A method and system for monitoring the operating state of a high-voltage distribution network

Abstract

The present application relates to the technical field of data processing, and more particularly to a method and system for monitoring the operating state of a high-voltage power distribution network. The method comprises the steps of: obtaining the voltage fluctuation intensity of each monitoring terminal node at each sampling time; obtaining the abnormal propagation weight of each monitoring terminal node and each adjacent node thereof; based on the abnormal propagation weight and the voltage fluctuation intensity of each monitoring terminal node at each sampling time, obtaining the cooperative voltage fluctuation degree of each monitoring terminal node at each sampling time; based on the cooperative voltage fluctuation degree, obtaining the fault saliency of each monitoring terminal node at each sampling time; based on the fault saliency, obtaining the fault abnormality degree of each monitoring terminal node at each sampling time; and determining whether a fault exists in the high-voltage power distribution network according to the fault abnormality degree. The present application improves the accurate identification capability of real faults in the power grid.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for monitoring the operating status of a high-voltage power distribution network. Background Technology

[0002] High-voltage distribution networks are a crucial link connecting the main transmission network to the user side, responsible for safely and reliably delivering electricity to end users. The safety and stability of the distribution network directly affect industrial production, commercial operations, and residential lives. Faults can lead to widespread power outages, equipment damage, and even safety accidents. To promptly detect and address potential faults in the power grid (such as line short circuits), numerous monitoring terminals are typically deployed at key nodes of the distribution network to collect electrical parameters such as voltage in real time.

[0003] Traditional fault detection methods are mostly based on analyzing the time-series data of a single monitoring terminal node. For example, by setting a fixed voltage or current threshold, an alarm is triggered when the monitored value exceeds the threshold, or some time-series analysis algorithms (such as moving average algorithms) are used to detect abnormal changes in a single data sequence.

[0004] However, fault monitoring methods that rely solely on analyzing the time-series data of a single monitoring terminal node have significant limitations in complex distribution network environments. Distribution networks are highly coupled physical systems, and a fault at one node can propagate rapidly along the line, causing anomalies in multiple adjacent monitoring nodes. Therefore, single-node analysis often fails to reflect the true fault. In addition, distribution networks frequently experience transient voltage fluctuations caused by non-faults, such as normal switching of reactive power compensation devices, instantaneous switching of large remote loads, and system dispatch commands. These fluctuations are extremely short in duration but may trigger threshold or timing anomaly detection, leading to false alarms. Summary of the Invention

[0005] To address the technical problem that anomaly analysis of time-series data from a single monitoring terminal node ignores the propagation between multiple adjacent monitoring nodes and misjudges instantaneous voltage fluctuations caused by non-fault factors as power grid faults, this invention provides a method and system for monitoring the operational status of a high-voltage distribution network.

[0006] In a first aspect, the present invention provides a method for monitoring the operating status of a high-voltage distribution network, employing the following technical solution:

[0007] A method for monitoring the operational status of a high-voltage distribution network, comprising the following steps:

[0008] Obtain the voltage amplitude of each monitoring terminal node at each sampling time and the neighboring nodes of each monitoring terminal node;

[0009] The voltage fluctuation intensity of each monitoring terminal node at each sampling time is obtained; based on the equivalent electrical impedance between the monitoring terminal node and its neighboring nodes, the anomaly propagation weight of each monitoring terminal node and each of its neighboring nodes is obtained; the voltage fluctuation intensity of the monitoring terminal node is combined with the sum of the voltage fluctuation intensity of all its neighboring nodes after anomaly propagation weighting to obtain the cooperative voltage fluctuation degree of each monitoring terminal node at each sampling time.

[0010] Based on the sum of the coordinated voltage fluctuations of the monitoring terminal nodes at continuous sampling times, the fault significance of each monitoring terminal node at each sampling time is obtained; based on the difference between the fault significance and a preset severity threshold, the fault severity factor of each monitoring terminal node at each sampling time is obtained; based on the difference in fault significance at adjacent sampling times, the fault urgency factor of each monitoring terminal node at each sampling time is obtained; based on the fault severity factor and the fault urgency factor, the fault anomaly degree of each monitoring terminal node at each sampling time is obtained; based on the fault anomaly degree, it is determined whether a fault exists in the high-voltage distribution network.

