Fault detection method and system for new energy parallel power grid based on power grid data

Abstract

The invention discloses a fault detection method and system for a new energy parallel power grid based on power grid data, and relates to the technical field of fault detection methods, a plurality of fault nodes are determined based on each fault power grid path, and a fault area of the new energy parallel power grid is determined according to the plurality of fault nodes. And determining a corresponding fault event according to the position of the fault area, the area form and the state of the new energy parallel power grid. Determining a fault maintenance sequence of the plurality of fault components based on the project content of the fault event, the corresponding fault components and the power supply path of the new energy parallel power grid, and determining a fault maintenance plan according to the fault maintenance sequence and the fault type of the plurality of fault components; maintenance of the multiple fault components is triggered according to the fault maintenance plan, autonomous regulation and control of the maintenance progress of the multiple fault components are triggered based on the maintenance progress of the multiple fault components, the component types of the multiple fault components and the power supply efficiency of the new energy parallel power grid, and autonomous maintenance of the multiple fault components is guaranteed.

Description

Technical Field

[0001] This invention relates to the technical field of fault detection methods, and in particular to a fault detection method and system for a new energy parallel power grid based on power grid data. Background Technology

[0002] With the development of technology, the new energy parallel grid refers to a power system structure that connects new energy power generation systems (mainly distributed or centralized new energy power plants) with existing traditional power systems (grids) to operate together. During operation, the new energy parallel grid transmits electricity and works in coordination with multiple components. In the current technology, the operation process of the new energy parallel grid is monitored in real time, and data from each grid is collected. Based on the detection of data from each grid, corresponding fault events are determined. However, the influence of the location and shape of the fault area is ignored, which leads to the accuracy of fault event detection and affects the maintenance of multiple faulty components. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a fault detection method and system for a new energy parallel power grid based on power grid data.

[0004] This invention provides a fault detection method for a new energy parallel grid based on grid data, comprising: determining multiple grid data based on the detection of multiple grid-connected areas in the new energy parallel grid; determining grid data combinations for each grid-connected area based on the multiple grid data and the location of each grid-connected area; determining corresponding faulty grid data based on the filtering of each grid data combination; determining corresponding faulty grid paths based on the tracing of the faulty grid data; determining multiple faulty nodes based on each faulty grid path; determining faulty areas of the new energy parallel grid based on the multiple faulty nodes; determining corresponding fault events based on the location, regional shape, and state of the faulty areas and the new energy parallel grid; determining the fault maintenance sequence of multiple faulty components based on the project content of the fault events, the corresponding faulty components, and the power supply path of the new energy parallel grid; determining a fault maintenance plan based on the fault maintenance sequence and fault type of multiple faulty components; triggering maintenance of multiple faulty components according to the fault maintenance plan; and triggering autonomous adjustment of the maintenance progress of multiple faulty components based on the maintenance progress of multiple faulty components, the component types of multiple faulty components, and the power supply efficiency of the new energy parallel grid.

[0005] This invention provides a fault detection system for a new energy parallel grid based on grid data. The fault detection system for a new energy parallel grid based on grid data is applied to the aforementioned fault detection method for a new energy parallel grid based on grid data. The fault detection system for a new energy parallel grid based on grid data includes: The power grid data combination module is used to determine multiple power grid data based on the detection of multiple grid-connected areas in the new energy parallel power grid, and to determine the combination of power grid data for each grid-connected area based on the multiple power grid data and the location of each grid-connected area; The fault grid path module is used to determine the corresponding fault grid data based on the filtering of various grid data combinations, and to determine the corresponding fault grid path based on the tracing of the fault grid data. The fault event module is used to determine multiple fault nodes based on each faulty power grid path, determine the fault area of ​​the new energy parallel grid based on the multiple fault nodes, and determine the corresponding fault event based on the location, shape and status of the fault area and the new energy parallel grid. The fault maintenance plan module is used to determine the fault maintenance sequence of multiple faulty components based on the project content of the fault event, the corresponding faulty components and the power supply path of the new energy parallel grid, and to determine the fault maintenance plan according to the fault maintenance sequence and fault type of multiple faulty components. The fault progress module is used to trigger the maintenance of multiple faulty components according to the fault maintenance plan. It can autonomously control the maintenance progress of multiple faulty components based on the maintenance progress of multiple faulty components, the component types of multiple faulty components, and the power supply efficiency of the new energy grid.

