A power grid phase cooperative monitoring method and system based on a distributed communication base station

Abstract

This invention discloses a power grid phase collaborative monitoring method and system based on distributed communication base stations, relating to the field of power equipment detection technology. The method is executed collaboratively by multiple phase monitoring terminals deployed at different communication base stations and a collaborative processing node. The method includes: each phase monitoring terminal collecting real-time phase data of its respective power supply line; the collaborative processing node dynamically generating a virtual phase reference for phase comparison based on the real-time phase data acquired from the multiple phase monitoring terminals; each phase monitoring terminal comparing its own collected real-time phase data with the virtual phase reference to obtain phase difference information; and judging local abnormal states based on the phase difference information. This invention overcomes the dependence on fixed physical reference points in traditional schemes by having the collaborative processing node dynamically generate a virtual phase reference based on data from multiple terminals, improving the fault tolerance of the phase monitoring system and ensuring continuous monitoring functionality.

Description

Technical Field

[0001] This invention relates to the field of power equipment testing technology, and in particular to a power grid phase collaborative monitoring method and system based on distributed communication base stations. Background Technology

[0002] With the rapid development of smart grids and the energy internet, real-time and accurate sensing of grid operation status has become crucial for ensuring power supply security. Phase, as a core characteristic of electrical energy, directly affects grid synchronization, power quality, and fault diagnosis. Utilizing widely distributed communication base station network resources and incorporating phase monitoring capabilities to construct a wide-area, real-time grid status sensing system has become an important technological direction in the industry.

[0003] In existing multi-node-based power grid phase monitoring schemes, one or more fixed physical nodes are typically relied upon as phase reference benchmarks. However, this reliance on fixed reference points has inherent drawbacks: when the reference point becomes inaccurate or its data becomes unavailable due to equipment failure, measurement errors, or local disturbances on the line, the judgment benchmark of the entire monitoring system becomes invalid, leading to widespread false alarms or paralysis of monitoring functions. Therefore, establishing a phase reference system that does not depend on any single point and can adaptively maintain high reliability has become a critical technical problem that urgently needs to be solved. Summary of the Invention

[0004] To address the problem in existing technologies that rely on one or more fixed physical nodes as phase references, which can lead to phase inaccuracies or data unavailability due to equipment failure, measurement errors, or local disturbances on the power line, this invention provides a power grid phase collaborative monitoring method and system based on distributed communication base stations. By dynamically generating a virtual phase reference based on multi-terminal data through collaborative processing nodes, this method effectively overcomes the dependence on fixed physical reference points in traditional solutions, improves the fault tolerance of the phase monitoring system, and ensures continuous monitoring functionality even in the event of individual node failures or local power grid disturbances. The specific technical solution is as follows:

[0005] This invention provides a power grid phase collaborative monitoring method based on distributed communication base stations. The method is executed collaboratively by multiple phase monitoring terminals deployed at different communication base stations and a collaborative processing node. The method includes:

[0006] The real-time phase data of the power supply line to which each of the aforementioned nuclear phase monitoring terminals is located is collected;

[0007] The collaborative processing node dynamically generates a virtual phase reference for phase comparison based on real-time phase data obtained from multiple nuclear phase monitoring terminals.

[0008] Each nuclear phase monitoring terminal compares its own collected real-time phase data with the virtual phase reference to obtain phase difference information; and performs local abnormal state judgment based on the phase difference information.

[0009] Preferably, the step of dynamically generating a virtual phase reference for phase comparison based on real-time phase data acquired from multiple nuclear phase monitoring terminals includes:

[0010] Phase data from multiple phase monitoring terminals are received, and node reliability indices for each terminal are calculated. The node reliability indices are determined based on at least one or a combination of the following factors: clock synchronization accuracy of the terminal equipment, signal acquisition quality score, network communication stability, and the position weight of the terminal in the power grid topology.

[0011] Based on the node reliability index, corresponding weights are assigned to the phase data of each nuclear phase monitoring terminal;

[0012] Based on the assigned weights, the virtual phase reference is dynamically calculated and generated using a preset fusion algorithm; wherein the virtual phase reference is not identical to the phase data of any fixed physical node.

