Operation and maintenance quick response method and system under power market monitoring and medium

Abstract

The invention discloses an operation and maintenance quick response method and system under power market monitoring and a medium, and relates to the technical field related to power operation and maintenance, and the method comprises the steps: carrying out the continuous monitoring of a power market, and obtaining a multi-source data set; calling a state sequence identification plan to identify and analyze the state data set; calling a composite current criterion plan to carry out criterion analysis on the current data set; cooperating with the real-time state sequence and the real-time electric leakage information to obtain a real-time fault list; obtaining a preset equipment weight, and analyzing the first fault in combination with the operation data set to obtain a first priority index; and performing operation and maintenance response on the first fault based on the first priority index. The technical problems that in the prior art, multi-source electric power data are dispersed, fault recognition lags behind, composite fault diagnosis precision is insufficient, and operation and maintenance response priorities are not clear are solved, and the technical effects of real-time accurate monitoring, rapid fault positioning, intelligent priority ranking and efficient operation and maintenance response are achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to power operation and maintenance, and specifically to a method, system and medium for rapid response to operation and maintenance under power market monitoring. Background Art

[0002] Monitoring the operating status of power equipment and rapidly responding to faults are key to ensuring the safe and stable operation of the power grid. Traditional power operations and maintenance struggle to cope with the high-frequency, multi-source, and dynamically changing operating environment of the modern power market. This is especially true in the context of power marketization, where factors such as power trading, supply and demand fluctuations, and the integration of distributed energy resources further exacerbate the uncertainty of power grid operation, increasing the probability and impact of faults. Furthermore, power system monitoring relies on distributed, heterogeneous data collected by devices such as SCADA (Supervisory Control and Data Acquisition) and PMUs (Synchrophasor Measurement Units), lacking effective collaborative analysis methods. This leads to delayed fault identification or misjudgment, and fault diagnosis struggles to adapt to complex and changing operating conditions. This is particularly true in complex fault scenarios such as leakage, short circuits, and equipment overloads, which can easily lead to missed or false alarms. This impacts real-time monitoring of the power system's operating status and fault location.

[0003] Therefore, at the current stage, relevant technologies have technical problems such as the dispersion of multi-source power data, delayed fault identification, insufficient accuracy in complex fault diagnosis, and unclear operation and maintenance response priorities. Summary of the Invention

[0004] This application solves the technical problems of multi-source power data dispersion, fault identification lag, insufficient accuracy of complex fault diagnosis, and unclear operation and maintenance response priority in the existing technology by providing a method, system and medium for rapid operation and maintenance response under power market monitoring, and achieves the technical effects of real-time accurate monitoring, rapid fault location, intelligent priority sorting and efficient operation and maintenance response.

[0005] The present application provides a method for rapid operation and maintenance response under power market monitoring, the method comprising: continuously monitoring the power market to obtain a multi-source data set; calling a state sequence identification plan to identify and analyze the state data set in the multi-source data set to obtain a real-time state sequence; calling a composite current criterion plan to perform criterion analysis on the current data set in the multi-source data set to obtain real-time leakage information; coordinating the real-time state sequence with the real-time leakage information to obtain a real-time fault list of the power market, wherein the real-time fault list includes multiple faults with equipment and degree identifications; obtaining a predetermined equipment weight, and combining the operation data set in the multi-source data set to analyze the first fault to obtain a first priority index, wherein the first fault refers to any one of the multiple faults with equipment and degree identifications; and performing an operation and maintenance response to the first fault based on the first priority index.

[0006] In a possible implementation, the operation and maintenance rapid response method under the power market monitoring further performs the following processing: performing status monitoring on the power system in the power market according to the physical memory mapping mechanism to obtain the status data set; acquiring the substation in the power market and performing current information monitoring on the long section of power cable of the substation to obtain the current data set; performing operation monitoring on the power market to obtain the operation data set; the status data set, the current data set and the operation data set constitute the multi-source data set.

[0007] In a possible implementation, the operation and maintenance rapid response method under power market monitoring further performs the following processing: obtaining the secondary equipment of the power system; obtaining the memory address of the secondary equipment according to the physical memory mapping mechanism; and accessing the memory address to obtain the status data set.

[0008] In one possible implementation, the operation and maintenance rapid response method under power market monitoring further performs the following processing: extracting the first state parameter in the state data set; standardizing the first state parameter according to the parameter standard strategy in the state sequence identification plan to obtain a first sequence value; and forming the real-time state sequence based on the first sequence value.

[0009] In one possible implementation, the operation and maintenance rapid response method under power market monitoring also performs the following processing: analyzing historical secondary equipment status records and forming a historical database, and constructing a status prediction model based on the historical database; performing predictive analysis on the status data set through the status prediction model to obtain a predicted status sequence; analyzing the predicted status sequence to obtain a status sequence limit, and verifying the real-time status sequence with the status sequence limit; which includes: extracting the first data set of the first historical stage in the historical database, wherein the first data set includes a first historical status data set and a first historical fault condition; matching the first predetermined status sequence corresponding to the first historical fault condition, and forming a training data set in combination with the first historical status data set; performing supervised learning on the training data set to obtain the status prediction model.

[0010] In one possible implementation, the operation and maintenance rapid response method under power market monitoring further performs the following processing: traversing the real-time status sequence in the historical database to obtain traversal results; analyzing the traversal results to obtain the best-matching data set, wherein the best-matching data set includes the best-matching fault condition; and adding the best-matching fault condition to the real-time fault list.