[0006] Compared with the prior art, the beneficial effects of the present invention are: In this embodiment of the invention, the corresponding fault grid path is determined by tracing the fault grid data; multiple fault nodes are determined based on each fault grid path; the fault area of ​​the new energy parallel grid is determined based on the multiple fault nodes; and the corresponding fault event is determined based on the location, shape, and state of the fault area and the new energy parallel grid. By introducing fault grid data, the invention incorporates a holistic consideration of the location, shape, and state of the fault area and the new energy parallel grid, thereby improving the accuracy of fault event detection.

[0007] Therefore, based on the project content of the fault event, the corresponding faulty component, and the power supply path of the new energy parallel grid, the fault maintenance sequence of multiple faulty components is determined. A fault maintenance plan is then determined based on the fault maintenance sequence and fault type of the multiple faulty components. Maintenance of multiple faulty components is triggered according to this fault maintenance plan. The maintenance progress of multiple faulty components is autonomously controlled based on their maintenance progress, component types, and the power supply efficiency of the new energy parallel grid. This introduces a fault maintenance plan, achieving a holistic consideration of the maintenance progress, component types, and power supply efficiency of multiple faulty components, thereby enabling autonomous control of the maintenance progress of multiple faulty components and ensuring autonomous maintenance of these components. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating the fault detection method for a new energy parallel power grid based on power grid data in an embodiment of the present invention. Figure 2 This is a flowchart illustrating step S11 in the fault detection method for new energy parallel power grids based on power grid data in an embodiment of the present invention. Figure 3 This is a flowchart illustrating step S12 in the fault detection method for new energy parallel power grids based on power grid data in an embodiment of the present invention. Figure 4 This is a flowchart illustrating step S13 in the fault detection method for new energy parallel power grids based on power grid data in an embodiment of the present invention. Figure 5 This is a flowchart illustrating step S14 in the fault detection method for new energy parallel power grids based on power grid data in an embodiment of the present invention. Figure 6 This is a flowchart illustrating step S15 in the fault detection method for new energy parallel power grids based on power grid data in an embodiment of the present invention. Figure 7 This is a schematic diagram of the structural composition of a fault detection system for a new energy parallel power grid based on power grid data in an embodiment of the present invention. Detailed Implementation

[0009] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0010] Please see Figures 1 to 7 A fault detection method for a new energy parallel grid based on grid data is proposed and applied to fault detection scenarios in new energy parallel grids. The fault detection method for a new energy parallel grid based on grid data includes: Step S11: Determine multiple grid data based on the detection of multiple grid-connected areas in the new energy parallel grid, and determine the combination of grid data for each grid-connected area based on the multiple grid data and the location of each grid-connected area; Step S12: Determine the corresponding faulty power grid data based on the filtering of various power grid data combinations, and determine the corresponding faulty power grid path based on the tracing of the faulty power grid data; Step S13: Determine multiple fault nodes based on each faulty power grid path, determine the fault area of ​​the new energy parallel power grid based on the multiple fault nodes, and determine the corresponding fault event based on the location, regional shape and state of the new energy parallel power grid. Step S14: Based on the project content of the fault event, the corresponding faulty component and the power supply path of the new energy parallel grid, determine the fault maintenance sequence of multiple faulty components, and determine the fault maintenance plan according to the fault maintenance sequence and fault type of multiple faulty components; Step S15: Trigger maintenance of multiple faulty components according to the fault maintenance plan, and trigger autonomous control of the maintenance progress of multiple faulty components based on the maintenance progress of multiple faulty components, the component types of multiple faulty components and the power supply efficiency of the new energy parallel grid. refer to Figure 2 In step S11, the specific steps are as follows: S111: Locate the grid connection of new energy sources and collect the distribution map of the grid connection of new energy sources. Based on the detection of the distribution map of the grid connection of new energy sources, determine multiple grid connection areas and mark the location of each grid connection area; detect multiple grid connection areas and determine multiple grid data based on the detection of multiple grid connection areas. S112: Each grid-connected area is matched with multiple corresponding grid data; in each grid-connected area, the combination of grid data for each grid-connected area is determined based on the multiple grid data, the location of each grid-connected area, and the corresponding grid-connected function.

