Quantum-sensing-based wide-area synchronized phasor intelligent monitoring method and system for power grid

Abstract

This invention provides a method and system for intelligent monitoring of synchronized phasors in a wide-area power grid based on quantum sensing, belonging to the field of power monitoring technology. The method includes: deploying a set of quantum sensing nodes and a set of conventional sensing nodes in a wide-area power grid; collecting phasor data from the quantum nodes for reliability analysis; screening out identified quantum sensing nodes with values ​​exceeding a preset threshold; constructing a wide-area power grid topology model; using the identified quantum sensing nodes to generate reference anchor point constraints to perform error propagation calculations on the wide-area topology model; obtaining a node error dataset for synchronization phasor calibration; and inputting the data into a monitoring terminal for processing. This invention solves the technical problems of insufficient monitoring accuracy and poor data reliability in existing wide-area power grid technologies. It achieves the technical effect of intelligent monitoring of synchronized phasors in a wide-area power grid based on quantum sensing technology, combined with node reliability analysis and reference anchor point error propagation, thereby improving the monitoring accuracy and data reliability of the wide-area power grid.

Description

Technical Field

[0001] This invention relates to the field of power monitoring technology, specifically to a method and system for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing. Background Technology

[0002] A wide-area power grid (WAN) refers to a power system that spans a large geographical area and encompasses multiple power plants, substations, and transmission lines. It is characterized by its large scale, complex structure, frequent fluctuations in power load, and significant impacts on its operational status from inter-regional power flows and sudden disturbances. To ensure the safe and stable operation of a WAN, real-time monitoring of key parameters across the entire network is typically required to achieve a comprehensive understanding of the grid's operational status. Existing WAN monitoring methods primarily rely on conventional sensors, such as voltage transformers, current transformers, and phasor measurement units. These devices collect voltage, current, and phase information from grid nodes and aggregate the data at a monitoring center for analysis and dispatch. While these methods can provide grid status information to some extent, they suffer from limitations in measurement accuracy, high data noise, and insufficient synchronization.

[0003] Furthermore, conventional sensing nodes lack effective error correction mechanisms and cannot perform network-wide calibration for measurement errors between nodes. This results in low reliability of data from critical nodes, and some node data may even mislead scheduling and stability analysis. It is evident that with the continuous expansion of the power grid and the increasing proportion of renewable energy integration, existing monitoring methods are significantly insufficient in handling large-scale synchronous phasor data monitoring and ensuring network-wide data consistency.

[0004] Existing technologies suffer from limited accuracy of conventional sensing nodes and a lack of effective error correction mechanisms, resulting in insufficient monitoring accuracy and poor data reliability in wide-area power grids. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing, in order to solve the technical problems of insufficient accuracy and poor data reliability in wide-area power grid monitoring caused by the limited accuracy of conventional sensing nodes and the lack of effective error correction mechanisms in existing technologies.

[0006] In view of the above problems, this application provides a method and system for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing.

[0007] The first aspect of this application provides a method for intelligent monitoring of wide-area synchronous phasors in a power grid based on quantum sensing. This method includes: deploying a set of quantum sensing nodes and a set of conventional sensing nodes in a wide-area power grid; collecting the quantum node phasor sets corresponding to the set of quantum sensing nodes; performing a reliability analysis on the set of quantum node phasors to obtain a set of reliability indicators; filtering out identified quantum sensing nodes whose indicators exceed a preset threshold based on the magnitude of the indicators in the set of reliability indicators; constructing a wide-area topology model of the power grid based on the set of quantum sensing nodes and the set of conventional sensing nodes; performing error propagation calculations on the wide-area topology model using the identified quantum sensing nodes as reference anchor constraints to obtain a node error dataset; performing synchronous phasor calibration on the node error dataset; and inputting the calibrated phasor monitoring dataset into a monitoring terminal for processing.

[0008] Optionally, the quantum node phasor set is time-aligned to obtain a quantum node time-series phasor set; the phase long-term stability index, noise characteristic index, drift rate index, and integrity index of the quantum node time-series phasor set are analyzed; a credibility function is constructed according to the phase long-term stability index, noise characteristic index, drift rate index, and integrity index; and the credibility function is used to perform credibility analysis on the quantum node phasor set to obtain a credibility index set.

[0009] Optionally, the current load mode of the wide-area power grid is analyzed, and the credibility influence weight is identified according to the current load mode; the credibility influence weight is used as a dynamic weight to fit the credibility function, and the credibility analysis of the quantum node phasor set is re-performed to obtain the credibility index set corresponding to the current load mode.

[0010] Optionally, identified quantum sensing nodes with index values ​​greater than a preset index threshold are selected according to the index size of the set of credibility indices, and the identified quantum sensing nodes serve as benchmark anchors; wherein, the preset index threshold is dynamically configured by identifying the average weighted value of key variables of the current load mode, and the key variables include disturbance sensitivity and signal stability margin.

[0011] Optionally, the real-time operating status dataset of the wide area power grid is obtained, and the wide area power grid is divided into multiple sub-power grids according to the real-time operating status dataset; multiple power grid topology sub-models corresponding to the multiple sub-power grids are obtained, error propagation calculations are performed based on the multiple power grid topology sub-models respectively, multiple sets of node error datasets are obtained, and the multiple sets of node error datasets are merged to output a node error dataset.

[0012] Optionally, the reference anchor constraint includes establishing a constraint that minimizes the sum of errors between the identified quantum sensing node and non-anchor nodes, using the identified quantum sensing node as the reference anchor; initializing the node phasor error covariance matrix of the remaining sensing nodes; performing error propagation calculations on the power grid wide-area topology model according to the error covariance matrix based on the smooth propagation algorithm to obtain the error propagation results; evaluating the node error confidence intervals in the error propagation results until all node error confidence intervals are greater than a preset confidence threshold to obtain a node error dataset.

[0013] Optionally, the power grid wide-area topology model establishes node error propagation relationships according to the error covariance matrix; establishes error influence relationships between anchor points and node pairs based on the node error propagation relationships; analyzes the influence weights of the error influence relationships based on the benchmark anchor point constraints, until the error update value of each non-anchor point node is obtained, repeats the iteration until error propagation converges, and outputs the node error distribution as the error propagation result.

[0014] Optionally, based on the node error propagation relationship, an error influence relationship between anchor point-node pairs is established. The categories of the error influence relationship include a first type of error influence relationship based on the relationship between anchor points and non-anchor point quantum sensing nodes, and a second type of error influence relationship based on the relationship between anchor points and conventional sensing nodes. When the anchor point-node pair is a first type of error influence relationship, the influence weight is updated by error recursion. When the anchor point-node pair is a second type of error influence relationship, the adjustment weight of the conventional sensing node based on historical accuracy, historical noise, and historical sampling completeness is calculated, and the influence weight is updated by error weighted fusion according to the adjustment weight.

[0015] Optionally, a priority identifier is obtained based on the importance of the node data in the anchor-node pair, and priority error propagation calculation is performed on the propagation nodes based on the priority identifier.