[0011] In one possible implementation, the operation and maintenance rapid response method under power market monitoring further performs the following processing: obtaining a first element among the predetermined elements, and matching a first current parameter corresponding to the first element in the current data set; forming a current vector based on the first current parameter, and calculating a current similarity index between the current vector and a reference current vector; if the current similarity index does not reach a predetermined similarity limit, identifying and determining the real-time leakage information based on a composite current criterion plan; wherein the predetermined elements include at least a steady-state residual current element, a residual current incremental element, a steady-state residual current differential element, and a single-phase residual current differential element.

[0012] In one possible implementation, the operation and maintenance rapid response method under power market monitoring further performs the following processing: establishing a leakage database, wherein the leakage database includes multiple current vectors with leakage type and leakage degree identifications; calculating a first similarity between the current vector and a first vector, wherein the first vector refers to any one of the multiple current vectors with leakage type and leakage degree identifications; if the first similarity reaches the predetermined similarity limit, matching the first leakage type and the first leakage degree corresponding to the first vector; and obtaining the real-time leakage information based on the first leakage type and the first leakage degree.

[0013] The present application also provides an operation and maintenance rapid response system under power market monitoring, the system comprising: a continuity monitoring module, used to perform continuous monitoring of the power market and obtain a multi-source data set; an identification and analysis module, used to call a state sequence identification plan to perform identification and analysis on the state data set in the multi-source data set to obtain a real-time state sequence; a criterion analysis module, used to call a composite current criterion plan to perform criterion analysis on the current data set in the multi-source data set to obtain real-time leakage information; a real-time fault list acquisition module, used to coordinate the real-time state sequence and the real-time leakage information to obtain a real-time fault list of the power market, wherein the real-time fault list includes multiple faults with equipment and degree identifications; a first fault analysis module, used to obtain a predetermined equipment weight, and in combination with the operating data set in the multi-source data set, analyze the first fault to obtain a first priority index, wherein the first fault refers to any one of the multiple faults with equipment and degree identifications; an operation and maintenance response module, used to perform an operation and maintenance response to the first fault based on the first priority index.

[0014] The present application also provides a computer-readable storage medium, comprising: a computer program stored thereon, which, when executed by a processor, implements a method for rapid operation and maintenance response under power market monitoring.

[0015] The proposed method, system, and medium for rapid operation and maintenance response under power market monitoring will be used to continuously monitor the power market and obtain a multi-source data set. The system will retrieve the state sequence identification plan to identify and analyze the state data set. The system will retrieve the composite current criterion plan to perform criterion analysis on the current data set. The system will coordinate the real-time state sequence with the real-time leakage information to obtain a real-time fault list. The system will obtain the predetermined equipment weight and analyze the first fault in combination with the operating data set to obtain a first priority index. The system will perform an operation and maintenance response to the first fault based on the first priority index. This solves the technical problems of dispersed multi-source power data, delayed fault identification, insufficient composite fault diagnosis accuracy, and unclear operation and maintenance response priorities in the prior art, achieving the technical effects of real-time accurate monitoring, rapid fault location, intelligent priority sorting, and efficient operation and maintenance response. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] Figure 1 A flowchart of a rapid operation and maintenance response method under power market monitoring provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of the structure of an operation and maintenance rapid response system under power market monitoring provided in an embodiment of the present application.

[0019] Description of the accompanying drawings: continuity monitoring module 10, identification and analysis module 20, judgment analysis module 30, real-time fault list acquisition module 40, first fault analysis module 50, operation and maintenance response module 60. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0023] The embodiment of the present application provides a method for rapid operation and maintenance response under power market monitoring, such as Figure 1 As shown, the method includes: Step S100: Continuously monitor the electricity market to obtain a multi-source data set.

[0024] Step S100 further includes step S110, performing status monitoring on the power system in the power market according to the physical memory mapping mechanism to obtain the status data set; step S120, obtaining the substation of the power market and monitoring the current information of the long section power cable of the substation to obtain the current data set; step S130, performing operation monitoring on the power market to obtain the operation data set; step S140, the status data set, the current data set and the operation data set constitute the multi-source data set.

[0025] Preferably, the power market is continuously monitored, that is, full-time coverage monitoring is carried out at the millisecond level (equipment status) and minute level (market transactions), and status data sets, current data sets and operation data sets are obtained. Specifically, according to the physical memory mapping mechanism, the real-time status register value is read from the embedded system or FPGA / CPLD of the power equipment through the MMU (memory management unit) or direct memory access (DMA), that is, the real-time operation status of the hardware equipment in the power system (such as relay protection devices, smart meters, circuit breakers, transformers, etc.) is monitored, and the status information in their registers, I / O ports or firmware is obtained to form a status data set, which may include the device switch status (such as open / closed), protection device action signal (such as overcurrent protection triggering), communication status (such as PLC, fiber optic channel connectivity) and equipment health (such as temperature, vibration, insulation aging monitoring data), such as the fault flag of the smart circuit breaker and the oil temperature data of the transformer.