[0013] Preferably, the virtual phase reference is a weighted average value, expressed as:

[0014]

[0015] in, For a moment Virtual phase reference, For the first Phase data reported by each terminal For its corresponding node reliability index, This represents the number of terminals participating in the computation.

[0016] Preferably, the dynamic generation of a virtual phase reference for phase comparison is performed based on a preset fixed period or an event-triggered method;

[0017] Among them, the event triggers include node phase data anomalies exceeding a preset proportion, network topology updates, or the reliability index of any of the nodes exceeding a preset change rate threshold compared to its previous calculation period value or historical sliding window average value.

[0018] Preferably, after the virtual phase reference is generated, it further includes:

[0019] Based on the power grid topology or electrical distance, multiple nuclear phase monitoring terminals are divided into different cooperative subgroups;

[0020] Calculate a virtual phase reference for each cooperative subgroup;

[0021] Among them, each nuclear phase monitoring terminal is preferentially compared with the subgroup virtual phase reference of its corresponding cooperative subgroup.

[0022] Preferably, a power grid phase collaborative monitoring method based on distributed communication base stations further includes:

[0023] When the virtual phase reference fails to be generated or is unavailable, a nuclear phase monitoring terminal performs the following operations:

[0024] Select a backup reference node from a list of pre-stored or dynamically acquired backup reference nodes based on the node reliability index;

[0025] The phase data of the backup reference node is obtained as a temporary comparison benchmark, and the phase difference is calculated and the status is determined.

[0026] Preferably, a power grid phase collaborative monitoring method based on distributed communication base stations further includes:

[0027] When a nuclear phase monitoring terminal detects a local phase difference abnormality, it acquires the phase difference status information of some or all nuclear phase monitoring terminals provided by the collaborative processing node.

[0028] By comparing the abnormal patterns of different terminals and the power grid topology, it is determined whether the abnormality originates from the local line, the upstream common connection point, or the distortion of the virtual phase reference itself, and an early warning message including the source of the abnormality is generated.

[0029] Preferably, the local abnormal state judgment based on phase difference information includes:

[0030] Calculate the derivative of the phase difference with time to obtain the phase drift rate;

[0031] When the phase drift rate continuously exceeds a preset rate threshold, it is determined to be a progressive phase loss warning;

[0032] When the absolute value of the change in phase difference at adjacent sampling times exceeds a preset abrupt change threshold, it is determined as an instantaneous phase jump warning.

[0033] Preferably, after the nuclear phase monitoring terminal acquires real-time phase data and before comparison, the method further includes:

[0034] The acquired real-time phase data is filtered to remove outliers caused by measurement noise or transient interference.

[0035] The filtered real-time phase data is bound to the timestamp provided by the time synchronization source.

[0036] The present invention also provides a power grid phase collaborative monitoring system based on distributed communication base stations, which, using the aforementioned method, includes:

[0037] Multiple phase monitoring terminals deployed at communication base stations are used to collect real-time phase data of the power supply lines where they are located;

[0038] The collaborative reference network module is used to establish communication connections between the multiple nuclear phase monitoring terminals and dynamically generate a virtual phase reference for phase comparison based on the real-time phase data reported by each terminal.

[0039] The nuclear phase monitoring terminal is also used to compare the real-time phase data it collects with the virtual phase reference to obtain phase difference information; and to make anomaly judgments based on the phase difference information.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] This invention discloses a power grid phase collaborative monitoring method based on distributed communication base stations. This method dynamically generates a virtual phase reference based on multi-terminal data by collaborative processing nodes. The virtual phase reference is a fusion product of multi-source reliable data. Even if some terminal data is abnormal, the system can still generate stable reference values ​​through algorithmic filtering and weighting. This effectively overcomes the dependence of traditional schemes on fixed physical reference points, improves the overall fault tolerance of the phase monitoring system, and ensures the continuity and accuracy of the system's monitoring function in the event of individual node failures or local power grid disturbances. Attached Figure Description

[0042] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0043] Figure 1 This is a flowchart of a power grid phase collaborative monitoring method based on a distributed communication base station according to the present invention.

[0044] Figure 2 This is a schematic diagram of the architecture of a power grid phase collaborative monitoring system based on a distributed communication base station according to the present invention.