[0016] A second aspect of this application provides a quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids. The system comprises: a sensor node deployment module for deploying a set of quantum sensor nodes and a set of conventional sensor nodes in a wide-area power grid; a sensor node screening module for collecting the quantum node phasor sets corresponding to the set of quantum sensor nodes, performing credibility analysis on the set of quantum node phasors, obtaining a credibility index set, and screening identified quantum sensor nodes whose index values ​​exceed a preset index threshold according to the index values ​​of the credibility index set; a topology model construction module for constructing a wide-area power grid topology model based on the set of quantum sensor nodes and the set of conventional sensor nodes; and a data calibration module for performing error propagation calculations on the wide-area power grid topology model using the reference anchor point constraints generated by the identified quantum sensor nodes, obtaining a node error dataset, performing synchronous phasor calibration on the node error dataset, and inputting the calibrated phasor monitoring dataset into a monitoring terminal for processing.

[0017] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in this application deploys a set of quantum sensing nodes and a set of conventional sensing nodes in a wide-area power grid; collects the phasor set of quantum nodes corresponding to the set of quantum sensing nodes; performs credibility analysis on the phasor set of quantum nodes to obtain a set of credibility indices; filters out identified quantum sensing nodes whose indices exceed a preset threshold based on the magnitude of the indices in the set of credibility indices; constructs a wide-area power grid topology model based on the set of quantum sensing nodes and the set of conventional sensing nodes; performs error propagation calculation on the wide-area power grid topology model using the reference anchor point constraints generated by the identified quantum sensing nodes to obtain a node error dataset; performs synchronous phasor calibration on the node error dataset; and inputs the calibrated phasor monitoring dataset into a monitoring terminal for processing. This achieves the technical effect of realizing synchronous intelligent phasor monitoring of a wide-area power grid based on quantum sensing technology, combined with node credibility analysis and reference anchor point error propagation, thereby improving the monitoring accuracy and data reliability of the wide-area power grid.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application 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 merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A flowchart illustrating the wide-area synchronous phasor intelligent monitoring method for power grids based on quantum sensing provided in this application.

[0021] Figure 2 A schematic diagram of the structure of the quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids provided in this application.

[0022] Figure labeling: Sensor node deployment module 11, sensor node selection module 12, topology model construction module 13, data calibration module 14. Detailed Implementation

[0023] This application provides a method and system for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing. It addresses the technical problems of insufficient accuracy and poor data reliability in wide-area power grid monitoring caused by the limited accuracy of conventional sensing nodes and the lack of effective error correction mechanisms in existing technologies. The system achieves the technical effect of realizing wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing technology, combined with node reliability analysis and reference anchor point error propagation, thereby improving the monitoring accuracy and data reliability of wide-area power grids.

[0024] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0025] Example 1, as Figure 1 As shown, this application provides a quantum sensing-based method for wide-area synchronous phasor intelligent monitoring of power grids. The quantum sensing-based method for wide-area synchronous phasor intelligent monitoring of power grids includes: Deploy quantum sensing node sets and conventional sensing node sets in a wide-area power grid.

[0026] Specifically, based on the geographical zoning of the wide-area power grid, the grid is divided into several regions, such as by region type or geographical characteristics. For each region, its power load characteristics are comprehensively analyzed. For example, industrial areas have large and fluctuating loads, while residential areas have relatively stable loads but with peak-valley differences. Combined with line density, areas with dense lines have a relatively higher probability of faults. At key power facilities in these regions, such as the incoming and outgoing lines of large substations, the busbars of regional hub substations, and the outlets of important power plants, quantum sensors are deployed using high-precision installation techniques. Based on the principles of quantum mechanics, their quantum properties are utilized to achieve precise measurement of parameters such as voltage, current, and phase, forming a quantum sensing node set. For other general power facilities, such as ordinary distribution transformers and shorter transmission lines, conventional sensors, such as electromagnetic voltage transformers and current transformers, are deployed evenly at certain intervals to monitor basic power parameters, forming a conventional sensing node set. This achieves a comprehensive and targeted monitoring layout for the wide-area power grid, ensuring the coverage, accuracy, and stability of the monitoring data.

[0027] Collect the quantum node phasor set corresponding to the quantum sensing node set, perform a credibility analysis on the quantum node phasor set, obtain a credibility index set, and filter out the quantum sensing nodes that are greater than the preset index threshold according to the index size of the credibility index set.

[0028] Furthermore, a credibility analysis is performed on the quantum node phasor set to obtain a credibility index set. The method further includes: temporally aligning the quantum node phasor set to obtain a quantum node temporal phasor set; analyzing the long-term phase stability index, noise characteristic index, drift rate index, and integrity index of the quantum node temporal phasor set; constructing a credibility function according to the long-term phase stability index, noise characteristic index, drift rate index, and integrity index; and using the credibility function to perform credibility analysis on the quantum node phasor set to obtain a credibility index set.

[0029] Specifically, after deploying the quantum sensing node set and the conventional sensing node set in the wide-area power grid, synchronous data acquisition is performed on each quantum sensing node in the quantum sensing node set to obtain a quantum node phasor set. This set includes parameters such as voltage, current, and phase acquired by each quantum sensing node. High-precision time synchronization technology, such as GPS-based time synchronization, is employed to provide a precise time reference for each quantum sensing node. Data acquired by different nodes are then aligned according to a unified time standard to obtain the quantum node time-series phasor set.

[0030] Furthermore, a multi-dimensional index analysis is performed on the quantum node time-series phasor set, including long-term phase stability, noise characteristics, drift rate, and integrity. Long-term phase stability refers to the ability of the power grid phase measured by the quantum sensor to remain stable over a long period. Stable phase data ensures the reliability of power grid monitoring. Long-term phase stability is evaluated by calculating the standard deviation of the quantum sensor node phase data over a period of time. Let the set of quantum node time-series phasors collected by the i-th quantum sensor node within the time window T be {θi(t1), θi(t2)…θi(tN)}. The specific calculation of long-term phase stability is as follows: ; Where, σθ i Let θ be the standard deviation of the phase data of the i-th node within the time window T. i (t k ) represents the state of the i-th quantum sensing node at time t. k The phase measurement value reflects the voltage phase angle of the node at that moment, and N is the total number of times phasor data is acquired within the time window T, i.e., the number of times the node acquires phasor data within that window. σ is the average phase value within the time window. ref The maximum permissible phase fluctuation threshold is set as the reference, and the long-term phase stability index S i The normalized interval [0,1] indicates that a larger value indicates better long-term phase stability, meaning the quantum sensing node is less susceptible to external interference and the measurement results are more reliable. Noise characteristic indicators are used to measure the magnitude of noise influence in quantum node data, including the effects of background noise, electromagnetic interference, etc. Signal processing techniques, such as spectral analysis or Fourier transform, are used to identify and analyze the noise components in the quantum node's time-series phasor set. By comparing the noise ratio with the signal, the magnitude of the noise influence is obtained. A Fourier transform is performed on the phase or voltage / current time-series signals in the quantum node's time-series phasor set: X i (f)=F{θ i In the frequency domain, the effective frequency band energy E of the signal is... s Energy E in the high-frequency noise band n Separate, then the noise characteristic index N i Defined as: N i =1-E n / (E s +E n The calculated noise characteristic indices are also normalized; the lower the proportion of noise energy, the better. iA value closer to 1 indicates a high signal-to-noise ratio (SNR) and excellent data quality in the quantum sensing node's measurement signal. The drift rate index represents the trend of phasor data changes over time. It is obtained by monitoring the changes in the quantum node's measurements over time and calculating its drift rate. A linear fit is then performed on the time-series phasor set of the quantum node using the least squares method, resulting in a linear equation θ. i (t)=a i t+b i , where b i The intercept of the fitted line represents the initial value or reference phase level of the i-th node at time t=0, a. i Let the slope be the fitting slope, representing the rate of phase drift over time. Then, the drift rate index is defined as: D i =1-|a i | / a max , where a max This is the maximum allowable drift rate threshold. The smaller the drift rate, the lower the D... i The larger the value, the better the long-term measurement stability of the quantum sensing node. The completeness index measures the completeness of the data collected by the quantum sensor. It is determined by statistically analyzing the proportion of valid data collected by the quantum node within a specific time period, and its calculation method is: C i =N v / N t , where N v N represents the number of valid data points that were successfully received and verified. t This indicates the theoretical number of data points that should be collected.