[0026] Optimally, data is collected from substations in the power market, using Rogowski coils or high-precision CTs (current transformers) for real-time sampling. High-precision current monitoring is performed on long-distance power cables (such as 10kV / 35kV transmission lines) in the substations, focusing on detecting abnormal currents (such as leakage, harmonics, and short-circuit currents). Harmonic components are analyzed using FFTs (Fast Fourier Transforms), or transient faults are detected using wavelet transforms. This generates a current dataset that may include three-phase current amplitudes, zero-sequence current, current harmonic distortion, and transient current waveforms, such as short-circuit surge currents. Wide-area synchronous monitoring of power market operations is performed, including data on the overall power market operation, including grid dispatch, trading, and load forecasting, obtained from SCADA / EMS (Energy Management Systems) or power trading platform APIs. This data is then used to generate an operational dataset that may include grid flow distribution, node voltage / frequency stability data, power market trading information (such as real-time electricity prices and supply-demand balance), and grid-connected power fluctuations of renewable energy (photovoltaic and wind power).

[0027] Preferably, the status dataset is used to determine whether the equipment is abnormal (such as a circuit breaker failure), the current dataset is used to locate the fault type and location (such as a cable leakage point), and the operation dataset is used to evaluate the impact of the fault on the entire power grid (such as whether it causes voltage collapse). Fusion of the status dataset, current dataset, and operation dataset to form a multi-source dataset can significantly improve the comprehensiveness and response speed of fault detection.

[0028] Furthermore, step S110 also includes step S111, obtaining the secondary device of the power system; step S112, obtaining the memory address of the secondary device according to the physical memory mapping mechanism; step S113, accessing the memory address to obtain the status data set.

[0029] Preferably, secondary equipment refers to intelligent electronic devices used to monitor, protect and control main equipment (primary equipment) in the power system, mainly including protection equipment (such as line protection devices, transformer differential protection devices), measurement and control equipment (such as merging units, intelligent terminals), automation equipment (such as automatic voltage control systems, synchronous phasor measurement devices) and communication equipment (such as protocol converters, time synchronization devices); the memory address of the secondary equipment is obtained according to the physical memory mapping mechanism. Specifically, each secondary equipment has a specific memory area for storing real-time operating parameters and status flags. These memory addresses are usually pre-defined by the equipment manufacturer during development and correspond to different functional registers. The device establishes a direct channel with the device memory through the underlying hardware interface (such as FPGA or dedicated communication chip), bypassing the conventional communication protocol stack to implement direct read and write operations at the register level; this includes identifying the memory mapping table of the target device, locating the memory address range where the key status registers are located, and finally accessing the memory address through direct memory access technology to read real-time data and obtain status data sets, such as device operating status (switch status, protection start / action flag), real-time measurement values (current / voltage sampling values, power calculation values), event records (protection action events, communication abnormality events, device alarm events) and device health status (internal temperature, power status, memory status, etc.).

[0030] Step S200 , calling a state sequence recognition plan to perform recognition and analysis on the state data set in the multi-source data set to obtain a real-time state sequence.

[0031] Preferably, the state data sets in the multi-source data sets are identified and analyzed through predefined intelligent analysis rules (i.e., state sequence identification plans), wherein the state sequence identification plans include pre-established equipment state assessment strategies, which may include normal operating parameter thresholds of various types of power equipment (such as standard values of temperature, insulation resistance, etc.), characteristic patterns of typical abnormal states (such as vibration waveform characteristics of mechanical failures of circuit breakers). Specifically, data preprocessing is performed on the state data sets in the multi-source data sets, that is, the original state data obtained by memory mapping is standardized and converted, including analog quantity normalization (converting sensor data of different units into standard dimensions) and state quantity encoding (converting discrete switching signals into codes recognizable by the state machine), and then multi-dimensional feature extraction is performed, such as extracting the rate of change of monitoring parameters, fluctuation amplitude, signal spectrum distribution, etc.; the extracted features are then compared with the standard rules in the state sequence identification plan, including checking whether they exceed the preset safety threshold and judging whether they meet the feature combination of typical failures, and then outputting a state chain with a timestamp, which is a real-time state sequence, to facilitate accurate real-time operation and maintenance of power equipment.

[0032] Furthermore, step S200 also includes step S210, extracting the first state parameter in the state data set; step S220, standardizing the first state parameter according to the parameter standard strategy in the state sequence identification plan to obtain a first sequence value; step S230, forming the real-time state sequence based on the first sequence value.

[0033] Preferably, the original state data of the equipment operation is converted into a state evaluation indicator chain with time series characteristics and standard comparability through the parameter standard strategy in the state sequence identification plan, wherein the parameter standard strategy is the data standardization rule defined in the state sequence identification plan. Specifically, the first state parameter is randomly extracted from the state data set, such as the circuit breaker contact resistance, transformer winding temperature, cable partial discharge, etc., and the first state parameter is matched in the parameter standard strategy and standardized. For example, the standard parameter corresponding to the equipment model and operating years in the parameter standard strategy is called to correct the first state parameter. At the same time, the continuous quantity is converted into a discrete state level, such as the temperature value is converted into normal / caution / abnormal / critical to obtain the first sequence value; finally, a real-time state sequence is formed according to the first sequence value, so that the operation and maintenance personnel can intuitively grasp the dynamic evolution law of the equipment state.