[0045] Figure 3 This is a schematic diagram of a power grid phase collaborative monitoring system based on a distributed communication base station according to the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] It should be understood that, when used in this specification, the terms “comprising” and “including” indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0048] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” are intended to include the plural forms.

[0049] It should also be further understood that the term "and / or" as used in this specification refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0050] Please refer to the following examples. Figures 1 to 3 .

[0051] Please see Figure 1 and Figure 2 This invention provides a power grid phase collaborative monitoring method based on distributed communication base stations. The method is executed collaboratively by multiple phase monitoring terminals deployed at different communication base stations and a collaborative processing node. The method includes:

[0052] Step S1: Each of the aforementioned phase monitoring terminals collects real-time phase data of its respective power supply line;

[0053] Each phase monitoring terminal is deployed on the power supply line of the communication base station, using high-precision voltage / current sensors to acquire voltage or current signals from the power supply line in real time. A high-speed analog-to-digital converter (ADC) converts the analog signal into a digital signal, and digital signal processing technology is used to calculate the current phase value from the AC signal in real time. The acquisition process is performed at a fixed high sampling rate to ensure that high dynamic changes in phase can be captured.

[0054] The process includes, after the nuclear phase monitoring terminal acquires real-time phase data, the following:

[0055] The acquired real-time phase data is filtered to remove outliers caused by measurement noise or transient interference.

[0056] Before uploading, the continuously sampled raw phase data undergoes digital filtering. Specifically, sliding window mean filtering, median filtering, or wavelet transform-based noise reduction methods can be used. For example, a time window can be set, and the mean or median of the phase data within the window can be calculated to replace the value of the current sampling point, thus smoothing out random noise. For obvious transient interference, such as sudden pulse interference, a change rate threshold can be set for judgment. If the phase change of adjacent sampling points exceeds this threshold, it is considered an anomaly and discarded, replaced by interpolation of the preceding and following normal data. This effectively suppresses unavoidable random noise and accidental interference during the measurement process, making the phase data smoother and more reliable, and avoiding false alarms caused by data anomalies.

[0057] In some embodiments, a two-stage progressive filtering is employed. The first-stage filtering uses a sliding window mid-range filter, with the window width set to an integer multiple of the power frequency period, effectively eliminating transient pulse interference caused by switching operations, lightning strikes, etc. The second-stage filtering uses an adaptive Kalman filter, dynamically adjusting the filtering parameters based on the statistical characteristics of the signal and noise.

[0058] The filtered real-time phase data is bound to the timestamp provided by the time synchronization source.

[0059] Each phase monitoring terminal is equipped with a high-precision timing module, such as through a global satellite navigation system like BeiDou. It receives pulse-per-second (1PPS) and standard time information, or obtains a synchronization clock from the network via a terrestrial precision time protocol. After filtering, each phase data sample is bound one-to-one with a timestamp accurate to microseconds or even nanoseconds provided by the timing module. Simultaneously, each terminal needs to perform clock synchronization calibration periodically. This provides a unified time coordinate for data collected by all distributed terminals, enabling phase data from different terminals to be compared and fused on a strictly synchronized time axis.

[0060] Step S2: The collaborative processing node dynamically generates a virtual phase reference for phase comparison based on real-time phase data obtained from multiple nuclear phase monitoring terminals.

[0061] Specifically, the step of dynamically generating a virtual phase reference for phase comparison based on real-time phase data acquired from multiple nuclear phase monitoring terminals includes:

[0062] Phase data from multiple phase monitoring terminals are received, and node reliability indices for each terminal are calculated. The node reliability indices are determined based on at least one or a combination of the following factors: clock synchronization accuracy of the terminal equipment, signal acquisition quality score, network communication stability, and the position weight of the terminal in the power grid topology.

[0063] The dynamic calculation of node reliability indicators is a comprehensive evaluation process involving multiple dimensions and online computation.

[0064] The clock synchronization accuracy score is calculated based on the deviation between the terminal's reported clock and the standard time; the smaller the deviation, the higher the score. The clock synchronization accuracy score is expressed as follows:

[0065]

[0066] in, To account for deviation from standard time, This is the decay time constant.

