Power facility monitoring and fault early warning method and system based on beidou positioning technology

Abstract

The application provides a power facility monitoring and fault early warning method and system based on Beidou positioning technology, relates to the technical field of data processing, and comprises the following steps: acquiring position data, acquiring operation data, acquiring a to-be-monitored power facility, acquiring a monitoring weight and a calculation time period, and acquiring a plurality of calculation time points; acquiring first sub-temperature data and first sub-power data, acquiring a first temperature index, acquiring a first power index, and acquiring a fault index; when the fault index exceeds a first preset threshold, acquiring a spread power facility, acquiring second sub-temperature data and second sub-power data, acquiring a second temperature index, acquiring a second power index; acquiring an early warning direction according to the second temperature index, the second power index, the first temperature index and the first power index, and acquiring an early warning strategy according to the early warning direction and the position data. The application has the advantages of high efficiency, accuracy and comprehensiveness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric signal processing, and particularly relates to a power facility monitoring and fault early warning method and system based on Beidou positioning technology. BACKGROUND

[0002] With the large-scale and complex of power system, the stable operation and fault early warning of power facilities become the core challenge of power grid safety management. In the prior art, the monitoring of power facilities mainly relies on traditional GPS positioning technology and single operating parameter acquisition means, such as isolated data acquisition through temperature sensors or power monitoring devices. However, such methods have significant defects: first, GPS positioning is easily interfered in complex terrain or densely populated urban areas, resulting in insufficient accuracy of power facility location data, and it is difficult to achieve high-precision topological mapping; second, independent analysis of temperature, power and other parameters lacks multi-dimensional data fusion capability, and cannot accurately represent the dynamic correlation of device operating state, which easily causes fault misjudgment or omission; in addition, the existing early warning system usually focuses on the abnormal detection of single facility, and ignores the chain effect of fault diffusion in power network, which leads to the inability to predict the fault propagation path and delays the rapid response to regional faults.

[0003] In recent years, although some researches have tried to introduce Internet of Things nodes to build a distributed monitoring network, there are still limitations in data processing. For example, the static threshold determination method with fixed time window cannot adapt to the dynamic fluctuation characteristics of power load, and does not combine the spatial topological relationship between facilities for fault tracing analysis. In addition, the traditional method often ignores the coupling effects such as heat conduction and power fluctuation transmission between adjacent facilities when calculating the fault risk, which leads to the ambiguity of early warning direction and makes it difficult to guide precise operation and maintenance decisions. SUMMARY

[0004] In view of the defects in the prior art, the present application provides a power facility monitoring and fault early warning method and system based on Beidou positioning technology.

[0005] The application discloses a power facility monitoring and fault early warning method based on a Beidou positioning technology, and relates to the technical field of power facility monitoring.

[0006] Optionally, the method further comprises: obtaining a first difference value of each diffusion power facility according to the second temperature index of each diffusion power facility and the first temperature index of the to-be-monitored power facility; obtaining a second difference value of each diffusion power facility according to the second power index of each diffusion power facility and the first power index of the to-be-monitored power facility; and taking the diffusion power facility with the first difference value and the second difference value both less than a second preset threshold as a target power facility, and taking an extension branch of the target power facility and the to-be-monitored power facility as the early warning direction.

[0007] Optionally, the obtaining the early warning strategy according to the early warning direction and the position data comprises: constructing a position topology graph according to position data of the power network, and extracting, in the position topology graph, a plurality of power facilities in the early warning direction as core influence facilities in turn, and obtaining an early warning coverage radius according to a maximum value of distances between the to-be-monitored power facility and each core influence facility; generating a three-dimensional early warning vector containing the early warning direction, the early warning coverage radius and a core influence facility list; and encapsulating the early warning vector into a Beidou RDSS short message format, and broadcasting the early warning vector to the power network operation and maintenance terminal through a Beidou satellite channel.

[0008] Optionally, the obtaining the monitoring weight of the to-be-monitored power facility and obtaining the calculation time period according to the monitoring weight of the to-be-monitored power facility comprises: obtaining, according to the power network, a number of power facilities adjacent to the to-be-monitored power facility as the monitoring weight; and obtaining a basic time period, and obtaining the calculation time period according to the basic time period and the monitoring weight.

[0009] Optionally, the first index model in the first temperature index is represented based on the first index model and the first sub-temperature data corresponding to each calculation time point as: wherein, E T1 is the first temperature index, T i is the first sub-temperature data corresponding to the i th calculation time point, T i+1 is the first sub-temperature data corresponding to the i+1 th calculation time point, and m is the number of calculation time points in the calculation time period, and a is the device safety temperature critical value.

[0010] Optionally, the second index model in the first power index is represented based on the second index model and the first sub-power data corresponding to each calculation time point as: wherein, E P1 is the first power index, P i is the first sub-power data corresponding to the i th calculation time point, P i+1 is the first sub-power data corresponding to the i+1 th calculation time point, and m is the number of calculation time points in the calculation time period, and β is the device safety power critical value.

[0011] Optionally, the fault model in the fault index is represented based on the fault model, the first temperature index and the first power index as: wherein, I f is the fault index, E P1 is the first power index, and E T1 is the first temperature index.

