Power distribution network state estimation method and system

Abstract

The invention relates to a power distribution network state estimation method and system in the technical field of power distribution networks, and the method comprises the following steps: building a power distribution network state estimation model which comprises a state equation and a measurement equation; performing state prediction based on the state equation to obtain a state prediction value and an error covariance prediction matrix, and performing measurement prediction to obtain an error covariance measurement initial matrix and a cross covariance initial matrix; calculating a fading factor, and updating the error covariance prediction matrix by using the fading factor to obtain an error covariance prediction adjustment matrix; performing measurement prediction based on the error covariance prediction adjustment matrix to obtain an error covariance measurement update matrix and a cross covariance update matrix; according to the method, the state estimation is updated, and the observation noise covariance is updated based on the updating result, so that the problem that the estimation precision and robustness are remarkably reduced due to the fact that an existing power distribution network state estimation method cannot accurately model unknown noise and is difficult to effectively track the rapid change of a system is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and specifically to a power distribution network state estimation method and system. Background Technology

[0002] The distribution network is a crucial component of the power system, responsible for distributing and transmitting electrical energy from the high-voltage grid to various end users such as households and businesses. Distribution network state estimation refers to the process of collecting and analyzing various data within the distribution network to provide real-time optimal estimates of the voltage (amplitude and phase) and power flow of each bus within the network, thereby achieving accurate monitoring and assessment of the network's operational status. Load surges are a common and significant problem in distribution network operation, and drastic load fluctuations pose substantial challenges to grid stability and state monitoring. In practical applications, due to the complex operating environment of distribution network systems, the statistical characteristics of system noise are often unknown. Therefore, how to accurately and in real-time estimate the distribution network state under conditions of load surges and unknown noise statistical characteristics is of great importance for ensuring the safe and stable operation of the power system.

[0003] In recent years, researchers have proposed various algorithms to improve the accuracy of dynamic state estimation under load abrupt changes. The first approach involves introducing adaptive factors to adjust the state prediction error covariance matrix, measurement error covariance matrix, and gain matrix online, thereby reducing the impact of load abrupt changes on state estimation. Furthermore, combining data from Wide Area Measurement System (WAMS) and SCADA system increases redundant measurements, further enhancing the system's ability to cope with load abrupt changes. The second approach proposes an adaptive dynamic state estimator that detects load abrupt changes by using the ratio of standardized innovation to weighted innovation, and utilizes standardized innovation and residuals to verify and eliminate abnormal data in the system, thus improving the performance of dynamic state estimation under load abrupt changes. The third approach proposes an improved robust adaptive unscented Kalman filter method, which enhances the algorithm's state estimation capability under load abrupt changes by accurately estimating noise parameters and ensuring positive semidefiniteness. The fourth approach proposes introducing the predicted load as a new measurement into the state estimation model to improve the accuracy of state estimation under load abrupt changes. Compared with traditional state prediction methods, this approach better reflects the dynamic characteristics of distribution networks and helps adapt to load fluctuations and abrupt changes.

[0004] However, due to the complexity and high dimensionality of power distribution system models, the aforementioned methods still suffer from low accuracy in state estimation. Furthermore, power load forecasting itself involves inherent uncertainties, such as the often unknown statistical characteristics of system process noise. When facing complex systems or sudden events, load forecasting errors can directly impact the accuracy of state estimation. Some studies have addressed the challenges posed by load abrupt changes by updating state at nodes and employing a maximum a posteriori probability estimation strategy. However, in complex load fluctuation environments, existing dynamic state estimation methods struggle to accurately model unknown noise and effectively track rapid system changes, leading to a significant decrease in estimation accuracy and robustness. Summary of the Invention

[0005] This invention addresses the shortcomings of existing technologies by providing a distribution network state estimation method and system. It solves the problem that existing distribution network state estimation methods cannot accurately model unknown noise and are difficult to effectively track rapid changes in the system, resulting in a significant decrease in estimation accuracy and robustness.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A method for estimating the state of a distribution network includes the following steps:

[0008] Establish a distribution network state estimation model, including state equations and measurement equations;

[0009] Based on the state equation, state prediction is performed to obtain the state prediction value and the error covariance prediction matrix. Based on the state prediction value, the error covariance prediction matrix and the measurement equation, measurement prediction is performed to obtain the error covariance measurement initial matrix and the cross-covariance initial matrix.