[0034] Furthermore, step S230 also includes step S231, analyzing historical secondary equipment status records and forming a historical database, and constructing a status prediction model based on the historical database; step S232, performing predictive analysis on the status data set through the status prediction model to obtain a predicted status sequence; step S233, analyzing the predicted status sequence to obtain a status sequence limit, and verifying the real-time status sequence with the status sequence limit; wherein, it includes: step a, extracting the first data set of the first historical stage in the historical database, wherein the first data set includes a first historical status data set and a first historical fault condition; step b, matching the first predetermined status sequence corresponding to the first historical fault condition, and forming a training data set in combination with the first historical status data set; step c, performing supervised learning on the training data set to obtain the status prediction model.

[0035] Preferably, during power system operation, secondary equipment continuously generates a large amount of operating status data, recording various parameter information of the equipment at different times and under different operating conditions. A historical database is established by collecting, organizing, and storing historical secondary equipment status records, including long-term operating records of the secondary equipment (such as protection device action logs and self-test reports), fault event archives (including fault type, occurrence time, and treatment measures), and environmental data (temperature, humidity, load factor, etc. at the time of the fault). Data for a specific time period (i.e., the first historical phase) is then extracted from the historical database to establish a first data set containing data from a typical operating phase of the secondary equipment's full lifecycle. The first data set includes a first historical status data set and a first historical fault condition. The first historical status data set covers various data on the normal operating status of the secondary equipment during this phase, and the first historical fault condition records data such as the time, type, and manifestation of equipment faults that occurred during this phase.

[0036] Preferably, the first historical fault condition is matched with the relevant state sequence in the first historical state data set to obtain the corresponding first predetermined state sequence, that is, the specific sequence pattern presented by the operating state of the equipment before the fault occurs. By matching the first predetermined state sequence and integrating it with the corresponding first historical state data set, a training data set is formed, which includes data on normal operating state and pre-fault state; then the relationship between the input data (historical state data in the training data set) and the output data (corresponding fault condition or state change) is clarified, and the training data set is input into the constructed machine learning model (LSTM, TCN and other time series neural networks). According to the correspondence between the input and output, its own parameters and structure are continuously adjusted to minimize the error between the predicted results and the actual results. After repeated training and optimization, a state prediction model that can accurately predict the state of the equipment is finally obtained, which is used to accurately predict the future state of the equipment based on the state data set, and output the predicted state sequence of the secondary equipment, including the future state prediction value and failure probability.

[0037] Preferably, a state prediction model is used to perform predictive analysis on a state data set. Specifically, a state data set (e.g., voltage, current, temperature, switch status, and other parameters of secondary equipment) collected in real time is input into the state prediction model. Trend features (e.g., continuous rise in equipment temperature), periodic patterns (e.g., load fluctuations during peak hours of daily electricity consumption), and abnormal fluctuation patterns are identified in the data. The model then predicts the equipment state at multiple future time points, generating a predicted state sequence containing the future values of each parameter. The predicted state sequence is then analyzed, including performing descriptive statistics on the predicted state sequence, calculating the mean and standard deviation of each parameter, and using the mean ± k × standard deviation as the normal range limit, where k is an empirical coefficient, such as k = 2, which covers 95% of the data, to obtain the state sequence limit. Finally, the real-time state sequence is verified using the state sequence limit. Specifically, the real-time collected state sequence and the predicted state sequence are aligned by timestamp to ensure that the parameter values at the same time point are comparable. The Mahalanobis distance between the real-time data and the predicted value is compared. If the real-time current value exceeds the upper limit of the predicted sequence limit, it is determined to be a potential fault and an early warning is triggered, generating a comprehensive verification result.

[0038] Step S300 , calling a composite current criterion plan to perform criterion analysis on the current data set in the multi-source data set to obtain real-time leakage information.

[0039] Preferably, the composite current criterion plan is a multi-dimensional, intelligent leakage detection solution that can analyze and judge current data based on multi-dimensional features such as steady-state current, transient current, and harmonic components to obtain real-time leakage information. The multi-dimensional current criterion data is shown in Table 1: Table 1 Multi-dimensional current criterion data table

[0040] Preferably, the composite current criterion plan is called to perform criterion analysis on the current data set in the multi-source data set. Specifically, the zero-sequence current and residual current criterion analysis is first performed. If the zero-sequence current is greater than the set threshold (such as 50mA) and the duration is >500ms, the zero-sequence current warning is triggered, and the residual current is greater than 30mA, then current mutation detection and spectrum feature extraction are performed. Specifically, the current change rate in the sliding window is calculated. When it is greater than 10A / ms and lasts for 2 sampling cycles (40ms), it is marked as an instantaneous leakage pulse; then the current signal is subjected to fast Fourier transform (FFT) to analyze the energy proportion of the 20kHz frequency band. If the energy of this frequency band increases by 30% compared with the historical average, The above is determined to be partial discharge caused by insulation aging; then a logical combination verification is performed, that is, if the zero-sequence current exceeds the standard and the residual current exceeds the standard, the leakage type is determined to be a metallic ground fault; if the current mutation rate exceeds the standard and the spectrum is abnormal, the leakage type is determined to be instantaneous cable breakdown; if the three-phase imbalance is greater than 20% and the zero-sequence current fluctuates, the leakage type is determined to be internal winding leakage of the equipment; finally, real-time leakage information is output, including the distance from the leakage point to the measuring end calculated by current traveling wave positioning (such as the two-end traveling wave method), as well as the leakage current size (such as 200mA), leakage duration (such as 2 minutes and 30 seconds) and hazard level, thereby improving the reliability and intelligence level of leakage protection of the power system.