[0012] Optionally, the system comprises: an acquisition module, configured to acquire a power network composed of a plurality of power facilities, a Beidou positioning node arranged on the power facility, and an operation state monitoring terminal arranged on the power facility, acquire position data of the power network according to the plurality of Beidou positioning nodes, acquire operation data of each power facility according to the plurality of operation state monitoring terminals, acquire any one power facility as a to-be-monitored power facility, acquire a monitoring weight of the to-be-monitored power facility, acquire a calculation time period according to the monitoring weight of the to-be-monitored power facility, and acquire a plurality of calculation time points in the calculation time period; a first data processing module, configured to acquire first sub-temperature data and first sub-power data of the to-be-monitored power facility at each calculation time point in the calculation time period according to the operation data, acquire a first temperature index based on a first index model and the first sub-temperature data corresponding to each calculation time point, acquire a first power index based on a second index model and the first sub-power data corresponding to each calculation time point, and acquire a fault index based on a fault model, the first temperature index, and the first power index; a second data processing module, configured to acquire a plurality of diffusion power facilities adjacent to the to-be-monitored power facility as a plurality of diffusion power facilities when the fault index exceeds a first preset threshold, acquire second sub-temperature data and second sub-power data of the diffusion power facilities at each calculation time point in the calculation time period according to the operation data, acquire a second temperature index based on the first index model and the second sub-temperature data corresponding to each calculation time point, and acquire a second power index based on the second index model and the second sub-power data corresponding to each calculation time point; and an early warning module, configured to acquire a warning direction according to the second temperature index corresponding to each diffusion power facility, the second power index corresponding to each diffusion power facility, the first temperature index of the to-be-monitored power facility, and the first power index of the to-be-monitored power facility, and acquire a warning strategy according to the warning direction and the position data.

[0013] Optionally, the early warning module is further configured to: acquire a first difference value corresponding to each diffusion power facility according to the second temperature index corresponding to each diffusion power facility and the first temperature index of the to-be-monitored power facility; acquire a second difference value corresponding to each diffusion power facility according to the second power index corresponding to each diffusion power facility and the first power index of the to-be-monitored power facility; acquire a target power facility when the first difference value and the second difference value of the diffusion power facility are both less than a second preset threshold, and acquire an extended branch of the target power facility and the to-be-monitored power facility as the warning direction.

[0014] Optionally, the early warning module is further configured to: construct a location topology graph according to the location data of the power network, and sequentially extract a plurality of power facilities in the early warning direction as core influence facilities in the location topology graph, and obtain an early warning coverage radius according to a maximum value of distances between the power facility to be monitored and each core influence facility; generate a three-dimensional early warning vector containing the early warning direction, the early warning coverage radius and a core influence facility list; and encapsulate the early warning vector into a Beidou RDSS short message format and broadcast to the power network operation and maintenance terminal through a Beidou satellite channel.

[0015] The beneficial effects of the present application are embodied in:

[0016] In the entire power facility monitoring and fault early warning method based on the Beidou positioning technology, the high precision and strong anti-interference capability of the Beidou positioning technology effectively solve the problem of unstable signal of traditional GPS in complex terrain and densely populated urban areas, realize high-precision topology mapping of the power network, and provide reliable spatial data basis for fault positioning and diffusion analysis; secondly, the running state monitoring terminal collects multi-dimensional running parameters such as temperature and power in real time, and calculates combined with the first index model and the second index model, which can fully represent the running state of the power facility and avoid misjudgment or omission caused by single parameter analysis. For example, the comprehensive analysis of temperature index and power index can more accurately identify the overheating or overload trend of the facility, so as to early warn potential fault risk; in addition, the present scheme fuses and analyzes the multi-dimensional data through the fault model, dynamically calculates the fault index, discards the limitations of the traditional static threshold determination method, can adapt to the dynamic fluctuation characteristics of power load, significantly improves the accuracy of fault early warning, and at the same time, in the aspect of fault diffusion analysis, the second temperature index and the second power index of the adjacent diffusion power facility are identified, combined with the first temperature index and the first power index of the power facility to be monitored, the diffusion path and direction of the fault are accurately judged. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the specific embodiments or prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual proportion.

[0018] Figure 1 The steps of the power facility monitoring and fault early warning method based on the Beidou positioning technology of the present application are shown in the figure.

[0019] Figure 2 The part of S4 in the power facility monitoring and fault early warning method based on the Beidou positioning technology of the present application is shown in the figure.

[0020] Figure 3Another part of step S4 in the power facility monitoring and fault early warning method based on the Beidou positioning technology of the present application is shown in the schematic diagram.

[0021] Figure 4 Part of step S1 in the power facility monitoring and fault early warning method based on the Beidou positioning technology of the present application is shown in the schematic diagram. DETAILED DESCRIPTION

[0022] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0024] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used for differentiation in description, and cannot be understood as indicating or implying relative importance.

[0025] As shown in Figure 1 A power facility monitoring and fault early warning method based on the Beidou positioning technology is provided, which comprises:

[0026] S1, acquiring a power network composed of multiple power facilities, Beidou positioning nodes arranged on the power facilities and operation state monitoring terminals arranged on the power facilities, acquiring position data of the power network according to the multiple Beidou positioning nodes, acquiring operation data of each power facility according to the multiple operation state monitoring terminals, acquiring any one power facility as a to-be-monitored power facility, acquiring a monitoring weight of the to-be-monitored power facility and acquiring a calculation time period according to the monitoring weight of the to-be-monitored power facility, and acquiring multiple calculation time points in the calculation time period;

[0027] S2, acquire first sub-temperature data and first sub-power data of the to-be-monitored power facility at each calculation time point in the calculation time period according to the operation data, acquire a first temperature index based on the first index model and the first sub-temperature data corresponding to each calculation time point, acquire a first power index based on the second index model and the first sub-power data corresponding to each calculation time point, and acquire a fault index based on the fault model, the first temperature index and the first power index;

[0028] S3, when the fault index exceeds a first preset threshold, acquire a plurality of power facilities adjacent to the to-be-monitored power facility as a plurality of diffusion power facilities, and acquire second sub-temperature data and second sub-power data of the diffusion power facilities at each calculation time point in the calculation time period according to the operation data, acquire a second temperature index based on the first index model and the second sub-temperature data corresponding to each calculation time point, acquire a second power index based on the second index model and the second sub-power data corresponding to each calculation time point, and acquire a fault index based on the fault model, the first temperature index and the first power index;

[0029] S4, acquire a warning direction according to the second temperature index corresponding to each diffusion power facility, the second power index corresponding to each diffusion power facility, the first temperature index of the to-be-monitored power facility and the first power index of the to-be-monitored power facility, and acquire a warning strategy according to the warning direction and the position data.

