Power grid frequency balancing intelligent control system based on distributed architecture

Abstract

The application discloses a power grid frequency balance intelligent control system based on a distributed architecture and relates to the technical field of power grid control, and is used for solving the problem that the existing WLS / UKF method generally assumes good synchronization, is not sensitive to heteroscedastic time delay and missing measurement, and is difficult to ensure robustness under real communication and equipment conditions; through a through link, under the conditions of real clock misalignment, time delay jitter and packet loss, measurement unified time axis and quality quantization are realized, frequency estimation bias and uncertain transmission are significantly reduced; the same confidence is used to penetrate reconstruction, filtering and triggering, abnormal measurement suppression and effective information utilization are improved; the fusion efficiency and timeliness of asynchronous arriving data are improved by using graph prior and information domain incremental updating; measurement abnormality and real power imbalance are distinguished by double model causal discrimination, and false event reporting is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid control, and more particularly, to a power grid frequency balance intelligent control system based on a distributed architecture. BACKGROUND

[0002] With the complication of modern power systems and the improvement of interconnection, the stable operation of power grid frequency is facing severe challenges. Frequency is a key indicator of power system operation state, and its balance is directly related to the safety and reliability of the power grid. However, in actual operation, the accurate perception and rapid control of power grid frequency are restricted by various factors.

[0003] The prior art has the following disadvantages:

[0004] Under the distributed power grid frequency balance scenario, the time stamp inconsistency and spatial observation fragmentation caused by the coexistence of clock misalignment, asynchronous time delay, packet loss and topology disturbance of PMU / RTU and other heterogeneous measurements, cause systematic bias in frequency and RoCoF estimation and distortion in the transmission of upper-layer frequency regulation link, and further induce AGC false triggering, over / under regulation and oscillation and other chain risks. The existing WLS / UKF method generally assumes good synchronization, is not sensitive to heteroscedastic time delay and missing data, and is difficult to ensure robustness under real communication and equipment conditions. The core difficulty is: under uncontrollable network and equipment quality fluctuations, realize unified time axis and measurement reliability quantification, structure reconstruction of missing data, develop robust state estimation with asynchronous, graph prior constraints, and distinguish measurement anomalies and real power imbalance, and form a closed loop of data quality to event triggering and control threshold.

[0005] In view of the above problems, the present application provides a solution. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a power grid frequency balance intelligent control system based on a distributed architecture to solve the problems raised in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0008] The power grid frequency balance intelligent control system based on a distributed architecture comprises:

[0009] The acquisition and timing module is configured to perform joint estimation of clock and time delay on edge measurements, generate corrected time stamps, jitter statistics and node identifiers, establish a time reference table, and provide the time reference table for unified time axis mapping and indexing;

[0010] A confidence calculation module configured to calculate a comprehensive confidence Ci according to clock quality, time delay statistics, residual consistency, graph space coherence and packet loss risk, form a weight table containing version number and time decay parameter, and provide the weight table for reconstruction and filtering calling;

[0011] A reconstruction module configured to perform weighted sparse reconstruction on fragmented measurements according to the Ci based on the grid graph model and the frequency domain basis representation, obtain reconstruction data of each node frequency sequence, and generate a reconstruction cache and an index pointer for regional agent retrieval;

[0012] A filtering module configured to perform state estimation on the asynchronous measurements and the reconstruction data in the form of information at the regional agent side, maintain an information matrix and an information vector, and advance [f,ḟ] in small steps, while outputting a residual set for subsequent determination and weight table updating;

[0013] A causal discrimination module configured to recursively determine the residual set and neighborhood consistency in combination with graph coherence and online change point detection, generate event labels and saliency, and store them together with node identification for triggering and master station parameter setting reference.

[0014] In a preferred embodiment, the collection and time correction module establishes a time reference table containing node identification, correction timestamp, link jitter and version number; synthesizes time delay and jitter through one-way and two-way timestamp packet analysis; and provides the time reference table for unified time axis mapping and subsequent module indexing.

[0015] In a preferred embodiment, the confidence calculation module factorizes the comprehensive confidence Ci into clock quality, time delay statistics, residual consistency, graph space coherence and packet loss risk; respectively generates them from the time reference table, round-trip delay statistics, previous residual, neighborhood consistency and hidden Markov estimation, and writes them into the weight table.