[0011] In the embodiments of this application, the new energy grid is located and a physical layout map of the grid is obtained. This typically includes the location information of substations, lines, switching stations, and key new energy power generation units (such as wind farms and photovoltaic power station groups). This distribution map is the basis for regional division. The distribution map is analyzed, and the grid is divided into several logically or physically independent "grid-connected areas" according to certain rules or objectives (e.g., geographical proximity, functional similarity, voltage level, control area, etc.). For example, a large wind farm and its connected step-up substation and transmission lines can be regarded as a grid-connected area, or all new energy grid-connected points in a certain region and their associated distribution network parts can be regarded as a region. Each divided grid-connected area is assigned a unique identifier, and the location information of its core nodes or boundaries (geographic coordinates and topology node numbers) is recorded.

[0012] Real-time or near-real-time status monitoring is performed on each marked grid-connected area, typically achieved through various sensors and monitoring devices deployed within the area. Actual grid operation data is collected from monitoring points in each grid-connected area (such as busbars, line outlets, and key switches). This data includes: electrical quantities: voltage amplitude, phase angle, frequency, current, active power, reactive power, harmonic components, etc.; status quantities: the position status of circuit breakers and disconnectors, the action signals of protection devices, the start-up and shutdown status of renewable energy generation units, etc.; environmental / status quantities: for renewable energy, this also includes wind speed, solar intensity, equipment temperature, etc.; measurement types: traditional SCADA points, more precise PMU (phasor measurement unit) data, or sequence of events (SOE) records from intelligent electronic devices (IEDs), etc.

[0013] Furthermore, for each defined grid-connected area, there is a clear data source (sensor, monitoring point) corresponding to it, and multi-dimensional and multi-type power grid data of that area can be obtained, which ensures the relevance and completeness of data collection. At the same time, based on multiple power grid data, the location of each grid-connected area and its corresponding grid-connected function, the power grid data combination of each grid-connected area is determined. The combination operation is carried out within each area. It is necessary to comprehensively consider: multiple power grid data: these are the raw materials for combination; these raw data need to be preprocessed (such as noise reduction, interpolation, and standardization).

[0014] Location of the grid-connected area: Location information is used for: Spatial feature construction: Combining geographic information to extract spatial distribution features, such as voltage / power differences between adjacent areas; Regional importance assessment: Faults located in critical transmission channels or load centers have a greater impact; Fault propagation analysis: Location information helps to trace fault paths later.

[0015] Corresponding grid connection functions: Each grid-connected area plays a different role in the power grid (e.g., a major power generation area, load center, interconnection hub, or end-point distribution network), resulting in different fault characteristics and impacts on the power grid. Different data focuses: For example, for power generation areas, the focus is on power output and voltage support capacity; for interconnection hubs, the focus is on power flow and interconnection line flow. Different data combination strategies or weights are designed for areas with different functions. The final output "power grid data combination" should be a data structure that effectively characterizes the overall operating status of the grid-connected area and highlights its characteristics (especially fault-related features). This combination consists of: a feature vector; a structured data record; a graph-based representation of the area's topology; and an estimated state that integrates physical measurements and model information.

