Power distribution network operation state estimation method, computer equipment, medium and product

Abstract

The invention relates to a power distribution network operation state estimation method, computer equipment, a medium and a product. The method comprises the following steps: constructing a three-order measurement state tensor of the power distribution network according to multi-source measurement data of the power distribution network in a historical time period, and constructing a state estimation model of noise tensor complementation of the power distribution network according to the multi-source measurement data, the three-order measurement state tensor and operation data of the power distribution network, and performing conversion processing on the state estimation model after noise tensor completion to obtain a converted state estimation model, and solving the converted state estimation model by using a direction alternation method to obtain an operation state estimation result of the power distribution network at the current moment, the current moment being the end moment of the historical time period. By adopting the method, the three-order measurement state tensor of the power distribution network can be constructed through low-quality and limited-type measurement data containing measurement noise, and state estimation is realized based on the three-order measurement state tensor, so that the observability of the power distribution network can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network technology, and in particular to a distribution network operating state estimation method, computer equipment, medium and product. Background Art

[0002] With the development of power grid technology, the number of distributed power sources connected to the distribution network is increasing. Therefore, it is particularly important to estimate the operating status of the distribution network on a fast time scale for real-time tracking.

[0003] In related technologies, the operating status of the distribution network is mainly estimated on a fast time scale based on multi-source measurement data obtained by the measurement system configured in the distribution network (such as Supervisory Control And Data Acquisition, SCADA, Advanced Metering Infrastructure, AMI and Phasor Measurement Unit, PMU)

[0004] However, in the related art, when estimating the operating status of the distribution network, there is a problem of low observability of the distribution network. Summary of the Invention

[0005] Based on this, it is necessary to provide a distribution network operating status estimation method, computer equipment, medium and product to address the above technical problems.

[0006] In a first aspect, the present application provides a method for estimating the operating state of a distribution network, comprising:

[0007] Based on the multi-source measurement data of the distribution network in the historical time period, a third-order measurement state tensor of the distribution network is constructed; the multi-source measurement data includes measurement data from the data acquisition and control system including measurement noise, measurement data from the advanced metering system, and measurement data from the synchronized phasor measurement unit;

[0008] Based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network, a state estimation model for noise tensor completion of the distribution network is constructed;

[0009] Performing conversion processing on the state estimation model of the noise tensor completion to obtain a converted state estimation model;

[0010] The converted state estimation model is solved using the direction alternation method to obtain the operating state estimation result of the distribution network at the current moment; the current moment is the end moment of the historical time period.

[0011] In one embodiment, a state estimation model includes an objective function and a constraint function. A state estimation model for noise tensor completion of a distribution network is constructed based on multi-source measurement data, a third-order measurement state tensor, and operation data of the distribution network, including:

[0012] Construct the objective function of the state estimation model based on the multi-source measurement data of the distribution network and the third-order measurement state tensor of the distribution network;

[0013] According to the operation data of the distribution network, the constraint function in the state estimation model is determined.

[0014] In one embodiment, an objective function in a state estimation model is constructed based on multi-source measurement data of a distribution network and a third-order measurement state tensor of the distribution network, including:

[0015] According to the principle of uniform distribution, multi-source sample data is selected from multi-source measurement data;

[0016] Obtaining a measurement error based on multi-source measurement data and multi-source sample data; and obtaining a tensor ring nuclear norm of a third-order measurement state tensor;

[0017] Taking the minimum measurement error as the criterion, the objective function in the state estimation model is determined according to the measurement error and the tensor ring nuclear norm.

[0018] In one embodiment, converting the state estimation model of the noise tensor completion to obtain the converted state estimation model includes:

[0019] Obtaining an auxiliary variable of the third-order measurement state tensor, and performing pattern expansion on the auxiliary variable of the third-order measurement state tensor according to a dimension index and a pattern index of the third-order measurement state tensor to obtain a pattern expansion variable;

[0020] According to the pattern expansion variables, the objective function in the state estimation model is deformed to obtain the deformed objective function;

[0021] According to the constraint function of the auxiliary variable, the constraint function in the state estimation model is updated to obtain the updated constraint function;

[0022] The deformed objective function and the updated constraint function are determined as the converted state estimation model.

[0023] In one embodiment, the converted state estimation model is solved using the direction alternation method to obtain the current state estimation result of the distribution network, including:

[0024] According to the converted state estimation model, an augmented Lagrangian function is constructed; the augmented Lagrangian function includes Lagrangian multipliers, auxiliary variables and a third-order measurement state tensor;

[0025] According to the augmented Lagrangian function, the gradient of the auxiliary variable is calculated to obtain the value of the third-order measurement state tensor;

[0026] According to the value of the third-order measurement state tensor, the estimated result of the distribution network operation state at the current moment is determined.

[0027] In one embodiment, determining an estimated result of the operating state of the distribution network at a current moment based on the value of the third-order measurement state tensor includes:

[0028] According to the augmented Lagrangian function, the gradient of the Lagrangian multiplier is calculated to obtain the value of the auxiliary variable;

[0029] According to the value of the third-order measurement state tensor and the value of the auxiliary variable, the Lagrange multiplier is updated to obtain the updated Lagrange multiplier;

[0030] If the updated Lagrangian multiplier does not satisfy a preset convergence condition, the augmented Lagrangian function is updated based on the value of the auxiliary variable, the updated Lagrangian multiplier, and the value of the auxiliary variable until the current updated Lagrangian multiplier satisfies the convergence condition;

[0031] According to the value of the current third-order measurement state tensor, the estimated result of the operation state of the distribution network at the current moment is determined.