[0067] The signal acquisition quality score is calculated based on the raw signal-to-noise ratio (SNR) reported by the terminal or the noise statistics before filtering. A higher SNR results in a higher score. The signal acquisition quality score is expressed as follows:

[0068]

[0069] in, For signal-to-noise ratio, The standard deviation of noise. The amplitude is the fundamental wave.

[0070] Network communication stability score is dynamically evaluated by cooperating nodes based on historical communication success rate, current packet latency, and jitter. Nodes with high consecutive success rates and low latency receive higher scores. The network communication stability score is expressed as follows:

[0071]

[0072] in, The effective communication duration within the statistical period, The total duration within the statistical period. For packet loss rate, For network jitter, This is the jitter tolerance constant.

[0073] The location weights in the power grid topology are pre-configured based on the power grid wiring diagram or obtained through dynamic learning. Terminals located in the backbone network and key substations are typically assigned higher inherent weights. The power grid topology location weights are expressed as follows:

[0074]

[0075] in, This represents the degree of a node in the communication topology. This is the normalized value of the electrical distance to the electrical center of the power grid. To represent the complete set of nodes consisting of all nuclear phase monitoring terminals participating in collaborative monitoring, j Indicates the corresponding node jNuclear phase monitoring terminal.

[0076] The above-mentioned sub-scores are weighted and integrated to generate a comprehensive reliability index, namely the node reliability index. Calculated dynamically using the following formula:

[0077]

[0078] in, Represents a node i At any moment t The overall reliability index To score the normalized clock synchronization accuracy, For normalized signal acquisition quality scoring, For normalized network communication stability scores, The fixed-location weighting coefficients are derived from power grid topology analysis. These are adjustable weighting coefficients, and .

[0079] Based on the node reliability index, corresponding weights are assigned to the phase data of each nuclear phase monitoring terminal;

[0080] Based on the assigned weights, the virtual phase reference is dynamically calculated and generated using a preset fusion algorithm; wherein the virtual phase reference is not identical to the phase data of any fixed physical node.

[0081] The reliability index is directly used for normalization and serves as the weight of each node's data in the fusion calculation.

[0082] The reliability weights are normalized as follows:

[0083]

[0084] The reliability index is directly used for normalization and serves as the weight for each node's data in the fusion calculation. This indicates that the higher the reliability of a node, the greater its phase data's impact on the final benchmark.

[0085] The weighted average virtual benchmark is represented as:

[0086]

[0087] in, For a moment Virtual phase reference, For the first Phase data reported by each terminal For its corresponding node reliability index, This represents the number of terminals participating in the computation.

[0088] because and These settings are updated over time, allowing the weight allocation to respond in real-time to changes in terminal status and network environment. For example, when a terminal's signal quality temporarily deteriorates due to interference, its weight will automatically decrease to prevent it from polluting the overall baseline, thus providing adaptive capabilities to resist local disturbances.

[0089] The weighted average virtual benchmark based on the above calculations Even if one or more terminals completely fail or data becomes abnormal, as long as there are still enough highly reliable terminals online, This allows for accurate calculation. The continuity of the virtual reference does not depend on any single physical node, overcoming the shortcomings of traditional fixed reference point schemes. Simultaneously, through reliability weighting, the algorithm automatically suppresses interference from low-quality, unreliable data and amplifies the contribution of high-quality data. This makes the generated virtual reference more stable and closer to the theoretically true system phase than any single physical measurement, effectively forming a many-core calibration reference.

[0090] In other embodiments, the virtual phase reference can also be generated by an iterative algorithm based on distributed consensus. The distributed consensus iterative algorithm is another implementation of virtual phase reference generation. It achieves consensus among all nodes on a common phase reference value through a limited number of guided information exchanges and state iterations among the nodes in the network, rather than having the central node directly calculate and distribute the value. The specific process is as follows:

[0091] Initialization, each node i Each node uses its preprocessed and timestamped real-time phase data, multiplied by its own reliability metric, as the initial consensus state value for iterative computation. Within one computation cycle, each node... i Neighboring nodes that communicate directly with it j Exchange their current consensus state values, and set the nodes... i In the k The consensus state of the next iteration is .node i After receiving the state values ​​from the neighbors, a weighted fusion is performed to generate the state values ​​for the next round, represented as:

[0092]