[0010] Calculate the fading factor and use the fading factor to update the error covariance prediction matrix to obtain the error covariance prediction adjustment matrix;

[0011] Based on the error covariance prediction adjustment matrix, measurement prediction is performed to obtain the error covariance measurement update matrix and the cross-covariance update matrix.

[0012] The state estimation is updated based on the error covariance measurement update matrix and the cross-covariance update matrix, and the observation noise covariance is updated based on the update results.

[0013] Optionally, state prediction is performed based on the state equation to obtain the predicted state value and the error covariance prediction matrix, including the following steps:

[0014] Calculate the volume point of the distribution network state variables at the current moment, and propagate the volume point at the current moment through the state equation to obtain the propagated volume point;

[0015] The predicted state value and error covariance prediction matrix for the next time step are calculated based on the propagation volume points.

[0016] Optionally, the predicted state value and error covariance prediction matrix for the next time step are calculated based on the propagated volume points, using the following formula:

[0017] in, P′ represents the predicted state value at time k+1. k+1∣k This represents the value of the error covariance prediction matrix; Q represents the volume point after propagation; k w represents the observation noise covariance matrix at time k; i Indicates the weight. n represents the dimension of the state variables, M = n(n-1) / 2.

[0018] Optionally, measurement prediction is performed based on the predicted state value, the error covariance prediction matrix, and the measurement equation, including the following steps:

[0019] Based on the error covariance prediction matrix and the state prediction value, the volume point of the distribution network measurement variables at the next time moment is calculated, and the volume point at the next time moment is propagated through the measurement equation to obtain the observation volume point.

[0020] The measurement prediction value for the next time step is calculated based on the observed volume points, and the initial measurement matrix and the initial cross-covariance matrix are calculated based on the measurement prediction value.

[0021] Optionally, the formulas for calculating the initial matrix of error covariance measurements and the initial matrix of cross-covariance are as follows:

[0022] in, This represents the initial matrix of the error covariance measurement; Represents the initial cross-covariance matrix; Indicates the observation volume point; R represents the predicted value of the measurement at time k+1; k+1 This represents the observation noise covariance at time k+1; The volume point represents the distribution network measurement variable at time k+1; (·) T Indicates transpose calculation; w i Indicates the weight.

[0023] Optionally, the formula for calculating the fading factor is as follows:

[0024] Here, tr() represents the trace of the matrix; the mathematical symbol N is defined. k+1 and M k+1 As shown below, V represents the initial matrix of error covariance measurements, κ≥1 is the weakening factor, and V 0,k+1 Let R be the covariance matrix of the residual sequence. k+1 This represents the observation noise covariance at time k+1. ρ is the forgetting factor, ε k+l Represents the innovation vector

[0025] Optionally, the expression for the error covariance prediction adjustment matrix is:

[0026] Where, λ k+1 This represents the fading factor at time k+1. This represents the predicted state value at time k+1; Q represents the volume point after propagation; k w represents the observation noise covariance matrix at time k; i Indicates the weight. n represents the dimension of the state variables, M = n(n-1) / 2.

[0027] Optionally, the update formula for performing state estimation updates is:

[0028] in, This represents the state estimate; y represents the predicted state value at time k+1; k+1 Indicates the value of the measurement equation; P represents the measured predicted value. k+1 P″ represents the numerical value of the error covariance. k+1∣k This represents the value of the error covariance prediction adjustment matrix; This represents the error covariance measurement update matrix; (·) T Indicates transpose calculation; K k+1 Indicates Kalman gain, This represents the mutual covariance update matrix.

[0029] Optionally, the update formula for updating the observation noise covariance based on the update results is as follows:

[0030] Where, d k = (1-b) / (1-b) k+1 ), where b is the forgetting factor, and 0 < b < 1; Q k K represents the observation noise covariance matrix at time k; k P is the filter gain; k|k To estimate the error covariance matrix; y represents the predicted value of the measurement at time k.k This represents the value of the measurement equation at time k.

[0031] A distribution network state estimation system, wherein the distribution network state estimation system performs the distribution network state estimation method as described in any one of the above.