[0011] The innovation of this invention lies in the fact that it no longer analyzes the time series of a single monitoring terminal node in isolation, but analyzes whether the failures of adjacent nodes occur in a coordinated manner, thereby obtaining the fault significance of each monitoring terminal node at each sampling time. This can accurately identify the fault events that truly need attention, greatly reduce the number of invalid alarms, and integrate the severity of fault significance and the trend of fault significance changes to obtain the degree of fault anomaly of each monitoring terminal node at each sampling time, making the fault assessment more accurate.

[0012] Preferably, obtaining the voltage fluctuation intensity of each monitoring terminal node at each sampling time includes:

[0013] The number of sampling times is preset to M. The window formed by the M sampling times before the t-th sampling time of the i-th monitoring terminal node is used as the local window of the i-th monitoring terminal node at the t-th sampling time.

[0014] , This represents the voltage fluctuation intensity of the i-th monitoring terminal node at the t-th sampling time. This represents the voltage amplitude of the i-th monitoring terminal node at the t-th sampling time; This represents the average of all voltage amplitudes in a local window of the i-th monitoring terminal node at the t-th sampling time. This represents the rated voltage value of the power distribution network area.

[0015] Preferably, obtaining the anomaly propagation weight of each monitoring terminal node and each of its neighboring nodes includes:

[0016] ;

[0017] In the formula, The anomaly propagation weight represents the weight of the i-th monitoring terminal node and its j-th neighboring node; represents the equivalent electrical impedance between the i-th monitoring terminal node and its j-th neighboring node; exp() represents an exponential function with the natural constant as the base.

[0018] Preferably, obtaining the degree of coordinated voltage fluctuation of each monitoring terminal node at each sampling time includes:

[0019] ;

[0020] In the formula, This represents the degree of coordinated voltage fluctuation of the i-th monitoring terminal node at time t. This represents the voltage fluctuation intensity of the i-th monitoring terminal node at time t. This represents the number of neighboring nodes of the i-th monitoring terminal node; This represents the voltage fluctuation intensity of the j-th neighboring node of the i-th monitoring terminal node at time t. This represents the anomaly propagation weight between the i-th monitoring terminal node and its j-th neighboring node.

[0021] By combining the voltage fluctuation intensity of the monitored terminal node with the sum of the voltage fluctuation intensity of all its neighboring nodes after anomaly propagation weighting, the degree of coordinated voltage fluctuation is obtained. This method utilizes the spatial propagation characteristics of power grid faults and can effectively distinguish fluctuations that are more likely to be real faults occurring in coordination on the topology.

[0022] Preferably, obtaining the fault significance of each monitoring terminal node at each sampling time includes:

[0023] ;

[0024] In the formula, This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. This represents the number of sampling times in the local window of the i-th monitoring terminal node at the t-th sampling time; This represents the degree of coordinated voltage fluctuation of the i-th monitoring terminal node at the k-th sampling time within a local window at the t-th sampling time. This represents the normalization function.

[0025] By utilizing the temporal persistence characteristics of real faults, it is possible to effectively filter out transient disturbances that may occur together in space but have extremely short durations and pose almost no threat.

[0026] Preferably, obtaining the fault severity factor of each monitoring terminal node at each sampling time includes:

[0027] ;

[0028] In the formula, This represents the fault severity factor of the i-th monitoring terminal node at the t-th sampling time. This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. This represents the preset severity threshold; exp() represents an exponential function with the natural constant as the base.

[0029] Preferably, obtaining the fault urgency factor of each monitoring terminal node at each sampling time includes:

[0030] ;

[0031] In the formula, This represents the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time. This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. represents the fault significance of the i-th monitoring terminal node at the (t-1)-th sampling time; tanh() represents the hyperbolic tangent function; max() represents the maximum value function.

[0032] Preferably, obtaining the fault anomaly degree of each monitoring terminal node at each sampling time includes:

[0033] ;

[0034] In the formula, This represents the degree of fault abnormality of the i-th monitoring terminal node at the t-th sampling time; This represents the fault severity factor of the i-th monitoring terminal node at the t-th sampling time. represents the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time; norm() represents the normalization function.

[0035] The difference between the fault significance and the preset severity threshold was taken into account, as well as whether the fault was rapidly deteriorating, resulting in a more accurate assessment of the fault anomaly.