[0031] A confidence function is constructed based on long-term phase stability, noise characteristics, drift rate, and integrity. This confidence function is a multi-index weighted fusion model, expressed as: R i =w1S i +w2N i+ w3D i+ w4C i , where R i Let w1, w2, w3, and w4 be the credibility index for the i-th quantum sensing node, and w1, w2, w3, and w4 be the weighting coefficients for phase long-term stability, noise characteristic index, drift rate index, and integrity index, respectively, with a sum of 1. The weighting coefficients can be dynamically set according to the power grid scenario. The credibility function is used to perform credibility analysis on the quantum node phasor set, calculating a credibility index for each quantum sensing node and obtaining a credibility index set. A preset index threshold is set, and the index values ​​of the credibility index set are compared with the preset index threshold. Quantum sensing nodes whose index values ​​are greater than the preset index threshold are identified. For example, in a region with 8 quantum sensing nodes deployed, the calculated index for a certain node is: Si =0.96, N i =0.92, D i =0.84, C i =0.99, w1, w2, w3, and w4 are 0.35, 0.25, 0.25, and 0.15 respectively, and their reliability index is: Ri = 0.35 × 0.96 + 0.25 × 0.92 + 0.25 × 0.94 + 0.15 × 0.99 ≈ 0.95. A preset threshold of 0.85 is set. Since the reliability index of this quantum sensing node is greater than the preset threshold, this quantum sensing node is identified as a high-reliability sensing node.

[0032] By analyzing indicators such as the long-term phase stability, noise characteristics, and drift rate of quantum nodes, the performance of quantum sensing nodes can be comprehensively evaluated. By conducting credibility analysis on the quantum node phasor set, identifying quantum sensing nodes with reliable measurement data and stable performance can be screened, further ensuring the accuracy and reliability of the entire monitoring scheme. This enables precise monitoring of synchronous phasors in a wide-area power grid, timely detection of anomalies in the power grid, and protection of the safe and stable operation of the power grid.

[0033] Furthermore, the method for obtaining the set of credibility indicators also includes: analyzing the current load mode of the wide area power grid, identifying the credibility influence weight according to the current load mode; using the credibility influence weight as a dynamic weight to fit the credibility function, re-analyzing the credibility of the quantum node phasor set, and obtaining the set of credibility indicators corresponding to the current load mode.

[0034] Specifically, real-time operational status data of key nodes within the wide-area power grid is collected using the power grid's energy management system (EMS) and load monitoring devices. This data includes key operating parameters such as active power load level, load change rate, frequency offset amplitude, and voltage fluctuation range. The collected real-time operational status data is then cleaned and standardized. Data cleaning removes outliers and missing values. For example, for active power load level data, if the data at a certain moment significantly exceeds the normal range, it is identified as an outlier and corrected using interpolation. The normal range can be set with a reasonable threshold based on historical data statistics, such as ±3 standard deviations. For missing values, linear interpolation is used to fill in the missing values ​​based on data from previous and subsequent moments. Data standardization unifies data with different dimensions to the same scale, such as using the z-score standardization method. Over a period of time, the average and standard deviation of active load levels (e.g., 3 hours), the average and maximum load change rate, the average and maximum frequency offset, and the maximum voltage fluctuation range are calculated as extracted features. Pattern recognition methods, such as k-means clustering analysis, are used to classify the extracted features. An appropriate number of clusters K is selected, such as 3 clusters corresponding to high-disturbance, stable, and periodically fluctuating load modes. By iteratively optimizing the cluster center and minimizing the intra-cluster squared error, time periods or regions with similar load characteristics are grouped into the same cluster. Based on the characteristic statistics of each cluster, such as the average load fluctuation amplitude, frequency offset, and voltage fluctuation within the cluster, the cluster label is mapped to a specific load mode type: clusters with large fluctuation amplitude and drastic load and frequency changes are labeled as high-disturbance load modes; clusters with stable loads and small fluctuations are labeled as stable load modes; and clusters with periodic fluctuations are labeled as periodically fluctuating load modes. Finally, the load mode corresponding to the cluster to which the current time period or region belongs is taken as the current load mode of the wide-area power grid.

[0035] After identifying the current load mode, the reliability influence weights corresponding to this load mode are further determined. These reliability influence weights refer to the degree of influence of various factors affecting reliability under different load modes, such as long-term phase stability, noise characteristics, drift rate, and integrity on the reliability of the quantum node phasor set. For example, under a high-disturbance load mode, the power grid is frequently subjected to load changes or disturbances. In this case, long-term phase stability and drift rate are more critical for judging the reliability of quantum sensing nodes, thus increasing their corresponding weights. Under a stable load mode, communication link stability and data continuity are more important for long-term monitoring, so the weights of integrity and noise characteristics are appropriately increased. The weight configuration can be determined through statistical analysis of historical operating data or experience, while satisfying weight normalization constraints. After obtaining the reliability influence weights corresponding to the current load mode, these weights are introduced as dynamic weights into the reliability function to fit and adjust the original reliability model. The adjusted reliability function is expressed as: R i (m) =w1(m) S i +w2 (m) N i +w3 (m) D i +w4 (m) C i , among which, Ri (m) w represents the credibility index for the i-th quantum sensing node under load mode m. k (m) These are the dynamic weighting coefficients corresponding to the load mode. Using this fitted confidence function, a new confidence analysis is performed on the quantum node phasor set to obtain a set of confidence indices that highly matches the current load mode, thereby reflecting the true reliability level of the quantum sensing node under actual operating conditions.

[0036] By introducing load pattern sensing and dynamic weight adjustment mechanisms, the credibility assessment no longer relies on static index combinations, but can adaptively adjust the evaluation focus in real time according to the power grid operation status. This significantly improves the credibility index's ability to characterize complex operating conditions, providing a more accurate decision-making basis for identifying quantum sensing nodes that conforms to on-site operating conditions. This enhances the rationality of the selection of benchmark anchor points and the stability of error propagation calculation.