[0032] Compared with the prior art, the technical solution provided by this invention has the following advantages:

[0033] By introducing a fading factor from a strong tracking filter into a high-order capillary Kalman filter, the error covariance matrix is ​​modified, thereby enhancing the algorithm's sensitivity to state abrupt changes. This strong tracking adaptive capillary Kalman filter (ST-AHCKF), which combines a noise statistical estimator and a fading factor, improves the algorithm's robustness by adaptively adjusting the noise and error covariance matrices. It better handles load abrupt changes and ensures the stability and accuracy of system state estimation. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of a power distribution network state estimation method proposed in Embodiment 1;

[0036] Figure 2 This is a diagram showing the estimation results of the voltage amplitude and phase angle of node 8 proposed in Embodiment 1.

[0037] Figure 3 The voltage amplitude and phase angle proposed in this embodiment are in Figure 2 Enlarged view of location ①;

[0038] Figure 4 The voltage amplitude and phase angle proposed in this embodiment are in Figure 2 Enlarged view of position ②;

[0039] Figure 5 This is a diagram showing the relative error between the voltage amplitude and phase angle of node 8 as proposed in Embodiment 1. Detailed Implementation

[0040] The present invention will be further described in detail below with reference to the embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0041] Example 1

[0042] like Figure 1 As shown, a distribution network state estimation method includes the following steps: establishing a distribution network state estimation model, including state equations and measurement equations. Since the daily load change in the distribution area is slow and the random fluctuations are small, it can be assumed that the distribution network operates under quasi-static conditions, that is, displacement current or induced current is ignored. The expressions for the state equations and measurement equations are as follows:

[0043] x k+1 =f(x) k )+ω k ;

[0044] y k+1 =h(x k+1 )+v k+1 ,

[0045] Where k represents the time series; x k+1 =[U,θ] T ∈R n Represents the n-dimensional distribution network state variables in the real number domain, where U and θ represent the node voltage magnitude and phase angle, respectively; y k+1 ∈R m ω represents an m-dimensional measurement in the real number domain, including node voltage magnitude and phase, node injected active and reactive power, and branch active and reactive power; f(·) represents the nonlinear equation in the state equation of the distribution network system; h(·) represents the nonlinear equation in the measurement equation of the distribution network system, which is a nonlinear function with node voltage vectors as state variables; k V represents the system process noise. k Let Q represent the observation noise, assuming both are uncorrelated Gaussian white noise with zero mean, and their covariance matrices are Q... k and R k .

[0046] Next, the strong tracking adaptive capacitive Kalman filter (ST-AHCKF) algorithm is performed. First, state prediction is executed, assuming that the distribution network state variable x at time k... k+1 Estimation error covariance P k and the observation noise covariance matrix Q k Given that the following are the steps for estimating the state of the distribution network at time k+1.

[0047] First, state prediction is performed based on the state equation to obtain the predicted state value and the error covariance prediction matrix. Then, measurement prediction is performed based on the predicted state value, the error covariance prediction matrix, and the measurement equation to obtain the initial error covariance measurement matrix and the initial cross-covariance matrix.

[0048] The process of predicting the state based on the state equation to obtain the predicted state value and the error covariance prediction matrix includes the following steps: calculating the volume point of the distribution network state variables at the current moment, and propagating the volume point at the current moment through the state equation to obtain the propagated volume point; and calculating the predicted state value and the error covariance prediction matrix at the next moment based on the propagated volume point.

[0049] Specifically, calculate the volume point (i = 1, 2, ..., 2n) of the distribution network state variables at time k. 2 +1), the calculation formula is:

[0050] in, P represents the estimated value of the state variable at time k; k Let be the covariance matrix of the state variables at time k; n is the dimension of the state variables, L k It is P k The lower triangular matrix obtained by Cholesky decomposition, that is Let M = n(n-1) / 2, and vector ξ i for:

[0051]

[0052] in ξ i It is a 2n-1 dimensional vector, e j Let j be an n-dimensional unit vector with row j being 1, and be a point set. and As shown respectively and

[0053] Then, through the nonlinear equations of the state equations, the volumetric point of the distribution network state variables at time k is determined. After propagation, the post-propagation volume point is obtained.