[0036] Preferably, determining whether a fault exists in the high-voltage distribution network based on the degree of fault abnormality includes:

[0037] A preset fault anomaly level threshold T is set; if the fault anomaly level of any monitoring terminal node at any sampling time is greater than or equal to the fault anomaly level threshold T, the power system issues an early warning to remind staff that there is a real fault in the high-voltage distribution network.

[0038] It improves the accuracy of real fault monitoring in high-voltage distribution networks.

[0039] Secondly, the present invention provides a high-voltage distribution network operation status monitoring system, which adopts the following technical solution:

[0040] A high-voltage distribution network operation status monitoring system includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned high-voltage distribution network operation status monitoring method is implemented.

[0041] By adopting the above technical solution, a computer program is generated from the above-mentioned method for monitoring the operation status of a high-voltage distribution network and stored in a memory so that it can be loaded and executed by a processor. Terminal equipment can then be made based on the memory and processor for convenient use.

[0042] The present invention has the following technical effects: By analyzing whether the fault occurs in conjunction with the fault between adjacent nodes and whether the joint fluctuation lasts for a period of time, the present invention obtains the fault significance of each monitoring terminal node at each sampling time, which can accurately identify the fault events that really need attention, avoid invalid warnings, and integrate the severity of fault significance and the trend of fault significance to obtain the degree of fault anomaly of each monitoring terminal node at each sampling time, making the assessment of faults more accurate. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method for monitoring the operating status of a high-voltage distribution network according to an embodiment of the present invention;

[0044] Figure 2 This is a diagram showing the comparison between fault monitoring results and existing technologies. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0046] This invention discloses a method for monitoring the operational status of a high-voltage distribution network, referring to... Figure 1 This includes steps S1-S4:

[0047] S1: Obtain the voltage amplitude of each monitoring terminal node at each sampling time and the neighboring nodes of each monitoring terminal node.

[0048] In this embodiment of the invention, multiple monitoring terminal nodes are located in a high-voltage distribution network area. The monitoring terminal nodes typically include sensors, data acquisition modules, communication modules, etc. The voltage amplitude of each monitoring terminal node is collected in real time at a sampling time of one second, for a total of two hours; the rated voltage value of the distribution network area is obtained.

[0049] For any monitoring terminal node, the sequence of voltage amplitude values ​​of the monitoring terminal node at all sampling times is denoted as the voltage amplitude sequence of the monitoring terminal node; and through the electrical topology information of the high-voltage distribution network, all monitoring terminal nodes directly connected to the monitoring terminal node via lines are obtained and denoted as the neighboring nodes of the monitoring terminal node.

[0050] For the j-th neighboring node of the i-th monitoring terminal node, the equivalent electrical impedance between the i-th monitoring terminal node and its j-th neighboring node is obtained based on the electrical topology information of the high-voltage distribution network. Similarly, the equivalent electrical impedance between each monitoring terminal node and each of its neighboring nodes is obtained.

[0051] S2: Obtain the voltage fluctuation intensity of each monitoring terminal node at each sampling time; obtain the abnormal propagation weight of each monitoring terminal node and each of its neighboring nodes; based on the abnormal propagation weight and the voltage fluctuation intensity of each monitoring terminal node at each sampling time, obtain the cooperative voltage fluctuation degree of each monitoring terminal node at each sampling time.

[0052] It should be noted that events such as line faults can cause voltage fluctuations. Therefore, this invention obtains the voltage fluctuation intensity of each monitoring terminal node at each sampling time based on the difference between the voltage amplitude of each monitoring terminal node at each sampling time and the recent average voltage amplitude.

[0053] In this embodiment of the invention, the number of sampling times is preset to M=10. In other embodiments, the implementer may preset the value of M according to the specific implementation method, and use the window formed by the M sampling times before the t sampling time of the i-th monitoring terminal node as the local window of the i-th monitoring terminal node at the t sampling time.

[0054] Obtain the voltage fluctuation intensity of each monitoring terminal node at each sampling time:

[0055] ;

[0056] In the formula, This represents the voltage fluctuation intensity of the i-th monitoring terminal node at the t-th sampling time. This represents the voltage amplitude of the i-th monitoring terminal node at the t-th sampling time; This represents the average of all voltage amplitudes in a local window of the i-th monitoring terminal node at the t-th sampling time. Represents the rated voltage value of the distribution network area, used for... The difference is normalized. The larger the value, the more significant the voltage of the i-th monitoring terminal node at the t-th sampling time deviates from the recent voltage amplitude, indicating that there may be an anomaly in the power grid.

