the simplified standby millisecond-level data set includes:

[0023] performing averaging processing on a plurality of the millisecond-level hub voltages to obtain a mean voltage value, and performing averaging processing on a plurality of the millisecond-level hub currents to obtain a mean current value;

[0024] Taking the hub node and the hub branch as starting points, a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch are derived through the average voltage value and the average current value.

[0025] Furthermore, the calculation formula of the voltage value in the stage is:

[0026] V jd (i) = α * V 2s (i)+(1-α)V 1s (i)

[0027] Among them, V jd (i) represents the stage voltage value of the i-th basic node, α represents the first weight, V 2s (i) represents the second-second voltage value of the i-th basic node, V 1s (i) represents the first-second voltage value of the i-th basic node;

[0028] The calculation formula of the current value in this stage is:

[0029] I jd (j) = β * I 2s (j)+(1-β)I 1s (j),

[0030] Among them, I jd (j) represents the stage current value of the j-th basic branch, β represents the second weight, I 2s (j) represents the second-second current value of the j-th basic branch, I 1s (j) represents the first-second current value of the j-th basic branch.

[0031] Furthermore, the step of obtaining the optimal voltage value and the optimal voltage phase angle corresponding to the basic node through the observation vector group includes:

[0032] Setting an initial voltage parameter value and an initial voltage phase angle corresponding to the basic node, and acquiring an initial current parameter value corresponding to the basic branch based on the initial voltage parameter value and the initial voltage phase angle;

[0033] Constructing a theoretical vector group based on the initial voltage parameter value and the initial current parameter value, and constructing an objective function through the observation vector group and the theoretical vector group;

[0034] The objective function is iteratively converged to optimize the initial voltage parameter value to an optimal voltage value, and optimize the initial voltage phase angle to an optimal voltage phase angle.

[0035] Furthermore, the objective function is:

[0036] M(x)=[gc-ll(x)] T R -1 [gc-ll(x)]+γ*||I jd (ZZ)-I ll (ZZ)|| 2 ,

[0037] Where M(x) represents the objective function, x represents the initial voltage parameter value and the initial current parameter value to be iteratively converged, gc represents the observation vector group, ll(x) represents the theoretical vector group, T represents the transpose symbol, and R -1 represents the inverse covariance matrix of the measurement error, γ represents the constraint weight, I jd (ZZ) represents the phase current value of the main branch, I ll (ZZ) represents the initial current parameter value of the main branch.

[0038] Furthermore, the step of obtaining the predicted voltage value and the predicted voltage phase angle through the prediction model, the optimal voltage value and the optimal voltage phase angle includes:

[0039] Constructing a prediction model based on the voltage variation characteristics of the basic node and the current variation characteristics of the basic branch;

[0040] The optimal voltage value and the optimal voltage phase angle are used as inputs of the prediction model to obtain an evaluation voltage value and a predicted voltage phase angle, and the optimal voltage value is used to replace the evaluation voltage value to obtain a predicted voltage value.

[0041] Furthermore, the step of evaluating the real-time state of the distribution network by using the predicted current value, the predicted voltage phase angle, and the predicted voltage value includes:

[0042] Performing difference processing on the predicted current value and the stage current value to obtain a current residual, comparing the current residual with a residual threshold, and triggering a branch current warning if the current residual is greater than the residual threshold;

[0043] Obtaining a voltage over-limit value based on the predicted voltage value, comparing the voltage over-limit value with an over-limit threshold, and triggering a node voltage over-limit warning if the voltage over-limit value is greater than the over-limit threshold;

[0044] The angle difference of the predicted voltage phase angle between two adjacent basic nodes is obtained, and the angle difference is compared with an angle threshold. If the angle difference is greater than the angle threshold, a power angle instability warning is triggered.