[0032] In a second aspect, the present application further provides a distribution network operating state estimation device, comprising:

[0033] A tensor construction module is used to construct a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network over a historical period; the multi-source measurement data includes measurement data from a data acquisition and control system including measurement noise, measurement data from an advanced metering system, and measurement data from a synchronized phasor measurement unit;

[0034] A model building module is used to build a state estimation model for the distribution network with noise tensor completion based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network;

[0035] A conversion processing module, used for performing conversion processing on the state estimation model of the noise tensor completion to obtain a converted state estimation model;

[0036] The solution module is used to solve the converted state estimation model using the direction alternation method to obtain the estimated result of the operation state of the distribution network at the current moment; the current moment is the end moment of the historical time period.

[0037] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method in any embodiment of the first aspect are implemented.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method in any embodiment of the first aspect above.

[0039] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method in any embodiment of the first aspect above.

[0040] The distribution network operation state estimation method, computer equipment, medium and product provided in the embodiments of the present application include: constructing a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network within a historical time period, constructing a noise tensor-complemented state estimation model of the distribution network based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network, converting the noise tensor-complemented state estimation model to obtain a converted state estimation model, and solving the converted state estimation model using the direction alternation method to obtain an operation state estimation result of the distribution network at the current moment, where the current moment is the end moment of the historical time period; the above method can construct a third-order measurement state tensor of the distribution network using low-quality and limited-type measurement data containing measurement noise, and realize state estimation based on the third-order measurement state tensor, which can improve the observability of the distribution network; at the same time, the above distribution network operation state estimation method can be applied to operation state estimation on a fast time scale to improve the observability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a diagram of the internal structure of a computer device in one embodiment;

[0042] Figure 2 1 is a flow chart of a method for estimating the operating status of a distribution network in one embodiment;

[0043] Figure 3 1 is a flow chart of a method for estimating the operating status of a distribution network according to another embodiment;

[0044] Figure 4 1 is a flow chart of a method for estimating the operating status of a distribution network according to another embodiment;

[0045] Figure 5 1 is a flow chart of a method for estimating the operating status of a distribution network according to another embodiment;

[0046] Figure 6 1 is a flow chart of a method for estimating the operating status of a distribution network according to another embodiment;

[0047] Figure 7 1 is a flow chart of a method for estimating the operating status of a distribution network according to another embodiment;

[0048] Figure 8 A topological diagram of a power distribution network in one embodiment;

[0049] Figure 9 A comparison diagram of estimation results of a method for estimating the operating status of a distribution network using three different schemes in one embodiment;

[0050] Figure 10 A diagram comparing the errors of estimation results of two different schemes for implementing the method for estimating the operating status of a distribution network in one embodiment;

[0051] Figure 11 A diagram comparing estimation results of a method for estimating the operating status of a distribution network using three different schemes in another embodiment;

[0052] Figure 12 A diagram comparing the errors of estimation results of two different schemes for implementing the method for estimating the operating state of a distribution network in another embodiment;

[0053] Figure 13 FIG. 4 is a structural block diagram of a device for estimating the operating status of a distribution network in an embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0055] The distribution network operation state estimation method provided in the embodiment of the present application can be applied to Figure 1 Computer devices shown. Computer devices may include, but are not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, smart car devices, and projectors. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Head-mounted devices may include virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, and the like.

[0056] In an exemplary embodiment, Figure 2 As shown in the figure, a distribution network operation state estimation method is provided, which is applied to Figure 1 The computer device in the example is used to illustrate the process, including the following steps:

[0057] S100: Construct a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network within a historical period, wherein the multi-source measurement data includes measurement data from a data acquisition and control system including measurement noise, measurement data from an advanced metering system, and measurement data from a synchronized phasor measurement unit.

[0058] It should be noted that the distribution network is equipped with a supervisory control and data acquisition (SCADA), an advanced metering infrastructure (AMI), and a synchronized phasor measurement unit (PMU). The data acquisition and control system measurement data may be data collected by the SCADA, the advanced metering system measurement data may be data collected by the advanced metering system, and the synchronized phasor measurement unit measurement data may be data collected by the PMU. Optionally, the multi-source measurement data may include data such as the topological connection relationship and line impedance parameters of the distribution network, the load of each node, and the access location and capacity of distributed power sources.

[0059] In the embodiment of the present application, the above-mentioned distribution network may be a distribution network with poor observability or a weakly observable distribution network.

[0060] In practical applications, computer equipment can obtain multi-source measurement data of the distribution network within a historical time period, call a construction tool for the third-order measurement state tensor, and then input the multi-source measurement data of the distribution network within the historical time period into the construction tool for the third-order measurement state tensor. The construction tool constructs and outputs the third-order measurement state tensor of the distribution network.

[0061] In addition, the computer device can also pre-train an algorithm model, and then input multi-source measurement data of the distribution network in a historical time period into the algorithm model, and the algorithm model outputs the constructed third-order measurement state tensor of the distribution network.

[0062] It should be noted that the computer constructs a third-order measurement state tensor for the current distribution network based on multi-source measurement data from the distribution network over a historical time period. For example, if the historical time period is [t-ΔT, t], then the third-order measurement state tensor for the distribution network at time t can be constructed accordingly.

[0063] In the embodiment of the present application, the third-order measurement state tensor of the distribution network is constructed It can be represented by a matrix, The subscript of each element in can represent node i in the distribution network × measurement type j of node i × measurement time t. The measurement type of each node in the distribution network can be SCADA, AMI, and / or PMU.