[0041] Furthermore, step S300 also includes step S310, obtaining the first element among the predetermined elements, and matching the first current parameter corresponding to the first element in the current data set; step S320, forming a current vector based on the first current parameter, and calculating the current similarity index between the current vector and the reference current vector; step S330, if the current similarity index does not reach the predetermined similarity limit, then determining the real-time leakage information according to the composite current criterion plan identification; wherein, the predetermined elements include at least a steady-state residual current element, a residual current incremental element, a steady-state residual current differential element and a single-phase residual current differential element.

[0042] Preferably, the predetermined element is a core parameter acquisition unit for leakage detection, which includes at least a steady-state residual current element, a residual current increment element, a steady-state residual current differential element and a single-phase residual current differential element. Among them, the steady-state residual current element is used to monitor the residual current (i.e., the vector sum of the three-phase currents) during normal operation of the power system, reflecting the leakage baseline value of the system under steady state. Under normal circumstances, the vector sum of the three-phase currents is theoretically zero. If a steady-state residual current exists, it may indicate a slow leakage fault such as insulation aging; the residual current increment element focuses on the dynamic change of the residual current, that is, the difference between the current residual current and the historical baseline value, and is used to identify sudden leakage events, such as the current mutation at the moment of equipment insulation breakdown; the steady-state residual current differential element is used to compare the difference in steady-state residual currents at different locations (such as the beginning and end of the line). If the difference between the two ends exceeds the threshold, it may indicate the existence of a leakage point in the interval (such as current loss caused by line damage); the single-phase residual current differential element compares the current difference between the phase line and the neutral line for the single-phase line. Under normal circumstances, the currents of the two are equal in magnitude and opposite in direction. If a differential current occurs, it directly points to a single-phase grounding fault.

[0043] Preferably, a detection element is randomly selected from predetermined elements as the first element, and the current monitoring data corresponding to the first element, i.e., the first current parameter, is matched and extracted in the current data set. If a steady-state residual current element is selected, the matching parameter is the vector sum of the three-phase currents at a certain moment, and then the current parameters (or multi-dimensional parameters) of the same element at different time points are combined into a vector to obtain a current vector, which includes multiple continuously monitored residual current values or harmonic amplitudes in different frequency bands. The reference current vector is a standard vector constructed based on historical normal operating data, which represents the current characteristics of the element when there is no fault. For example, the residual current values of 1,000 normal operating samples are extracted through the historical database, and the average value is taken to form the reference current vector.

[0044] Preferably, a vector similarity algorithm (such as cosine similarity) is used to calculate the degree of matching between the current current vector and the reference current vector to obtain a current similarity index. Specifically, the smaller the vector angle, the higher the similarity, indicating that the current characteristics are closer to the normal state; the larger the angle, the lower the similarity, and there may be a leakage anomaly; if the current similarity index does not reach a predetermined similarity limit (such as a threshold value set to 0.9), it means that the current current characteristics are significantly different from the normal state, triggering a leakage warning, and combined with the composite current judgment plan, the similarity index results of multiple components are integrated to determine the leakage location and degree. For example, if the steady-state residual current component similarity is low, and the incremental component shows a current mutation, it may be determined as a progressive insulation fault; if the steady-state residual current differential component similarity is low, and the current difference between the two ends exceeds the threshold, the leakage point in the interval is directly located, and ultimately normal load fluctuations and real leakage faults are effectively distinguished, the false alarm rate is reduced, and real-time leakage information is determined.

[0045] Furthermore, step S330 also includes step S331, establishing a leakage database, wherein the leakage database includes multiple current vectors with leakage type and leakage degree identifications; step S332, calculating a first similarity between the current vector and the first vector, wherein the first vector refers to any one of the multiple current vectors with leakage type and leakage degree identifications; step S333, if the first similarity reaches the predetermined similarity limit, matching the first leakage type and the first leakage degree corresponding to the first vector; step S334, obtaining the real-time leakage information based on the first leakage type and the first leakage degree.

[0046] Preferably, a leakage database is established using multiple current vectors with leakage type and leakage degree identifiers in historical fault data and power system simulation data, wherein the current vector records the multi-dimensional characteristics of the current at the time of the fault (such as amplitude, phase, harmonic components, etc.), usually in the form of a time series or a multi-dimensional parameter combination. Specifically, the leakage type identifier is a classification of the nature of the fault, which may include but is not limited to single-phase ground leakage (such as grounding due to insulation damage of phase A), phase-to-phase leakage (such as short circuit of phases AB), arc leakage (such as arcing caused by poor line contact), and internal leakage of equipment (such as insulation breakdown of transformer windings). The leakage degree identifier quantifies the severity of the fault, which is usually divided into mild (such as slight moisture in the insulation, leakage current <100mA), moderate (such as partial discharge, leakage current 100mA~500mA) and severe (such as metallic grounding, leakage current >500mA).

[0047] Preferably, a marked current vector is arbitrarily selected from the leakage database as the first vector, which is used to compare with the current vector to calculate the similarity, that is, a vector similarity algorithm (such as cosine similarity, Euclidean distance) is used to calculate the matching degree between the real-time current vector and the first vector to determine the first similarity; a predetermined similarity limit is set according to historical data, and if the similarity between the real-time vector and a vector in the database reaches the limit, it is determined that the two belong to the same type of leakage fault, and then the leakage type and leakage degree corresponding to the first vector are matched in the leakage database as the first leakage type and the first leakage degree; finally, the first leakage type and the first leakage degree are matched and integrated into real-time leakage information, thereby realizing rapid positioning and severity assessment of the leakage fault.

