Main power distribution network integrated dispatching monitoring method and system

Abstract

The invention relates to the technical field of main power distribution network monitoring, in particular to a main power distribution network integrated dispatching monitoring method and system, and the system achieves main power distribution network integrated dispatching monitoring through the method steps of main network and distribution network data acquisition, data preprocessing, data fusion, main power distribution network fusion topology model establishment and the like. When the operation data of the main power distribution network is fused, the traditional Bayesian fusion algorithm is improved by using a confidence factor, so that the heterogeneous data sources can be more effectively integrated, and more accurate information is provided for the operation and maintenance of the main power distribution network; the fused state estimation value reflects the most probable value of the state parameter of the main power distribution network under the condition that all observation data and priori knowledge are given, the information of all data sources is integrated, and the reliability of different data sources is constrained through confidence factors, so that the state estimation value is more accurate and reliable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of main distribution network monitoring, and particularly relates to a main distribution network integrated dispatching and monitoring method and system. BACKGROUND

[0002] With the rapid development of social economy, the scale of the power system is continuously expanding, and the connection of the main distribution network is increasingly close. The traditional mode of separate dispatching and monitoring of the main network and the distribution network has been difficult to meet the demand of efficient and reliable operation of the modern power system. In order to improve the operation and management level of the main distribution network and realize the unified dispatching and monitoring of the main distribution network, a dispatching and monitoring method capable of integrating the main distribution network is needed. In the traditional power system, the dispatching and monitoring of the main network and the distribution network are usually managed by different departments: the main network dispatching focuses on the connection of large-capacity power sources, the safety of power transmission channels and the power balance between regions, and the SCADA system is used to monitor the macro parameters such as the bus voltage of the substation and the line flow; the distribution network dispatching focuses on the reliability of user-side power supply, and the DAS is used to monitor the micro data such as the feeder switch state and the distribution transformer load. There are significant differences between the two in data standards, communication protocols, monitoring interfaces and fault handling processes.

[0003] In the prior art, when the main distribution network is integrated for dispatching and monitoring, due to the characteristics of multiple nodes and complex logic of the main distribution network, the fusion topology model established for the main distribution network cannot accurately reflect the fusion state of the main distribution network, thereby affecting the dispatching and monitoring of the main distribution network. Meanwhile, in the multi-source data fusion of the main distribution network, there are differences in the reliability and accuracy of different data sources. The core of the traditional Bayesian fusion algorithm is to use the Bayesian theorem to integrate the uncertain information provided by multiple data sources, and finally obtain the optimal estimation of the target state. It assumes that all data sources have the same contribution weight to the target state, i.e. there is no difference in their reliability. This means that whether the data comes from a high-precision sensor or a low-precision terminal, the algorithm gives them the same trust degree. At the same time, the data source characteristics are not taken into account, and factors such as the type of data source, environmental conditions and operation state are not considered to affect the observation reliability, which may lead to deviation of the fusion result in actual application. SUMMARY

[0004] In order to solve the above technical problems, the present application provides a main distribution network integrated dispatching and monitoring method and system to solve the problems in the prior art.

[0005] The present application provides a main distribution network integrated dispatching and monitoring method, which comprises the following steps: S1: obtaining operation data of the main network and the distribution network; S2: performing data preprocessing operation on the operation data; S3: performing data fusion operation on the preprocessed operation data; Specifically, S3 consists of: S3.1: calculating the initial confidence factor for each data source in the running data; S3.2: normalizing the initial confidence factor for each data source to obtain the confidence factor for each data source; S3.3: using an improved Bayesian fusion algorithm to perform data fusion operation on the preprocessed running data based on the confidence factor for each data source. Input the multi-source data to be fused; For each data source, in the state estimate update stage, an observation model is established, a likelihood function is calculated for the new observation data and corresponding confidence factor of each data source, the posterior probability is updated according to the likelihood function, and the fused state estimate is updated according to the posterior probability. The observation model is as follows: ; In the formula, D k The data is the observation data at time k, h( ) is the observation function, v k It is observation noise. The formula for calculating the likelihood function is: ; In the formula, Given parameters θ, let the data D observed by the i-th data source at time k be... k,i likelihood Given parameter θ, let the observation data D from the i-th data source be... k,i The probability of occurrence; The confidence level is iteratively updated based on the state estimate to achieve data fusion; S4: Establish a fusion topology model of the main and distribution networks; S5: Based on the integrated operation data and the integrated topology model of the main and distribution networks, realize integrated scheduling and monitoring of the main and distribution networks.