[0037] Furthermore, the method includes: selecting identified quantum sensing nodes whose index size is greater than a preset index threshold according to the index size of the set of credibility indices, wherein the identified quantum sensing nodes are benchmark anchors; wherein the preset index threshold is dynamically configured by identifying the average weighted value of key variables of the current load mode, and the key variables include disturbance sensitivity and signal stability margin.

[0038] Specifically, after obtaining the set of credibility indices corresponding to the quantum sensing nodes, the quantum sensing nodes are sorted according to the value of each credibility index, and quantum sensing nodes with credibility indices greater than the preset index threshold are selected as identified quantum sensing nodes. The identified quantum sensing nodes are used as reference anchors for subsequent error propagation and synchronous phasor calibration. The reference anchor refers to the data source node that is considered to have high measurement accuracy, strong stability and long-term reliability in wide-area power grid monitoring. Its phasor data plays the role of reference coordinates and constraint center in the error propagation process.

[0039] Furthermore, unlike fixed threshold screening methods, the preset index thresholds are not statically set but dynamically configured based on the current power grid operating status. First, the current load mode of the wide-area power grid is identified. By analyzing real-time operating status data, such as load level, power fluctuation amplitude, and frequency offset trend, the current operating status is categorized into light load mode, normal load mode, or high-disturbance load mode. Then, key variables highly correlated with the reliability of quantum sensing nodes are extracted, including disturbance sensitivity and signal stability margin. Disturbance sensitivity refers to the degree of response of the phasor parameter change amplitude to disturbance input when the power grid is affected by external factors such as load abrupt changes or fault disturbances. It can be quantified by the amount of phase or frequency change caused by a unit disturbance. Signal stability margin refers to the ability of the quantum sensing node's output signal to maintain stable measurement accuracy within a certain disturbance range, characterized by the ratio of the stable operating range to the extreme operating range.

[0040] After obtaining the key variable perturbation sensitivity and signal stability margin, they are normalized using z-score or max-min normalization methods. Based on the current load pattern, corresponding weighting coefficients are set, and a dynamic threshold calculation expression is constructed, such as: T = α × S d +β×M s Where T is the preset index threshold, and S d M is the normalized value of the disturbance sensitivity. s Let α and β be the normalized value of the signal stability margin, respectively, and let their weights be 1. Under high-disturbance load conditions, the weight corresponding to disturbance sensitivity is increased to ensure that only quantum sensing nodes that remain stable under severe operating conditions are selected. Under normal load conditions, the weight of signal stability margin is appropriately increased to balance measurement accuracy and coverage quantity. The preset index threshold is dynamically configured through average weighting values, generating a reliability threshold in real time that matches the current load mode. Based on this, the reliability index set is screened to determine the set of quantum sensing nodes to identify, enhancing the adaptability of the screening process to changes in power grid operating conditions. For example, if at a certain moment the wide-area power grid is in a high-load, high-fluctuation operating mode, the normalized value of disturbance sensitivity is 0.82, and the normalized value of signal stability margin is 0.76. With weights set to α=0.6 and β=0.4, the dynamic threshold is calculated as: T=0.6×0.82+0.4×0.76≈0.8, where 0.8 is used as the current preset index threshold.

[0041] By combining the reliability assessment results of quantum sensing nodes with the operating conditions of the power grid, an adaptive selection mechanism for the reference anchor point is achieved. Through dynamic threshold adjustment, unstable nodes are avoided as error propagation references under complex loads or strong disturbances, thereby improving the reliability of the error calibration process from the source. This provides a stable and highly reliable reference benchmark for wide-area synchronous phasor monitoring, further enhancing the effectiveness and accuracy of intelligent wide-area synchronous phasor monitoring of the power grid.

[0042] Furthermore, the method for analyzing the current load mode of the wide area power grid also includes: obtaining a real-time operating status dataset of the wide area power grid; dividing the wide area power grid into multiple sub-power grids according to the real-time operating status dataset; obtaining multiple power grid topology sub-models corresponding to the multiple sub-power grids; performing error propagation calculations based on the multiple power grid topology sub-models respectively; obtaining multiple sets of node error datasets; and fusing the multiple sets of node error datasets to output a node error dataset.

[0043] Specifically, the power grid's energy management system (EMS) and load monitoring devices are used to collect real-time operational status data of key nodes within the wide-area power grid. This data includes key operating parameters such as active load level, load change rate, frequency offset amplitude, and voltage fluctuation range. The collected real-time operational status data is cleaned and standardized to form a real-time operational status dataset for the wide-area power grid. After obtaining the real-time operational status dataset, the wide-area power grid is divided into multiple sub-grids based on factors such as geographical region, voltage level, or load characteristics. These sub-grids are local components of the wide-area power grid, and each sub-grid is tightly coupled in topology and has similar operating characteristics. For example, based on geographical region, the power grid of a large city is divided into a city center sub-grid, a suburban sub-grid, etc.; based on voltage level, it is divided into a high-voltage sub-grid, a medium-voltage sub-grid, and a low-voltage sub-grid, in order to perform localized processing of the power grid.

[0044] For each sub-grid, a corresponding sub-model of the power grid topology is constructed using the power grid's geographic information system and power grid planning database. For example, the geographic location information of each substation and transmission line in the sub-grid is obtained using a GIS system, and combined with line parameters such as resistance and reactance from the power grid planning database, a sub-model of the power grid topology for each sub-grid is constructed. This sub-model reflects the electrical connection relationships between nodes and lines, power flow distribution, and the arrangement of sensor nodes within the corresponding sub-grid area. Based on each sub-model, a smoothing error propagation algorithm is used to calculate the error distribution of each node in the sub-grid. Error propagation calculation analyzes the propagation of errors caused by factors such as measurement errors and model errors between grid nodes during power grid operation. For example, Monte Carlo simulation is used to calculate error propagation by randomly sampling and simulating various possible error scenarios to calculate the degree to which each node is affected by the error. For example, in a certain power grid topology sub-model, the error range of voltage measurement is ±0.5%, and the error range of current measurement is ±1%. By performing 1000 sampling calculations through Monte Carlo simulation, the distribution of voltage and current errors at each node is obtained, and then the node error dataset of the sub-power grid is obtained. After performing the above error propagation calculation on each power grid topology sub-model, multiple sets of node error datasets corresponding to multiple sub-power grids are obtained.

[0045] Multiple node error datasets calculated from each sub-grid are fused using methods such as weighted averaging, covariance fusion, or Bayesian update. By combining the node error data from different sub-grids, a node error dataset is output to reflect the overall node error information of the wide-area power grid. For example, a weighted averaging method can be used for data fusion. Different weights are assigned to the node error datasets of each sub-grid based on factors such as its size and importance. If a sub-grid contains a large number of key nodes and bears a large load, it is assigned a higher weight. For instance, the weights of the five sub-grids are 0.1, 0.2, 0.2, 0.3, and 0.2, respectively. The error data of the corresponding nodes in each sub-grid are then weighted and averaged to obtain the final node error dataset.

[0046] By acquiring real-time operational status datasets and dividing the power grid into subgrids, local error calculations can be performed, allowing for a more precise focus on the local characteristics of the power grid. This improves computational efficiency while maintaining the accuracy of error analysis. Furthermore, by fusing multiple sets of node error data, a comprehensive and reliable node error distribution can be generated, enhancing the accuracy and comprehensiveness of node error data and thus improving the reliability and precision of wide-area power grid monitoring.