[0016] In a preferred embodiment, the weight table contains time decay parameter and effective time; when new measurements or regional side residual write-back arrive, performs overwrite or merge update according to node identification and version number; the updated entries maintain a one-to-one indexing relationship for calling by the reconstruction module and the filtering module.

[0017] In a preferred embodiment, the reconstruction module constructs frequency domain representation based on grid graph Laplacian eigenbasis, forms diagonal weighting according to the Ci and sampling matrix Sk; uses iterative sparse solution and previous time solution as warm start, limits maximum iteration and stop threshold, and outputs reconstruction cache and index pointer according to node and time.

[0018] In a preferred embodiment, the filtering module maintains an information matrix Omega and an information vector xi, which are advanced by a fixed small step frequency; performs an incremental update on the arriving measurement or reconstruction data by scaling with Ri by Ci; only exchanges the incremental difference of the boundary nodes when cross-region cooperation, sets the boundary variable change threshold and the round limit.

[0019] In a preferred embodiment, the causal discrimination module adopts a double model online variable point detection, maintains a running length and a posteriori for a measurement anomaly model and a power imbalance model respectively; recursively outputs an event label and a significance with node identification association storage by taking node residual and graph coherence score as observation.

[0020] In a preferred embodiment, it further includes a trigger arrangement module, which triggers according to the joint indicators of innovation quantity norm, average comprehensive confidence and the significance compared with the congestion level threshold; when triggered, it packages the boundary incremental information quantity, event label and index field, and sets the minimum trigger interval and hysteresis rule.

[0021] In a preferred embodiment, it further includes a control interface module, which performs boundary consistency on regional estimation at the master station side, generates interface data containing frequency, frequency rate of change and covariance; sets control threshold according to the event label and the Ci, and forms prior and threshold parameter set to issue region and edge.

[0022] In a preferred embodiment, the message structure between edge agent and region agent contains node identification, correction timestamp, version number, incremental information quantity, event label and significance, cache pointer and source identification; each field is packaged according to unified encoding and check scheme and transmitted through secure channel.

[0023] The technical effects and advantages of the power grid frequency balance intelligent control system based on distributed architecture of the application are as follows:

[0024] The application realizes measurement unified time axis and quality quantization under the conditions of real clock misalignment, delay jitter and packet loss through the through link, significantly reduces the frequency estimation bias and uncertain transmission; uses the same confidence to penetrate reconstruction, filtering and triggering, improves abnormal measurement suppression and effective information utilization; uses graph prior and information domain incremental update to improve the fusion efficiency and timeliness of asynchronously arriving data; uses double model causal discrimination to distinguish measurement anomaly and real power imbalance, reduces false event reporting; uses innovation quantity and significance based trigger arrangement to preferentially transmit key increments under congestion; the master station sets control threshold and writes back prior and threshold according to event label and confidence, so that estimation, communication and control form a self-consistent closed loop, thereby obtaining more robust, low latency and measurable uncertainty perception and frequency modulation decision support under distributed architecture. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a schematic diagram of a layered distributed architecture of a power grid frequency balancing intelligent control system according to an embodiment of the present application;

[0026] Figure 2 is a non-divisible function flowchart of a power grid frequency balancing intelligent control system according to an embodiment of the present application;

[0027] Figure 3 is a schematic diagram of a module structure of a power grid frequency balancing intelligent control device according to an embodiment of the present application;

[0028] Figure 4 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] The present application relates to the technical field of power grid control, and in particular to a power grid frequency balancing intelligent control system based on a distributed architecture.

[0030] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0031] Embodiment, see Figure 1 The present application provides a power grid frequency balancing intelligent control system based on a distributed architecture. The system adopts a layered distributed structure, aiming to solve the problem of unbiased, robust, and low-delay perception of power grid node frequency and frequency change rate RoCoF, and output the uncertainty to frequency modulation control after quantization.

[0032] The system mainly includes the following three levels:

[0033] 1. Edge agent ; deployed on the side of measurement devices such as PMU / RTU, responsible for preliminary processing of raw measurement data.