[0006] Preferably, S4 specifically comprises: S4.1: Construct a static integrated topology model of the main distribution network; S4.2: Data association and mapping of main distribution network; S4.3: Perform dynamic association based on the main and distribution network integrated topology model.

[0007] Preferably, S4.3 specifically involves: establishing a mapping table of association relationships between the main network and distribution network equipment, wherein the mapping table of association relationships between the main network and distribution network equipment includes the static connection relationships between substation busbars and corresponding feeders and switching station incoming lines; When the main grid side substation busbar is detected to be switched from operation to maintenance, all downstream feeders supplied by the main grid side substation busbar are located according to the mapping table of the association between the main grid and distribution network equipment, and the status of all downstream feeders is automatically updated to out of power. Furthermore, the corresponding distribution transformer is associated and its status is marked as de-energized. At the same time, the load information of the corresponding line is updated in the main grid and distribution network integrated topology model. When the switching switch of the ring main unit on the distribution network side is detected to switch from open to closed, the distribution network line segment where the switching switch is located and its power supply range are first determined. Then, according to the mapping table of the relationship between the main network and the distribution network equipment, the corresponding feeder of the switching switch on the main network side is deduced in reverse, and the power supply area of ​​the feeder on the main network side is automatically updated.

[0008] Preferably, S1 specifically includes: the main network data acquired includes bus voltage / current, active / reactive power of main transformer, circuit breaker / disconnector status, line power flow, and protection action signals within the substation; the distribution network data acquired includes feeder switch position, distribution transformer load rate, predicted and actual output values ​​of distributed power sources, user-side load, and low-voltage distribution area voltage.

[0009] Preferably, both the main network data and the distribution network data are collected in real time through intelligent terminals, with a collection frequency of ≥100Hz for key nodes of the main network and ≥1Hz for regular nodes of the distribution network; wherein, the intelligent terminal includes a synchronous phasor measurement device for the main network and a distribution terminal unit / feeder terminal unit for the distribution network.

[0010] Preferably, the data preprocessing operation includes data cleaning and data filtering; the data cleaning includes noise removal, missing value filling, and erroneous data correction. The noise removal specifically involves removing noise from the running data using a Kalman filter. Specifically, filling in the missing values ​​involves using a spline interpolation algorithm to fill in the missing values ​​in the running data. The specific method for correcting error data is to mark and correct the operational data that exceeds a set threshold.

[0011] Preferably, the data filtering specifically involves filtering out key data based on the importance of the cleaned data and its impact on scheduling and monitoring.

[0012] Preferably, in step S3.1, the initial confidence factor for each data source is calculated using the following formula: ; In the formula, CF i This is the initial confidence factor for the i-th data source, where α and β are weighting coefficients used to balance the influence of different factors. MAE iIt is the mean absolute error (RMSE) of the i-th data source. i It is the root mean square error of the i-th data source.

[0013] Preferably, in step S3.2, the sum of the confidence factors of all data sources is 1; Specifically, S3.2 involves: calculating the sum of the initial confidence factors of all data sources, and then normalizing the initial confidence factors of each data source. The formula for calculating the sum of the initial confidence factors for all data sources is as follows: ; In the formula, SumCF is the sum of the initial confidence factors of all data sources, and N is the number of data source types. The formula for normalizing the initial confidence factor for each data source is as follows: ; In the formula, CF i ′ is the confidence factor for the i-th data source.