[0016] refer to Figure 3 In step S12, the specific steps are as follows: S121: Collect data combinations from various power grids and filter the data combinations. Based on the filtering of the data combinations, determine multiple power grid data of different levels. Based on the fault detection of multiple power grid data of different levels, determine the corresponding faulty power grid data. At this time, the multiple power grid data of different levels adopt the corresponding fault detection mode for fault detection. S122: Collect multiple faulty power grid data and mark the fault level of the faulty power grid data. Determine the corresponding tracing method based on the multiple faulty power grid data and the corresponding fault level. Trace each faulty power grid data along the corresponding tracing method and mark the corresponding tracing node. Construct the corresponding faulty power grid path based on the multiple tracing nodes. In the embodiments of this application, various power grid data combinations are collected. The system needs to extract data from the power grid data combinations of various grid-connected areas generated in the S112 stage in real time or at a set frequency. These combined data are stored in a database or transmitted in real time through a message queue.

[0017] The purpose of screening various power grid data combinations is to initially identify "abnormal" or "suspicious" data combinations. Screening can be based on various rules: threshold method: checking whether certain key indicators in the data combination exceed the preset normal range; for example, line current, power, voltage, frequency, combination score, etc.; statistical method: comparing the statistical characteristics (such as mean, variance, quantile) of the current data combination with historical data to identify values ​​with significant deviations; pattern matching: comparing the current data combination with a known fault mode library to see if there is a match; rate of change detection: detecting whether the rate of change of certain indicators in the data combination is abnormally fast.

[0018] Based on the screening results, the power grid data is categorized into different levels. Common levels include: Normal, Attention, Warning, Alarm, and Emergency. Different levels reflect the severity of the anomaly and the urgency of the issue. More in-depth and precise fault detection is then performed on the screened suspicious data. The key is that "different levels employ corresponding fault detection modes": For the "Normal" level, no further detection is needed; it can be ignored or a lightweight health assessment can be performed. For the "Attention / Warning" level, a relatively simple detection mode is used, such as detailed threshold-based checks, simple time-series analysis, or lightweight machine learning models, with the aim of quickly eliminating false alarms or confirming minor anomalies. For the "Alarm / Emergency" level, a more complex and precise detection mode is used, such as detailed model-based simulation analysis, complex machine learning / deep learning models (such as LSTM, CNN), expert system reasoning, etc., with the aim of accurately identifying the fault type and severity. At this point, multiple power grid data at different levels are detected using the corresponding fault detection modes.

[0019] Furthermore, from the output of stage S121, collect all records that are identified as "faulty power grid data"; each record not only contains the data combination information when the fault occurred, but also needs to specify its corresponding fault level (such as overload, short circuit, grounding, etc.) and severity.

[0020] Different fault types and levels require different tracing methods. Tracing methods refer to the search and location of faults starting from known fault data points and tracing upstream and downstream or related parts of the power grid. Simultaneously, based on power flow direction: for faults such as overload and voltage anomalies, tracing is usually performed along the power flow direction (from power source to load) or against the power flow direction. Based on protection action information: for faults caused by protection device actions (such as tripping), the fault area can be directly determined using the protection device's action information (such as the action element and action time); protection action information often provides the most direct clues for fault location. Based on topology: using the power grid's topology diagram, traversing from the fault point along connected lines, switches, transformers, and other equipment. Based on time series analysis: for cascading faults, it is necessary to analyze the causal relationship between different fault points based on the time sequence of fault occurrence.

[0021] The selected tracing method is executed to search for the equipment or location related to the fault step by step along the power grid structure or data flow; each key point related to the fault (such as a switch, a line segment, a transformer, or the action point of a protection device) is recorded and called a "tracing node".

[0022] All tagged traceable nodes are connected in series or in parallel according to their physical or logical connections in the power grid and their temporal or causal relationships during the fault occurrence process, forming a directional path that represents the scope of the fault's impact or the chain of fault locations.