[0034] 2. Regional agent ; deployed in substations / region master stations, responsible for data aggregation, processing, and event determination in the region.

[0035] 3. Master station MS; responsible for coordinated control and decision-making in the whole network.

[0036] For example, Figure 2As shown, this system achieves its goals through the following inseparable functional processes, specifically including:

[0037] Step S101: Time synchronization and alignment with the timeline;

[0038] Edge proxy On the other hand, joint clock and latency estimation is performed on the raw measurement data. Specifically, this process generates a corrected timestamp and link jitter metric, maps the measurements of each node to a unified time axis, and registers the time base and node identification metadata for subsequent processing. This step solves the problems of incomparability of timestamps between different nodes caused by inaccuracies in PTP / NTP, Precision Time Protocol / Network Time Protocol, and link jitter, ensuring time consistency in subsequent data processing.

[0039] Furthermore, a recursive model incorporating offset and drift states is used for the joint estimation of clock and delay. Combined with one-way and two-way timestamp message parsing, the combined network and device delay is estimated. and its vibration The corrected timestamps, jitter metrics, and node identifiers are written into the edge agent's time base table for reference in subsequent steps.

[0040] Step S102: Calculate the overall confidence level;

[0041] On the edge agent side, the overall confidence level is calculated based on clock quality, latency statistics, residual consistency, graph spatial coherence, and packet loss risk. .Should The data is written into the edge-side weight table, serving as a weighted input and unified index key for reconstruction, filtering, event triggering, and communication orchestration, thus quantifying and transmitting the uncertainty of measurement data. This step addresses the issue of varying observation quality requiring unified quantification and transmission to subsequent stages.

[0042] Furthermore, for abnormal jumps in local clock skew... A robust cost function is used for suppression to prevent outlier data from interfering with confidence calculation.

[0043] Optionally, after this step is completed, the... Write the weights to the edge-side weight table along with time decay parameters and version numbers; when new measurements arrive or residuals are written back, retrieve the corresponding entries according to the node identifier and version number to overwrite or merge and update them, ensuring that subsequent steps consistently reference the same weight entries.

[0044] Step S103: Weighted graph compressed sensing missing measurement reconstruction;

[0045] On the edge agent side, based on the power grid graph model and frequency domain basis representation, fragmented measurements on a unified time axis are executed according to the aforementioned... Weighted sparse reconstruction, obtaining reconstructed data of frequency sequence of each node. This step solves the problem of spatial fragmentation of measurement data caused by packet loss or late arrival, affecting the integrity of state estimation. The reconstruction process generates a reconstructed cache and an index pointer for the regional side to read.

[0046] Further, this step constructs a frequency domain representation based on the eigenbasis of the graph Laplacian, and adopts the reconstruction strategy according to the Weighted sparse reconstruction strategy. The reconstruction takes the solution of the previous time as the warm start initialization, limits the number of iteration steps and the stop threshold, and generates a reconstructed data cache and an index pointer for the regional agent to retrieve according to the node and time.

[0047] Optionally, the selection of fragmented measurements is based on the sampling matrix Indication, the sampling matrix And the weight matrix according to the Generate diagonal weight; and set the convergence criterion according to the change for the iteration process, for example, the relative error is less than , and the maximum iteration upper limit, for example, 5-10 iterations, when any condition is met, output the reconstructed data of the corresponding node and register the cache timestamp and source identification.

[0048] Step S104: asynchronous graph information filtering and causal variable point detection;

[0049] On the regional agent side, the asynchronously arrived measurement and the reconstructed data are subjected to information form state estimation. This step solves the problem of robust state estimation needed due to the asynchronous arrival and different quality of measurement data.

[0050] Specifically, this step adopts an information filtering method and injects a graph smoothing prior. When the measurement of a node Arrives, it is updated incrementally, where Is the measurement noise covariance matrix, scaled by . The information matrix , dimension , where Is the number of nodes, and the information vector , dimension , is updated, and is advanced by a fixed small step frequency, for example, 20-50ms. Further, when cross-regional cooperation, only the incremental differential components of the information matrix and the information vector of the boundary nodes are exchanged

[0051] Further, when cross-regional cooperation, only the incremental differential components of the information matrix and the information vector of the boundary nodes are exchanged ​, consensus algorithm or alternating direction method of multipliers, ADMM, is used to maintain the boundary consistency. This step sets the boundary variable change threshold and the upper limit of the round, for example, 5-20 iterations, and outputs the regional level estimation when the condition is met and writes back the residual set to the edge agent EA to update the relevant entries.