[0064] S200: Construct a state estimation model for noise tensor completion of the distribution network based on multi-source measurement data, third-order measurement state tensor and operation data of the distribution network.

[0065] Specifically, the computer device can construct a state estimation model for noise tensor completion of the distribution network according to the preset completion state estimation strategy, multi-source measurement data, third-order measurement state tensor and operation data of the distribution network.

[0066] In addition, the computer device can send the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network to a third-party device, instructing the third-party device to construct a state estimation model of the distribution network's noise tensor completion based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network.

[0067] Correspondingly, the computer device can receive the state estimation model of the noise tensor completion of the distribution network constructed by the third-party device.

[0068] S300 , converting the state estimation model completed with the noise tensor to obtain a converted state estimation model.

[0069] Specifically, the computer device may use a model conversion processing method to convert the state estimation model of the noise tensor completion to obtain a converted state estimation model.

[0070] The above-mentioned model conversion processing method may be a conversion method based on filtering theory, a conversion method based on optimization theory, a method for adjusting the model structure and parameters, etc.

[0071] In addition, the computer device can deform the state estimation model of the noise tensor completion to achieve conversion processing and obtain a converted state estimation model.

[0072] S400: Solve the converted state estimation model using the direction alternation method to obtain an estimated result of the distribution network operation state at the current moment, where the current moment is the end moment of the historical time period.

[0073] In practical applications, a computer device can use the alternating direction method to solve the converted state estimation model to obtain an estimated result of the current distribution network operating state. Alternatively, the alternating direction method can be referred to as the alternating direction multiplier method. The historical time period can be the time period from the historical moment to the current moment.

[0074] In an embodiment of the present application, the above-mentioned operating state estimation result of the distribution network may include the voltage phasor, active power injection and reactive power injection of each node in the distribution network.

[0075] The steps in S100-S400 can be executed cyclically at an estimation interval Δt, where Δt is relatively small, to achieve fast-time-scale operating state estimation and improve the observability of the distribution network. For example, the historical time period corresponding to the next state estimation may be [t+Δt-ΔT, t+Δt].

[0076] The technical solution in the embodiment of the present application is to construct a third-order measurement state tensor of the distribution network based on the multi-source measurement data of the distribution network in the historical time period, and to construct a state estimation model of the distribution network with noise tensor completion based on the multi-source measurement data, the third-order measurement state tensor and the operation data of the distribution network. The state estimation model with noise tensor completion is converted to obtain a converted state estimation model, and the converted state estimation model is solved using the direction alternation method to obtain the operation state estimation result of the distribution network at the current moment, where the current moment is the end moment of the historical time period. The above method can construct the third-order measurement state tensor of the distribution network using low-quality and limited-type measurement data containing measurement noise, and realize state estimation based on the third-order measurement state tensor, which can improve the observability of the distribution network. At the same time, the above distribution network operation state estimation method can be applied to operation state estimation on a fast time scale to improve the observability of the distribution network.

[0077] The following describes the process of constructing a state estimation model for noise tensor completion of a distribution network based on multi-source measurement data, third-order measurement state tensor, and operation data of the distribution network. In one embodiment, the state estimation model includes an objective function and a constraint function; Figure 3 As shown, the steps in S200 above can be implemented in the following ways:

[0078] S210. Construct an objective function in a state estimation model based on multi-source measurement data of the distribution network and a third-order measurement state tensor of the distribution network.

[0079] In practical applications, computer equipment can pre-train an objective function construction model, and then input the multi-source measurement data of the distribution network and the third-order measurement state tensor of the distribution network into the objective function construction model. The objective function construction model constructs a state estimation model of the noise tensor completion of the distribution network based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network and outputs it.

[0080] Optionally, the above-mentioned objective function construction model can be a combination of at least one of a convolutional neural network model, a long short-term memory neural network model, a fully connected neural network model, a recurrent neural network model, etc.

[0081] S220. Determine a constraint function in a state estimation model based on operating data of the distribution network.

[0082] Specifically, the computer device can obtain the constraint function in the state estimation model according to the operation data of the distribution network in accordance with the preset constraint conditions.

[0083] In addition, the computer device can pre-train an algorithm model, and then input the operating data of the distribution network into the algorithm model, and the algorithm model outputs the constraint function in the state estimation model.

[0084] In an embodiment of the present application, the computer device can determine the admittance matrix of the distribution network based on the operating data of the distribution network, and then determine the constraint function in the state estimation model based on the admittance matrix. The constraint function in the state estimation model can be expressed by the following formulas (1)-(4):

[0085]

[0086]

[0087]

[0088]

[0089] in, express The infinite norm of , δ represents the norm threshold, A and C represent the linearized power flow coefficient matrix, s -1 and v -1 The power injection and voltage of the balancing node in the distribution network are respectively, and Respectively represent the real part and imaginary part, |v -1 | means taking v -1 The amplitude of Indicates taking the conjugate of v1, s1 and v1 represent the power injection and voltage of the unbalanced node in the distribution network respectively, w represents the voltage compensation term, They represent the correlation matrix of the balanced nodes and the correlation matrix of the unbalanced nodes in the admittance matrix of the distribution network, τ r , τ c ,γ,α r , α c They represent the tidal constraint and elastic constraint coefficients respectively.

[0090] In one embodiment, if Figure 4 As shown, the step of constructing the objective function in the state estimation model according to the multi-source measurement data of the distribution network and the third-order measurement state tensor of the distribution network in the above S210 can be implemented in the following way:

[0091] S211. Select multi-source sample data from multi-source measurement data according to the principle of uniform distribution.