[0047] Based on the set of quantum sensing nodes and the set of conventional sensing nodes, a wide-area topology model of the wide-area power grid is constructed.

[0048] Specifically, basic topology information of the wide-area power grid is obtained from the power grid energy management system and dispatch automation system, including power plants, substations, transmission lines and their electrical parameters, such as line impedance, rated capacity, and node type. Based on this, quantum sensing nodes and conventional sensing nodes are mapped to corresponding grid nodes or line locations, forming a sensor node-grid node association. A wide-area power grid topology model is constructed based on the electrical coupling relationships and power flow distribution characteristics between grid nodes. This model reflects the physical connection structure, electrical characteristics, and sensor node distribution relationships of the power grid. Nodes represent electrical nodes or key equipment in the power grid, and edges represent transmission lines or electrical relationships. Information such as sensor node type, measurement accuracy, and reliability can be further embedded in the node attributes. By fusing data from quantum sensing nodes and conventional sensing nodes to construct the wide-area power grid topology model, the actual physical structure and electrical connection relationships of the wide-area power grid can be accurately reflected.

[0049] The error propagation calculation is performed on the wide-area topology model of the power grid using the reference anchor point constraint generated by the identified quantum sensing node to obtain the node error dataset. The node error dataset is then synchronized with the phasor calibration, and the calibrated phasor monitoring dataset is input into the monitoring terminal for processing.

[0050] Furthermore, using the identified quantum sensing node as a reference anchor point constraint, error propagation calculation is performed on the wide-area topology model of the power grid to obtain a node error dataset. The method includes: wherein the reference anchor point constraint includes establishing a constraint that minimizes the sum of errors between the identified quantum sensing node and non-anchor nodes, using the identified quantum sensing node as the reference anchor point; initializing the node phasor error covariance matrix of the remaining sensing nodes; performing error propagation calculation on the wide-area topology model of the power grid according to the error covariance matrix based on the smooth propagation algorithm to obtain the error propagation result; evaluating the node error confidence interval in the error propagation result until all node error confidence intervals are greater than a preset confidence threshold to obtain the node error dataset.

[0051] Specifically, a benchmark anchor constraint is generated using identified quantum sensing nodes. These identified quantum sensing nodes are those whose confidence index set has an index value greater than a preset threshold, and whose phasor measurement errors are limited to a small range. The benchmark anchor constraint is established by using the measured phasor of the identified quantum sensing node as a reference. An objective function is constructed to minimize the error difference between anchor and non-anchor nodes, forming a constraint that minimizes the sum of errors across the entire network. This minimizes the sum of errors between the identified quantum sensing node and conventional sensing nodes, as well as other quantum sensing nodes not used as benchmarks. This ensures the accuracy and consistency of error calculations for each node in the entire power grid topology model, providing a stable reference boundary for error propagation.

[0052] For all sensing nodes except the reference anchor point, an initial node phasor error covariance matrix is ​​established. This matrix is ​​a mathematical matrix describing the correlation between measurement errors of node phasors, such as voltage and current phasors, reflecting the degree of mutual influence between measurement errors of different nodes. Through historical data statistical analysis and theoretical modeling, combined with the performance parameters of the sensing nodes and the power grid topology, an error covariance matrix is ​​initialized for each remaining sensing node. For example, voltage and current phasor data of each node during stable operation are extracted from historical synchronous phasor measurement records, and their measurement residual sequences are calculated. The mean, variance, and covariance of the residuals in the real and imaginary directions are statistically analyzed to obtain the statistical characteristics of the node's own measurement error. The statistical results are then corrected and constrained by the sensing node's performance parameters, such as rated accuracy level, sampling resolution, temperature drift coefficient, and long-term drift index. Furthermore, power grid topology information is introduced. By analyzing the relative positions and electrical coupling strength of nodes in the topology, a theoretical model of the error correlation between adjacent nodes is established. For example, nodes with lower line impedance and stronger power coupling are assigned higher error correlation coefficients. Finally, by integrating historical statistical characteristics, sensor performance parameters, and topological correlations, a corresponding node phasor error covariance matrix is ​​constructed for each remaining sensing node. The diagonal elements of the matrix represent the measurement error variances of the real and imaginary parts of the node's phasor, while the off-diagonal elements reflect the correlation between the real and imaginary parts, as well as the errors with neighboring nodes. For example, for a conventional voltage sensing node, a 2×2 error covariance matrix is ​​initialized based on the standard deviation of its historical measurement data and its correlation with other nodes. The diagonal elements represent the measurement error variances of the node's voltage phasor in the real and imaginary parts, while the off-diagonal elements represent the covariance between the real and imaginary measurement errors.

[0053] A smooth propagation algorithm is employed to perform error propagation calculations on a wide-area power grid topology model based on an initialized error covariance matrix. This algorithm utilizes the graph structure constructed from the power grid topology model, treating power grid nodes as vertices and transmission lines as edges, and using the node error estimates and their covariance as node state variables. During the iteration process, constraints are first imposed on the benchmark anchor point to keep its error estimate within a preset small range, preventing it from changing with each iteration. Subsequently, for non-anchor nodes, error information is received from their neighboring nodes based on topological connections, and the current node error is corrected using a weighted average or least squares update method, combined with line weights and the error covariance matrix. Each iteration, while satisfying the benchmark anchor point constraints, gradually smooths and unifies the error estimates of neighboring nodes in the spatial topology, while progressively reducing the overall network error uncertainty. By continuously repeating this update process, the error propagation process is considered converged when the change in node error between two adjacent iterations falls below a preset convergence threshold, ultimately outputting a stable error propagation result, thus achieving network-wide error consistency calibration based on topology and statistical characteristics.

[0054] After obtaining the error propagation results, the error estimates for each node are statistically evaluated, and the corresponding node error confidence intervals are calculated. These confidence intervals represent the range within which the true value of the node error might exist at a certain confidence level. Using probabilistic statistical methods, based on the distribution characteristics of the error propagation results, the confidence interval for each node error is calculated. For example, using the normal distribution assumption, the confidence interval at a 95% confidence level is calculated based on the mean and standard deviation of the node errors. It is then determined whether all node error confidence intervals are greater than a preset confidence threshold. This preset confidence threshold is determined based on the accuracy requirements of power grid monitoring and the actual application scenario, and is generally between 95% and 99%. If any node error confidence interval is less than the preset confidence threshold, it indicates that the error estimate for that node is not reliable enough. The process involves re-initializing the error covariance matrix or adjusting the parameters of the smoothing propagation algorithm, and re-calculating the error propagation and evaluating the confidence intervals until all node error confidence intervals are greater than the preset confidence threshold. The resulting error propagation result is the final node error dataset.