[0045] In a second aspect, an embodiment of the present application provides a distribution network real-time status assessment system, which is applied to the distribution network real-time status assessment method as described in the first aspect above, and the system includes:

[0046] a processing module, configured to select a plurality of hub nodes from the plurality of basic nodes, select a plurality of hub branches corresponding to the hub nodes from the plurality of basic branches, obtain initial millisecond-level data sets of the hub nodes and the hub branches, obtain first-second-level voltage values ​​of the basic nodes and first-second-level current values ​​of the basic branches, and perform data processing on the initial millisecond-level data sets to obtain a stand-by millisecond-level data set;

[0047] an adjustment module, configured to perform data simplification on the standby millisecond-level data set, obtain a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch based on the simplified standby millisecond-level data set, obtain a stage voltage value corresponding to the basic node based on the first-second-level voltage value and the second-second-level voltage value, and obtain a stage current value corresponding to the basic branch using the first-second-level current value and the second-second-level current value;

[0048] an execution module, configured to construct an observation vector group based on the stage current value and the stage voltage value, obtain an optimal voltage value and an optimal voltage phase angle corresponding to the basic node through the observation vector group, and obtain a predicted voltage value and a predicted voltage phase angle through a prediction model, the optimal voltage value, and the optimal voltage phase angle;

[0049] An evaluation module is used to obtain a predicted current value based on the predicted voltage value and the predicted voltage phase angle, and evaluate the real-time status of the distribution network through the predicted current value and the predicted voltage value.

[0050] In a third aspect, an embodiment of the present application provides a computer comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for real-time status assessment of a distribution network as described in the first aspect above is implemented.

[0051] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for real-time status assessment of a distribution network as described in the first aspect above is implemented.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: by obtaining the initial millisecond-level data set of the hub node and the hub branch, and combining the first-second-level current value and the first-second-level voltage value, multi-time-scale data fusion solves the problem that the traditional static model is insensitive to high-frequency data fluctuations; by processing the initial millisecond-level data set, the interference of noise is reduced, so that the stand-by millisecond-level data set is closer to the dynamic changes of the actual distribution network, and the accuracy of subsequent state evaluation is improved; by constructing the objective function and performing iterative convergence, the problem of parameter solidification of the traditional static model is avoided, and further By combining the prediction model for dynamic prediction and replacing the evaluated voltage value with the optimal voltage value, it effectively avoids the frequent jumps in voltage prediction caused by the noise contained in millisecond-level data after the introduction of the prediction model. After data processing and data fusion, the optimal voltage value provides higher accuracy, avoids the cumulative error of dynamic prediction, and improves the accuracy of state assessment. As for the predicted voltage phase angle, as high-frequency information, it cannot be captured by traditional static models. Through the continuous updating of the prediction model, the dynamic characteristics of the distribution network are continuously tracked, and the real-time status of the distribution network can be grasped more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for evaluating the real-time status of a power distribution network according to a first embodiment of the present invention;

[0054] Figure 2 This is a structural block diagram of a distribution network real-time status assessment system according to a second embodiment of the present invention;

[0055] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0056] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0057] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0059] See also Figure 1 A first embodiment of the present invention provides a method for evaluating the real-time status of a distribution network, which is used to evaluate the real-time status of a distribution network. The distribution network includes a plurality of basic nodes and a plurality of basic branches, wherein the basic branches are used to connect two of the basic nodes. It can be understood that the interconnection between the basic nodes and the basic branches can form a topological structure of the distribution network. The method for evaluating the real-time status of a distribution network includes the following steps:

[0060] S10: Selecting a plurality of hub nodes from the plurality of basic nodes, selecting a plurality of hub branches corresponding to the hub nodes from the plurality of basic branches, obtaining initial millisecond-level data sets of the hub nodes and the hub branches, obtaining first-second-level voltage values ​​of the basic nodes and first-second-level current values ​​of the basic branches, and performing data processing on the initial millisecond-level data sets to obtain a stand-by millisecond-level data set;

[0061] It should be noted that when obtaining the first-second voltage value and the first-second current value, it is also necessary to collect and obtain the hub node and the hub branch. In fact, the hub node and the hub branch each correspond to two sets of data, the initial millisecond data set and the first-second voltage value / the first-second current value.