[0014] According to another aspect of the present invention, an integrated dispatching and monitoring system for a main and distribution network is provided. The system employs the aforementioned integrated dispatching and monitoring method for a main and distribution network, and the system includes: The data acquisition module is used to acquire operational data from the main network and distribution network. The data preprocessing module is used to perform data preprocessing operations on the running data; The data fusion module is used to perform data fusion operations on the preprocessed running data; The model building module is used to build a fusion topology model of the main and distribution networks; The scheduling and monitoring module is used to realize integrated scheduling and monitoring of the main and distribution networks based on the integrated operation data and the integrated topology model of the main and distribution networks.

[0015] Compared with the prior art, the present invention has the following technical effects: This invention improves upon existing Bayesian fusion algorithms when fusing main and distribution network operation data. By using a confidence factor to enhance the traditional Bayesian fusion algorithm, it effectively integrates main and distribution network data, providing more accurate information for the operation and maintenance of the main and distribution networks. The fused state estimate reflects the most probable values ​​of the main and distribution network state parameters given all observation data and prior knowledge. It integrates information from all data sources and constrains the reliability of different data sources through a confidence factor, thus making the fused state estimate more accurate and reliable.

[0016] This invention addresses two scenarios that have the greatest impact on the integrated topology model of the main and distribution networks during the integration of the main and distribution networks: "the main grid-side substation busbar switching from operation to maintenance" and "the distribution network-side ring main unit interconnection switch switching from open to closed." It dynamically correlates the integrated topology model of the main and distribution networks, enabling it to reflect changes in the grid's operating status in real time. This provides timely and accurate grid model support for dispatching decisions, effectively shortening fault response time and improving grid reliability. Simultaneously, it strengthens the correlation and interaction between the main grid topology model and the distribution network topology model, achieving cross-level collaborative dispatching and management. In scenarios such as fault handling and load transfer, it can comprehensively consider the overall operation of the main and distribution networks, formulate better control strategies, and improve the efficiency of grid resource utilization. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an integrated dispatching and monitoring method for main and distribution networks provided in an embodiment of the present invention; Figure 2 This is a flowchart of a data fusion operation on preprocessed running data provided in an embodiment of the present invention; Figure 3 This is a flowchart of establishing a fusion topology model of the main and distribution networks provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] Example 1 like Figure 1 As shown, an integrated dispatching and monitoring method for main and distribution networks includes the following steps: S1: Obtain operational data from the main network and distribution network; The acquired main network data includes bus voltage / current within the substation, active / reactive power of the main transformer, circuit breaker / disconnector status, line power flow, and protection operation signals; the acquired distribution network data includes feeder switch position, distribution transformer load rate, predicted and actual output values ​​of distributed power sources, user-side load, and low-voltage distribution area voltage.

[0021] Both main grid data and distribution network data are collected in real time through intelligent terminals. The collection frequency is ≥100Hz for key nodes of the main grid to meet dynamic monitoring requirements, and ≥1Hz for regular nodes of the distribution network to meet steady-state analysis requirements. The intelligent terminals include the synchronous phasor measurement device of the main grid and the distribution terminal unit / feeder terminal unit of the distribution network.

[0022] S2: Perform data preprocessing operations on the running data obtained by S1; The data preprocessing operations include data cleaning and data filtering. Data cleaning includes noise removal, missing value imputation, and erroneous data correction. During data acquisition and transmission, noise may be generated due to environmental interference or other factors. In this step, Kalman filtering is used to remove noise from the operational data, improving data accuracy. Data loss may occur during data transmission. In this step, spline interpolation is used to imput missing values ​​in the operational data, ensuring data integrity. Obvious erroneous operational data is corrected by setting a threshold; operational data exceeding the threshold is marked and corrected.

[0023] The data screening process involves selecting key data based on the importance of the cleaned data and its impact on scheduling and monitoring. For example, data such as voltage and current, which directly affect the safe operation of the power grid, are given higher priority for screening and processing; while auxiliary data are given lower priority.

[0024] S3: Perform data fusion operation on the preprocessed runtime data; In the fusion of multi-source data from power grids, the reliability and accuracy of different data sources vary. Traditional Bayesian fusion algorithms assume that all data sources have the same reliability, which may lead to deviations in the fusion results in practical applications. This embodiment introduces a confidence factor to improve the traditional Bayesian fusion algorithm, thereby enhancing the accuracy and reliability of the fusion results.