[0092] In the embodiment of the present application, the computer device may select multi-source sample data from multi-source measurement data according to the principle of uniform distribution.

[0093] S212 , obtaining a measurement error based on the multi-source measurement data and the multi-source sample data; and obtaining a tensor ring nuclear norm of a third-order measurement state tensor.

[0094] The computer device can use the relative error method, the absolute error method or the standard deviation method to calculate the measurement error of multi-source measurement data and multi-source sample data; and the computer device can calculate the tensor ring nuclear norm of the third-order measurement state tensor, which can be expressed as

[0095] In the embodiment of the present application, the measurement error can be expressed as y represents multi-source measurement data, Represents multi-source sample data, where represents the tensor uniform sampling operator, M represents the number of multi-source sample data, They represent independent and identically distributed unit tensor bases.

[0096] S213. Taking the minimization of the measurement error as a criterion, determine the objective function in the state estimation model according to the measurement error and the tensor ring nuclear norm.

[0097] In practical applications, the computer device can use the minimum measurement error as the criterion and perform arithmetic operations based on the measurement error and the tensor ring nuclear norm to obtain the objective function in the state estimation model.

[0098] Optionally, the arithmetic operation may be implemented by at least one of addition, subtraction, multiplication, division, logarithm, exponential operation, etc.

[0099] Alternatively, the computer device can input the measurement error and the tensor ring nuclear norm into the objective function construction model, which takes minimizing the measurement error as a criterion and constructs the objective function in the state estimation model based on the measurement error and the tensor ring nuclear norm.

[0100] In the embodiment of the present application, the objective function in the constructed state estimation model can be expressed as:

[0101]

[0102] Among them, λ represents the penalty coefficient.

[0103] The technical solution in the embodiment of the present application obtains measurement error based on multi-source measurement data and multi-source sample data, and obtains the tensor ring nuclear norm of the third-order measurement state tensor, and determines the objective function in the state estimation model based on the measurement error and the tensor ring nuclear norm, with the minimum measurement error as the criterion; in the process of constructing the objective function in the state estimation model of the distribution network, the above method can use the tensor ring nuclear norm of the third-order measurement state tensor to eliminate the measurement noise in the multi-source measurement data, thereby improving the accuracy of the constructed objective function, and preparing for the subsequent improvement of the accuracy of the obtained state estimation results.

[0104] The following describes the process of converting the state estimation model completed with the noise tensor to obtain the converted state estimation model. Figure 5 As shown, the steps in S300 above can be implemented in the following ways:

[0105] S310 , obtaining auxiliary variables of the third-order measurement state tensor, and performing pattern expansion on the auxiliary variables of the third-order measurement state tensor according to the dimension index and pattern index of the third-order measurement state tensor to obtain pattern expansion variables.

[0106] In practical applications, computer equipment can obtain auxiliary variables of the third-order measurement state tensor The dimensional index and pattern index of the third-order measurement state tensor and the auxiliary variables of the third-order measurement state tensor are input into a pre-trained algorithm model. The algorithm model performs pattern expansion on the auxiliary variables of the third-order measurement state tensor according to the dimensional index and pattern index of the third-order measurement state tensor and outputs the pattern expansion variables.

[0107] Among them, the dimension index of the above-mentioned third-order measurement state tensor can be expressed as s, the mode index of the third-order measurement state tensor is k, and the above-mentioned mode expansion variable can be represented as the kth tensor in the auxiliary variable The pattern-(k,s) expansion.

[0108] S320 , according to the pattern expansion variable, deform the objective function in the state estimation model to obtain a deformed objective function.

[0109] Furthermore, the objective function in the state estimation model can be deformed by expanding the variables according to the pattern, and the deformed objective function can be expressed as:

[0110]

[0111] Among them, α k represents the weight of the pattern-(k,s) expansion, α k ∈[0,1] and Indicates taking The infinite norm of Indicates l ∞ Norm indicator function.

[0112] S330. Update the constraint function in the state estimation model according to the constraint function of the auxiliary variable to obtain an updated constraint function.

[0113] Among them, the constraint function of the auxiliary variable can be expressed as:

[0114]

[0115] K represents the mode number of the third-order measurement state tensor.

[0116] S340: Determine the transformed objective function and the updated constraint function as a converted state estimation model.

[0117] The technical solution in the embodiment of the present application can convert the objective function and constraint function in the state estimation model of the distribution network to obtain a converted state estimation model, which can reduce the complexity of solving the subsequent state estimation model.

[0118] The following describes the process of solving the converted state estimation model using the direction alternation method to obtain the current state estimation result of the distribution network. Figure 6 As shown, the steps in the above S400 may include:

[0119] S410: Constructing an augmented Lagrangian function based on the converted state estimation model, wherein the augmented Lagrangian function includes Lagrangian multipliers, auxiliary variables, and a third-order measurement state tensor.

[0120] In the embodiment of the present application, the augmented Lagrangian function constructed according to the converted state estimation model can be expressed as:

[0121]

[0122] in, represents the Lagrange multiplier.

[0123] S420. According to the augmented Lagrangian function, the gradient of the auxiliary variable is calculated to obtain the value of the third-order measurement state tensor.

[0124] In practical applications, the gradient of the auxiliary variable is obtained according to the augmented Lagrangian function:

[0125]

[0126] Where vec(·) represents the vectorized expansion of the third-order measurement state tensor, X represents the vectorized form of the random tensor basis, l represents the number of iterations in the solution process.