[0062] The initial millisecond-level dataset includes several type units, each of which includes several node sub-units, each of which includes initial sub-data in several time series. In this embodiment, the initial millisecond-level dataset includes a voltage type unit and a current type unit. Taking the voltage type unit as an example, if there are five hub nodes, the voltage type unit includes five node sub-units. In this embodiment, the collection interval of the initial millisecond-level dataset is 100 milliseconds. Taking the sampling interval of the first second-level voltage value as an example, which is 1 second, the node sub-unit includes the initial sub-data in 10 time series. The initial sub-data is the millisecond-level hub voltage. It can be understood that, taking the current type unit as an example, the initial sub-data is the millisecond-level hub current.

[0063] The step S10 includes:

[0064] S110: Obtaining a plurality of first median values ​​of the initial sub-data, obtaining a plurality of numerical differences between the initial sub-data and the first median values, and obtaining a plurality of second median values ​​of the numerical differences;

[0065] S120: Determine whether the initial sub-data is abnormal data based on the first median value and the second median value; if the initial sub-data is abnormal data, remove the initial sub-data to obtain a plurality of stage sub-data;

[0066] The judgment formula for abnormal data is:

[0067] |X i -X z1 |>T*θ*X z2 ,

[0068] Among them, X i represents the i-th initial sub-data, X z1 represents the first median, T represents the critical value, δ represents the standard deviation, X z2 Indicates the second median value.

[0069] S130: determining whether there is a missing region between two adjacent stage sub-data based on the time series; if there is a missing region between the two adjacent stage sub-data, generating a filling value based on the two stage sub-data, and filling the missing region with the filling value to obtain a plurality of final sub-data;

[0070] The formula for obtaining the fill value is:

[0071]

[0072] Among them, Z(t m ) represents the filling value at the mth moment, t n represents the nth moment, t o represents the oth moment, t m represents the mth moment, and t o <t m <t n , A o represents the second-order derivative at the oth moment, A n represents the second-order derivative at the nth moment, Z(t o ) represents the stage sub-data at the oth moment, Z(t n ) represents the stage sub-data at the nth moment.

[0073] S140: Several of the final sub-data are combined into a final node sub-unit, several of the final node sub-units are combined into a final type unit, and several of the final type units are combined into a stand-by millisecond-level data set.

[0074] S20: Simplifying the standby millisecond-level data set, obtaining a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch based on the simplified standby millisecond-level data set, obtaining a stage voltage value corresponding to the basic node based on the first-second-level voltage value and the second-second-level voltage value, and obtaining a stage current value corresponding to the basic branch based on the first-second-level current value and the second-second-level current value;

[0075] The in-use millisecond-level data set includes a first data packet corresponding to the hub node and a second data packet corresponding to the hub branch. The first data packet and the second data packet respectively include a plurality of millisecond-level hub voltages and a plurality of millisecond-level hub currents. It should be noted that the first data packet, the type unit, and the node subunit all represent different ways of dividing the millisecond-level data set; in essence, they are all data.

[0076] The step S20 includes:

[0077] S210: performing averaging processing on the plurality of millisecond-level hub voltages to obtain a mean voltage value, and performing averaging processing on the plurality of millisecond-level hub currents to obtain a mean current value;

[0078] The purpose of averaging is to synchronize the millisecond-level hub voltage with the first-second-level voltage value, and the millisecond-level hub current with the first-second-level current value in terms of time state, thereby ensuring that the subsequent merged data between the two meets the same time conditions and improves data accuracy.

[0079] S220: Taking the hub node and the hub branch as starting points, deriving a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch through the average voltage value and the average current value;

[0080] After obtaining the voltage of some nodes and the current of the branch, the voltage, current and other data of the adjacent nodes can be deduced by combining the voltage, current and the impedance of the branch.