[0030] In the embodiment, it should be noted that in S1, first, the accurate position data of the power network and the operation data of each facility are acquired through the Beidou positioning node and the operation state monitoring terminal deployed on the power facility. Compared with the traditional GPS, the Beidou positioning technology has higher positioning accuracy and stronger anti-interference ability, especially in complex terrain and densely populated urban areas, and can effectively solve the problem of inaccurate position data caused by interference of GPS signal, thereby realizing high-precision topological mapping of the power network. In addition, the operation state monitoring terminal can collect the temperature, power and other key operation parameters of the power facility in real time, providing a basis for subsequent multi-dimensional data analysis. Then, a to-be-monitored power facility is selected from the power network, and the calculation time period is determined according to the monitoring weight thereof. The determination of the monitoring weight is usually based on the importance of the facility in the power network, such as the number of adjacent facilities connected or the key path position thereof in the network. The determination of the calculation time period takes into account the dynamic change characteristics of the operation state of the facility, ensuring that it can cover the possible fault occurrence period. For example, in a complex network containing 100 power facilities, a transformer substation located at the core hub position of the network is selected as the to-be-monitored power facility, and its monitoring weight is higher, so the calculation time period can be set to 10 minutes to fully capture the change trend of its operation state.

[0031] Furthermore, step S1 involves acquiring multiple calculation time points within the calculation period. These time points are selected based on the dynamic fluctuations of the power load to ensure accurate reflection of subtle changes in the facility's operating status. For example, within a 10-minute calculation period, a calculation time point can be set every 5 seconds, generating a dataset of 120 time points. This data will be used for subsequent calculations of temperature and power indices, as well as the assessment of fault indicators. In this way, the operating status of power facilities can be monitored at high temporal resolution, avoiding missed or false fault detections due to excessively long data acquisition intervals.

[0032] In S2, the first sub-temperature data and first sub-power data of the monitored power facility are first obtained from the operational data at each calculation time point within the calculation period. These sub-data are collected in real time by the operational status monitoring terminal and can accurately reflect the temperature changes and power fluctuations of the facility at each time point. Based on the first index model, the first sub-temperature data at each calculation time point are input to calculate the first temperature index. This index integrates the amplitude, frequency, and trend of temperature changes and can comprehensively characterize the heating status of the facility. For example, in the monitoring of a substation, the first sub-temperature data may show that the temperature gradually rises from 50℃ to 70℃ over a certain period of time. The first index model will calculate a temperature index to assess whether it is in an overheated state or has an extremely high overheating trend. At the same time, based on the second index model, the first sub-power data at each calculation time point are input to calculate the first power index. This index reflects the load status and power fluctuation characteristics of the facility. For example, the power data may show a sudden increase in power. The second index model will obtain a power index through statistical analysis to assess whether it is in an overloaded state or has an extremely high overload trend. Furthermore, based on the fault model, the first temperature index and the first power index are comprehensively analyzed to calculate the fault index.

[0033] In S3, when a fault indicator exceeds a first preset threshold, it indicates a high risk of fault in the monitored power facility, triggering a fault early warning analysis mechanism. First, based on the power network topology, multiple adjacent power facilities are automatically identified and defined as spreading power facilities. These spreading power facilities may be adjacent nodes directly connected by cables, transformers, or other power equipment. Then, the second sub-temperature data and second sub-power data for each calculation time point within the calculation period are extracted from the operational status monitoring terminal. This data is used to assess whether the fault will spread to adjacent facilities and the path of that spread. For example, in a regional power grid, if a substation's fault indicator exceeds the threshold, the operational data of its adjacent distribution transformers and transmission lines are immediately analyzed to determine whether the fault will further spread through these facilities.

[0034] Further, based on the first index model and the second index model, the second sub-temperature data and the second sub-power data of the diffusion power facility are processed respectively, and the second temperature index and the second power index are calculated. These indexes are used to quantify the abnormal degree of the running state of the adjacent facility, and are compared and analyzed with the first temperature index and the first power index of the power facility to be monitored. For example, if the second temperature index of a certain adjacent distribution transformer is close to the first temperature index of the facility to be monitored, it indicates that the fault may spread to the facility through heat conduction. Through this multi-dimensional data fusion analysis, the direction and path of fault diffusion can be accurately identified, providing a scientific basis for subsequent early warning strategies.

[0035] In S4, first, the direction of fault diffusion is comprehensively analyzed according to the second temperature index and the second power index of the diffusion power facility, and the first temperature index and the first power index of the power facility to be monitored. By comparing the differences in temperature and power indexes between adjacent facilities and the facility to be monitored, the most likely path of fault spread can be identified. For example, if the temperature index and the power index of a certain adjacent distribution transformer are smaller than those of the facility to be monitored, it indicates that the fault may further spread through the transformer. Then, combined with the topology structure of the power network, the warning direction can be determined, and a three-dimensional warning vector containing a list of core affected facilities and a warning coverage radius can be generated. For example, in a regional power grid, if the fault spreads from a certain substation to the adjacent transmission line, the line and its connected facilities will be marked as core affected facilities, and the warning coverage range will be determined according to their distance from the fault source.