[0127] Furthermore, the value of the third-order measurement state tensor can be obtained by solving formula (9).

[0128] S430: Determine an estimated result of the operating state of the distribution network at the current moment according to the value of the third-order measurement state tensor.

[0129] Specifically, when the Lagrange multiplier reaches the optimal value, the estimated operating state of the distribution network at the current moment can be determined directly based on the value of the third-order measurement state tensor.

[0130] In one embodiment, if Figure 7 As shown, the step of determining the estimated result of the operating state of the distribution network at the current moment according to the value of the third-order measurement state tensor in the above S430 may include:

[0131] S431. According to the augmented Lagrangian function, the gradient of the Lagrangian multiplier is calculated to obtain the value of the auxiliary variable.

[0132] In practical applications, computer equipment can express the result of calculating the gradient of the Lagrange multiplier based on the augmented Lagrangian function as follows:

[0133]

[0134] Where τ = λα k / μ l , μ l represents the shrinkage coefficient, represents the singular value threshold operator, [U,∑,V]=SVD(A) represents singular value decomposition.

[0135] Furthermore, we can solve formula (10) to obtain the value of the auxiliary variable M (k,s) .

[0136] S432. Update the Lagrange multiplier according to the value of the third-order measurement state tensor and the value of the auxiliary variable to obtain an updated Lagrange multiplier.

[0137] In practical applications, the computer device can update the Lagrange multiplier according to the value of the third-order measurement state tensor and the value of the auxiliary variable according to the preset update rule to obtain the updated Lagrange multiplier.

[0138] In addition, the computer device may perform an arithmetic operation on the value of the third-order measurement state tensor and the value of the auxiliary variable, and update the Lagrange multiplier based on the arithmetic operation result to obtain an updated Lagrange multiplier. Optionally, the arithmetic operation may be at least one of addition, subtraction, multiplication, division, logarithm, and / or exponential operation.

[0139] In the embodiment of the present application, the computer device can use the gradient ascent method to update the Lagrange multiplier according to the value of the third-order measurement state tensor and the value of the auxiliary variable to obtain the updated Lagrange multiplier. Expressed as:

[0140]

[0141] S433. If the updated Lagrangian multiplier does not satisfy a preset convergence condition, the augmented Lagrangian function is updated based on the value of the auxiliary variable, the updated Lagrangian multiplier, and the value of the auxiliary variable until the current updated Lagrangian multiplier satisfies the convergence condition.

[0142] Specifically, when determining that the updated Lagrangian multiplier does not meet the preset convergence condition, the computer device may update the augmented Lagrangian function based on the value of the auxiliary variable, the updated Lagrangian multiplier and the value of the auxiliary variable, and replace the augmented Lagrangian function with the updated augmented Lagrangian function, and continue to execute the steps in S420-S430 above until the current updated Lagrangian multiplier meets the convergence condition.

[0143] Optionally, the fact that the updated Lagrangian multiplier does not meet the preset convergence condition can be understood as the error between the updated Lagrangian multiplier and the optimal Lagrangian multiplier being greater than or equal to a preset threshold, or the number of updates or iterations being less than or equal to a preset number. Optionally, the preset threshold can be a positive number approaching 0.

[0144] S434. Determine an estimated result of the operating state of the distribution network at the current moment according to the value of the current third-order measurement state tensor.

[0145] Furthermore, the estimated result of the operating state of the distribution network at the current moment can be determined based on the value of the currently acquired third-order measurement state tensor.

[0146] For example, taking the improved IEEE 33-node distribution network as an example, the network topology and measurement configuration of the distribution network are as follows: Figure 8As shown in the figure, nodes 5, 9, 16, 18, 20, and 24 are all connected to photovoltaic (PV) networks, and nodes 10, 11, 21, 29, 17, 31, and 33 are all connected to wind turbines (WT). Each node is equipped with an AMI, and some nodes and lines are equipped with SCADA and PMU. The measurement standard deviations of AMI, SCADA, and PMU are 5%, 1%, and 0.1%, respectively, and the time scales are 15 minutes, 1 minute, and 20 milliseconds, respectively. Using the above distribution network operation state estimation method, the distribution network operation state estimation result can be obtained. In order to verify the effectiveness of the above method, the following three schemes are used for comparison:

[0147] Solution 1: Use open-source distribution network simulation software to directly obtain the true value of the distribution network's operating status;

[0148] Solution 2: Use low-rank tensor completion method to estimate the operating status of the distribution network;

[0149] Solution 3: Use the above distribution network operation status estimation method to estimate the operation status of the distribution network.

[0150] Specifically, the results of estimating the operating status of the distribution network using the above distribution network operating status estimation method are shown in Tables 1 and Figures 9-12 As shown. Among them, Figure 9 The comparison diagram of the voltage amplitude in the operating status results of each node in the distribution network estimated using schemes one, two and three respectively. Figure 10 It is a comparison chart of the voltage amplitude error (i.e. relative error) calculated by dividing the difference between the estimated value and the true value of the voltage amplitude by the true value of the voltage amplitude. Figure 11 This is a comparison diagram of the voltage phase angles in the estimated operating state results of Schemes 1, 2, and 3. Figure 12 It is a comparison chart of the voltage phase angle error (i.e., absolute error) obtained by using the difference between the estimated value and the true value of the voltage phase angle.