[0057] It should be noted that real faults in high-voltage distribution networks, such as line faults and load surges, will cause voltage propagation in the power grid. The voltage amplitude between adjacent monitoring terminal nodes will almost synchronously become abnormal. Therefore, this invention combines the voltage fluctuation intensity of each monitoring terminal node and its neighboring nodes at each sampling time to obtain the degree of coordinated voltage fluctuation of each monitoring terminal node at each sampling time.

[0058] In this embodiment of the invention, the anomaly propagation weight of each monitoring terminal node and each of its neighboring nodes is obtained:

[0059] ;

[0060] In the formula, The anomaly propagation weight represents the weight of the i-th monitoring terminal node and its j-th neighboring node; represents the equivalent electrical impedance between the i-th monitoring terminal node and its j-th neighboring node; exp() represents an exponential function with the natural constant as the base. If the equivalent electrical impedance between the i-th monitoring terminal node and its j-th neighboring node is smaller, the voltage change is almost unimpeded, resulting in synchronous voltage fluctuations between the two monitoring terminal nodes. In this case, the anomaly propagation weight between the i-th monitoring terminal node and its j-th neighboring node is larger. Conversely, if the equivalent electrical impedance between the i-th monitoring terminal node and its j-th neighboring node is larger, the voltage change is blocked. In this case, the anomaly propagation weight between the i-th monitoring terminal node and its j-th neighboring node is smaller.

[0061] In this embodiment of the invention, the degree of coordinated voltage fluctuation of each monitoring terminal node at each sampling time is obtained:

[0062] ;

[0063] In the formula, This represents the degree of coordinated voltage fluctuation of the i-th monitoring terminal node at time t. This represents the voltage fluctuation intensity of the i-th monitoring terminal node at time t. This represents the number of neighboring nodes of the i-th monitoring terminal node; This represents the voltage fluctuation intensity of the j-th neighboring node of the i-th monitoring terminal node at time t. The anomaly propagation weight represents the weight of the i-th monitoring terminal node and its j-th neighboring node;

[0064] The greater the voltage fluctuation intensity of the i-th monitoring terminal node at time t, the greater the weighted voltage fluctuation intensity of all its neighboring nodes at time t. The larger the value, the greater the voltage fluctuation intensity of the i-th monitoring terminal node at time t. At the same time, the smaller the weighted voltage fluctuation intensity of all neighboring nodes of the i-th monitoring terminal node at time t. The value approaches .

[0065] S3: Based on the degree of coordinated voltage fluctuation of the monitoring terminal nodes at continuous sampling times, obtain the fault significance of each monitoring terminal node at each sampling time; based on the fault significance, obtain the fault severity factor and fault urgency factor of each monitoring terminal node at each sampling time; based on the fault severity factor and fault urgency factor, obtain the fault anomaly degree of each monitoring terminal node at each sampling time.

[0066] It should be noted that real faults in high-voltage distribution networks, such as line faults, will cause synchronous voltage amplitude fluctuations between adjacent monitoring terminal nodes, and these fluctuations will last for a period of time, posing a real threat to power supply reliability. However, instantaneous voltage fluctuations caused by non-faults in high-voltage distribution networks (such as normal switching of reactive power compensation devices, instantaneous switching of large remote loads, system dispatch commands, etc.) will also cause synchronous fluctuations between adjacent monitoring terminal nodes, but their duration is extremely short and has almost no lasting impact on the system. Therefore, if the degree of coordinated voltage fluctuation of any monitoring terminal node is greater at continuous intervals, it indicates that an actual fault may have occurred at that monitoring terminal node in the high-voltage distribution network, requiring timely alarm triggering, fault isolation, or further in-depth analysis.

[0067] In this embodiment of the invention, the fault significance of each monitoring terminal node at each sampling time is obtained:

[0068] ;

[0069] In the formula, This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. This represents the number of sampling times in the local window of the i-th monitoring terminal node at the t-th sampling time; This represents the degree of coordinated voltage fluctuation of the i-th monitoring terminal node at the k-th sampling time within a local window at the t-th sampling time. This represents the normalization function. The normalization method used is linear normalization, and the normalization object is the i-th monitoring terminal node at all sampling times. The value; The larger the value, the greater the degree of coordinated voltage fluctuation of the monitoring terminal node at continuous time, indicating that an actual fault may have occurred in the high-voltage distribution network.