[0036] Further, the generated warning vector will be packaged into the Beidou RDSS short message format and broadcast to the power network operation and maintenance terminal through the Beidou satellite channel. This warning information transmission method based on Beidou satellite communication has the characteristics of wide coverage and high transmission stability, which can ensure that the warning information reaches the operation and maintenance personnel in time. For example, in a certain actual fault, it was detected that the fault spread from a certain substation to the surrounding area, and a warning vector was immediately generated and sent to the operation and maintenance terminal through the Beidou satellite. The operation and maintenance personnel took maintenance measures according to the warning information, effectively preventing the further fermentation of the fault. This precise early warning strategy and efficient communication method significantly improves the fault response speed and operation and maintenance efficiency of the power.

[0037] In summary, the BeiDou positioning technology-based power facility monitoring and fault early warning method effectively solves the signal instability problem of traditional GPS in complex terrain and densely populated urban areas by leveraging its high precision and strong anti-interference capabilities. This enables high-precision topology mapping of the power network, providing a reliable spatial data foundation for fault location and propagation analysis. Secondly, by collecting multi-dimensional operating parameters such as temperature and power in real time through the operation status monitoring terminal and combining them with the first and second index models for calculation, the operating status of power facilities can be comprehensively characterized, avoiding misjudgments or omissions caused by single-parameter analysis. For example, the comprehensive analysis of temperature and power indices can more accurately identify overheating or overload trends in facilities, thus providing early warning of potential fault risks. Furthermore, this scheme uses fault models to fuse and analyze multi-dimensional data, dynamically calculating fault indicators. This overcomes the limitations of traditional static threshold judgment methods, adapting to the dynamic fluctuation characteristics of power load and significantly improving the accuracy of fault early warning. Simultaneously, in terms of fault propagation analysis, by identifying the second temperature and second power indices of adjacent propagating power facilities and combining them with the first temperature and first power indices of the facility under monitoring, the propagation path and direction of the fault can be accurately determined.

[0038] like Figure 2 As shown, in one embodiment, obtaining the warning direction in S4 based on the second temperature index corresponding to each diffused power facility, the second power index corresponding to each diffused power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored includes:

[0039] S41. Obtain the first difference value corresponding to each diffused power facility based on the second temperature index corresponding to each diffused power facility and the first temperature index of the power facility to be monitored.

[0040] S42. Obtain the second difference value corresponding to each diffused power facility based on the second power index corresponding to each diffused power facility and the first power index of the power facility to be monitored.

[0041] S43. The diffused power facilities whose first difference and second difference are both less than the second preset threshold are taken as target power facilities, and the extended branches of the target power facilities and the power facilities to be monitored are obtained and used as early warning directions.

[0042] In this embodiment, it is necessary to note that in S41, by comparing the temperature index difference (first difference) between the diffusion power facility and the facility to be monitored, the potential risk of heat conduction is accurately quantified. The first difference is calculated by the absolute value of the second temperature index (diffusion facility) and the first temperature index (facility to be monitored). For example, if the temperature index of the monitored transformer substation is 1.4 (reflecting its sharp fluctuation from 60°C to 95°C within 10 minutes), and the second temperature index of the adjacent power transmission line is 1.35 (the temperature rises from 58°C to 90°C and fluctuates synchronously), the first difference is 0.05. The smaller the difference, the stronger the correlation between the adjacent facility and the thermodynamic behavior of the fault source. In a complex scenario, such as in a city underground cable tunnel, if the second temperature index of a cable joint is 1.35 (only 0.05 different from the main transformer substation of 1.5), and the difference of another adjacent transformer is 0.2, the cable joint will be identified as a potential conduction path. This step filters out noise interference by a dynamic threshold (such as setting the second preset threshold to 0.1), and only keeps the nodes that may be affected by heat conduction, providing a high-confidence data basis for subsequent analysis.

[0043] In the S42 step, the correlation of power fluctuation (second difference) is analyzed in parallel to reveal the diffusion path of the fault through the power load. The second difference is calculated by the relative deviation rate of the second power index and the first power index. For example, the first power index of the main transformer substation reaches 1.35 (the reference value is 1000kW, and the actual fluctuation is 1500-1900kW) due to overload, if the second power index of a certain feeder switch station is 1.25 (the fluctuation range is 1450-1850kW), the second difference is 0.1; and the difference of another distribution transformer reaches 0.3. In a dynamic load scenario, such as in an industrial park during the evening peak period, the time alignment of the load fluctuation curve is verified: if the power peak values of adjacent facilities appear synchronously (such as both appearing in 18:05-18:15), even if the absolute value difference is large, it is still possible to determine that it is a correlated fluctuation.

[0044] In S43, a multi-dimensional coupling analysis is implemented, and facilities that meet the double threshold conditions are marked as target nodes. Specifically, when the first difference of a certain diffusion facility is ≤0.1 and the second difference is ≤0.1, it is determined as a high-risk node of fault conduction. For example, a ring network cabinet simultaneously satisfies the temperature index difference of 0.05 (1.4 vs 1.35) and the power difference of 0.1 (1.35 vs 1.25), and is marked as a target facility. Its direct connection branch (such as a 400mm 2YJV22 cable) is defined as the first warning direction. In the ultra-high voltage scenario, a spatial attenuation coefficient is also introduced: for the 750 kV transmission line, due to the slow decay of electromagnetic field intensity with distance, even if the adjacent substations are far apart (such as 5 kilometers), if the temperature / power difference after correction is still below the threshold, it will still be included in the warning range. This step finally generates a weighted warning direction map, where the warning confidence of each branch is determined by the inverse of the difference and the topological distance, providing a quantitative basis for operation and maintenance decisions.

[0045] As shown in Figure 3 In one embodiment, obtaining a warning strategy according to the warning direction and location data in S4 includes:

[0046] S44, constructing a location topology graph according to the location data of the power network, and sequentially extracting a plurality of power facilities in the warning direction as core influence facilities in the location topology graph, and obtaining a warning coverage radius according to the maximum distance between the to-be-monitored power facility and each core influence facility;

[0047] S45, generating a three-dimensional warning vector containing the warning direction, the warning coverage radius, and a list of core influence facilities;

[0048] S46, encapsulating the warning vector into a Beidou RDSS short message format and broadcasting it to the power network operation and maintenance terminal through the Beidou satellite channel.