[0048] Step S400 : Coordinate the real-time status sequence and the real-time leakage information to obtain a real-time fault list of the power market, wherein the real-time fault list includes a plurality of faults with equipment and degree identifiers.

[0049] Preferably, the real-time status sequence and real-time leakage information are coordinated. Specifically, the current vector in the leakage information is matched with the equipment operation data in the real-time status sequence to locate the specific faulty equipment. At the same time, the real-time status of the equipment (such as switch trip signal, temperature abnormality) and the leakage degree are combined to assess the scope of the fault impact. The abstract leakage information is converted into a fault identifier of a specific physical equipment. Each fault record is then formatted according to the device identifier + degree identifier to obtain a real-time fault list of the power market. An exemplary real-time fault list of the power market is shown in Table 2: Table 2 List of real-time faults in the electricity market (partial examples)

[0050] Step S400 further includes step S410, traversing the real-time status sequence in the historical database to obtain a traversal result; step S420, analyzing the traversal result to obtain a best-matching data set, wherein the best-matching data set includes a best-matching fault condition; step S430, adding the best-matching fault condition to the real-time fault list.

[0051] Preferably, the real-time state sequence is traversed in the historical database. That is, the current real-time state data of the power equipment is traversed and matched in the fault case library (historical database) to determine the most similar historical fault and add it to the real-time fault list, assisting in more accurate judgment and positioning of the current real-time fault. Specifically, the current real-time state data is compared with the fault data in the historical database one by one to determine which historical fault data features are related to it, and the degree of match is recorded (for example, similarity 80%, 60%, etc.); the traversal results and the historical fault data that are most similar to the real-time state are then analyzed to form the most matching data set. For example, if the real-time data is "a sudden increase in the current of line M, accompanied by intermittent arcing characteristics", after traversing the historical database, three historical faults are found: "line Z short circuit due to tree obstacle (similarity 90%)", "line X tripped due to lightning strike (similarity 70%)", and "internal discharge of transformer D (similarity 40%)". The "line Z short circuit due to tree obstacle" is selected as the most matching fault situation and added to the real-time fault list, ensuring more intelligent fault diagnosis and more efficient processing.

[0052] Step S500: obtaining a predetermined device weight, and analyzing a first fault in combination with an operating data set in the multi-source data set to obtain a first priority index, wherein the first fault refers to any one of the multiple faults with device and degree identifiers.

[0053] Preferably, a predetermined equipment weight, i.e., an importance score of the power equipment, is set based on the grid topology (equipment location), power supply responsibility (number of users), equipment value, etc., reflecting the criticality of the equipment in the grid. For example, substations in the core area of a city and main lines supplying power to residents have higher weights (e.g., 0.8, 0.9), while small branches in remote areas and non-critical auxiliary equipment have lower weights (e.g., 0.3, 0.4). The first fault is analyzed in combination with the operation data set in the multi-source data set, wherein the first fault refers to any one of multiple faults with equipment and severity identifiers. For example, the weight of the equipment itself and the fault severity reflected by the current operation data (e.g., real-time current exceeds the standard by 200%, triggering a protection alarm) are weighted and calculated. The priority index = equipment weight × basic coefficient + operation data abnormality × impact coefficient, wherein the basic coefficient reflects the inherent importance of the equipment and the impact coefficient reflects the impact of the current fault on the operation, thereby obtaining a first priority index corresponding to the first fault, thereby ensuring the response and operation efficiency of the power equipment fault.

[0054] Step S600: Perform an operation and maintenance response to the first fault based on the first priority index.

[0055] Preferably, the fault levels of all faults are determined based on the first priority index mapping, and a processing sequence table is obtained. The response speed, resource input, and handling process are matched to different fault levels. For example, if transformer T3 internal leakage (priority index 1.2) is determined to be an urgent fault, a repair team is dispatched within 10 minutes, a backup transformer and inspection vehicle are deployed, and the power supply to the faulty transformer is immediately remotely cut off (to avoid explosion). The backup transformer is replaced first to restore power, and then the faulty equipment is repaired. If switch station K5 busbar arc leakage (index 0.85) is determined to be a general fault, an operations and maintenance team is dispatched within 2 hours, equipped with conventional detection tools such as multimeters and infrared imaging. The fault is included in the next-day inspection plan and handled during a power outage, or at night when load is low, a short power outage can be arranged for repair. By dynamically calculating the priority index, critical faults are prioritized, grid restoration efficiency is improved, and the automation level and reliability of power equipment operation and maintenance are significantly enhanced, thereby ensuring the safe and efficient operation of smart grids and power markets.

[0056] In the above, refer to Figure 1 The operation and maintenance rapid response method under power market monitoring according to an embodiment of the present invention is described in detail. Figure 2 The operation and maintenance rapid response system under power market monitoring according to an embodiment of the present invention is described.