[0070] It should be noted that if the fault significance of any monitoring terminal node at any sampling time does not exceed the severity threshold, the voltage fluctuation of that monitoring terminal node at that sampling time is more likely to be normal, thus avoiding misjudging normal fluctuations as faults. If the fault significance of any monitoring terminal node at any sampling time exceeds the severity threshold, it indicates that the voltage of that monitoring terminal node at that sampling time is more likely to be abnormal. Furthermore, if the fault significance increases in a short period of time, it indicates that the fault is accelerating in a short period of time, and the voltage is more likely to be abnormal. Therefore, this invention obtains the degree of fault abnormality of each monitoring terminal node at each sampling time based on the difference between the fault significance and the severity threshold of each monitoring terminal node at each sampling time, as well as the difference in fault significance over a short period of time.

[0071] In this embodiment of the invention, the fault severity factor of each monitoring terminal node at each sampling time is obtained:

[0072] ;

[0073] In the formula, This represents the fault severity factor of the i-th monitoring terminal node at the t-th sampling time. This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. Represents the preset severity threshold; exp() represents an exponential function with the natural constant as the base. The larger the value, the greater the fault severity factor of the i-th monitoring terminal node at the t-th sampling time; in this embodiment of the invention, a preset severity threshold is used. In other embodiments, implementers may preset the severity threshold value according to the specific implementation situation;

[0074] Obtain the fault urgency factor for each monitoring terminal node at each sampling time:

[0075] ;

[0076] In the formula, This represents the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time. This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. represents the fault significance of the i-th monitoring terminal node at the (t-1)-th sampling time; tanh() represents the hyperbolic tangent function; max() represents the maximum value function; The larger the value, the faster the fault is developing in a short period of time. At this time, the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time is greater.

[0077] Obtain the fault anomaly level of each monitoring terminal node at each sampling time:

[0078] ;

[0079] In the formula, This represents the degree of fault abnormality of the i-th monitoring terminal node at the t-th sampling time; This represents the fault severity factor of the i-th monitoring terminal node at the t-th sampling time. represents the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time; norm() represents the normalization function; the larger the fault severity factor and fault urgency factor of the i-th monitoring terminal node at the t-th sampling time, the greater the degree of fault abnormality of the i-th monitoring terminal node at the t-th sampling time.

[0080] S4: Determine whether there is a fault in the high-voltage distribution network based on the degree of fault abnormality of each monitoring terminal node at each sampling time.

[0081] In this embodiment of the invention, the preset fault anomaly threshold T = 0.65. In other embodiments, the implementer can preset the value of T according to the specific implementation situation. If the fault anomaly of any monitoring terminal node at any sampling time is greater than the fault anomaly threshold T, the power system issues an early warning to remind the staff that there is a real fault in the high-voltage distribution network, such as a line fault.

[0082] Figure 2The graph shows a comparison between the fault monitoring results and existing technologies. The black dashed line represents the threshold line for the degree of fault abnormality. During the instantaneous interference period (around 20 seconds), the gray curve of the existing technology instantly exceeded the threshold. Although the red curve of the present invention showed slight fluctuations, it never exceeded the threshold, proving that the present invention can effectively suppress instantaneous interference and avoid false alarms. During the actual fault period (60-80 seconds), the red curve of the present invention responded quickly and had a higher and more stable value. This is due to the amplification effect of the synergistic voltage fluctuation degree, fault severity factor, and fault urgency factor on the fault characteristics, proving that the present invention can accurately identify and continuously alarm when a real fault occurs.

[0083] This invention also discloses a high-voltage distribution network operation status monitoring system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a high-voltage distribution network operation status monitoring method provided by this invention is implemented.