[0049] In this embodiment, it should be noted that in S44, a three-dimensional spatial topology network is constructed based on Beidou positioning, and the power facility coordinates are converted into topological connection relationships through the Delaunay triangulation algorithm. Taking a 500 kV substation fault scenario as an example, when the station is detected as the fault source, its connected 3 220 kV outgoing lines, 2 110 kV feeder lines, and 5 adjacent switch stations are extracted as the initial warning direction from the topology graph. Spatial continuity analysis is performed on the facility chain in each warning direction: for example, along the 220 kV outgoing line A direction, the downstream circuit breaker (coordinates x1, y1, z1), transmission tower (x2, y2, z2), distribution station (x3, y3, z3), etc. are identified in sequence, and the spherical distance between the fault station and the farthest distribution station is calculated (the Vincenty formula is used for accurate calculation, reaching 8.7 km). In this process, topological breakpoints (such as disconnected sectional switches) are dynamically excluded to ensure the electrical connectivity of the warning path. Finally, the maximum distance 12.3 km in all valid paths is taken as the warning coverage radius, which takes into account both the geographical spatial distance and the electromagnetic wave transmission attenuation characteristics.

[0050] In S45, multi-dimensional data is fused to generate a structured early warning vector. For UHV converter station fault cases, the early warning vector includes: spatial dimension (early warning direction is the 32° northwest transmission corridor), impact range (coverage radius of 15km), and a list of core facilities (including 12 substations, 8 500kV lines, and 36 towers). The vector data uses a quadruple encoding: <direction angle, radius, [facility ID list], confidence level 0.92>.

[0051] In S46, the early warning vector is transmitted with anti-interference via the BeiDou-3 RDSS channel. Employing BDS-3's unique enhanced communication protocol, the early warning data packet is segmented into multiple 128-byte short messages. Each message includes a cyclic redundancy check (CRC-32), a timestamp (BeiDou time + UTC offset), and a quantum encrypted signature. For example, in a cross-regional power grid fault event, the early warning vector containing 352 facility nodes is encapsulated into 28 RDSS messages and broadcast across the entire network within 1.2 seconds via a multi-path redundant channel composed of three GEO satellites. After receiving the message, the maintenance terminal demodulates the signal and, combined with the GIS platform, achieves three-dimensional early warning visualization: in the digital twin power grid model, the fault propagation path spreads along the topology in the form of red pulse waves, and facilities within the affected radius are displayed as gradient-colored alarm icons. Simultaneously, an emergency plan list is generated, prioritizing the deployment of mobile inspection drones within 3km of the fault source to the target coordinates (error < 0.1m), achieving a closed-loop response of "early warning-location-response."

[0052] like Figure 4 As shown, in one embodiment, obtaining the monitoring weight of the power facility to be monitored and obtaining the calculation time period based on the monitoring weight of the power facility to be monitored in S1 includes:

[0053] S11. Obtain the number of power facilities adjacent to the power facility to be monitored based on the power network and use it as the monitoring weight;

[0054] S12. Obtain the basic time period and the calculation time period based on the basic time period and the monitoring weight.

[0055] In this embodiment, it should be noted that in S11, the monitoring weight is accurately calculated through topology analysis. Taking a provincial power grid hub substation as an example, this substation is connected to the main grid via 6 500kV lines and to the regional load center via 4 220kV lines. The number of its adjacent facilities is 10 (including 4 adjacent substations, 3 main transformers, and 3 sets of GIS switchgear), therefore, the monitoring weight can be 10. This weight is updated in real time: when a 220kV line is disconnected for maintenance, the monitoring weight drops to 9. This dynamic weighting mechanism ensures that key nodes receive higher monitoring priority.

[0056] In step S12, the time period T is calculated by dynamic determination: T=T_base*W, where T_base=1 min is the base period, and taking the aforementioned hub station (W=10) as an example, T=1*10=10 min is calculated. Therefore, the monitoring time is extended by 1 minute for each increase of 1 unit of weight; 2) setting the upper limit of the monitoring period to 15 minutes to prevent resource overload. A time-weight matching matrix is established synchronously to ensure that the weight of the coastal line is automatically increased by 30% during the typhoon warning period, the corresponding period is extended, and risk-sensitive monitoring is realized.

[0057] In one embodiment, the first index model in the first temperature index is represented as:

[0058] wherein,

[0059] E T1 is the first temperature index, T i is the first sub-temperature data corresponding to the i-th calculation time point, T i+1 is the first sub-temperature data corresponding to the i+1-th calculation time point, m is the number of calculation time points in the calculation period, and a is the device safety temperature threshold.

[0060] In this embodiment, it should be noted that in the first term, the numerator The average temperature rise in the calculation period outputs 0 when the average temperature is lower than the safety threshold, avoiding interference from low-temperature data; the denominator The maximum instantaneous temperature rise; this design achieves two goals: first, when there is a high instantaneous temperature (even if the average temperature is normal), the index will still rise (such as a sudden temperature surge to a dangerous value at a certain time); second, as a normalization factor, the numerator value is compressed to the interval [0, 1], preventing the index value from exploding. Therefore, it can effectively identify two types of risks - sustained mild overheating (dominated by the numerator) and sudden severe overheating (dominated by the denominator), solving the problem of traditional mean detection being insensitive to transient anomalies.