[0052] In addition, this step combines the graph coherence and change point detection to generate event labels and significance, and writes back the residual statistics to update the edge side confidence related data entries. This step solves the problem of distinguishing between "measurement anomalies" and "real power imbalances" and avoiding false control.

[0053] Specifically, the causal change point determination uses a double model Bayesian online change point detection, which uses the BOCPD framework to separately model measurement anomalies, such as model and real power imbalance model , and maintains the run length and posterior probability. The graph space coherence score and node residual are used as observations to participate in the recursion, and the event label and significance are output and stored together with the node identifier for subsequent steps to read. The prior settings of BOCPD can be determined according to historical data or expert experience, for example, the prior distribution of change point probability can be set as Beta distribution, and its parameters can be adjusted according to the expected change point frequency.

[0054] Step S105: Event triggering and communication arrangement

[0055] On the edge agent and regional agent side, event triggering and communication pruning are performed according to the comprehensive analysis quantity. This step solves the problem of ensuring the priority transmission of critical state information and avoiding excessive reporting when the communication is congested.

[0056] Specifically, the trigger criterion is obtained by comparing the joint index of the innovation norm , the average comprehensive confidence and the significance with the threshold related to the congestion level. Among them, the exponential parameter in the delay quality can be adjusted according to the network jitter characteristics, and the typical value range is 0.1-10, which is used to balance the influence of delay jitter on confidence. When the trigger condition is met, only the boundary incremental information quantity, event label and necessary index field are packaged, and the minimum trigger interval, for example, 50-200ms, is used for throttle control and repetition suppression with the hysteresis rule.

[0057] Step S106: Master station threshold setting and parameter downlink

[0058] At the master station MS side, the estimation result and the event label are received, and interface data such as the frequency and the frequency change rate RoCoF and the confidence interval are generated. This step solves the problem that the uncertainty should be reflected on the control threshold to suppress false triggering and over-regulation.

[0059] Specifically, the master station generates an interface data structure including the system frequency, the frequency change rate RoCoF, and the covariance after boundary consistency processing of the regional estimation. And according to the event label and the The control threshold is set, and the priori and threshold parameter set is issued to the regional agent and the edge agent to update the local configuration.

[0060] Further, the priori and threshold parameter set issued by the master station includes the graph priori intensity, the triggering threshold, the time advancement step, and the weight table retention strategy. The regional agent updates the priori item of the information matrix and the triggering logic after receiving, and the edge agent updates the decay parameter of the weight table and the version number after receiving, and records the effective time and the configuration source.

[0061] Optional implementation:

[0062] In addition, the message structure between the edge agent and the regional agent includes the node identification, the correction timestamp, the version number, the incremental information amount, the event label and the significance, the cache pointer, and the source identification; each field is packaged according to a unified encoding and checking scheme, and is transmitted through a secure channel for index parsing by the step four and the step five.

[0063] In order to accurately describe and analyze the power grid frequency balance intelligent control system, the power grid graph model and the frequency field state model are constructed.

[0064] Power grid graph model:

[0065] The power grid is abstracted as a graph model , where represents the nodes in the power grid, such as generators, loads, and substations, represents the connections between nodes, such as transmission lines. The edge weight can be defined according to electrical characteristics, for example, in proportion to the inverse of the line reactance , or generated according to the inverse of the electrical distance. Based on this graph model, the graph Laplacian matrix can be constructed, where is the degree matrix, is the weight matrix. The graph Laplacian matrix plays a key role in subsequent graph signal processing and state estimation.

[0066] Frequency field state model:

[0067] At discrete time , the frequency field state of the power grid Defined as including the frequencies of all nodes and the rate of change of frequency RoCoF, vector:

[0068] ;

[0069] in This refers to the number of nodes in the power grid. The evolution of the frequency field state follows a linear dynamic system model:

[0070] ;

[0071] in It is the state transition matrix. It is a control input matrix. It is a control input. It is process noise, with a mean of 0 and a covariance of . The Gaussian distribution.