[0025] Specifically, such as Figure 2 As shown, S3 above specifically refers to: S3.1: Calculate the initial confidence factor for each data source in the running data; In calculating the initial confidence factor for each data source, the historical error of the data source is considered, including mean absolute error (MAE) and root mean square error (RMSE), to assess the data source's past performance.

[0026] Specifically, the formula for calculating the initial confidence factor for each data source is as follows: ; In the formula, CF i This is the initial confidence factor for the i-th data source, where α and β are weighting coefficients used to balance the influence of different factors. MAE i It is the mean absolute error (RMSE) of the i-th data source. i It is the root mean square error of the i-th data source.

[0027] S3.2: Normalize the initial confidence factor for each data source to obtain the confidence factor for each data source; The sum of the confidence factors of all data sources is 1; Specifically, S3.2 above involves: calculating the sum of the initial confidence factors of all data sources, and then normalizing the initial confidence factors of each data source. The formula for calculating the sum of the initial confidence factors for all data sources is as follows: ; In the formula, SumCF is the sum of the initial confidence factors of all data sources, and N is the number of data source types. The formula for normalizing the initial confidence factor for each data source is as follows: ; In the formula, CF i ′ is the confidence factor for the i-th data source.

[0028] S3.3: Based on the confidence factor of each data source, an improved Bayesian fusion algorithm is used to perform data fusion operation on the preprocessed running data; The above S3.3 specifically refers to: Establish a dynamic model of the main distribution network; this dynamic model is used to describe the evolution of the network state and can be represented as: ; In the formula, θ k It represents the power grid state at time k, which can include parameters such as voltage, current, and power; θ k-1 This is the power grid state at time k-1, f( () is a dynamic function of the main distribution network, describing the evolution of the main distribution network's state. The dynamic function can be an analytical function based on a physical model, or an empirical model obtained through data-driven methods. k-1 It is the control input at time k-1, including scheduling instructions, equipment operations, etc., ω k-1 It is process noise, representing the uncertainty and randomness in the evolution of the main distribution network state.

[0029] Predict the next time step based on the dynamic model of the main distribution network; The specific formula is as follows: ; In the formula, For the first k The predicted state value at time 10:00. For the first k State estimate at time 1; Then, predict the state error covariance matrix; the specific formula is: ; In the formula, For the first k The state error covariance matrix at time t. For Jacobian matrices, For the kth The state error covariance matrix at time 1 The process noise covariance matrix; The state estimate is updated based on the new observation data; specifically: An observation model is established, and a likelihood function is calculated for new observation data from each data source and the corresponding confidence factor. The posterior probability is updated based on the likelihood function, and the merged state estimate is updated based on the posterior probability. The merged state estimate is updated using the maximum a posteriori (MAP) estimate. The above merged state estimate is the operating data of the merged main distribution network.

[0030] The observation model is as follows: ; In the formula, D k The data is the observation data at time k, h( ) is the observation function, v k It is observation noise. The formula for calculating the likelihood function is: ; In the formula, Given parameters θ, let the data D observed by the i-th data source at time k be... k,i likelihood Given parameter θ, let the observation data D from the i-th data source be... k,i The possibility of it occurring.

[0031] The fused state estimate reflects the most probable values ​​of the main distribution network state parameters given all observation data and prior knowledge. It integrates information from all data sources and constrains the reliability of different data sources through a confidence factor, thus making the fused state estimate more accurate and reliable. When fusing main distribution network operation data, improving the traditional Bayesian fusion algorithm using a confidence factor can more effectively integrate these heterogeneous data sources, providing more accurate information for the operation and maintenance of the main distribution network.

[0032] S4: Establish a fusion topology model of the main and distribution networks; Among them, such as Figure 3 As shown, S4 above specifically refers to: S4.1: Construct a static integrated topology model of the main distribution network; The physical connections between the main grid and distribution network are analyzed separately to form a static integrated topology model of the main grid and distribution network. The static integrated topology model of the main grid and distribution network includes a main grid topology model and a distribution network topology model. The main grid topology model is based on the electrical connection relationship of primary equipment in the substation, combined with dispatch naming and equipment ledger, to construct a main grid hierarchical topology model of substation-bus-line-transformer. The distribution network topology model is a distribution network radiation / ring network topology model of substation outgoing line-branch box-distribution transformer-user, which is analyzed for distribution network equipment such as distribution transformer area, switching station, and branch line through GIS, ledger data, and point table information of automation terminal.