[0055] After acquiring the node error dataset, it undergoes synchronization phasor calibration. This calibration utilizes a high-precision synchronous clock signal, such as the precise time signal provided by the Global Positioning System (GPS), to synchronize the voltage and current phasors in the node error dataset and correct their phases. This eliminates errors caused by asynchronous measurement times and phase differences between different nodes, ensuring that the phasor data of all nodes have accurate phase relationships under the same time reference, thus obtaining calibrated synchronized phasors. Wide-area synchronized phasors in a power grid refer to the phasor information of electrical quantities of the power grid obtained synchronously from multiple measurement points distributed across different geographical locations within a wide-area power grid, under a unified and precise time reference. The calibrated phasor monitoring dataset is then input into a monitoring terminal for processing. The monitoring terminal possesses powerful data processing and analysis capabilities, enabling real-time monitoring, fault diagnosis, load forecasting, and other analytical processing of the calibrated phasor monitoring data. For example, by monitoring changes in voltage and current phasors in real time, the monitoring terminal can promptly detect abnormal conditions such as short-circuit faults and overloads in the power grid and issue early warning signals. It can also use historical phasor monitoring data for load forecasting, providing decision support for power grid scheduling and operation.

[0056] By setting reference anchor point constraints for the identified quantum sensing nodes, the error propagation process is optimized to ensure that the calculation error can be effectively propagated and updated, thereby obtaining accurate and reliable wide-area power grid synchronous phasor monitoring data. This improves the accuracy and reliability of power grid monitoring, enables efficient real-time monitoring and analysis of the status of the wide-area power grid, and ensures the safe and stable operation of the power grid.

[0057] Furthermore, the power grid wide-area topology model performs error propagation calculations according to the error covariance matrix. The method includes: establishing node error propagation relationships according to the error covariance matrix; establishing error influence relationships between anchor points and node pairs based on the node error propagation relationships; analyzing the influence weights of the error influence relationships based on the benchmark anchor point constraints until the error update value of each non-anchor point node is obtained; repeating the iteration until error propagation converges; and outputting the node error distribution as the error propagation result.

[0058] Furthermore, based on the node error propagation relationship, an error influence relationship between anchor point-node pairs is established. The categories of the error influence relationship include a first type of error influence relationship based on the relationship between anchor points and non-anchor point quantum sensing nodes, and a second type of error influence relationship based on the relationship between anchor points and conventional sensing nodes. When the anchor point-node pair is a first type of error influence relationship, the influence weight is updated recursively based on error. When the anchor point-node pair is a second type of error influence relationship, the adjustment weight of the conventional sensing node based on historical accuracy, historical noise, and historical sampling completeness is calculated, and the influence weight is updated by error weighted fusion according to the adjustment weight.

[0059] Specifically, based on the constructed wide-area power grid topology model, the initialized node phasor error covariance matrix is ​​embedded into the wide-area power grid topology model to form a node error propagation relationship. This node error propagation relationship refers to how, under power grid topology constraints, the phasor error of a certain node affects the error estimation of adjacent nodes through factors such as line impedance and power coupling strength. The node error propagation relationship can be described by a weighted adjacency matrix between nodes, where the weights are jointly determined by the reciprocal of the line impedance, the node error covariance, and the electrical distance. First, the equivalent line impedance between two nodes is obtained from the power grid parameters. The impedance is used to characterize the electrical coupling strength between two nodes. The smaller the impedance, the stronger the error propagation capability. The electrical distance between two nodes is calculated according to the power grid topology. If it is expressed as the weighted shortest path length, the closer the distance, the more significant the error impact. The reciprocal of the reciprocal is calculated. The node phasor error covariance matrix is ​​introduced, and the largest eigenvalue is used as a quantitative index of node error uncertainty to suppress the influence of highly uncertain nodes on the propagation weight. The reciprocal value is obtained. The reciprocals of the above factors are multiplied together and normalized so that the sum of the weights of all adjacent nodes is 1, thus forming a stable node error propagation weight distribution.

[0060] Based on the node error propagation relationship, the error influence relationship between anchor point and node pairs is established. That is, taking the reference anchor point as the error reference source, the propagation path and influence intensity of the anchor point error on the error update of other nodes are analyzed, and the nodes are divided into two categories according to the type of sensing nodes: one is the first type of error influence relationship between anchor point and non-anchor point quantum sensing nodes. The nodes in the first type of error influence relationship have high measurement accuracy and stable error characteristics. Therefore, a recursive update method is adopted in the error propagation process. The influence weight is gradually corrected according to the error residual of adjacent iterations, so that it quickly converges to the anchor point error reference.

[0061] Another type is the second type of error influence relationship between the anchor point and conventional sensing nodes. Since conventional sensing nodes are significantly affected by historical accuracy, historical noise levels, and historical sampling completeness, adjustment weights are determined based on these factors. Historical accuracy indicators for the conventional sensing node, such as the mean or root mean square error of historical measurement errors, are statistically analyzed from historical operational data and normalized. Higher accuracy corresponds to a larger weight component. Historical noise levels are analyzed, and noise intensity is assessed using signal-to-noise ratio or noise power spectral density; lower noise levels result in a higher weight component. Furthermore, historical sampling completeness, i.e., the ratio of effective sampling points to theoretical sampling points within a given time window, is used to measure data missingness; higher completeness indicates greater reliability. Based on this, the three normalized components are weighted and fused according to preset coefficients to construct adjustment weights. These weights are used to correct the error influence weights between the anchor point and conventional sensing nodes, allowing conventional nodes with better historical performance to participate more in error propagation, while effectively weakening the impact of nodes with poorer performance on error updates, thereby improving the overall stability and reliability of the error propagation results. For example, for a typical node with a historical measurement accuracy of ±0.5%, a noise signal-to-noise ratio of 35dB, and a sampling completeness of 95%, a combined adjustment weight of 0.72 can be obtained. Based on this, the anchor-node influence weights are updated using error-weighted fusion to reduce the interference of unreliable data on the error propagation results. Under the condition of satisfying the baseline anchor point constraint, error update iterations are performed on all non-anchor nodes until the change in node error between two adjacent iterations is less than a preset threshold, indicating that the error propagation process has converged. Finally, the error distribution of all nodes in the network is output as the error propagation result.

[0062] By distinguishing different types of sensor nodes and introducing a differentiated error propagation mechanism based on covariance and historical performance, the advantages of high-reliability quantum sensor nodes can be effectively transmitted to the entire network, while suppressing the risk of error amplification of conventional sensor nodes. This achieves overall convergence and consistency calibration of phasor errors in the wide-area power grid, thereby improving the accuracy and reliability of phasor monitoring data.

[0063] Furthermore, a priority identifier is obtained based on the importance of the node data in the anchor-node pair, and priority error propagation calculation is performed on the propagation nodes based on the priority identifier.