[0054] Finally, calculate the state prediction value at time k+1. and prediction error covariance matrix P k+1∣k The calculation formula is:

[0055] in, P′ represents the predicted state value at time k+1. k+1∣k This represents the value of the error covariance prediction matrix; Q represents the volume point after propagation; k w represents the observation noise covariance matrix at time k; i Indicates the weight. n represents the dimension of the state variables, M = n(n-1) / 2.

[0056] Once the state prediction value and error covariance prediction matrix for the next time step are obtained, measurement prediction can be performed. This involves the following steps: calculating the volume points of the distribution network measurement variables for the next time step based on the error covariance prediction matrix and the state prediction value, and propagating these volume points through the measurement equations to obtain the observation volume points; calculating the measurement prediction value for the next time step based on the observation volume points, and calculating the initial error covariance measurement matrix and the initial cross-covariance matrix based on the measurement prediction value.

[0057] Specifically, a volume transformation is performed on the propagation volume point to obtain the volume point at the next time step (i = 1, 2, ..., 2n). 2 +1), the calculation formula is: Among them, P′ k+1∣k =L k+1∣k L k+1∣k T Substituting the volume point after volume transformation into the measurement equation h(.), the observed volume point is obtained by propagation of the volume point. The calculation formula is as follows:

[0058] Next, the predicted measurement value at time k+1 is calculated. The calculation formula is:

[0059] Finally, the initial matrix of error covariance measurement at time k+1 is calculated. and the initial matrix of cross-covariance The calculation formulas for both are as follows:

[0060] in, Indicates the observation volume point; R represents the predicted value of the measurement at time k+1; k+1 This represents the observation noise covariance at time k+1; The volume point represents the distribution network measurement variable at time k+1; (·) T Indicates transpose calculation; w i Indicates the weight.

[0061] Furthermore, since sudden load changes can cause drastic fluctuations in the power grid state, and the tracking filter (STF) has adaptive characteristics, it can quickly adjust the state estimate according to load changes, improving the accuracy and robustness of the estimate. Therefore, this application focuses on the prediction error covariance matrix P. k+1∣k Introducing the fading factor λ k+1 The fading factor is calculated as follows:

[0062] Here, tr() represents the trace of the matrix; the mathematical symbol N is defined. k+1 and M k+1 As shown below, V represents the initial matrix for measuring error covariance, where κ≥1 is a weakening factor that can be set differently depending on the actual application scenario and selected empirically. 0,k+1 Let be the covariance matrix of the residual sequence. ρ is the forgetting factor, which can take values ​​from [0.95, 0.995], and ε... k+1 Represents the innovation vector

[0063] After obtaining the fading factor, the error covariance prediction matrix can be updated using the fading factor to obtain the error covariance prediction adjustment matrix, where the expression for the error covariance prediction adjustment matrix is:

[0064] Where, λ k+1 This represents the fading factor at time k+1. This represents the predicted state value at time k+1; Q represents the volume point after propagation; k w represents the observation noise covariance matrix at time k; i Indicates the weight.

[0065] Furthermore, measurement prediction can be performed based on the error covariance prediction adjustment matrix to obtain the error covariance measurement update matrix and the cross-covariance update matrix. The measurement prediction process is as follows:

[0066] Specifically, due to P′ k+1∣k =L k+1∣k L k+1∣k T Therefore, when P′ k+1∣k Introducing a fading factor yields the error covariance prediction adjustment matrix P″. k+1∣k Then, the error covariance prediction adjustment matrix P″ is used. k+1∣k right Update and get Then, regarding the volume point... The updated observation volume points are obtained through propagation. The calculation formula is as follows: Next, calculate the updated measurement prediction value at time k+1. The calculation formula is:

[0067] Finally, the error covariance measurement update matrix at time k+1 is calculated. and the mutual covariance update matrix The calculation formulas for both are as follows:

[0068]

[0069] Furthermore, state estimation updates are performed based on the error covariance measurement update matrix and the cross-covariance update matrix. Specifically, this is combined with the actual measured value y. k+1 and measurement prediction value Solve for the Kalman gain K k+1 and the state estimate With error covariance P k+1 Update.