[0072] The measurement model takes into account asynchronous / delay / missing measurements;

[0073] node Local time Generate observations Considering the asynchronicity, time delay, and missing data in actual power grid measurements, the measurement model is expressed as follows:

[0074] ;

[0075] in Represents a node In real time The frequency value, It is the combined latency of the network and devices. It is measurement noise, with a mean of 0 and a variance of . The Gaussian distribution.

[0076] Local clock model:

[0077] Each node Local clock With global real time There is offset and drift between them:

[0078] ;

[0079] in It's clock offset. It's clock drift.

[0080] The missing measurement data is due to the sampling matrix It is indicated that the matrix is a diagonal matrix, and the diagonal elements are 1, indicating that the corresponding node has measurement, and 0, indicating that there is no measurement.

[0081] These mathematical models provide a theoretical basis for subsequent time alignment, confidence calculation, missing measurement reconstruction, state estimation and event determination of the application.

[0082] The application constructs a comprehensive confidence for each observation The index runs through each level of the system, and is used to quantify the quality and reliability of the observation data and guide the subsequent processing process. The comprehensive confidence The calculation formula of the comprehensive confidence is as follows:

[0083] ;

[0084] Wherein:

[0085] Clock quality : The index reflects the accuracy of the local clock. It is based on the edge agent, EA, side to the Kalman estimation variance of the local clock offset And drift , the variance is mapped to Interval. The smaller the variance, the higher the clock quality, Closer to 1.

[0086] Further, the mapping adopts a piecewise linear function to avoid threshold jitter and ensure Smooth change.

[0087] Delay quality : The index reflects the stability and predictability of network transmission delay. It is mapped from round-trip time, RTT, and jitter statistics Into the form of , wherein Represents the delay jitter, the larger the jitter, The smaller, the worse the delay quality. The parameter Can be adjusted according to the network jitter characteristics, and the typical value range is 0.1-10, which is used to balance the influence of delay jitter on confidence.

[0088] Residual consistency : The index reflects the degree of agreement between the current observation and the system state estimation. It is mapped from the Mahalanobis distance of the last filtering residual Into the form of The larger the Mahalanobis distance, the larger the residual, indicating that the deviation between the observation and the estimation is larger, The smaller.

[0089] Figure space coherence : This index reflects the consistency of the current node frequency with the frequencies of the electrical neighborhood. It is obtained by calculating the consistency score of the current node with the frequencies of the electrical neighborhood, and then normalized after temperature scaling. When the node frequency is highly consistent with its neighborhood frequencies, the index is higher, indicating that the observation has better spatial coherence.

[0090]

[0091] Packet loss risk : This index reflects the probability of packet loss of the measurement data at the next moment. It is obtained by online estimation of the two-state hidden Markov chain good or congestion. The higher the packet loss risk, the smaller the comprehensive confidence, thereby reducing the comprehensive confidence.

[0092] Comprehensive confidence It has multiple purposes in the entire system, ensuring consistent transmission of evidence, including:

[0093] Weighted reconstruction: In the missing measurement reconstruction module, it is used for weighted sparse reconstruction of fragmented measurements, so that high-quality measurement data plays a greater role in reconstruction.

[0094] Measurement covariance scaling: In the regional estimation event determination module, it is used to scale the measurement covariance, so that high-quality measurement data has a higher weight in information filtering.

[0095] Event triggering threshold calculation: In the trigger arrangement module, it is one of the core inputs of the event triggering criterion, affecting the dynamic adjustment of the triggering threshold.

[0096] Through this comprehensive confidence system, the invention can comprehensively and dynamically evaluate the quality of each measurement data, and effectively transmit this uncertainty information to each processing link of the system, thereby improving the robustness and accuracy of the entire system.

[0097] Referring to Figure 3 , the embodiment also provides a power grid frequency balance intelligent control system, which can include but is not limited to the following modules:

[0098] Acquisition and time correction module: used for performing clock and time delay joint estimation on edge measurements, generating corrected time stamps and link jitter indicators, and outputting corrected time and clock quality , time delay quality . This module maps each node measurement to a unified time axis, and registers time reference and node identification metadata for subsequent processing reference.