[0093] In the formula, i and j This indicates the node number of the nuclear phase monitoring terminal on different communication base stations in the power grid; Represents a node i The set of neighboring nodes; This is the iteration number; At any moment tnode i Assign it to its neighbor nodes j Dynamic fusion weights; Represents a node j In the k The consensus state of the next iteration;

[0094] The elements of the weight matrix are designed as follows:

[0095]

[0096] In the formula, For nodes j At any moment t Node reliability metrics; For nodes i To the node j Normalized communication cost or logical distance;

[0097] Iterate repeatedly in the node network, from arrive Under a connected network and a properly designed weighting scheme, the state values ​​of all nodes... Through information dissemination and weighted fusion, the initial weighted opinions will gradually converge to a single, stable consensus value.

[0098] The iteration stops and the algorithm converges when the preset number of iterations is reached, or when the difference between the state values ​​of all nodes is less than a minimum threshold. After convergence, the virtual baseline is:

[0099]

[0100] in, This represents the number of terminals participating in the computation.

[0101] The above describes a fully decentralized computation process with no single point of failure. Even if a collaborative processing node temporarily fails, the consensus process can still proceed locally as long as the node network remains connected. Unreliable nodes and nodes with communication problems are automatically assigned low weights during the iteration process, effectively diluting the impact of their abnormal data on the final result. Weights It can adapt to changes in node reliability and network status, ensuring that the benchmark generation process always maintains optimal fusion.

[0102] Step S3: Each nuclear phase monitoring terminal compares its own collected real-time phase data with the virtual phase reference to obtain phase difference information; and performs local abnormal state judgment based on the phase difference information.

[0103] Specifically, the local abnormal state judgment based on phase difference information includes:

[0104] The phase drift rate is obtained by calculating the derivative of the phase difference with time; the phase drift rate is calculated as follows:

[0105]

[0106] in, , For the calculation window, This refers to the real-time phase data of the terminal at time t.

[0107] When the phase drift rate continuously exceeds a preset rate threshold, it is determined to be a progressive phase loss warning;

[0108] Gradual loss of synchronization criterion:

[0109]

[0110] In the formula, This is the preset rate threshold.

[0111] When the absolute value of the change in phase difference at adjacent sampling times exceeds a preset abrupt change threshold, it is determined as an instantaneous phase jump warning.

[0112] Instantaneous phase jump detection is as follows:

[0113]

[0114] The instantaneous jump criterion is:

[0115]

[0116] in The sampling period is This is the preset mutation threshold.

[0117] By employing a dual-channel parallel analysis mechanism, two completely different types of phase anomalies were identified:

[0118] The progressive phase drift warning focuses on the trend of phase difference changes rather than instantaneous values. It calculates the rate of change of phase difference over time to obtain the phase drift rate. When the absolute value of the phase drift rate consistently exceeds a preset threshold, an alert is triggered. This mode is specifically designed to detect chronic problems caused by equipment aging, insulation degradation, slow increase in load imbalance, and minor generator governor malfunctions. It enables maintenance personnel to intervene before problems escalate into failures, achieving predictive maintenance.

[0119] Instantaneous phase jump early warning focuses on the magnitude of sudden changes in phase difference within a very short time. It identifies step jumps by calculating the absolute value of the change in phase difference between two adjacent high-precision sampling points. Once this absolute value exceeds a preset threshold for a single change, the system immediately triggers an early warning. This mode is extremely sensitive to sudden events such as switching operations, line faults, protection device activation, and sudden loss of synchronization. It provides near real-time alarms for dispatching and fault handling, ensuring the transient stability of the power grid.

[0120] This invention discloses a power grid phase collaborative monitoring method based on distributed communication base stations. This method dynamically generates a virtual phase reference based on multi-terminal data by collaborative processing nodes. The virtual phase reference is a fusion product of multi-source reliable data. Even if some terminal data is abnormal, the system can still generate stable reference values ​​through algorithmic filtering and weighting. This effectively overcomes the dependence of traditional schemes on fixed physical reference points, improves the overall fault tolerance of the phase monitoring system, and ensures the continuity and accuracy of the system's monitoring function in the event of individual node failures or local power grid disturbances.

[0121] Specifically, in a preferred embodiment of this application, the dynamic generation of a virtual phase reference for phase comparison is performed based on a preset fixed period or an event-triggered method.