[0084] The system also includes other components well-known to those skilled in the art, such as communication buses and communication interfaces, the setup and functions of which are known in the art and will not be described in detail here. In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0085] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for monitoring the operational status of a high-voltage distribution network, characterized in that, include: Obtain the voltage amplitude of each monitoring terminal node at each sampling time and the neighboring nodes of each monitoring terminal node; The voltage fluctuation intensity of each monitoring terminal node at each sampling time is obtained; based on the equivalent electrical impedance between the monitoring terminal node and its neighboring nodes, the anomaly propagation weight between each monitoring terminal node and each of its neighboring nodes is obtained. The voltage fluctuation intensity of the monitoring terminal node is combined with the sum of the voltage fluctuation intensity of all its neighboring nodes after anomaly propagation weighting to obtain the cooperative voltage fluctuation degree of each monitoring terminal node at each sampling time, including: , This represents the degree of coordinated voltage fluctuation of the i-th monitoring terminal node at time t. This represents the voltage fluctuation intensity of the i-th monitoring terminal node at time t. This represents the number of neighboring nodes of the i-th monitoring terminal node. This represents the voltage fluctuation intensity of the j-th neighboring node of the i-th monitoring terminal node at time t. The anomaly propagation weight represents the weight of the i-th monitoring terminal node and its j-th neighboring node; Based on the sum of the coordinated voltage fluctuations of the monitoring terminal nodes at continuous sampling times, the fault significance of each monitoring terminal node at each sampling time is obtained. Based on the difference between the fault significance and the preset severity threshold, the fault severity factor of each monitoring terminal node at each sampling time is obtained, including: , This represents the fault severity factor of the i-th monitoring terminal node at the t-th sampling time. This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. This represents the preset severity threshold, and exp() represents an exponential function with the natural constant as the base. Based on the difference in fault significance at adjacent sampling times, the fault urgency factor for each monitoring terminal node at each sampling time is obtained, including: , This represents the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time. represents the fault significance of the i-th monitoring terminal node at the (t-1)-th sampling time, tanh() represents the hyperbolic tangent function, and max() represents the maximum value function; Based on the fault severity factor and fault urgency factor, the fault anomaly level of each monitoring terminal node at each sampling time is obtained; based on the fault anomaly level, it is determined whether there is a fault in the high-voltage distribution network.

2. The method for monitoring the operating status of a high-voltage distribution network according to claim 1, characterized in that, The process of acquiring the voltage fluctuation intensity of each monitoring terminal node at each sampling time includes: The number of sampling times is preset to M. The window consisting of the M sampling times before the t-th sampling time of the i-th monitoring terminal node is used as the local window of the i-th monitoring terminal node at the t-th sampling time. , This represents the voltage fluctuation intensity of the i-th monitoring terminal node at the t-th sampling time. This represents the voltage amplitude of the i-th monitoring terminal node at the t-th sampling time; This represents the average of all voltage amplitudes in a local window of the i-th monitoring terminal node at the t-th sampling time. This represents the rated voltage value of the power distribution network area.

3. The method for monitoring the operating status of a high-voltage distribution network according to claim 1, characterized in that, The process of obtaining the anomaly propagation weight of each monitoring terminal node and each of its neighboring nodes includes: ； In the formula, The anomaly propagation weight represents the weight of the i-th monitoring terminal node and its j-th neighboring node; represents the equivalent electrical impedance between the i-th monitoring terminal node and its j-th neighboring node; exp() represents an exponential function with the natural constant as the base.

4. The method for monitoring the operating status of a high-voltage distribution network according to claim 1, characterized in that, The acquisition of the fault significance of each monitoring terminal node at each sampling time includes: ； In the formula, This represents the fault significance of the i-th monitoring terminal node at the t-th sampling time. This represents the number of sampling times in the local window of the i-th monitoring terminal node at the t-th sampling time; This represents the degree of coordinated voltage fluctuation of the i-th monitoring terminal node at the k-th sampling time within a local window at the t-th sampling time. This represents the normalization function.

5. The method for monitoring the operating status of a high-voltage distribution network according to claim 1, characterized in that, The process of obtaining the fault anomaly level of each monitoring terminal node at each sampling time includes: ； In the formula, This represents the degree of fault abnormality of the i-th monitoring terminal node at the t-th sampling time; This represents the fault severity factor of the i-th monitoring terminal node at the t-th sampling time. represents the fault urgency factor of the i-th monitoring terminal node at the t-th sampling time; norm() represents the normalization function.

6. The method for monitoring the operating status of a high-voltage distribution network according to claim 1, characterized in that, The determination of whether a fault exists in the high-voltage distribution network based on the degree of fault abnormality includes: A preset fault anomaly level threshold T is set; if the fault anomaly level of any monitoring terminal node at any sampling time is greater than or equal to the fault anomaly level threshold T, the power system issues an early warning to remind staff that there is a real fault in the high-voltage distribution network.

7. A high-voltage distribution network operation status monitoring system, characterized in that, include: A processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement a method for monitoring the operating status of a high-voltage power distribution network according to any one of claims 1-6.

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