[0151] Among them, compared with Scheme 2, Scheme 3 has advantages in terms of voltage amplitude and phase angle estimation accuracy, especially in terms of phase angle. Therefore, it can be concluded that the distribution network operation state estimation method in this application can more accurately obtain the operation state results of the weakly observable distribution network. The accuracy of Scheme 2 mainly depends on the number of ranks in the typical multi-far decomposition. Although the increase in the number of ranks will improve the accuracy, the corresponding calculation speed will also increase, increasing the computational burden. On the contrary, the distribution network operation state estimation method in this application decomposes the third-order measurement state tensor into a series of smaller tensors, while ensuring the calculation accuracy and taking into account the calculation speed. In addition, another source of error in Scheme 2 is its inability to handle measurement noise. It ensures the accuracy of recovering lost elements under the premise of noise-free measurement. When measurement noise exists in the actual distribution network, its accuracy is greatly reduced. However, the distribution network operation state estimation method in this application takes measurement noise into account in the modeling process, greatly improving the robustness, further verifying the advantages of the distribution network operation state estimation method in this application. Table 2 lists the state estimation calculation time of the three schemes. The calculation efficiency of the distribution network operation state estimation method in this application is more than doubled, which can meet the real-time requirements.

[0152] Table 1. Error comparison of state estimation results

[0153]

[0154] Table 2 Comparison of state estimation calculation time

[0155]

[0156] Based on the above comparison, it can be concluded that the distribution network operating state estimation method in this application can accurately estimate the operating state of a weakly observable distribution network, and has a faster calculation speed, which can greatly improve the observability of such distribution networks.

[0157] The technical solution in the embodiment of the present application is to construct an augmented Lagrangian function based on the converted state estimation model, wherein the augmented Lagrangian function includes a Lagrangian multiplier, an auxiliary variable and the third-order measurement state tensor. According to the augmented Lagrangian function, the gradient of the auxiliary variable is calculated to obtain the value of the third-order measurement state tensor, and the operating state estimation result of the distribution network at the current moment is determined based on the value of the third-order measurement state tensor. The processing process in the above method is relatively simple and does not require the participation of complex algorithms, thereby reducing the complexity of determining the operating state estimation result of the distribution network and accelerating the determination speed of the operating state estimation result.

[0158] In one embodiment, the present application also provides a method for estimating the operating state of a distribution network, the method comprising the following process:

[0159] (1) Constructing a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network in a historical period; the multi-source measurement data includes measurement data of the data acquisition and control system including measurement noise, measurement data of the advanced metering system, and measurement data of the synchronized phasor measurement unit;

[0160] (2) Select multi-source sample data from multi-source measurement data according to the principle of uniform distribution;

[0161] (3) obtaining a measurement error based on the multi-source measurement data and the multi-source sample data; and obtaining a tensor ring nuclear norm of a third-order measurement state tensor;

[0162] (4) Taking the minimum measurement error as the criterion, determine the objective function in the state estimation model according to the measurement error and the tensor ring nuclear norm;

[0163] (5) Determine the constraint function in the state estimation model based on the operating data of the distribution network;

[0164] (6) obtaining auxiliary variables of the third-order measurement state tensor, and performing pattern expansion on the auxiliary variables of the third-order measurement state tensor according to the dimension index and pattern index of the third-order measurement state tensor to obtain pattern expansion variables;

[0165] (7) According to the pattern expansion variables, the objective function in the state estimation model is deformed to obtain the deformed objective function;

[0166] (8) According to the constraint function of the auxiliary variable, the constraint function in the state estimation model is updated to obtain the updated constraint function;

[0167] (9) Determine the transformed objective function and the updated constraint function as the converted state estimation model;

[0168] (10) constructing an augmented Lagrangian function based on the transformed state estimation model; the augmented Lagrangian function includes Lagrangian multipliers, auxiliary variables and a third-order measurement state tensor;

[0169] (11) According to the augmented Lagrangian function, the gradient of the auxiliary variable is calculated to obtain the value of the third-order measurement state tensor;

[0170] (12) According to the augmented Lagrangian function, the gradient of the Lagrangian multiplier is calculated to obtain the value of the auxiliary variable;

[0171] (13) updating the Lagrange multiplier according to the value of the third-order measurement state tensor and the value of the auxiliary variable to obtain an updated Lagrange multiplier;

[0172] (14) When the updated Lagrangian multiplier does not satisfy the preset convergence condition, the augmented Lagrangian function is updated based on the value of the auxiliary variable, the updated Lagrangian multiplier, and the value of the auxiliary variable until the current updated Lagrangian multiplier satisfies the convergence condition;

[0173] (15) According to the value of the current third-order measurement state tensor, the estimated operating state of the distribution network at the current moment is determined.

[0174] The execution process of the above (1) to (15) can be specifically referred to the description of the above embodiment. The implementation principles and technical effects are similar and will not be repeated here.

[0175] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0176] Based on the same inventive concept, embodiments of the present application further provide a device for estimating the operating state of a distribution network, for implementing the aforementioned method for estimating the operating state of a distribution network. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for estimating the operating state of a distribution network provided below can be found in the aforementioned limitations of the method for estimating the operating state of a distribution network, and will not be further elaborated here.