[0099] ​​​​​A confidence calculation module is configured to calculate a comprehensive confidence based on clock quality, latency statistics, residual consistency, graph spatial coherence, and packet loss risk. The module calculates the comprehensive confidence based on clock quality, latency statistics, residual consistency, graph spatial coherence, and packet loss risk. The module writes the edge-side weight table as a weighted input and unified index key for reconstruction, filtering, event triggering, and communication orchestration.

[0100] A reconstruction module is configured to perform sparse reconstruction based on the grid model and frequency domain basis representation, and to obtain reconstructed data of the frequency sequence of each node and generate a reconstruction cache and index pointer for the regional side to read.

[0101] A filtering module is configured to perform asynchronous graph information filtering, perform state estimation on the information form of the asynchronously arrived measurement and reconstructed data, and output the estimated frequency, rate of change of frequency (RoCoF), and residual set.

[0102] A causal discrimination module is configured to analyze the residual output by the filtering module in combination with graph coherence and change point detection to generate event labels and significance.

[0103] A trigger orchestration module is configured to perform event triggering and communication orchestration based on comprehensive analysis quantities such as the innovation norm, average comprehensive confidence, and significance, to achieve intelligent orchestration of communication.

[0104] A control interface module is configured to provide estimated quantities, confidence intervals, and adaptive thresholds to primary / secondary frequency regulation and energy storage scheduling, and to receive threshold write-back from the master station to update the local configuration.

[0105] The above modules can be independent hardware units, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or can be implemented by software / firmware instructions of a processor. In different embodiments, the functions of the modules can be combined or split according to actual needs, for example, in some scenarios, the regional agent (RA) can be deployed to an industrial computing platform in the station, and the edge agent (EA) and the regional agent (RA) can be combined in the same physical device to reduce link delay.

[0106] Referring to Figure 4 The electronic device can be used to implement the functions of the edge agent, regional agent, or master station in the power grid frequency balance intelligent control system. The electronic device includes one or more processors, a memory, a communication interface, and a user interface.

[0107] ​​​​​Processor: is the control center of electronic device, responsible for executing program instructions in memory to realize the functions described in the invention. The processor can be a central processing unit, CPU, graphics processing unit, GPU, digital signal processor, DSP, or any other programmable logic device.

[0108] Memory: used to store program instructions, data and intermediate results generated during program execution. Memory can be random access memory, RAM, read-only memory, ROM, flash memory, FlashMemory, or any other type of storage medium. When the program instructions stored in the memory are executed by the processor, the electronic device performs one or more steps of the embodiment, thereby realizing the function of the power grid frequency balance intelligent control system.

[0109] Communication interface: used for data exchange between electronic device and other devices or network. For example, edge agent connects with PMU / RTU and communicates with regional agent through communication interface; regional agent communicates with edge agent and master station through communication interface. The communication interface can be an Ethernet interface, a wireless communication module such as Wi-Fi, 5G, or any other wired or wireless communication interface.

[0110] User interface: used for user interaction with electronic device, such as displaying running status, configuration parameters or receiving user instructions. The user interface can be a display screen, keyboard, mouse, touch screen or other input / output device.

[0111] The embodiment also provides a computer readable storage medium having program instructions stored thereon. The program instructions realize the flow of the embodiment when running on the electronic device. The computer readable storage medium can be memory or removable storage medium such as U disk, SD card, optical disc, etc. By storing program instructions on computer readable storage medium, the technical solution of the invention can be conveniently deployed and updated.

[0112] In order to ensure the actual operation effect and engineering feasibility of the system described in the invention, some key parameter settings and engineering suggestions are provided in this embodiment:

[0113] Mapping of PTP quality level and The piecewise linear function is used for mapping to avoid sharp fluctuations of at certain thresholds, ensuring its smoothness and stability.

[0114] Prior strength of graph Adaptive adjustment of smoothing strength: the smoothing strength of the smoothing prior is adjusted adaptively according to the size of the rate of change of frequency, RoCoF. When the grid disturbance is large, the strength of the smoothing prior is weakened to better preserve the boundary information and transient characteristics; when the grid operation is stable, the smoothing prior is strengthened to improve the stability of the estimation.