[0057] The operation and maintenance rapid response system under power market monitoring according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as the dispersion of multi-source power data, delayed fault identification, insufficient accuracy of complex fault diagnosis, and unclear operation and maintenance response priorities, and achieves the technical effects of real-time accurate monitoring, rapid fault location, intelligent priority sorting, and efficient operation and maintenance response. Figure 2 As shown, the operation and maintenance rapid response system under power market monitoring includes: a continuity monitoring module 10, an identification and analysis module 20, a judgment analysis module 30, a real-time fault list acquisition module 40, a first fault analysis module 50, and an operation and maintenance response module 60.

[0058] The continuity monitoring module 10 is used to perform continuity monitoring on the power market and obtain a multi-source data set; the identification and analysis module 20 is used to call the state sequence identification plan to identify and analyze the state data set in the multi-source data set to obtain a real-time state sequence; the judgment analysis module 30 is used to call the composite current judgment plan to perform judgment analysis on the current data set in the multi-source data set to obtain real-time leakage information; the real-time fault list acquisition module 40 is used to coordinate the real-time state sequence and the real-time leakage information to obtain a real-time fault list of the power market, wherein the real-time fault list includes multiple faults with equipment and degree identifications; the first fault analysis module 50 is used to obtain a predetermined equipment weight, and in combination with the operation data set in the multi-source data set, analyze the first fault to obtain a first priority index, wherein the first fault refers to any one of the multiple faults with equipment and degree identifications; the operation and maintenance response module 60 is used to perform an operation and maintenance response to the first fault based on the first priority index.

[0059] The specific configuration of the continuity monitoring module 10 will be described in detail below. The continuity monitoring module 10 further includes: performing status monitoring on the power system in the power market according to the physical memory mapping mechanism to obtain the status dataset; acquiring substations in the power market and monitoring the current information of long power cables in the substations to obtain the current dataset; and performing operation monitoring on the power market to obtain the operation dataset; the status dataset, the current dataset, and the operation dataset forming the multi-source dataset.

[0060] The following will further describe the specific configuration of the continuity monitoring module 10. The continuity monitoring module 10 further includes: obtaining a secondary device of the power system; obtaining a memory address of the secondary device according to the physical memory mapping mechanism; and accessing the memory address to obtain the status data set.

[0061] The specific configuration of the identification and analysis module 20 will be described in detail below. The identification and analysis module 20 further includes: extracting a first state parameter from the state data set; normalizing the first state parameter according to the parameter standard strategy in the state sequence identification plan to obtain a first sequence value; and forming the real-time state sequence based on the first sequence value.

[0062] The specific configuration of the identification and analysis module 20 will be described in detail below. The identification and analysis module 20 further includes: analyzing historical secondary equipment status records and forming a historical database, and constructing a state prediction model based on the historical database; performing predictive analysis on the state data set through the state prediction model to obtain a predicted state sequence; analyzing the predicted state sequence to obtain a state sequence limit, and verifying the real-time state sequence with the state sequence limit; wherein, it includes: extracting the first data set of the first historical stage in the historical database, wherein the first data set includes a first historical state data set and a first historical fault condition; matching the first predetermined state sequence corresponding to the first historical fault condition, and forming a training data set in combination with the first historical state data set; performing supervised learning on the training data set to obtain the state prediction model.

[0063] The specific configuration of the real-time fault list acquisition module 40 will be described in detail below. The real-time fault list acquisition module 40 further includes: traversing the real-time state sequence in the historical database to obtain traversal results; analyzing the traversal results to obtain a best-matching dataset, wherein the best-matching dataset includes a best-matching fault condition; and adding the best-matching fault condition to the real-time fault list.

[0064] The specific configuration of the criterion analysis module 30 will be described in detail below. The criterion analysis module 30 further includes: obtaining a first element from the predetermined elements and matching a first current parameter corresponding to the first element in the current data set; forming a current vector based on the first current parameter, and calculating a current similarity index between the current vector and a reference current vector; If the current similarity index does not reach the predetermined similarity limit, the real-time leakage information is identified and determined according to the composite current criterion plan; wherein the predetermined elements include at least a steady-state residual current element, a residual current incremental element, a steady-state residual current differential element and a single-phase residual current differential element.

[0065] The specific configuration of the criterion analysis module 30 will be described in detail below. The criterion analysis module 30 further includes: establishing a leakage database, wherein the leakage database includes multiple current vectors with leakage type and leakage degree identifiers; calculating a first similarity between the current vector and a first vector, wherein the first vector is any current vector among the multiple current vectors with leakage type and leakage degree identifiers; if the first similarity reaches the predetermined similarity limit, matching the first leakage type and first leakage degree corresponding to the first vector; and obtaining the real-time leakage information based on the first leakage type and the first leakage degree.

[0066] The operation and maintenance rapid response system under power market monitoring provided by the embodiment of the present invention can execute the operation and maintenance rapid response method under power market monitoring provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0067] Based on the above embodiments, an embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement the rapid operation and maintenance response method under power market monitoring as described in any of the previous embodiments.

[0068] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0069] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A rapid operation and maintenance response method under power market monitoring, characterized in that: include: Conduct continuous monitoring of the electricity market to obtain multi-source data sets; Retrieving a state sequence recognition plan to perform recognition and analysis on the state data set in the multi-source data set to obtain a real-time state sequence; Retrieving a composite current criterion plan to perform criterion analysis on the current data set in the multi-source data set to obtain real-time leakage information; Cooperating the real-time status sequence with the real-time leakage information to obtain a real-time fault list of the power market, wherein the real-time fault list includes a plurality of faults with equipment and degree identifiers; Obtaining a predetermined device weight, and analyzing a first fault in combination with an operating data set in the multi-source data set to obtain a first priority index, wherein the first fault is any one of the multiple faults with device and degree identifiers; Perform an operation and maintenance response to the first fault based on the first priority index.