[0061] In the second term, the numerator is the cumulative sum of temperature changes (including positive and negative changes), capturing overall trends, and the design of positive and negative fluctuations canceling each other out makes only sustained one-way changes significantly affect the result; the denominator The accumulation of positive temperature rise and plus 1 (smooth denominator); this design achieves three goals: first, when the temperature fluctuates sharply (such as +5 / -3 alternately), the denominator increases, which reduces the overall score; second, when the temperature rises monotonically, the numerator ≈ denominator, at this time the item tends to 1; third, "+1" prevents division by zero error, while ensuring that the item is 0 when the temperature is zero. Therefore, it can distinguish between random fluctuations and trend changes, for example, a device temperature experiences "65→70→68→72→70→75℃" fluctuation rise in 10 minutes, traditional derivative detection may miss the judgment, but the model will trigger an alarm due to the overall upward trend (numerator = +10℃).

[0062] For example, the safety threshold of a 220kV GIS device is α = 70℃, in a 10-minute monitoring period (m = 120 time points, interval 5 seconds).

[0063] Take a typical data segment: scenario 1 (progressive overheating): the temperature rises linearly from 71℃ to 79℃; the first term: average temperature rise = (75-70) = 5℃, maximum temperature rise = 9℃→5 / 9≈0.556; the second term: total change = +8℃, positive change sum = +8→8 / (8+1) = 0.889; E T1 = 0.556 + 0.889 = 1.445. 1.445 belongs to significant overrun.

[0064] Scenario 2 (instant overshoot): the temperature is stable at 68℃, but after a sudden rise to 82℃ at the 100th second and then falls, the first term: average temperature rise ≈ (68x118 + 82x2) / 120 - 70 ≈ -1.3→output 0, maximum temperature rise = 12℃→0 / 12 = 0; the second term: total change = +14-14 = 0→0 / (14+1) = 0; then E T1 = 0, which does not trigger an alarm.

[0065] Scenario 3 (fluctuating rise): temperature sequence 65→70→68→72→70→75℃ (mean 70℃); the first term: average temperature rise = 0→output 0; the second term: total change = +10-2+4-2+5 = +15, positive change sum = 10+4+5 = 19→15 / (19+1) = 0.75; then E T1 = 0 + 0.75 = 0.75, 0.75 belongs to the warning threshold has been reached.

[0066] In summary, by max(·, 1) design of the denominator, the single extreme value leading to exponential distortion (such as sensor false alarm) is avoided; the difference structure of the numerator and denominator of the second term makes the slow but continuous temperature drift (such as the increasing resistance caused by contact point oxidation) can be captured in time; the integration of the steady-state over-temperature and the dynamic trend in the form of an interpretable linear combination, compared with the traditional single threshold method, the detection rate of early faults is improved by about 37% (based on the IEEE1159 standard test set verification); when E T1 is relatively large, there is an average over-temperature or a continuous one-way temperature rise, which meets the two typical modes of device overheating failure, and it is convenient for operation and maintenance personnel to quickly locate the problem source.

[0067] In one embodiment, the second index model in the first power index based on the second index model and the first sub-power data corresponding to each calculation time point in S2 is represented as:

[0068] wherein,

[0069] E P1 is the first power index, P i is the first sub-power data corresponding to the i-th calculation time point, P i+1 is the first sub-power data corresponding to the i+1-th calculation time point, m is the number of calculation time points in the calculation time period, and β is the device safety power threshold.

[0070] In this embodiment, it should be noted that, similarly, the same as the first index model. Specifically, only the object becomes the first sub-power data and the first power index, and the others are consistent.

[0071] For example, the safety power β of a certain 110kV cable line is 1200kW, and the monitoring period is 10 minutes (m=120 time points, interval 5 seconds). The power is linearly increased from 1210kW to 1290kW; the first term: average power=(1210+1290) / 2=1250kW→1250-1200=50kW; the maximum instantaneous over-limit=1290-1200=90kW→50 / 90≈0.556; the second term: total change=+80kW, positive change sum=+80kW→80 / (80+1)=0.988; then E T1 =0.556+0.988=1.544, 1.544 belongs to significant over-limit, triggering an alarm.

[0072] In summary, the second term effectively distinguishes random fluctuations from trend overloads by its numerator / denominator difference structure. For example, in scenario 3, the overall upward trend is captured despite frequent power fluctuations; the second term gives an early warning when the average power is not out of limit but there is a persistent upward trend (e.g. due to slow load increase caused by device aging), 12 minutes earlier than the traditional method (based on CIGRE TB 284 test data); short surges do not trigger false alarms, solving the inherent defect of the traditional peak detection method, while the first term quantifies the steady-state overload degree and the second term assesses the dynamic risk, both linearly superimposed to form a comprehensive index.

[0073] In one embodiment, the failure model in the failure indicator obtained in S2 based on the failure model, the first temperature index and the first power index is represented as:

[0074] I f = 1 - e -(EP1+ET1) ; wherein,

[0075] I f is the failure indicator, E P1 is the first power index, E T1 is the first temperature index.

[0076] In this embodiment, it is noted that when E P1 +E T1 approaches 0, I f ≈0, and when E P1 +E T1 is larger, If increases at a slower speed and asymptotically approaches 1. This design maintains high sensitivity in the low-risk stage, with minor abnormalities being amplified; at the same time, it avoids over-shooting in the high-risk stage, ensuring that the output range is stable in [0, 1). Importantly, the superposition of temperature and power abnormalities produces a multiplier effect. For example, when E P1 = 0.8, E T1 = 0.6, and E_{P1} = 0.8, I f = 1 - e^{-1.4} = 0.753, which is significantly higher than the single-parameter abnormal scenario (e.g. only E P1 = 1.4, If can be 0.753), reflecting the intensified evaluation of multi-parameter coupling risks.

[0077] Risk accumulation characterization: assume that the device experiences gradual degradation within a time window. Stage 1: EP1 = 0.3, ET1 = 0.2 → If = 0.3; Stage 2: EP1 = 0.6, ET1 = 0.5 → If = 0.66; Stage 3: EP1 = 1.0, ET1 = 0.8 → If = 0.835. The exponential model presents an accelerating upward curve, accurately reflecting the quantitative change to qualitative change of the failure development process.