[0081] The calculation formula of the voltage value in this stage is:

[0082] V jd (i) = α * V 2s (i)+(1-α)V 1s (i)

[0083] Among them, V jd (i) represents the stage voltage value of the i-th basic node, α represents the first weight, V 2s (i) represents the second-second voltage value of the i-th basic node, V1s (i) represents the first-second voltage value of the i-th basic node;

[0084] The calculation formula of the current value in this stage is:

[0085] I jd (j) = β * I 2s (j)+(1-β)I 1s (j),

[0086] Among them, I jd (j) represents the stage current value of the j-th basic branch, β represents the second weight, I 2s (j) represents the second-second current value of the j-th basic branch, I 1s (j) represents the first-second current value of the j-th basic branch.

[0087] S30: constructing an observation vector group based on the stage current value and the stage voltage value, obtaining an optimal voltage value and an optimal voltage phase angle corresponding to the base node through the observation vector group, and obtaining a predicted voltage value and a predicted voltage phase angle through a prediction model, the optimal voltage value and the optimal voltage phase angle;

[0088] Assume that there are three nodes, namely node 1, node 2 and node 3, node 1 and node 2 are connected through branch 12, and node 2 and node 3 are connected through branch 23, then the observation vector group is (the stage voltage value of node 1, the stage current value of branch 12, the stage voltage value of node 2, the stage current value of branch 23, and the stage voltage value of node 3).

[0089] The step S30 includes:

[0090] S310: Setting an initial voltage parameter value and an initial voltage phase angle corresponding to the basic node, and acquiring an initial current parameter value corresponding to the basic branch based on the initial voltage parameter value and the initial voltage phase angle;

[0091] S320: constructing a theoretical vector group based on the initial voltage parameter value and the initial current parameter value, and constructing an objective function through the observation vector group and the theoretical vector group;

[0092] It can be understood that after setting the initial voltage parameter value and the initial voltage phase angle of the two basic nodes, the initial current parameter value of the basic branch connecting the two basic nodes can be obtained by combining the admittance matrix. This is widely used in electrical engineering and will not be described in detail here.

[0093] S330: Iteratively converge the objective function to optimize the initial voltage parameter value to an optimal voltage value, and optimize the initial voltage phase angle to an optimal voltage phase angle;

[0094] The objective function is:

[0095] M(x)=[gc-ll(x)] T R -1 [gc-ll(x)]+γ*||I jd (ZZ)-I ll (ZZ)|| 2 ,

[0096] Where M(x) represents the objective function, x represents the initial voltage parameter value and the initial current parameter value to be iteratively converged, gc represents the observation vector group, ll(x) represents the theoretical vector group, T represents the transpose symbol, and R -1 represents the inverse covariance matrix of the measurement error, γ represents the constraint weight, I jd (ZZ) represents the phase current value of the main branch, I ll (ZZ) represents the initial current parameter value of the main branch. The main branch refers to an extremely important branch in the distribution network, which plays the role of a physical constraint in the objective function.

[0097] After obtaining the theoretical vector combination, by constructing the objective function, the initial voltage parameter value and the initial voltage phase angle are iteratively adjusted so that the initial voltage parameter value approaches the stage voltage value, and the initial current parameter value approaches the stage current value, thereby obtaining the optimal voltage value and the optimal voltage phase angle.

[0098] S340: Constructing a prediction model based on the voltage variation characteristics of the basic node and the current variation characteristics of the basic branch;

[0099] Taking the voltage of a certain basic node as an example, the fluctuations of the voltage at different time nodes have time correlation, which is its changing characteristic. Through this time correlation, a state transition unit can be constructed. Based on different data types, different state transition units are constructed, and then combined into the prediction model.

[0100] S350: Using the optimal voltage value and the optimal voltage phase angle as inputs of the prediction model to obtain an evaluation voltage value and a predicted voltage phase angle, and replacing the evaluation voltage value with the optimal voltage value to obtain a predicted voltage value.

[0101] S40: Obtaining a predicted current value based on the predicted voltage value and the predicted voltage phase angle, and evaluating a real-time state of the distribution network through the predicted current value, the predicted voltage phase angle, and the predicted voltage value;

[0102] The predicted current value is obtained in the same manner as the initial current parameter value, and will not be further elaborated here.