[0122] Among them, the event triggers include node phase data anomalies exceeding a preset proportion, network topology updates, or the reliability index of any of the nodes exceeding a preset change rate threshold compared to its previous calculation period value or historical sliding window average value.

[0123] In practice, the collaborative processing nodes automatically initiate the virtual phase reference calculation process at preset fixed time intervals to ensure the reference is refreshed periodically. Outside of the fixed period, specific events are continuously monitored, and a reference recalculation is immediately triggered upon their occurrence. These events include:

[0124] Regional anomaly events: When the system detects that more than a preset proportion of nodes have reported phase data that are marked as abnormal, it determines that the power grid may be experiencing regional disturbances and needs to immediately generate a baseline reflecting the new state.

[0125] When a network topology update event is received from the power grid dispatch system, the baseline calculation needs to adjust the weights or participating nodes according to the new electrical connection relationships.

[0126] The system continuously monitors the reliability metrics of each node in response to sudden changes in node status. If the rate of change of any node's reliability metric relative to its previous period value or recent historical average exceeds a preset threshold, the node's status is considered to have undergone a significant change, and its contribution to the overall benchmark must be immediately reassessed.

[0127] The fixed cycle ensures the basic frequency of benchmark updates and the stability of system computational load; the event triggering mechanism gives the system the ability to respond quickly to sudden power grid conditions, avoiding misjudgments by using outdated benchmarks after critical events occur.

[0128] Specifically, in a preferred embodiment of this application, after the virtual phase reference is generated, it further includes:

[0129] Based on the power grid topology or electrical distance, multiple nuclear phase monitoring terminals are divided into different cooperative subgroups;

[0130] Calculate a virtual phase reference for each cooperative subgroup;

[0131] Among them, each nuclear phase monitoring terminal is preferentially compared with the subgroup virtual phase reference of its corresponding cooperative subgroup.

[0132] In practice, the collaborative processing nodes divide the vast terminal network into several collaborative subgroups based on the physical topology of the power grid or the electrical distance between nodes. Within each subgroup, a dynamic generation algorithm similar to the global benchmark runs independently, using data from the nodes within the subgroup to calculate a virtual phase benchmark for that subgroup.

[0133] Under normal circumstances, each nuclear phase monitoring terminal prioritizes using the virtual phase reference of its subgroup for comparison and anomaly detection. Simultaneously, the system still calculates a global reference for cross-validation of subgroup references.

[0134] Because nodes within the same subgroup are electrically close and similarly affected by the same disturbances, the subgroup benchmark can more sensitively reflect subtle changes in the local power grid, reducing interference from natural phase difference fluctuations caused by long electrical distances. Since most calculations and comparisons are performed within the subgroup, the need for wide-area data exchange is reduced, lowering network bandwidth pressure and the computational burden on central nodes.

[0135] When an anomaly occurs, by observing which subgroup's baseline or node commonly experiences alarms, the fault can be quickly located to specific areas such as substations and feeders.

[0136] Specifically, in a preferred embodiment of this application, a power grid phase collaborative monitoring method based on distributed communication base stations further includes:

[0137] When the virtual phase reference fails to be generated or is unavailable, a nuclear phase monitoring terminal performs the following operations:

[0138] Select a backup reference node from a list of pre-stored or dynamically acquired backup reference nodes based on the node reliability index;

[0139] The phase data of the backup reference node is obtained as a temporary comparison benchmark, and the phase difference is calculated and the status is determined.

[0140] In practice, when the nuclear phase monitoring terminal detects that it cannot receive a valid virtual phase reference, or that the received reference is marked as low confidence, it determines that the main reference generation has failed. The nuclear phase monitoring terminal retrieves a list of backup reference nodes that is pre-stored locally or dynamically obtained from the collaborative processing node. All nodes in this list are recognized as high-reliability nodes. Based on the latest node reliability indicators, the terminal selects the node with the highest reliability from the list as a temporary reference point.

[0141] The nuclear phase monitoring terminal requests phase data from the selected backup reference node as a temporary comparison benchmark to continue phase difference calculation and status judgment until the main reference service is restored.