[0033] S4.2: Data association and mapping between main and distribution networks; In this step, the association and mapping of main and distribution network data includes the unification of main and distribution network equipment identifiers and the mapping of topology nodes; Specifically, the unified identification of main and distribution network equipment is as follows: a unique coding rule is established for main and distribution network equipment to ensure that the same physical equipment has the same name and code in the main and distribution network models; the coding rule is the CID / CIM model coding of the IEC61968 / 61970 standard; for example, a 10kV feeder is named "XX Transformer 10kV Feeder 1" on the main network side, and needs to be associated with the same logical code or mapping table on the distribution network side.

[0034] Specifically, the topology node mapping is as follows: Define the relationship between main grid nodes and distribution network nodes: On the main grid side, the 10kV busbar and outgoing line bay of the substation are the core connection points; on the distribution network side, the incoming line of the switchgear, the ring main unit interconnection switch, and the high voltage side of the distribution transformer are the core connection points; through cross-verification of equipment ledgers and spatial geographic information, ensure that the outgoing lines of the main grid and the incoming lines of the distribution network correspond one-to-one.

[0035] S4.3: Perform dynamic correlation based on the integrated topology model of the main and distribution networks; In this step, although a fusion model was established and the main distribution network data was associated and mapped, the established fusion topology model cannot accurately reflect the fusion status of the main distribution network due to the large number of nodes and complex logic of the main distribution network topology. Therefore, based on the above situation, a mechanism for dynamic association and real-time updating of the fusion topology model of the main distribution network based on consistency verification and event-driven methods was developed to improve the scientificity and accuracy of the construction of the static fusion topology model of the main distribution network.

[0036] Specifically, S4.3 above refers to: establishing a mapping table of relationships between the main network and distribution network equipment. This mapping table includes the static connection relationships between substation buses and corresponding feeders and switching station incoming lines. For example, the mapping table records in detail the distribution network locations, distribution transformer distribution, and load allocation of each main network feeder, providing accurate association basis for topology updates.

[0037] When the main grid substation busbar is detected to be switched from operation to maintenance, the system locates all downstream feeders supplied by the main grid substation busbar based on the mapping table of the relationship between the main grid and distribution network equipment, and automatically updates the status of all downstream feeders to out of power. The system then further associates the corresponding distribution transformers and marks the status of the distribution transformers as de-energized. At the same time, the system updates the load information of the corresponding lines in the distribution network topology model to ensure the power balance and logical consistency of the entire topology model. When the switch of the tie switch of the distribution network ring network cabinet is detected to switch from open to closed, the distribution network line segment where the tie switch is located and its power supply range are first determined. Then, according to the mapping table of the relationship between the main network and the distribution network equipment, the corresponding feeder of the tie switch on the main network side is deduced in reverse, and the power supply area of ​​the feeder on the main network side is automatically updated to reflect the feedback effect of the topology change on the distribution network side on the main network.

[0038] This embodiment addresses two scenarios that have the greatest impact on the integrated topology model of the main and distribution networks: "the main grid-side substation busbar switching from operation to maintenance" and "the distribution network-side ring network cabinet interconnection switch switching from open to closed." It dynamically correlates the integrated topology model of the main and distribution networks, enabling it to reflect changes in the grid's operating status in real time. This provides timely and accurate grid model support for dispatching decisions, effectively shortening fault response time and improving grid reliability. Simultaneously, it strengthens the correlation and interaction between the main grid topology model and the distribution network topology model, achieving cross-level collaborative dispatching and management. In scenarios such as fault handling and load transfer, it comprehensively considers the overall operation of the main and distribution networks, formulating better control strategies and improving grid resource utilization efficiency.