[0103] The step S40 includes:

[0104] S410: performing difference processing on the predicted current value and the stage current value to obtain a current residual, comparing the current residual with a residual threshold, and triggering a branch current warning if the current residual is greater than the residual threshold;

[0105] In this embodiment, the residual threshold is 0.1.

[0106] S420: Obtaining a voltage over-limit value based on the predicted voltage value, comparing the voltage over-limit value with an over-limit threshold, and triggering a node voltage over-limit warning if the voltage over-limit value is greater than the over-limit threshold;

[0107] The step of obtaining the voltage over-limit value is as follows: obtaining the absolute value of the difference between the predicted voltage value and 1; dividing the absolute value by 1 and then multiplying it by 100% to obtain the voltage over-limit value.

[0108] In this embodiment, the crossing threshold is 5%.

[0109] S430: Obtaining an angle difference between the predicted voltage phase angles of two adjacent base nodes, comparing the angle difference with an angle threshold, and triggering a power angle instability warning if the angle difference is greater than the angle threshold;

[0110] In this embodiment, the angle threshold is 30°.

[0111] By obtaining the initial millisecond-level data sets of the hub node and the hub branch, and combining the first second-level current value and the first second-level voltage value, multi-time-scale data fusion solves the problem that traditional static models are insensitive to high-frequency data fluctuations. By processing the initial millisecond-level data sets, noise interference is reduced, making the stand-by millisecond-level data sets closer to the dynamic changes of the actual distribution network, and improving the accuracy of subsequent state assessment. By constructing the objective function and performing iterative convergence, the problem of parameter solidification in traditional static models is avoided. Furthermore, by combining the prediction model for dynamic prediction and replacing the evaluated voltage value with the optimal voltage value, it effectively avoids frequent voltage jumps caused by noise contained in the millisecond-level data after the introduction of the prediction model. After data processing and data fusion, the optimal voltage value provides higher accuracy, avoids cumulative errors in dynamic prediction, and improves the accuracy of state assessment. As high-frequency information, the predicted voltage phase angle cannot be captured by traditional static models. Through continuous updating of the prediction model, continuous tracking of the dynamic characteristics of the distribution network is achieved, and the real-time state of the distribution network can be more accurately grasped.

[0112] See also Figure 2 The second embodiment of the present invention provides a distribution network real-time status assessment system, which is applied to the distribution network real-time status assessment method described in the above embodiment. The details that have been explained will not be repeated here. As used below, the terms "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0113] The system comprises:

[0114] The processing module 10 is configured to select a plurality of hub nodes from the plurality of basic nodes, select a plurality of hub branches corresponding to the hub nodes from the plurality of basic branches, obtain initial millisecond-level data sets of the hub nodes and the hub branches, obtain first-second-level voltage values ​​of the basic nodes and first-second-level current values ​​of the basic branches, and perform data processing on the initial millisecond-level data sets to obtain a stand-by millisecond-level data set;

[0115] The processing module 10 includes:

[0116] A first unit is configured to obtain a first median value of a plurality of the initial sub-data, obtain a plurality of numerical differences between the initial sub-data and the first median value, and obtain a plurality of second median values ​​of the data differences;

[0117] a second unit, configured to determine whether the initial sub-data is abnormal data based on the first median value and the second median value, and if the initial sub-data is abnormal data, remove the initial sub-data to obtain a plurality of stage sub-data;

[0118] a third unit configured to determine, based on the time series, whether there is a missing region between two adjacent stage sub-data, and if so, generate a filling value based on the two stage sub-data, and fill the missing region with the filling value to obtain a plurality of final sub-data;

[0119] A fourth unit is used to combine several of the final sub-data into a final node sub-unit, combine several of the final node sub-units into a final type unit, and combine several of the final type units into a stand-by millisecond-level data set;