[0064] Specifically, for each anchor node pair, a comprehensive evaluation of its node data importance index is conducted. Node data importance refers to the degree of impact of the node's measurement data on the perception and stability analysis of the wide-area power grid operation. Evaluation factors include the node's criticality in the power grid topology, such as whether it is a hub substation or tie-line node, the power capacity it carries, the node's sensitivity to frequency and phase angle stability, and the number of downstream nodes connected to the node. By normalizing and weighting these factors, a node importance score is obtained, quantifying topology criticality. Values ​​are assigned based on the node's structural characteristics in the wide-area power grid topology; for example, hub substations and inter-regional tie-line nodes are assigned the highest topology level, with a value of 1, while other general main grid nodes are assigned 0.7, and ordinary distribution nodes are assigned 0.4. Secondly, the power capacity is quantified by statistically analyzing the node's active power or power flow throughput under operating conditions and mapping it to [0,1] using a maximum-minimum normalization method. Within the range, the larger the power scale, the closer the normalized value is to 1. The sensitivity of frequency and phase angle stability is evaluated. Through small disturbance analysis or historical disturbance event data, the sensitivity coefficient of the frequency offset or phase angle change of the node to the stability indicators of the whole network, such as the maximum phase angle difference or frequency offset, is calculated, and the sensitivity coefficient is normalized. The higher the sensitivity, the larger the normalized value. At the same time, the number of downstream nodes is counted, and the proportion of the number of downstream nodes directly or indirectly affected by the node to the total number of nodes in the whole network is used as the evaluation value, and normalized.

[0065] After normalizing each of the above individual indicators, a weighted fusion is performed according to pre-set weights or weights obtained from historical operational data statistics. For example, a node importance scoring model can be constructed: I = x1 × I 拓扑 +x2×I 功率 +x3×I 灵敏度 +x4×I 下游In this equation, x1, x2, x3, and x4 represent the topological criticality, carrying capacity, sensitivity, and downstream node weight coefficients, respectively, with a sum of 1. Factors related to hubs and stability can be assigned higher weights. The final comprehensive score I is the node importance score, which is used to assign priority labels. For example, nodes with an importance score greater than 0.8 are marked as high-priority nodes, those between 0.5 and 0.8 as medium-priority nodes, and those below 0.5 as low-priority nodes. In error propagation calculation, propagation nodes are scheduled hierarchically according to their priority labels. Error update iterations are performed on high-priority nodes first, allowing their error estimates to converge quickly under the constraint of the baseline anchor point. Subsequently, error propagation updates are performed sequentially on medium- and low-priority nodes, incorporating the latest error information from high-priority nodes during the update process. This forms an error propagation path that diffuses from critical nodes to general nodes. For example, in a wide-area power grid, there is an anchor-node pair involving a 500kV hub substation node. Its quantitative indicators are as follows: This node is an inter-regional interconnection hub substation, with a topological importance value of 1. Under typical load, the node has an active power of 2800MW, while the maximum node power in the grid is 3000MW. Using maximum-minimum normalization, its carrying capacity is 0.933. Historical disturbance analysis shows that the contribution coefficient of this node's frequency change to the maximum phase angle difference of the entire network is 0.25, with a maximum coefficient of 0.3. The normalized sensitivity value is 0.833. This node directly or indirectly... The problem affects 18 nodes, with a total of 25 nodes in the wide area power grid. The normalized downstream node value is 0.72. Based on experience and historical data, the topology criticality, carrying capacity, sensitivity, and downstream node weights are set to 0.35, 0.25, 0.25, and 0.15, respectively. Therefore, the comprehensive importance score of this node is: I = 0.35 × 1 + 0.25 × 0.933 + 0.25 × 0.833 + 0.15 × 0.72 ≈ 0.90. According to the scoring rules, this node is marked as a high-priority node and will be updated iteratively first in the priority error propagation calculation.

[0066] By introducing a priority error propagation strategy driven by node importance, the high-precision calibration results of critical nodes can participate in the network-wide error update process earlier and more stably. This not only accelerates the overall convergence speed of error propagation but also improves the phasor measurement accuracy of critical nodes that have the greatest impact on the safe operation of the power grid, thereby enhancing the accuracy and reliability of the synchronous phasor monitoring results of the wide-area power grid.

[0067] Example 2, based on the same inventive concept as the quantum sensing-based wide-area synchronous phasor intelligent monitoring method for power grids in the foregoing examples, such as... Figure 2 As shown, this application provides a quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids, wherein the quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids includes: The sensor node deployment module 11 is used to deploy a set of quantum sensor nodes and a set of conventional sensor nodes in a wide-area power grid; the sensor node screening module 12 is used to collect the set of quantum node phasors corresponding to the set of quantum sensor nodes, perform credibility analysis on the set of quantum node phasors, obtain a set of credibility indicators, and screen the identified quantum sensor nodes that are greater than a preset index threshold according to the index size of the set of credibility indicators; the topology model construction module 13 is used to construct a wide-area power grid topology model of the wide-area power grid based on the set of quantum sensor nodes and the set of conventional sensor nodes; the data calibration module 14 is used to perform error propagation calculation on the wide-area power grid topology model with the reference anchor point constraints generated by the identified quantum sensor nodes, obtain a node error dataset, perform synchronous phasor calibration on the node error dataset, and input the calibrated phasor monitoring dataset into the monitoring terminal for processing.

[0068] Optionally, the sensor node screening module 12 is further configured to: perform time-series alignment on the quantum node phasor set to obtain a quantum node time-series phasor set; analyze the phase long-term stability index, noise characteristic index, drift rate index, and integrity index of the quantum node time-series phasor set; construct a confidence function according to the phase long-term stability index, noise characteristic index, drift rate index, and integrity index; and use the confidence function to perform confidence analysis on the quantum node phasor set to obtain a confidence index set.

[0069] Optionally, the sensor node screening module 12 is further configured to: analyze the current load mode of the wide area power grid, identify the credibility influence weight according to the current load mode; use the credibility influence weight as a dynamic weight to fit the credibility function, re-analyze the credibility of the quantum node phasor set, and obtain the credibility index set corresponding to the current load mode.

[0070] Optionally, the sensor node screening module 12 is further configured to: screen identified quantum sensor nodes whose index size is greater than a preset index threshold according to the index size of the set of confidence indices, wherein the identified quantum sensor nodes are benchmark anchors; wherein the preset index threshold is dynamically configured by identifying the average weighted value of key variables of the current load mode, and the key variables include disturbance sensitivity and signal stability margin.

[0071] Optionally, the sensor node filtering module 12 is further configured to: obtain the real-time operating status dataset of the wide area power grid, divide the wide area power grid into multiple sub-power grids according to the real-time operating status dataset; obtain multiple power grid topology sub-models corresponding to the multiple sub-power grids, perform error propagation calculations based on the multiple power grid topology sub-models respectively, obtain multiple sets of node error datasets, and fuse the multiple sets of node error datasets to output a node error dataset.

[0072] Optionally, the data calibration module 14 is further configured to: the reference anchor point constraint includes establishing a constraint that minimizes the sum of errors between the identified quantum sensing node and non-anchor point nodes, using the identified quantum sensing node as the reference anchor point; initialize the node phasor error covariance matrix of the remaining sensing nodes; perform error propagation calculations on the power grid wide-area topology model according to the error covariance matrix based on the smooth propagation algorithm to obtain the error propagation results; evaluate the node error confidence intervals in the error propagation results until all node error confidence intervals are greater than a preset confidence threshold to obtain a node error dataset.

[0073] Optionally, the data calibration module 14 is further configured to: establish node error propagation relationships in the power grid wide-area topology model according to the error covariance matrix; establish error influence relationships between anchor points and node pairs based on the node error propagation relationships; analyze the influence weights of the error influence relationships based on the benchmark anchor point constraints, until the error update value of each non-anchor point node is obtained, repeat the iteration until the error propagation converges, and output the node error distribution as the error propagation result.