2. The method for rapid operation and maintenance response under power market monitoring according to claim 1, characterized in that: Continuous monitoring of the electricity market, generating multi-source datasets including: Performing status monitoring on the power system in the power market according to a physical memory mapping mechanism to obtain the status data set; Obtaining a substation in the power market, and monitoring current information of a long section of a power cable in the substation to obtain the current data set; Performing operation monitoring on the power market to obtain the operation data set; The state data set, the current data set, and the operation data set constitute the multi-source data set.

3. The method for rapid operation and maintenance response under power market monitoring according to claim 2, characterized in that: Performing status monitoring on the power system in the power market according to the physical memory mapping mechanism to obtain the status data set includes: obtaining secondary equipment of the power system; Acquire the memory address of the secondary device according to the physical memory mapping mechanism; Access the memory address to obtain the status data set.

4. The method for rapid operation and maintenance response under power market monitoring according to claim 1, characterized in that: Retrieving a state sequence identification plan to identify and analyze the state data set in the multi-source data set to obtain a real-time state sequence, including: extracting a first state parameter from the state data set; Standardize the first state parameter according to the parameter standard strategy in the state sequence identification plan to obtain a first sequence value; The real-time status sequence is constructed based on the first sequence value.

5. The method for rapid operation and maintenance response under power market monitoring according to claim 4, characterized in that: After forming the real-time status sequence based on the first sequence value, the method further includes: Analyze historical secondary equipment status records and build a historical database, and construct a status prediction model based on the historical database; Performing prediction analysis on the state data set using the state prediction model to obtain a predicted state sequence; Analyzing the predicted state sequence to obtain a state sequence limit value, and verifying the real-time state sequence with the state sequence limit value; Among them, including: Extracting a first data set of a first historical stage from the historical database, wherein the first data set includes a first historical state data set and a first historical fault condition; Matching a first predetermined state sequence corresponding to the first historical fault condition, and forming a training data set in combination with the first historical state data set; Supervised learning is performed on the training data set to obtain the state prediction model.

6. The method for rapid operation and maintenance response under power market monitoring according to claim 5, characterized in that: The real-time status sequence and the real-time leakage information are coordinated to obtain a real-time fault list of the power market, including: Traversing the real-time state sequence in the historical database to obtain a traversal result; Analyzing the traversal results to obtain a best-matching data set, wherein the best-matching data set includes a best-matching fault condition; The best matching fault condition is added to the real-time fault list.

7. The method for rapid operation and maintenance response under power market monitoring according to claim 1, characterized in that: The current data set includes current parameters of predetermined components, and a composite current criterion plan is called to perform criterion analysis on the current data set in the multi-source data set to obtain real-time leakage information, including: Acquire a first element from the predetermined elements, and match a first current parameter corresponding to the first element in the current data set; forming a current vector based on the first current parameter, and calculating a current similarity index between the current vector and a reference current vector; If the current similarity index does not reach the predetermined similarity limit, identifying and determining the real-time leakage information according to the composite current criterion plan; Wherein, the predetermined elements at least include a steady-state residual current element, a residual current increment element, a steady-state residual current differential element and a single-phase residual current differential element.

8. The method for rapid operation and maintenance response under power market monitoring according to claim 7, characterized in that: If the current similarity index does not reach the predetermined similarity limit, identifying and determining the real-time leakage information according to the composite current criterion plan includes: Establishing a leakage database, wherein the leakage database includes a plurality of current vectors with leakage type and leakage degree identification; Calculating a first similarity between the current vector and a first vector, wherein the first vector is any one of the multiple current vectors having leakage type and leakage degree identifiers; If the first similarity reaches the predetermined similarity limit, matching the first leakage type and the first leakage degree corresponding to the first vector; The real-time leakage information is obtained based on the first leakage type and the first leakage degree.

9. The operation and maintenance rapid response system under power market monitoring is characterized by: The system is used to implement the operation and maintenance rapid response method under power market monitoring according to any one of claims 1 to 8, and the system includes: Continuity monitoring module, used to continuously monitor the power market and obtain multi-source data sets; An identification and analysis module is used to call a state sequence identification plan to identify and analyze the state data set in the multi-source data set to obtain a real-time state sequence; A criterion analysis module is used to call a composite current criterion plan to perform criterion analysis on the current data set in the multi-source data set to obtain real-time leakage information; A real-time fault list obtaining module, configured to coordinate the real-time status sequence and the real-time leakage information to obtain a real-time fault list of the power market, wherein the real-time fault list includes a plurality of faults with equipment and degree identifiers; a first fault analysis module, configured to obtain a predetermined device weight and analyze a first fault in combination with an operating data set in the multi-source data set to obtain a first priority index, wherein the first fault is any one of the multiple faults having device and degree identifiers; An operation and maintenance response module is used to perform an operation and maintenance response to the first fault based on the first priority index.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the operation and maintenance rapid response method under power market monitoring according to any one of claims 1 to 8 is implemented.

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