[0023] refer to Figure 4 In step S13, multiple fault nodes are determined based on each faulty power grid path, the fault area of ​​the new energy parallel power grid is determined based on the multiple fault nodes, and the corresponding fault event is determined based on the location, regional shape and state of the new energy parallel power grid. In the specific implementation of this invention, the specific steps are as follows: S131: Collect data on each faulty power grid path, identify multiple faulty nodes based on the detection of each faulty power grid path, and match each faulty node with different working functions. S132: Collect the distance between two adjacent fault nodes, and determine the main fault area and multiple sub-fault areas of the new energy parallel grid based on the node location, corresponding work function of each fault node and the distance between two adjacent fault nodes. S133: Collect the status of the new energy grid in parallel, determine the first fault coefficient based on the location and shape of the fault area, determine the second fault coefficient based on the location of the fault area and the status of the new energy grid in parallel, and determine the corresponding fault event based on the mapping relationship between the first fault coefficient, the second fault coefficient and the fault event. In the embodiments of this application, each faulty power grid path is collected. In the data processing flow, the faulty power grid path information generated in step S122 and marked is extracted and used as the input of the current step S131. This path information usually exists in the form of a data structure (such as a list, array or edge in a graph structure), which includes the starting point, ending point and key node sequence passed through the path.

[0024] Each faulty power grid path is inspected, and the raw data (from S11 and S12) of each node on the path is analyzed, along with the node type and their location within the path. Here, "inspection" does not mean re-inspecting the fault, but rather assessing the "importance" or "correlation" of nodes on the identified path to find the most suspicious fault location. This typically involves: assessing the degree of data anomaly: reviewing the severity of the data anomaly when the node was marked as faulty in S12; for example, the extent to which parameters such as current, voltage, frequency, and temperature deviate from normal values; and so on. Point type analysis: Different types of nodes (such as switches, transformers, lines, protection devices, and sensors) have different behaviors and functions in faults; for example, maloperation or failure to operate of protection devices, abnormal tripping of switches, and overheating of lines all indicate the location of the fault; Path location analysis: The location of nodes in the path also provides clues; for example, the beginning or end of a path usually connects different areas or equipment, while intermediate nodes are located on the transmission path; Association rule application: There are some predefined rules, such as "If the switch at the end of the line trips and the protection operates, then the switch itself and its adjacent line segment are highly suspicious nodes".

[0025] "Work function" refers to the specific tasks or functions that a node undertakes in the power grid. Clarifying the work function of a node helps to understand the causes and impacts of faults. These functions are usually derived from the standardized design of the power grid or equipment ledger information. Common work functions include: On / off control: such as circuit breakers and disconnecting switches, responsible for connecting or disconnecting circuits; Voltage transformation: such as transformers, responsible for changing voltage levels; Power transmission: such as transmission lines and busbars, responsible for transmitting electrical energy; Measurement and monitoring: such as CTs (current transformers), PTs (voltage transformers), and smart terminals (DTUs / FTUs), responsible for collecting power grid operation data; Protection and control: such as relay protection devices and measurement and control devices, responsible for fault detection, isolation, and recovery; Grid connection interface: such as inverters and grid connection switches, responsible for connecting new energy power generation units to the power grid.

[0026] Specifically, suppose we have two fault grid paths: Path 1: Photovoltaic power plant B outlet switch > associated instantaneous overcurrent protection device > A phase CT; Path 2: Wind farm A bus > Wind farm A to substation F line > Substation F incoming line CT; The system obtains Path 1: [Switch B, Protection device B, CT_A]; The system obtains Path 2: [Bus A, Line AF, CT_F].

[0027] Processing path 1: Checking switch B: In S12, it is marked as a high-level fault; data shows tripping and overheating; the type is switch; initially judged as a suspicious node. Checking protection device B: In S12, it operates due to a detected fault; data shows protection action signal; the type is protection device; initially judged as a suspicious node, but more likely a fault responder. Checking CT_A: In S12, it detects abnormal current (such as grounding current); data shows abnormal current; the type is sensor; initially judged as a suspicious node, but more likely a fault detector. Determining the fault node: Considering all factors, switch B is the most critical fault node (directly tripping), and protection device B and CT_A are also related nodes, but switch B is the primary fault node of concern. Assume we determine switch B and protection device B as fault nodes.