[0039] S5: Based on the integrated operational data and the integrated topology model of the main and distribution networks, realize integrated scheduling and monitoring of the main and distribution networks; Based on the fused operational data, and according to preset monitoring indicators and thresholds, the operational status of the main distribution network is monitored in real time, including equipment operating status, power flow distribution, voltage quality, frequency stability, etc. Data analysis models are used to deeply mine and analyze the fused operational data to promptly identify potential anomalies and fault hazards, such as equipment overload, voltage exceeding limits, abnormal power factor, and line faults. These anomalies are classified and evaluated to determine their severity and development trend, providing a basis for dispatching decisions. The data analysis model is one of the existing deep learning models and reinforcement learning models.

[0040] Meanwhile, based on the monitoring results of the main and distribution networks' operation status, the dispatch center formulates an integrated dispatch decision-making scheme based on dispatch rules. This dispatch decision-making scheme comprehensively considers the safety, economy, and reliability of the main and distribution networks, and coordinates operations such as the adjustment of operation modes, reactive power and voltage control, reserve capacity allocation, and distributed power source access control between the main and distribution networks, so as to achieve the overall optimized operation of the main and distribution networks.

[0041] When formulating dispatching decision-making schemes, we should fully consider the real-time operating status and constraints of the power grid and make multi-objective optimization decisions. For example, under the premise of ensuring the safety and stability of the power grid, we should reduce the active power loss of the power grid, improve power quality, and optimize the output of distributed power sources. At the same time, we should also consider the feasibility and convenience of dispatching operations to ensure that dispatching decisions can be implemented smoothly.

[0042] Example 2: Based on the above-described integrated dispatching and monitoring method for main and distribution networks, the present invention also provides an integrated dispatching and monitoring system for main and distribution networks, comprising: The data acquisition module is used to acquire operational data from the main network and distribution network. The data preprocessing module is used to perform data preprocessing operations on the running data; The data fusion module is used to perform data fusion operations on the preprocessed running data; The model building module is used to build a fusion topology model of the main and distribution networks; The scheduling and monitoring module is used to realize integrated scheduling and monitoring of the main and distribution networks based on the integrated operation data and the integrated topology model of the main and distribution networks.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A main power distribution network integrated dispatching and monitoring method, characterized in that: The method comprises the following steps: S1: obtaining operation data of a main grid and a distribution grid; S2: performing data preprocessing on the operation data; S3: performing data fusion on the preprocessed operation data; The S3 is specifically: S3.1: calculating an initial confidence factor for each data source in the operation data; S3.2: normalizing the initial confidence factor of each data source to obtain a confidence factor of each data source; S3.3: performing data fusion on the preprocessed operation data based on the confidence factor of each data source by using an improved Bayesian fusion algorithm; The improved Bayesian fusion algorithm is specifically: inputting multi-source data to be fused; for each data source, establishing an observation model at a state estimation value updating link, calculating a likelihood function for new observation data of each data source and a corresponding confidence factor, updating a posterior probability according to the likelihood function, and updating a fused state estimation value according to the posterior probability; wherein the observation model is: ； In the formula, D k is the observation data at the kth moment, h( ) is an observation function, v k is observation noise, the formula for calculating the likelihood function is: ； wherein, is the likelihood of the ith data source observing data D k,i at time k given the parameter θ, is the probability of the ith data source observing data D k,i given the parameter θ. iteratively updating the confidence according to the state estimation value to realize data fusion; S4: establishing a main-distribution grid fusion topology model; S5: realizing integrated dispatching and monitoring of the main-distribution grid according to the fused operation data and the main-distribution grid fusion topology model.

2. The integrated dispatching and monitoring method of the main-distribution grid according to claim 1, characterized in that: The S4 is specifically: S4.1: constructing a main-distribution grid static fusion topology model; S4.2: main-distribution grid data correlation and mapping; S4.3: dynamically correlating according to the main-distribution grid fusion topology model.