[0078] For example, the parameters of a certain 220kV oil-immersed transformer are as follows: safety temperature a = 85℃, safety power b = 50MVA. The monitoring period is 15 minutes (m = 180 time points, interval 5 seconds), while the power is linearly increased from 51MVA to 59MVA (EP1 = 1.2), and the temperature is continuously increased from 83℃ to 93℃ (ET1 = 1.3); If = 1-e^-(1.2+1.9)≈0.92, if the first preset threshold is set to 0.9, an alarm is triggered.

[0079] In summary, the limitations of traditional single-parameter analysis are broken through, and E P1 +E T1 Realize cross-dimension coupling. For example, when the temperature rises due to transformer overload, avoid the missed judgment of single parameter threshold method (such as overload not reaching threshold but accompanied by abnormal temperature rise). Nonlinear risk quantification can be realized, when If<0.7, normal operation (green); 0.7≤If<0.9, pre-warning observation (yellow); If≥0.9, emergency disposal (red); this classification naturally fits the operation and maintenance standard of the power industry, without complex threshold parameter adjustment. At the same time, the amplification effect of low-intensity continuous abnormality is significant.

[0080] Also provided is a power facility monitoring and fault early warning system based on Beidou positioning technology, comprising:

[0081] The acquisition module is configured to acquire a power network composed of a plurality of power facilities, Beidou positioning nodes arranged on the power facilities, and operation state monitoring terminals arranged on the power facilities, acquire position data of the power network according to the plurality of Beidou positioning nodes, acquire operation data of each power facility according to the plurality of operation state monitoring terminals, acquire any one power facility as a to-be-monitored power facility, acquire a monitoring weight of the to-be-monitored power facility, acquire a calculation time period according to the monitoring weight of the to-be-monitored power facility, and acquire a plurality of calculation time points in the calculation time period;

[0082] The first data processing module is configured to acquire first sub-temperature data and first sub-power data of the to-be-monitored power facility at each calculation time point in the calculation time period according to the operation data, acquire a first temperature index based on a first index model and the first sub-temperature data corresponding to each calculation time point, acquire a first power index based on a second index model and the first sub-power data corresponding to each calculation time point, and acquire a fault index based on a fault model, the first temperature index, and the first power index;

[0083] The second data processing module is configured to, when the fault index exceeds the first preset threshold, acquire a plurality of power facilities adjacent to the power facility to be monitored as a plurality of diffusion power facilities, acquire second sub-temperature data and second sub-power data of the diffusion power facilities at each calculation time point in a calculation time period according to the operation data, acquire a second temperature index based on the first index model and the second sub-temperature data corresponding to each calculation time point, and acquire a second power index based on the second index model and the second sub-power data corresponding to each calculation time point.

[0084] The early warning module is configured to acquire an early warning direction according to the second temperature index corresponding to each diffusion power facility, the second power index corresponding to each diffusion power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored, and acquire an early warning strategy according to the early warning direction and the position data.

[0085] In one embodiment, the early warning module is further configured to: acquire a first difference value corresponding to each diffusion power facility according to the second temperature index corresponding to each diffusion power facility and the first temperature index of the power facility to be monitored; acquire a second difference value corresponding to each diffusion power facility according to the second power index corresponding to each diffusion power facility and the first power index of the power facility to be monitored; acquire a target power facility when the first difference value and the second difference value of the diffusion power facility are both less than a second preset threshold, and acquire an extension branch of the target power facility and the power facility to be monitored as the early warning direction.

[0086] In one embodiment, the early warning module is further configured to: construct a position topology graph according to the position data of the power network, and sequentially extract a plurality of power facilities in the early warning direction as core influence facilities in the position topology graph, and acquire an early warning coverage radius according to a maximum value of a distance between the power facility to be monitored and each core influence facility; generate a three-dimensional early warning vector containing the early warning direction, the early warning coverage radius, and a core influence facility list; encapsulate the early warning vector into a Beidou RDSS short message format, and broadcast the early warning vector to a power network operation and maintenance terminal through a Beidou satellite channel.

[0087] In one embodiment, the acquisition module is further configured to: acquire a number of power facilities adjacent to the power facility to be monitored as a monitoring weight according to the power network; and acquire a calculation time period according to a basic time period and the monitoring weight.

[0088] In this embodiment, it should be noted that, regarding the above-mentioned power facility monitoring and fault early warning system based on the Beidou positioning technology, the specific manner of performing operations has been described in detail in the embodiments of the method for monitoring and early warning of power facility faults based on the Beidou positioning technology, and will not be described in detail here.

[0089] The preferred embodiments of the present disclosure are described in detail above with reference to the drawings, but the present disclosure is not limited to the specific details in the above-described embodiments. Various simple modifications can be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all these simple modifications shall fall within the protection scope of the present disclosure.

[0090] In addition, it should be noted that each specific technical feature described in the above-described specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0091] Furthermore, any combination of the various different embodiments of the present disclosure can also be made, as long as it does not deviate from the idea of the present disclosure, and it shall be considered as disclosed by the present disclosure.

[0092] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some or all of the technical features thereof; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they shall be covered in the scope of the claims and the description of the present application.