[0177] In one embodiment, Figure 13 This is a schematic diagram of the structure of a distribution network operating state estimation device in one embodiment of the present application. The distribution network operating state estimation device provided in the embodiment of the present application can be applied to computer equipment. Figure 13 As shown, the distribution network operating state estimation device according to the embodiment of the present application may include: a tensor construction module 11, a model construction module 12, a conversion processing module 13 and a solution module 14, wherein:

[0178] A tensor construction module 11 is configured to construct a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network within a historical period; the multi-source measurement data includes measurement data of a data acquisition and control system including measurement noise, measurement data of an advanced metering system, and measurement data of a synchronized phasor measurement unit;

[0179] A model building module 12 is used to build a state estimation model of the distribution network with noise tensor completion based on multi-source measurement data, third-order measurement state tensor and operation data of the distribution network;

[0180] A conversion processing module 13 is used to convert the state estimation model of the noise tensor completion to obtain a converted state estimation model;

[0181] The solving module 14 is used to solve the converted state estimation model using the direction alternation method to obtain the estimated result of the operation state of the distribution network at the current moment; the current moment is the end moment of the historical time period.

[0182] The distribution network operating state estimation device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned distribution network operating state estimation method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0183] In one embodiment, the state estimation model includes an objective function and a constraint function; the model construction module 12 includes: a first construction unit and a second construction unit, wherein:

[0184] A first construction unit is configured to construct an objective function in a state estimation model based on multi-source measurement data of the distribution network and a third-order measurement state tensor of the distribution network;

[0185] The second construction unit is used to determine the constraint function in the state estimation model according to the operation data of the distribution network.

[0186] The distribution network operating state estimation device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned distribution network operating state estimation method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0187] In one embodiment, the first construction unit includes: a data selection subunit, an acquisition subunit, and a construction subunit, wherein:

[0188] The data selection subunit is used to select multi-source sample data from multi-source measurement data according to the principle of uniform distribution;

[0189] an acquisition subunit, configured to acquire a measurement error based on multi-source measurement data and multi-source sample data; and to acquire a tensor ring nuclear norm of a third-order measurement state tensor;

[0190] A subunit is constructed to determine the objective function in the state estimation model based on the measurement error and the tensor ring nuclear norm, taking the minimum measurement error as the criterion.

[0191] The distribution network operating state estimation device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned distribution network operating state estimation method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0192] In one embodiment, the conversion processing module 13 is specifically configured to:

[0193] Obtaining an auxiliary variable of the third-order measurement state tensor, and performing pattern expansion on the auxiliary variable of the third-order measurement state tensor according to a dimension index and a pattern index of the third-order measurement state tensor to obtain a pattern expansion variable;

[0194] According to the pattern expansion variables, the objective function in the state estimation model is deformed to obtain the deformed objective function;

[0195] According to the constraint function of the auxiliary variable, the constraint function in the state estimation model is updated to obtain the updated constraint function;

[0196] The deformed objective function and the updated constraint function are determined as the converted state estimation model.

[0197] The distribution network operating state estimation device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned distribution network operating state estimation method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0198] In one embodiment, the solution module 14 includes: a third construction unit, an acquisition unit, and a solution unit, wherein:

[0199] The third construction unit is used to construct an augmented Lagrangian function according to the converted state estimation model; the augmented Lagrangian function includes Lagrangian multipliers, auxiliary variables and a third-order measurement state tensor;

[0200] An acquisition unit is used to obtain the gradient of the auxiliary variable according to the augmented Lagrangian function to obtain the value of the third-order measurement state tensor;

[0201] The solving unit is used to determine the estimated result of the operation state of the distribution network at the current moment according to the value of the third-order measurement state tensor.

[0202] The distribution network operating state estimation device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned distribution network operating state estimation method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0203] In one embodiment, the solving unit is specifically configured to:

[0204] According to the augmented Lagrangian function, the gradient of the Lagrangian multiplier is calculated to obtain the value of the auxiliary variable;

[0205] According to the value of the third-order measurement state tensor and the value of the auxiliary variable, the Lagrange multiplier is updated to obtain the updated Lagrange multiplier;

[0206] If the updated Lagrangian multiplier does not satisfy a preset convergence condition, the augmented Lagrangian function is updated based on the value of the auxiliary variable, the updated Lagrangian multiplier, and the value of the auxiliary variable until the current updated Lagrangian multiplier satisfies the convergence condition;

[0207] According to the value of the current third-order measurement state tensor, the estimated result of the operation state of the distribution network at the current moment is determined.

[0208] The distribution network operating state estimation device provided in the embodiment of the present application can be used to execute the technical solution in the above-mentioned distribution network operating state estimation method embodiment of the present application. Its implementation principle and technical effects are similar and will not be repeated here.

[0209] Each module in the above-mentioned distribution network operating state estimation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0210] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 1As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a method for estimating the operating status of a distribution network is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0211] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0212] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0213] Based on the multi-source measurement data of the distribution network in the historical time period, a third-order measurement state tensor of the distribution network is constructed; the multi-source measurement data includes measurement data from the data acquisition and control system including measurement noise, measurement data from the advanced metering system, and measurement data from the synchronized phasor measurement unit;

[0214] Based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network, a state estimation model for noise tensor completion of the distribution network is constructed;

[0215] Performing conversion processing on the state estimation model of the noise tensor completion to obtain a converted state estimation model;

[0216] The converted state estimation model is solved using the direction alternation method to obtain the operating state estimation result of the distribution network at the current moment; the current moment is the end moment of the historical time period.

[0217] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0218] Based on the multi-source measurement data of the distribution network in the historical time period, a third-order measurement state tensor of the distribution network is constructed; the multi-source measurement data includes measurement data from the data acquisition and control system including measurement noise, measurement data from the advanced metering system, and measurement data from the synchronized phasor measurement unit;

[0219] Based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network, a state estimation model for noise tensor completion of the distribution network is constructed;

[0220] Performing conversion processing on the state estimation model of the noise tensor completion to obtain a converted state estimation model;

[0221] The converted state estimation model is solved using the direction alternation method to obtain the operating state estimation result of the distribution network at the current moment; the current moment is the end moment of the historical time period.