[0120] an adjustment module 20, configured to perform data simplification on the standby millisecond-level data set, obtain a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch based on the simplified standby millisecond-level data set, obtain a stage voltage value corresponding to the basic node based on the first-second-level voltage value and the second-second-level voltage value, and obtain a stage current value corresponding to the basic branch using the first-second-level current value and the second-second-level current value;

[0121] The adjustment module 20 includes:

[0122] a fifth unit, configured to perform averaging processing on the plurality of millisecond-level hub voltages to obtain a mean voltage value, and perform averaging processing on the plurality of millisecond-level hub currents to obtain a mean current value;

[0123] a sixth unit, configured to derive, with the hub node and the hub branch as starting points, a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch through the average voltage value and the average current value;

[0124] an execution module 30, configured to construct an observation vector group based on the stage current value and the stage voltage value, obtain an optimal voltage value and an optimal voltage phase angle corresponding to the base node through the observation vector group, and obtain a predicted voltage value and a predicted voltage phase angle through a prediction model, the optimal voltage value, and the optimal voltage phase angle;

[0125] The execution module 30 includes:

[0126] a seventh unit, configured to set an initial voltage parameter value and an initial voltage phase angle corresponding to the basic node, and acquire an initial current parameter value corresponding to the basic branch based on the initial voltage parameter value and the initial voltage phase angle;

[0127] An eighth unit is configured to construct a theoretical vector group based on the initial voltage parameter value and the initial current parameter value, and to construct an objective function using the observation vector group and the theoretical vector group;

[0128] a ninth unit, configured to iteratively converge the objective function to optimize the initial voltage parameter value to an optimal voltage value, and optimize the initial voltage phase angle to an optimal voltage phase angle;

[0129] A tenth unit, configured to construct a prediction model based on the voltage variation characteristics of the basic node and the current variation characteristics of the basic branch;

[0130] an eleventh unit, configured to use the optimal voltage value and the optimal voltage phase angle as inputs of the prediction model to obtain an evaluation voltage value and a predicted voltage phase angle, and replace the evaluation voltage value with the optimal voltage value to obtain a predicted voltage value;

[0131] an evaluation module 40, configured to obtain a predicted current value based on the predicted voltage value and the predicted voltage phase angle, and evaluate a real-time state of the distribution network through the predicted current value and the predicted voltage value;

[0132] The evaluation module 40 includes:

[0133] A twelfth unit is configured to perform difference processing between the predicted current value and the stage current value to obtain a current residual, compare the current residual with a residual threshold, and trigger a branch current warning if the current residual is greater than the residual threshold;

[0134] A thirteenth unit is configured to obtain a voltage over-limit value based on the predicted voltage value, compare the voltage over-limit value with an over-limit threshold, and trigger a node voltage over-limit warning if the voltage over-limit value is greater than the over-limit threshold;

[0135] The fourteenth unit is used to obtain the angle difference of the predicted voltage phase angle between two adjacent basic nodes, compare the angle difference with the angle threshold, and trigger a power angle instability warning if the angle difference is greater than the angle threshold.

[0136] The present invention also provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for evaluating the real-time status of a distribution network as described in the above technical solution is implemented.

[0137] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for real-time status evaluation of a distribution network as described in the above technical solution is implemented.