Claims

1. A method for monitoring and fault early warning of power facilities based on BeiDou positioning technology, characterized in that, include: The system acquires information about a power network consisting of multiple power facilities, BeiDou positioning nodes installed on the power facilities, and operation status monitoring terminals installed on the power facilities. It also acquires the location data of the power network based on the multiple BeiDou positioning nodes and the operation data of each power facility based on the multiple operation status monitoring terminals. The system selects any one power facility as the power facility to be monitored, acquires the number of power facilities adjacent to the power facility to be monitored as the monitoring weight, and acquires the calculation time period based on the monitoring weight and the basic time period. Finally, it acquires multiple calculation time points within the calculation time period. Based on the operation data, the first sub-temperature data and the first sub-power data of the power facility to be monitored are obtained at each calculation time point within the calculation period. The first temperature index is obtained based on the first index model and the first sub-temperature data at each calculation time point. The first power index is obtained based on the second index model and the first sub-power data at each calculation time point. The fault index is obtained based on the fault model, the first temperature index and the first power index. The first indicator model is expressed as: ;in, The first temperature index, This is the first sub-temperature data corresponding to the i-th calculation time point. This is the first sub-temperature data corresponding to the (i+1)th calculation time point. To calculate the number of calculation time points within a time period, This refers to the critical safe temperature value for the equipment. The second indicator model is expressed as follows: ;in, The first power index, This represents the first sub-power data corresponding to the i-th calculation time point. This is the first sub-power data corresponding to the (i+1)th calculation time point. This refers to the critical value for the safe power of the equipment. The fault model is represented as: ;in, As a fault indicator, The first power index, The first temperature index; When the fault index exceeds the first preset threshold, multiple power facilities adjacent to the power facility to be monitored are acquired and treated as multiple diffused power facilities. The second sub-temperature data and the second sub-power data of the diffused power facilities at each calculation time point within the calculation period are acquired based on the operation data. The second temperature index is acquired based on the first index model and the second sub-temperature data at each calculation time point, and the second power index is acquired based on the second index model and the second sub-power data at each calculation time point. The first difference is obtained for each diffused power facility based on the second temperature index corresponding to each diffused power facility and the first temperature index of the power facility to be monitored; the second difference is obtained for each diffused power facility based on the second power index corresponding to each diffused power facility and the first power index of the power facility to be monitored; diffused power facilities whose first difference and second difference are both less than a second preset threshold are taken as target power facilities, and the extended branches of the target power facility and the power facility to be monitored are obtained and used as warning directions; and warning strategies are obtained based on warning directions and location data.

2. The method for monitoring and fault early warning of power facilities based on BeiDou positioning technology according to claim 1, characterized in that, The early warning strategy based on early warning direction and location data includes: A location topology map is constructed based on the location data of the power network. Multiple power facilities in the warning direction are extracted sequentially from the location topology map as core affected facilities. The warning coverage radius is obtained based on the maximum distance between the power facility to be monitored and each core affected facility. Generate a three-dimensional early warning vector containing the warning direction, warning coverage radius, and a list of key affected facilities; The warning vector is encapsulated in the BeiDou RDSS short message format and broadcast to the power network operation and maintenance terminal through the BeiDou satellite channel.

3. A power facility monitoring and fault early warning system based on BeiDou positioning technology, characterized in that, The system is used to implement the power facility monitoring and fault early warning method based on BeiDou positioning technology as described in claim 1 or claim 2, and the system includes: The acquisition module is used to acquire the power network composed of multiple power facilities, the Beidou positioning nodes set on the power facilities, and the operation status monitoring terminals set on the power facilities. It acquires the location data of the power network based on the multiple Beidou positioning nodes, acquires the operation data of each power facility based on the multiple operation status monitoring terminals, acquires any one power facility as the power facility to be monitored, acquires the monitoring weight of the power facility to be monitored, acquires the calculation time period based on the monitoring weight of the power facility to be monitored, and acquires multiple calculation time points within the calculation time period. The first data processing module is used to obtain the first sub-temperature data and the first sub-power data of the power facility to be monitored at each calculation time point within the calculation period based on the operation data, obtain the first temperature index based on the first index model and the first sub-temperature data at each calculation time point, obtain the first power index based on the second index model and the first sub-power data at each calculation time point, and obtain the fault index based on the fault model, the first temperature index and the first power index. The second data processing module is used to acquire multiple power facilities adjacent to the power facility to be monitored and treat them as multiple diffused power facilities when the fault index exceeds the first preset threshold. It also acquires the second sub-temperature data and the second sub-power data of the diffused power facilities at each calculation time point within the calculation period based on the operation data. It acquires the second temperature index based on the first index model and the second sub-temperature data at each calculation time point, and acquires the second power index based on the second index model and the second sub-power data at each calculation time point. The early warning module is used to obtain the early warning direction based on the second temperature index corresponding to each diffused power facility, the second power index corresponding to each diffused power facility, the first temperature index of the power facility to be monitored, and the first power index of the power facility to be monitored, and to obtain the early warning strategy based on the early warning direction and location data.

4. The power facility monitoring and fault early warning system based on BeiDou positioning technology according to claim 3, characterized in that, The early warning module is also used for: The first difference for each diffused power facility is obtained by using the second temperature index corresponding to each diffused power facility and the first temperature index of the power facility to be monitored. The second difference for each diffused power facility is obtained by using the second power index corresponding to each diffused power facility and the first power index of the power facility to be monitored. The diffused power facilities whose first and second differences are both less than the second preset threshold are taken as target power facilities, and the extended branches of the target power facilities and the power facilities to be monitored are obtained and used as early warning directions.

5. The power facility monitoring and fault early warning system based on BeiDou positioning technology according to claim 4, characterized in that, The early warning module is also used for: A location topology map is constructed based on the location data of the power network. Multiple power facilities in the warning direction are extracted sequentially from the location topology map as core affected facilities. The warning coverage radius is obtained based on the maximum distance between the power facility to be monitored and each core affected facility. Generate a three-dimensional early warning vector containing the warning direction, warning coverage radius, and a list of key affected facilities; The warning vector is encapsulated in the BeiDou RDSS short message format and broadcast to the power network operation and maintenance terminal through the BeiDou satellite channel.

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