[0074] Optionally, the data calibration module 14 is further configured to: establish an error influence relationship between anchor point-node pairs based on the node error propagation relationship, wherein the categories of the error influence relationship include a first type of error influence relationship based on the relationship between anchor points and non-anchor point quantum sensing nodes, and a second type of error influence relationship based on the relationship between anchor points and conventional sensing nodes; when the anchor point-node pair is a first type of error influence relationship, perform error recursive updating on the influence weights; when the anchor point-node pair is a second type of error influence relationship, calculate the adjustment weights of the conventional sensing nodes based on historical accuracy, historical noise, and historical sampling completeness, and perform error weighted fusion updating on the influence weights according to the adjustment weights.

[0075] Optionally, the data calibration module 14 is further configured to: obtain a priority identifier based on the importance of the node data of the anchor-node pair, and perform priority error propagation calculation on the propagation node based on the priority identifier.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The quantum sensing-based wide-area synchronous phasor intelligent monitoring method and specific examples in Embodiment 1 are also applicable to the quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids in this embodiment. Through the foregoing detailed description of the quantum sensing-based wide-area synchronous phasor intelligent monitoring method for power grids, those skilled in the art can clearly understand the quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing, characterized in that, The method includes: Deploy quantum sensing node sets and conventional sensing node sets in a wide-area power grid; Collect the quantum node phasor set corresponding to the quantum sensing node set, perform a credibility analysis on the quantum node phasor set, obtain a credibility index set, and filter out the quantum sensing nodes that are greater than the preset index threshold according to the index size of the credibility index set. Based on the quantum sensing node set and the conventional sensing node set, a power grid wide-area topology model of the wide-area power grid is constructed. The error propagation calculation is performed on the wide-area topology model of the power grid using the reference anchor point constraint generated by the identified quantum sensing node to obtain the node error dataset. The node error dataset is then synchronized with the phasor calibration, and the calibrated phasor monitoring dataset is input into the monitoring terminal for processing.

2. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 1, characterized in that, The method further includes performing a credibility analysis on the set of quantum node phasors to obtain a set of credibility indices. Perform time-order alignment on the set of quantum node phasors to obtain the time-ordered set of quantum node phasors; The phase long-term stability index, noise characteristic index, drift rate index, and integrity index of the quantum node temporal phasor set are analyzed. A credibility function is constructed based on the phase long-term stability index, noise characteristic index, drift rate index, and integrity index. The credibility function is then used to perform credibility analysis on the quantum node phasor set to obtain a credibility index set.

3. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 2, characterized in that, Other methods for obtaining a set of credibility metrics include: Analyze the current load pattern of the wide area power grid and identify the credibility influence weight according to the current load pattern; The credibility influence weight is used as a dynamic weight to fit the credibility function, and the credibility analysis of the quantum node phasor set is re-performed to obtain the credibility index set corresponding to the current load mode.

4. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 3, characterized in that, Quantum sensing nodes with identified indicators that are greater than a preset threshold are selected according to the index size of the set of credibility indicators, and the identified quantum sensing nodes serve as benchmark anchors. The preset index threshold is dynamically configured by identifying the average weighted value of key variables in the current load mode, including disturbance sensitivity and signal stability margin.

5. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 3, characterized in that, The method for analyzing the current load pattern of the wide-area power grid further includes: Obtain the real-time operation status dataset of the wide area power grid, and divide the wide area power grid into multiple sub-power grids according to the real-time operation status dataset; Multiple power grid topology sub-models corresponding to the multiple sub-power grids are obtained. Error propagation calculations are performed on the multiple power grid topology sub-models respectively to obtain multiple sets of node error datasets. The multiple sets of node error datasets are then merged to output a node error dataset.

6. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 1, characterized in that, Using the identified quantum sensing nodes to generate reference anchor point constraints, error propagation calculations are performed on the wide-area topology model of the power grid to obtain a node error dataset. include: The reference anchor point constraint includes establishing a constraint that minimizes the sum of errors between the identified quantum sensing node and the non-anchor point node, using the identified quantum sensing node as the reference anchor point. Initialize the nodal phasor error covariance matrix of the remaining sensing nodes; According to the smooth propagation algorithm, the power grid wide-area topology model performs error propagation calculations based on the error covariance matrix to obtain the error propagation results. Evaluate the node error confidence intervals in the error propagation results until all node error confidence intervals are greater than a preset confidence threshold, and obtain the node error dataset.

7. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 6, characterized in that, The power grid wide-area topology model performs error propagation calculations according to the error covariance matrix, and the method includes: The power grid wide-area topology model establishes node error propagation relationships based on the error covariance matrix. Based on the node error propagation relationship, establish the error influence relationship between anchor point and node pair; Based on the baseline anchor point constraint, the influence weight of the error influence relationship is analyzed until the error update value of each non-anchor point node is obtained. The iteration is repeated until the error propagation converges, and the node error distribution is output as the error propagation result.

8. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 7, characterized in that, Based on the node error propagation relationship, an error influence relationship between anchor point and node pairs is established. The categories of the error influence relationship include a first type of error influence relationship based on the relationship between anchor point and non-anchor point quantum sensing nodes, and a second type of error influence relationship based on the relationship between anchor point and conventional sensing nodes. When the anchor point-node pair is a first type of error influence relationship, the influence weight is updated recursively based on error. When the anchor-node pair is a second type of error influence relationship, the adjustment weight of the conventional sensing node is calculated based on historical accuracy, historical noise and historical sampling integrity, and the influence weight is updated by error weighted fusion according to the adjustment weight.

9. The method for wide-area synchronous phasor intelligent monitoring of power grids based on quantum sensing as described in claim 7, characterized in that, Priority identifiers are obtained based on the importance of node data in the anchor-node pair, and priority error propagation calculations are performed on the propagation nodes based on the priority identifiers.

10. A wide-area synchronous phasor intelligent monitoring system for power grids based on quantum sensing, characterized in that, The steps for implementing the quantum sensing-based wide-area synchronous phasor intelligent monitoring method for power grids according to any one of claims 1 to 9, wherein the quantum sensing-based wide-area synchronous phasor intelligent monitoring system for power grids comprises: The sensor node deployment module is used to deploy quantum sensor node sets and conventional sensor node sets in a wide-area power grid; The sensor node screening module is used to collect the quantum node phasor set corresponding to the quantum sensor node set, perform a credibility analysis on the quantum node phasor set, obtain a credibility index set, and screen the identified quantum sensor nodes that are greater than a preset index threshold according to the index size of the credibility index set. The topology model construction module is used to construct the wide-area topology model of the wide-area power grid based on the set of quantum sensing nodes and the set of conventional sensing nodes. The data calibration module is used to perform error propagation calculations on the wide-area topology model of the power grid using the reference anchor point constraints generated by the identified quantum sensing nodes, obtain the node error dataset, perform synchronous phasor calibration on the node error dataset, and input the calibrated phasor monitoring dataset into the monitoring terminal for processing.

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