[0222] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0223] Based on the multi-source measurement data of the distribution network in the historical time period, a third-order measurement state tensor of the distribution network is constructed; the multi-source measurement data includes measurement data from the data acquisition and control system including measurement noise, measurement data from the advanced metering system, and measurement data from the synchronized phasor measurement unit;

[0224] Based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network, a state estimation model for noise tensor completion of the distribution network is constructed;

[0225] Performing conversion processing on the state estimation model of the noise tensor completion to obtain a converted state estimation model;

[0226] The converted state estimation model is solved using the direction alternation method to obtain the operating state estimation result of the distribution network at the current moment; the current moment is the end moment of the historical time period.

[0227] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic device (PLD), a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0228] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0229] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for estimating the operating status of a distribution network, characterized in that: The method comprises: Constructing a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network within a historical period, wherein the multi-source measurement data includes measurement data from a data acquisition and control system including measurement noise, measurement data from an advanced metering system, and measurement data from a synchronized phasor measurement unit; Constructing a state estimation model for noise tensor completion of the distribution network based on multi-source measurement data, third-order measurement state tensor and operation data of the distribution network; Performing conversion processing on the noise tensor-completed state estimation model to obtain a converted state estimation model; The converted state estimation model is solved using a direction alternation method to obtain an estimated result of the operating state of the distribution network at the current moment; the current moment is the end moment of the historical time period.

2. The method according to claim 1, characterized in that The state estimation model includes an objective function and a constraint function; the state estimation model for noise tensor completion of the distribution network is constructed based on the multi-source measurement data, third-order measurement state tensor and operation data of the distribution network, including: constructing an objective function in the state estimation model according to the multi-source measurement data of the distribution network and the third-order measurement state tensor of the distribution network; A constraint function in the state estimation model is determined according to the operating data of the distribution network.

3. The method according to claim 2, characterized in that The objective function in the state estimation model is constructed according to the multi-source measurement data of the distribution network and the third-order measurement state tensor of the distribution network, including: Selecting multi-source sample data from the multi-source measurement data according to a uniform distribution principle; Obtaining a measurement error based on the multi-source measurement data and the multi-source sample data; and obtaining a tensor ring nuclear norm of the third-order measurement state tensor; Taking minimizing the measurement error as a criterion, an objective function in the state estimation model is determined according to the measurement error and the tensor ring nuclear norm.

4. The method according to any one of claims 1 to 3, characterized in that The converting process of the noise tensor-completed state estimation model to obtain a converted state estimation model includes: Acquire an auxiliary variable of the third-order measurement state tensor, and perform pattern expansion on the auxiliary variable of the third-order measurement state tensor according to a dimension index and a pattern index of the third-order measurement state tensor to obtain a pattern expansion variable; Expanding variables according to the pattern, deforming the objective function in the state estimation model to obtain a deformed objective function; updating the constraint function in the state estimation model according to the constraint function of the auxiliary variable to obtain an updated constraint function; The deformed objective function and the updated constraint function are determined as the converted state estimation model.

5. The method according to any one of claims 1 to 3, characterized in that The method of solving the converted state estimation model using the direction alternation method to obtain an estimated result of the operation state of the distribution network at the current moment includes: Constructing an augmented Lagrangian function according to the converted state estimation model; the augmented Lagrangian function includes Lagrangian multipliers, auxiliary variables and the third-order measurement state tensor; According to the augmented Lagrangian function, the gradient of the auxiliary variable is calculated to obtain the value of the third-order measurement state tensor; An estimated result of the operating state of the distribution network at the current moment is determined according to the value of the third-order measurement state tensor.

6. The method according to claim 5, characterized in that Determining the estimated result of the operating state of the distribution network at the current moment according to the value of the third-order measurement state tensor includes: According to the augmented Lagrangian function, the gradient of the Lagrangian multiplier is calculated to obtain the value of the auxiliary variable; updating the Lagrange multiplier according to the value of the third-order measurement state tensor and the value of the auxiliary variable to obtain an updated Lagrange multiplier; If the updated Lagrangian multiplier does not satisfy a preset convergence condition, updating the augmented Lagrangian function based on the value of the auxiliary variable, the updated Lagrangian multiplier, and the value of the auxiliary variable until the current updated Lagrangian multiplier satisfies the convergence condition; According to the value of the current third-order measurement state tensor, an estimated result of the operating state of the distribution network at the current moment is determined.

7. A distribution network operating state estimation device, characterized in that: The device comprises: A tensor construction module is configured to construct a third-order measurement state tensor of the distribution network based on multi-source measurement data of the distribution network within a historical time period; the multi-source measurement data is measurement data of a data acquisition and control system including measurement noise, measurement data of an advanced metering system, and measurement data of a synchronized phasor measurement unit; A model building module, configured to build a state estimation model of the distribution network with noise tensor completion based on the multi-source measurement data, the third-order measurement state tensor and the operation data of the distribution network; a conversion processing module, configured to perform conversion processing on the noise tensor-completed state estimation model to obtain a converted state estimation model; A solution module is used to solve the converted state estimation model using a direction alternation method to obtain an estimated result of the operating state of the distribution network at the current moment; the current moment is the end moment of the historical time period.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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