[0138] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0139] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for evaluating the real-time status of a distribution network, wherein the method is used to evaluate the real-time status of the distribution network, wherein the distribution network comprises a plurality of basic nodes and a plurality of basic branches, wherein the basic branches are used to connect two of the basic nodes, and wherein: The method for evaluating the real-time status of a power distribution network comprises the following steps: Selecting a plurality of hub nodes from the plurality of basic nodes, selecting a plurality of hub branches corresponding to the hub nodes from the plurality of basic branches, obtaining initial millisecond-level data sets of the hub nodes and the hub branches, obtaining first-second-level voltage values ​​of the basic nodes and first-second-level current values ​​of the basic branches, and performing data processing on the initial millisecond-level data sets to obtain a stand-by millisecond-level data set; Simplifying the standby millisecond-level data set, obtaining a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch based on the simplified standby millisecond-level data set, obtaining a stage voltage value corresponding to the basic node based on the first-second-level voltage value and the second-second-level voltage value, and obtaining a stage current value corresponding to the basic branch using the first-second-level current value and the second-second-level current value; constructing an observation vector group based on the stage current value and the stage voltage value, obtaining an optimal voltage value and an optimal voltage phase angle corresponding to the base node through the observation vector group, and obtaining a predicted voltage value and a predicted voltage phase angle through a prediction model, the optimal voltage value and the optimal voltage phase angle; A predicted current value is obtained based on the predicted voltage value and the predicted voltage phase angle, and a real-time state of the distribution network is evaluated through the predicted current value, the predicted voltage phase angle, and the predicted voltage value.

2. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The initial millisecond-level dataset includes a plurality of type units, each of which includes a plurality of node sub-units, each of which includes initial sub-data in a plurality of time series. The step of processing the initial millisecond-level dataset to obtain a millisecond-level dataset to be used includes: Obtaining a plurality of first median values ​​of the initial sub-data, obtaining a plurality of numerical differences between the initial sub-data and the first median value, and obtaining a plurality of second median values ​​of the numerical differences; determining whether the initial sub-data is abnormal data based on the first median value and the second median value, and if the initial sub-data is abnormal data, removing the initial sub-data to obtain a plurality of stage sub-data; determining whether there is a missing region between two adjacent stage sub-data based on the time series; if there is a missing region between the two adjacent stage sub-data, generating a filling value based on the two stage sub-data, and filling the missing region with the filling value to obtain a plurality of final sub-data; Several of the final sub-data are combined into a final node sub-unit, several of the final node sub-units are combined into a final type unit, and several of the final type units are combined into a stand-by millisecond-level data set.

3. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The judgment formula for abnormal data is: |X i -X z1 |>T*θ*X z2 , Among them, X i represents the i-th initial sub-data, X z1 represents the first median, T represents the critical value, δ represents the standard deviation, X z2 represents the second median value; The formula for obtaining the filling value is: Among them, Z(t m ) represents the filling value at the mth moment, t n represents the nth moment, t o represents the oth moment, t m represents the mth moment, and t o <t m <t n , A o represents the second-order derivative at the oth moment, A n represents the second-order derivative at the nth moment, Z(t o ) represents the stage sub-data at the oth moment, Z(t n ) represents the stage sub-data at the nth moment.

4. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The standby millisecond-level data set includes a first data packet corresponding to the hub node and a second data packet corresponding to the hub branch, the first data packet and the second data packet respectively including a plurality of millisecond-level hub voltages and a plurality of millisecond-level hub currents. The step of performing data simplification on the standby millisecond-level data set and obtaining a second-second-level voltage value corresponding to the basic node and a second-second-level current value corresponding to the basic branch based on the simplified standby millisecond-level data set includes: performing averaging processing on a plurality of the millisecond-level hub voltages to obtain a mean voltage value, and performing averaging processing on a plurality of the millisecond-level hub currents to obtain a mean current value; Taking the hub node and the hub branch as starting points, a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch are derived through the average voltage value and the average current value.

5. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The calculation formula of the voltage value in this stage is: V jd (i)=α*V 2s (i)+(1-a)V 1s (i), Among them, V jd (i) represents the stage voltage value of the i-th basic node, α represents the first weight, V 2s (i) represents the second-second voltage value of the i-th basic node, V 1s (i) represents the first-second voltage value of the i-th basic node; The calculation formula of the current value in this stage is: I jd (j)=β*I 2s (j)+(1-β)I 1s (j), Among them, I jd (j) represents the stage current value of the j-th basic branch, β represents the second weight, I 2s (j) represents the second-second current value of the j-th basic branch, I 1s (j) represents the first-second current value of the j-th basic branch.

6. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The step of obtaining the optimal voltage value and the optimal voltage phase angle corresponding to the basic node through the observation vector group includes: Setting an initial voltage parameter value and an initial voltage phase angle corresponding to the basic node, and acquiring an initial current parameter value corresponding to the basic branch based on the initial voltage parameter value and the initial voltage phase angle; Constructing a theoretical vector group based on the initial voltage parameter value and the initial current parameter value, and constructing an objective function through the observation vector group and the theoretical vector group; The objective function is iteratively converged to optimize the initial voltage parameter value to an optimal voltage value, and optimize the initial voltage phase angle to an optimal voltage phase angle.

7. The method for real-time status assessment of a distribution network according to claim 6, characterized in that: The objective function is: M(x)=[gc-ll(x)] T R -1 [gc-ll(x)]+γ*||I jd (ZZ)-I ll (ZZ)|| 2 , Where M(x) represents the objective function, x represents the initial voltage parameter value and the initial current parameter value to be iteratively converged, gc represents the observation vector group, ll(x) represents the theoretical vector group, T represents the transpose symbol, and R -1 represents the inverse covariance matrix of the measurement error, γ represents the constraint weight, I id (ZZ) represents the phase current value of the main branch, I ll (ZZ) represents the initial current parameter value of the main branch.

8. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The step of obtaining the predicted voltage value and the predicted voltage phase angle through the prediction model, the optimal voltage value and the optimal voltage phase angle comprises: Constructing a prediction model based on the voltage variation characteristics of the basic node and the current variation characteristics of the basic branch; The optimal voltage value and the optimal voltage phase angle are used as inputs of the prediction model to obtain an evaluation voltage value and a predicted voltage phase angle, and the optimal voltage value is used to replace the evaluation voltage value to obtain a predicted voltage value.

9. The method for real-time status assessment of a distribution network according to claim 1, characterized in that: The step of evaluating the real-time state of the power distribution network by using the predicted current value, the predicted voltage phase angle, and the predicted voltage value includes: Performing difference processing on the predicted current value and the stage current value to obtain a current residual, comparing the current residual with a residual threshold, and triggering a branch current warning if the current residual is greater than the residual threshold; Obtaining a voltage over-limit value based on the predicted voltage value, comparing the voltage over-limit value with an over-limit threshold, and triggering a node voltage over-limit warning if the voltage over-limit value is greater than the over-limit threshold; The angle difference of the predicted voltage phase angle between two adjacent basic nodes is obtained, and the angle difference is compared with an angle threshold. If the angle difference is greater than the angle threshold, a power angle instability warning is triggered.

10. A distribution network real-time status assessment system, applied to the distribution network real-time status assessment method according to any one of claims 1 to 9, characterized in that: The system comprises: a processing module, configured to select a plurality of hub nodes from the plurality of basic nodes, select a plurality of hub branches corresponding to the hub nodes from the plurality of basic branches, obtain initial millisecond-level data sets of the hub nodes and the hub branches, obtain first-second-level voltage values ​​of the basic nodes and first-second-level current values ​​of the basic branches, and perform data processing on the initial millisecond-level data sets to obtain a stand-by millisecond-level data set; an adjustment module, configured to perform data simplification on the standby millisecond-level data set, obtain a second-second voltage value corresponding to the basic node and a second-second current value corresponding to the basic branch based on the simplified standby millisecond-level data set, obtain a stage voltage value corresponding to the basic node based on the first-second-level voltage value and the second-second-level voltage value, and obtain a stage current value corresponding to the basic branch using the first-second-level current value and the second-second-level current value; an execution module, configured to construct an observation vector group based on the stage current value and the stage voltage value, obtain an optimal voltage value and an optimal voltage phase angle corresponding to the basic node through the observation vector group, and obtain a predicted voltage value and a predicted voltage phase angle through a prediction model, the optimal voltage value, and the optimal voltage phase angle; An evaluation module is used to obtain a predicted current value based on the predicted voltage value and the predicted voltage phase angle, and evaluate the real-time status of the distribution network through the predicted current value and the predicted voltage value.

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