[0115] Hot start of EA side reconstruction with FISTA: the missing data reconstruction on the edge agent, EA, side uses the fast iterative shrinkage thresholding algorithm, FISTA, and uses the solution at the previous time as the hot start initialization. Experience shows that in typical cases, 5-10 iterations can meet the real-time requirements, and the convergence criterion can be set to a relative error of less than , effectively balancing the calculation efficiency and reconstruction accuracy.

[0116] Small step frequency advancement of RA side information filtering: the information filtering on the regional agent, RA, side is advanced in small steps of 20-50 ms, and the measurement data is updated as it comes. This high-frequency update mechanism ensures the system's rapid response capability to changes in the grid state.

[0117] Event-triggered minimum interval: the minimum interval of event-triggering is recommended to be set to 50-200 ms and dynamically adjusted according to the communication congestion level. When the communication congestion is severe, the triggering interval is appropriately lengthened to avoid excessive communication leading to network paralysis; when the communication conditions are good, the triggering interval can be shortened to improve the system response speed.

[0118] Upper limit of ADMM rounds: when the ADMM algorithm is used for cross-regional collaboration, the typical upper limit of iteration times can be set to 5-20 times to balance the convergence speed and communication overhead.

[0119] BOCPD prior: the prior setting of Bayesian online change point detection, BOCPD, has an important influence on the detection performance. For example, the prior distribution of the change point probability can be set to a Beta distribution, and its parameters can be adjusted according to the expected change point frequency, such as one change point per hour, to guide the algorithm to balance between detection sensitivity and false alarm rate.

[0120] In order to evaluate the performance and effect of the system described in the present invention, the present embodiment proposes a series of verifiability indicators:

[0121] Estimation error:

[0122] Mean absolute error of frequency estimation.

[0123] Root mean square error of the rate of change of frequency, RoCoF, estimation.

[0124] Unbiasedness: within the undisturbed window, the expected deviation between the system frequency estimate and the reference frequency is Approaching 0, indicating the accuracy of the system estimate.

[0125] Trigger quality:

[0126] False trigger rate: the proportion of non-events that the system judges as events.

[0127] Missed trigger rate: the proportion of real events that the system fails to detect.

[0128] These indicators can be based on event labels Statistics with artificial labels or real labels generated by simulation.

[0129] Communication load: compare the occupation of communication bandwidth before and after event triggering, evaluate the improvement effect of event triggering and communication arrangement mechanism on communication efficiency.

[0130] Control benefit:

[0131] Over / under adjustment ratio: evaluate the suppression effect of the control system on frequency deviation.

[0132] AGC / energy storage action times and equivalent life consumption: evaluate the economy of control action and the impact on equipment life.

[0133] The present application also includes some optional embodiments to adapt to different application scenarios and needs:

[0134] RA offloading and EA / RA co-location: in some scenarios, the functions of regional agent RA can be offloaded to the industrial computing platform in the station, and even the edge agent EA and the regional agent RA can be co-located in the same physical device to further reduce link delay and improve system response speed.

[0135] Degradation processing of : when some nodes lack neighborhood data, the graph space coherence Can be degraded to local consistency estimation weighted by electrical distance to ensure the effectiveness of the indicator in the case of incomplete data.

[0136] Large-scale system cross-zone consensus optimization: for large-scale systems, cross-zone consensus can use incremental transmission strategy by boundary block, only exchange the information increment of boundary nodes, reduce redundant exchange, and thus reduce communication load.

[0137] The above embodiments constitute a continuous and inseparable link from time calibration, confidence calculation, missing data reconstruction, asynchronous graph information filtering, causal variable point discrimination, event triggering and communication arrangement to master station threshold setting. The comprehensive analysis results are connected through weights, covariances, threshold values and other forms at each link, so that the system can still provide reliable, robust and low-latency state estimation and decision basis for distributed frequency balance control under the constraints of real communication and hardware. Those skilled in the art can make modifications and substitutions to the above embodiments without departing from the spirit of the present application, and all should fall within the protection scope of the present application.