3. The integrated dispatching and monitoring method of the main-distribution grid according to claim 2, characterized in that: The S4.3 is specifically: establishing a correlation relationship mapping table between main grid and distribution grid devices, wherein the correlation relationship mapping table between the main grid and the distribution grid devices includes a static connection relationship between a transformer substation bus and a corresponding feeder and an open-close station incoming line; when it is monitored that a transformer substation bus on the main grid side is switched from operation to maintenance, all downstream feeders powered by the transformer substation bus are located according to the correlation relationship mapping table between the main grid and the distribution grid devices, and the states of all downstream feeders are automatically updated to power failure, and are further correlated to corresponding distribution transformers to mark the states of the distribution transformers as power failure; meanwhile, load information of corresponding lines in the main-distribution grid fusion topology model is updated; when it is monitored that a tie switch of a ring network cabinet on the distribution grid side is switched from open to close, first, a distribution grid line section and a power supply range of the tie switch are determined, and then, a corresponding feeder of the tie switch on the main grid side is reversely deduced according to the correlation relationship mapping table between the main grid and the distribution grid devices, and a power supply area of the feeder on the main grid side is automatically updated.

4. The integrated dispatching and monitoring method of the main-distribution grid according to claim 1, characterized in that: The S1 is specifically: the obtained main grid data includes bus voltage / current in the substation, active / reactive power of the main transformer, on / off state of the circuit breaker / disconnector, line power flow, and protection action signal; and the obtained distribution network data includes feeder switch position, distribution transformer load rate, distributed power output prediction value and actual value, user side load, and low voltage area voltage.

5. The integrated dispatching and monitoring method of the main and distribution power grid according to claim 4, characterized in that: The main grid data and the distribution network data are both collected by intelligent terminals in real time, and the collection frequency is ≥100 Hz for the key nodes of the main grid and ≥1 Hz for the conventional nodes of the distribution network; wherein the intelligent terminals include a synchronous phasor measurement device of the main grid and a distribution terminal unit / feeder terminal unit of the distribution network.

6. The integrated dispatching and monitoring method of the main and distribution power grid according to claim 1, characterized in that: The data preprocessing operation includes data cleaning and data screening; the data cleaning includes removing noise, filling in missing values, and correcting erroneous data; The removing noise is specifically: removing noise in the operation data by Kalman filtering; The filling in missing values is specifically: filling in missing values of the operation data by spline interpolation algorithm; The correcting erroneous data is specifically: marking and correcting the operation data exceeding the threshold value by setting a threshold value.

7. The integrated dispatching and monitoring method of the main and distribution power grid according to claim 6, characterized in that: The data screening is specifically: screening out key data according to the importance of the data after cleaning and the influence degree on the dispatching and monitoring.

8. The integrated dispatching and monitoring method of the main and distribution power grid according to claim 1, characterized in that: In S3.1, the calculation formula of the initial confidence factor of each data source is: ； CFi = α * MAEi + β * RMSEi i is the initial confidence factor of the i th data source, α, β are weight coefficients for balancing the influence of different factors, MAE i is the mean absolute error of the i th data source, RMSE i is the root mean square error of the i th data source.

9. The integrated dispatching and monitoring method of the main and distribution power grid according to claim 8, characterized in that: In S3.2, the sum of the confidence factors of all data sources is 1; S3.2 is specifically: calculating the sum of the initial confidence factors of all data sources, and then normalizing the initial confidence factor of each data source; Wherein, the formula for calculating the sum of the initial confidence factors of all data sources is: ； In the formula, SumCF is the sum of the initial confidence factors of all data sources, and N is the number of types of data sources, The formula for normalizing the initial confidence factor of each data source is: ； where CF i is the confidence factor of the i-th data source.

10. A dispatching and monitoring system integrated with a main power distribution network, characterized in that, The system adopts the integrated dispatching and monitoring method of the main and distribution power grid according to any one of claims 1-9, and the system comprises: A data acquisition module for acquiring operation data of the main grid and the distribution network; A data preprocessing module for performing data preprocessing operation on the operation data; A data fusion module for performing data fusion operation on the preprocessed operation data; A model establishment module for establishing a main and distribution power grid fusion topology model; A dispatching and monitoring module for realizing integrated dispatching and monitoring of the main and distribution power grid according to the fused operation data and the main and distribution power grid fusion topology model.

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