[0070] Wherein, Kalman gain K k+1 The calculation formula is: Next, the state estimate and error covariance P are calculated based on the Kalman gain. k+1 The calculation formulas for both are as follows:

[0071] in, y represents the predicted state value at time k+1; k+1 Indicates the value of the measurement equation; P represents the measured predicted value. k+1 P″ represents the numerical value of the error covariance. k+1∣k This represents the value of the error covariance prediction adjustment matrix; represents the error covariance measurement update matrix; (·)T represents the transpose calculation.

[0072] Finally, the observation noise covariance is updated based on the update results. Specifically, the observation noise covariance Q at time k+1 is updated. k+1 To address noise in time-varying processes, an improved suboptimal Sage-Husa method is employed, which allows... Let Q represent the predicted measurement value at time k+1. k+1 It can be estimated online using the following formula:

[0073] Among them, K k P is the filter gain; k|k-1 To predict the covariance matrix in one step; P k|k To estimate the error covariance matrix; innovation y represents the predicted value of the measurement at time k. k The value of the measurement equation at time k; coefficient d k = (1-b) / (1-b) k+1 ), where b is the forgetting factor, and 0 < b < 1.

[0074] On the other hand, due to the aforementioned process noise covariance matrix Q k+1 The calculation results may be non-positive definite. Therefore, to solve this problem, the process noise covariance matrix Q is calculated using the following formula. k+1 Recalculate, and the calculation formula is as follows:

[0075] Thus, through the above calculation process, the problem of unknown process noise statistical characteristics in the distribution network system is solved, by analyzing the process noise covariance matrix Q. k+1 The recalculation can estimate the system process noise covariance matrix in real time.

[0076] To verify the above distribution network state estimation results, this embodiment uses an IEEE 33-node distribution system to validate the ST-AHCKF algorithm, and compares it with dynamic state estimation algorithms based on HCKF and AHCKF. State estimation is performed every 5 minutes throughout the day. Specifically, during the 130th state estimation, the load on node 8 is suddenly increased to five times its normal value; after a period of stable operation, the load on node 8 is restored to normal levels during the 160th state estimation. Through this process, the tracking accuracy and dynamic adaptability of each algorithm during load surges and recovery phases are observed. The experimental results are as follows: Figure 2-5 As shown.

[0077] like Figure 2 As shown, it can be seen that all three algorithms can basically track the system state quite well; Figure 3 and Figure 4 It can be seen that during the 130th state estimation, the distribution network system state changed abruptly due to the sudden increase in load to five times the normal value. Under these circumstances, both the voltage amplitude and phase angle decreased significantly. During the 160th state estimation, the load returned to normal, and the system state changed abruptly again. Under these circumstances, the voltage amplitude and phase angle quickly rebounded. All three dynamic state estimation algorithms can continue to track the changes in system state after a certain number of calculations.

[0078] Depend on Figure 5 It is evident that the filtering performance of the AHCKF algorithm, which provides real-time estimation of noise variance statistical characteristics, is superior to HCKF, but still weaker than the ST-AHCKF algorithm proposed in this application. This is because the real-time estimation of noise variance statistical characteristics in this application can effectively handle the estimation problem of time-varying noise. Furthermore, the ST-AHCKF algorithm significantly enhances its ability to track system mutations by introducing a fading factor to correct the error covariance matrix. The ST-AHCKF algorithm combines the advantages of real-time estimation of noise variance statistical characteristics and a fading factor, resulting in stronger robustness and higher estimation accuracy.

[0079] Example 2

[0080] A distribution network state estimation system is provided. Since the distribution network state estimation system implements the distribution network state estimation method described in Embodiment 1, it will not be repeated in this embodiment.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any form or substance. It should be noted that those skilled in the art can make various improvements and additions without departing from the method of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention. Any modifications, alterations, and equivalent changes made by those skilled in the art based on the above-disclosed technical content without departing from the spirit and scope of the present invention are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, and evolutions made to the above embodiments based on the essential technology of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for estimating the state of a distribution network, characterized in that, Includes the following steps: Establish a distribution network state estimation model, including state equations and measurement equations; Based on the state equation, state prediction is performed to obtain the state prediction value and the error covariance prediction matrix. Based on the state prediction value, the error covariance prediction matrix and the measurement equation, measurement prediction is performed to obtain the error covariance measurement initial matrix and the cross-covariance initial matrix. Calculate the fading factor and use the fading factor to update the error covariance prediction matrix to obtain the error covariance prediction adjustment matrix; Based on the error covariance prediction adjustment matrix, measurement prediction is performed to obtain the error covariance measurement update matrix and the cross-covariance update matrix. The state estimation is updated based on the error covariance measurement update matrix and the cross-covariance update matrix, and the observation noise covariance is updated based on the update results.