[0138] Those skilled in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application of the technical solution and the constraints of the invention. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0139] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0140] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this. Any skilled person in the art can easily think of changes or substitutions within the technical range disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0141] Finally: the above is only the preferred embodiment of the present application, and is not used to limit the present application. Any modification, equivalent substitution, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A power grid frequency balance intelligent control system based on a distributed architecture, characterized in that, include: The time acquisition module is configured to perform joint estimation of clock and delay for edge measurements, generate correction timestamps, jitter statistics and node identifiers, establish a time reference table, and make the time reference table available for unified time axis mapping and indexing. The confidence calculation module is configured to calculate a comprehensive confidence score Ci based on clock quality, delay statistics, residual consistency, spatial coherence, and packet loss risk, forming a weight table containing version number and time decay parameters, and making the weight table available for reconstruction and filtering. The reconstruction module is configured to perform weighted sparse reconstruction on fragmented measurements based on the power grid diagram model and frequency domain basis representation, according to the comprehensive confidence level Ci, to obtain the reconstruction data of the frequency sequence of each node, and generate reconstruction cache and index pointer for regional agent retrieval. The filtering module is configured to fuse asynchronous measurements and the reconstructed data in the form of information on the regional agent side to perform state estimation, maintain information matrix and information vector, and advance [f,ḟ] at a small step frequency, while outputting a residual set for subsequent judgment and weight table update; The causal discrimination module is configured to combine graph coherence and online change point detection to recursively determine the consistency between the residual set and the neighborhood, generate event labels and saliency, and store them together with node identifiers for triggering and reference in the main station parameter settings.

2. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 1, characterized in that: The time acquisition and synchronization module establishes a time reference table, which includes node identifier, correction timestamp, link jitter and version number; it synthesizes latency and jitter by parsing one-way and two-way timestamp messages, and uses the time reference table for unified time axis mapping and subsequent module indexing.

3. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 1, characterized in that: The confidence calculation module decomposes the comprehensive confidence Ci into clock quality, delay statistics, residual consistency, graph space coherence, and packet loss risk. The data are generated from the time base table, round-trip delay statistics, previous residuals, neighborhood consistency and hidden Markov estimation, respectively, and written into the weight table.

4. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 3, characterized in that... ； The weight table includes time decay parameters and effective time; when a new measurement or regional residual write-back arrives, an overwrite or merge update is performed based on the node identifier and version number; the updated entries maintain a one-to-one index relationship for use by the reconstruction module and the filtering module.

5. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 4, characterized in that: The reconstruction module constructs a frequency domain representation based on the Laplace feature basis of the power grid graph, and forms a diagonal weighting with Ci and the sampling matrix Sk; it adopts iterative sparse solution and starts with deheating at the previous time step, limits the maximum iteration and stopping threshold, and outputs the reconstruction buffer and index pointers by node and time step.

6. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 5, characterized in that, The filtering module maintains the information matrix Ω and the information vector ξ, and advances at a fixed small step frequency; it performs incremental updates on the arriving measurement or reconstructed data by scaling Ri with Ci; in cross-regional collaboration, it only exchanges the incremental difference components of the boundary nodes, and sets the threshold for boundary variable changes and the upper limit of the number of rounds.

7. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 6, characterized in that, The causal discrimination module adopts a dual-model online change point detection, maintaining the run-length and posterior of the measurement anomaly model and the power imbalance model respectively; it uses the node residuals and graph coherence scores as observations for recursion, outputs event labels and saliency, and stores them in association with node identifiers.

8. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 1, characterized in that, It also includes a trigger orchestration module, which triggers the event based on a joint indicator of the innovation norm, average comprehensive confidence, and significance, compared with the congestion level threshold; when triggering, it packages the boundary incremental information, event label, and index field, and sets the minimum trigger interval and hysteresis rules.

9. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 1, characterized in that, It also includes a control interface module, which performs boundary consistency on the region estimation on the main station side, generates interface data including frequency, frequency change rate and covariance; sets control thresholds based on event tags and Ci, and distributes the prior and threshold parameter sets to the region and edge.

10. The intelligent power grid frequency balance control system based on a distributed architecture according to claim 9, characterized in that, The message structure between edge agents and regional agents includes node identifier, correction timestamp, version number, incremental information, event label and salience, cache pointer and source identifier; Each field is encapsulated according to a unified encoding and verification scheme and transmitted through a secure channel.

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