[0142] In the extreme case of a complete failure of the primary baseline generation system, this mechanism provides a final safeguard, ensuring that monitoring functionality is not interrupted for each terminal. By intelligently selecting highly reliable backup nodes, even in degraded mode, the temporary baseline still possesses a certain degree of reliability, maintaining the high reference value of the monitoring results.

[0143] Specifically, in a preferred embodiment of this application, a power grid phase collaborative monitoring method based on distributed communication base stations further includes:

[0144] When a nuclear phase monitoring terminal detects a local phase difference abnormality, it acquires the phase difference status information of some or all nuclear phase monitoring terminals provided by the collaborative processing node.

[0145] By comparing the abnormal patterns of different terminals and the power grid topology, it is determined whether the abnormality originates from the local line, the upstream common connection point, or the distortion of the virtual phase reference itself, and an early warning message including the source of the abnormality is generated.

[0146] In practice, when a nuclear phase monitoring terminal detects a local phase difference anomaly and generates a preliminary warning, it requests the phase difference status information of other relevant terminals at the current moment through the collaborative processing node.

[0147] After receiving the information, the current phase monitoring terminal checks whether other terminals also exhibit abnormalities and the patterns of these abnormalities. If only the current phase monitoring terminal is abnormal, and its upstream nodes are normal, the problem is identified as a local line issue. If the current phase monitoring terminal and multiple terminals in its downstream area exhibit similar abnormalities, but the upstream nodes exhibit different abnormality patterns or are normal, the problem is identified as an upstream common connection point issue. If a large number of widely distributed terminals with loose electrical connections simultaneously exhibit similar abnormal deviations, and their deviations from the virtual reference are in the same direction, it may indicate that the virtual phase reference itself is inaccurate due to collective distortion of the data source used for calculation.

[0148] In this embodiment, special cases of distortion of the benchmark itself can be identified and special alarms can be issued to avoid the system from continuously outputting misleading conclusions when the benchmark is incorrect.

[0149] Please see Figure 3 This invention also provides a power grid phase collaborative monitoring system based on distributed communication base stations, which applies the aforementioned method and includes:

[0150] Multiple phase monitoring terminals deployed at communication base stations are used to collect real-time phase data of the power supply lines where they are located;

[0151] The collaborative reference network module is used to establish communication connections between the multiple nuclear phase monitoring terminals and dynamically generate a virtual phase reference for phase comparison based on the real-time phase data reported by each terminal.

[0152] The nuclear phase monitoring terminal is also used to compare the real-time phase data it collects with the virtual phase reference to obtain phase difference information; and to make anomaly judgments based on the phase difference information.

[0153] It should be noted that in a power grid phase collaborative monitoring system based on distributed communication base stations, in addition to the phase difference information used by the core phase monitoring terminal to make anomaly judgments, a virtual phase reference can also be uploaded to the central service platform through the collaborative reference network module. Each core phase monitoring terminal will also upload the real-time phase data it collects to the central service platform. The central service platform will then compare the real-time phase data with the virtual phase reference to obtain the phase difference information and make anomaly judgments based on the phase difference information.

[0154] Placing the core comparison and judgment functions on a central service platform / server, versus placing them on edge monitoring terminals, represents two different technical architectures, each with its own advantages and applicable scenarios. The appropriate architecture can be configured and implemented based on specific circumstances.

[0155] The functional explanations of each unit in this embodiment are the same as those of a power grid phase collaborative monitoring method based on a distributed communication base station, and the technical effects are the same, so they will not be repeated here.

[0156] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0157] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0158] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0159] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the specification of the present invention.

Claims

1. A method for grid phase coordination monitoring based on distributed communication base stations, characterized in that, The method is executed collaboratively by multiple nuclear phase monitoring terminals deployed at different communication base stations and a collaborative processing node, and the method includes: The real-time phase data of the power supply line to which each of the aforementioned nuclear phase monitoring terminals is located is collected; The collaborative processing node dynamically generates a virtual phase reference for phase comparison based on real-time phase data obtained from multiple nuclear phase monitoring terminals. Each nuclear phase monitoring terminal compares its own collected real-time phase data with the virtual phase reference to obtain phase difference information; and judges local abnormal states based on the phase difference information.