2. The distribution network state estimation method according to claim 1, characterized in that, Based on the state equation, state prediction is performed to obtain the predicted state value and the error covariance prediction matrix, including the following steps: Calculate the volume point of the distribution network state variables at the current moment, and propagate the volume point at the current moment through the state equation to obtain the propagated volume point; The predicted state value and error covariance prediction matrix for the next time step are calculated based on the propagation volume points.

3. The distribution network state estimation method according to claim 2, characterized in that, Based on the propagated volume points, the predicted state value and error covariance prediction matrix for the next time step are calculated using the following formulas: in, P′ represents the predicted state value at time k+1. k+1∣k This represents the value of the error covariance prediction matrix; Q represents the volume point after propagation; k w represents the observation noise covariance matrix at time k; i Indicates the weight. n represents the dimension of the state variables, M = n(n-1) / 2.

4. The distribution network state estimation method according to claim 1, characterized in that, Measurement prediction is performed based on the predicted state value, the error covariance prediction matrix, and the measurement equation, including the following steps: Based on the error covariance prediction matrix and the state prediction value, the volume point of the distribution network measurement variables at the next time moment is calculated, and the volume point at the next time moment is propagated through the measurement equation to obtain the observation volume point. The measurement prediction value for the next time step is calculated based on the observed volume points, and the initial measurement matrix and the initial cross-covariance matrix are calculated based on the measurement prediction value.

5. The distribution network state estimation method according to claim 4, characterized in that, The formulas for calculating the initial matrix of measurement error covariance and the initial matrix of cross-covariance are as follows: in, This represents the initial matrix of the error covariance measurement; Represents the initial cross-covariance matrix; Indicates the observation volume point; R represents the predicted value of the measurement at time k+1; k+1 This represents the observation noise covariance at time k+1; The volume point represents the distribution network measurement variable at time k+1; (·) T Indicates transpose calculation; w i Indicates the weight.

6. The distribution network state estimation method according to claim 1, characterized in that, The formula for calculating the fading factor is as follows: Here, tr() represents the trace of the matrix; the mathematical symbol N is defined. k+1 and M k+1 As shown below, V represents the initial matrix of error covariance measurements, κ≥1 is the weakening factor, and V 0,k+1 Let R be the covariance matrix of the residual sequence. k+1 This represents the observation noise covariance at time k+1. ρ is the forgetting factor, ε k+1 Represents the innovation vector 7. The distribution network state estimation method according to claim 6, characterized in that, The expression for the error covariance prediction adjustment matrix is: Where, λ k+1 This represents the fading factor at time k+1. This represents the predicted state value at time k+1; Q represents the volume point after propagation; k w represents the observation noise covariance matrix at time k; i Indicates the weight. n represents the dimension of the state variables, M = n(n-1) / 2.

8. The distribution network state estimation method according to claim 1, characterized in that, The update formula for state estimation updates is: in, This represents the state estimate; y represents the predicted state value at time k+1; k+1 Indicates the value of the measurement equation; P represents the measured predicted value. k+1 P″ represents the numerical value of the error covariance. k+1∣k This represents the value of the error covariance prediction adjustment matrix; This represents the measurement update matrix of the error covariance; (·) T Indicates transpose calculation; K k+1 Indicates the Kalman gain; This represents the mutual covariance update matrix.

9. The distribution network state estimation method according to claim 1, characterized in that, The update formula for updating the observation noise covariance based on the update results is as follows: Where, d k = (1-b) / (1-b) k+1 ), where b is the forgetting factor, and 0 < b < 1; Q k K represents the observation noise covariance matrix at time k; k P is the filter gain; k|k To estimate the error covariance matrix; y represents the predicted value of the measurement at time k. k This represents the value of the measurement equation at time k.

10. A power distribution network state estimation system, characterized in that, The power distribution network state estimation system performs the power distribution network state estimation method as described in any one of claims 1-9.

Images