2. The power grid phase collaborative monitoring method based on distributed communication base stations according to claim 1, characterized in that, The step of dynamically generating a virtual phase reference for phase comparison based on real-time phase data obtained from multiple nuclear phase monitoring terminals includes: Phase data from multiple phase monitoring terminals are received, and node reliability indices for each terminal are calculated. The node reliability indices are determined based on at least one or a combination of the following factors: clock synchronization accuracy of the terminal equipment, signal acquisition quality score, network communication stability, and the position weight of the terminal in the power grid topology. Based on the node reliability index, corresponding weights are assigned to the phase data of each nuclear phase monitoring terminal; Based on the assigned weights, the virtual phase reference is dynamically calculated and generated using a preset fusion algorithm; wherein the virtual phase reference is not identical to the phase data of any fixed physical node.

3. The power grid phase collaborative monitoring method based on distributed communication base stations according to claim 2, characterized in that, The virtual phase reference is a weighted average value, expressed as: in, For a moment Virtual phase reference, For the first Phase data reported by each terminal For its corresponding node reliability index, This represents the number of terminals participating in the computation.

4. The power grid phase collaborative monitoring method based on distributed communication base stations according to claim 2, characterized in that, The dynamic generation of a virtual phase reference for phase comparison is performed based on a preset fixed period or an event-triggered method. Among them, the event triggers include node phase data anomalies exceeding a preset proportion, network topology updates, or the reliability index of any of the nodes exceeding a preset change rate threshold compared to its previous calculation period value or historical sliding window average value.

5. A power grid phase collaborative monitoring method based on distributed communication base stations according to claim 2, characterized in that, After the virtual phase reference is generated, it also includes: Based on the power grid topology or electrical distance, multiple nuclear phase monitoring terminals are divided into different cooperative subgroups; Calculate a virtual phase reference for each cooperative subgroup; Among them, each nuclear phase monitoring terminal is preferentially compared with the subgroup virtual phase reference of its corresponding cooperative subgroup.

6. The power grid phase collaborative monitoring method based on distributed communication base stations according to claim 2, characterized in that, Also includes: When the virtual phase reference fails to be generated or is unavailable, a nuclear phase monitoring terminal performs the following operations: Select a backup reference node from a list of pre-stored or dynamically acquired backup reference nodes based on the node reliability index; The phase data of the backup reference node is obtained as a temporary comparison benchmark, and the phase difference is calculated and the status is determined.

7. The power grid phase collaborative monitoring method based on distributed communication base stations according to claim 1, characterized in that, Also includes: When a nuclear phase monitoring terminal detects a local phase difference abnormality, it acquires the phase difference status information of some or all nuclear phase monitoring terminals provided by the collaborative processing node. By comparing the abnormal patterns of different terminals and the power grid topology, it is determined whether the abnormality originates from the local line, the upstream common connection point, or the distortion of the virtual phase reference itself, and an early warning message including the source of the abnormality is generated.

8. The power grid phase collaborative monitoring method based on distributed communication base stations according to claim 1, characterized in that, Judging local abnormal states based on the phase difference information includes: Calculate the derivative of the phase difference with time to obtain the phase drift rate; When the phase drift rate continuously exceeds a preset rate threshold, it is determined to be a progressive phase loss warning; When the absolute value of the change in phase difference at adjacent sampling times exceeds a preset abrupt change threshold, it is determined as an instantaneous phase jump warning.

9. A power grid phase collaborative monitoring method based on distributed communication base stations according to claim 1, characterized in that, After the nuclear phase monitoring terminal acquires real-time phase data and before comparison, the method further includes: The acquired real-time phase data is filtered to remove outliers caused by measurement noise or transient interference. The filtered real-time phase data is bound to the timestamp provided by the time synchronization source.

10. A power grid phase collaborative monitoring system based on distributed communication base stations, characterized in that, The method described by any one of claims 1 to 9 includes: Multiple phase monitoring terminals deployed at communication base stations are used to collect real-time phase data of the power supply lines where they are located; The collaborative reference network module is used to establish communication connections between the multiple nuclear phase monitoring terminals and dynamically generate a virtual phase reference for phase comparison based on the real-time phase data reported by each terminal. The nuclear phase monitoring terminal is also used to compare the real-time phase data it collects with the virtual phase reference to obtain phase difference information; and to make anomaly judgments based on the phase difference information.

Images