[0028] Processing path 2: Check bus A: Data is normal or slightly abnormal; type is bus; low suspicion. Check line AF: In S12, it is marked as a medium-level fault; data shows excessive current and rising temperature; type is line; preliminary judgment is a suspicious node. Check CT_F: Data shows abnormal current; type is sensor; preliminary judgment is a suspicious node. Determine the fault node: line AF is the most critical fault node (overload or potential short circuit), and CT_F is an associated node; let's assume we determine line AF as the fault node.

[0029] Matching functions for node switch B: Querying grid information reveals that switch B is the grid-connected output switch for photovoltaic power station B; its function is "on / off control" (used to connect / disconnect the photovoltaic power station from the grid). Matching functions for node protection device B: Querying grid information reveals that protection device B is the associated protection for switch B; its function is "protection control" (used to detect faults and drive the switch to operate). Matching functions for node line AF: Querying grid information reveals that line AF is a transmission line used to transmit power from wind farm A to substation F; its function is "power transmission".

[0030] Through S131, we obtained the following information: Fault node 1: Switch B, function: on / off control (grid connection outlet of photovoltaic power station B); Fault node 2: Protection device B, function: protection control (related protection of switch B); Fault node 3: Line AF, function: power transmission (from wind farm A to substation F).

[0031] Furthermore, the distance between two adjacent fault nodes is collected. The distance collection usually requires reference to the power grid's geographic information system (GIS) data or topology database. "Adjacent" refers to two nodes that are directly connected on the fault path determined in S131. At the same time, the system will query the power grid database or GIS system to obtain the distance information of each pair of adjacent fault nodes on the path.

[0032] This section introduces the node location, corresponding functional role, and distance between adjacent faulty nodes. Node location: The specific coordinates or location description of the faulty node on the power grid geographic map or topology map; Functional role: The function that the faulty node performs in the power grid (such as switch, protection, transformer, line, etc.); The scope and nature of the impact of a faulty node are different for nodes with different functions; for example, a line fault usually affects the entire line, while a switch fault affects both sides of its connection; Distance between adjacent nodes: The distance between nodes is used to determine the continuity and scope of the fault's impact; nodes that are close together belong to the same fault area, while nodes that are far apart belong to different areas or sub-areas.

[0033] Specifically, the fault points are: switch B (the grid connection outlet of photovoltaic power station B, with on / off control function); protection device B (the associated protection of switch B, with protection control function); and line AF (from wind farm A to substation F, with power transmission function).

[0034] Distance from switch B to protection device B: 50 meters (physically very close, installed together); Distance from protection device B to a critical point on line AF (e.g., the end of line AF closest to protection device B): 500 meters; Node location: Switch B and protection device B are geographically very close; Line AF is another physical line; Function: Switch B and protection device B are both critical control / protection devices directly related to photovoltaic power station B; Line AF is used to transmit electrical energy from wind farm A; Distance between adjacent nodes: Switch B-protection device B is extremely close (50 meters); Protection device B-line AF is relatively far (500 meters).

[0035] Logically: Main Fault Area: Since switch B and protection device B are very close and are both key equipment (on / off control, protection control) of photovoltaic power station B, they belong to the core impact range of the same fault event; therefore, switch B and protection device B are classified into the main fault area; the fact that protection device B detects the abnormality and drives switch B to operate indicates that the problem lies in these two and their directly related equipment; Sub-Fault Area: Although line AF is also a fault node, it is far from protection device B (500 meters); the function of line AF is power transmission, which is important, but its direct correlation with switch B and protection device B is slightly weaker; therefore, line AF can be classified into a sub-fault area, which indicates that there is also a problem on line AF, or its state is affected by the main fault area (switch B / protection device B).

[0036] The system integrates the identified main and sub-fault areas to form a final description of the entire fault's impact range. "Fault area" is a more macroscopic concept that includes the main fault area (where the core problem lies) and all related sub-fault areas (affected or associated areas). The system merges the identified main fault area and each sub-fault area to form a general fault area description, which can be a geographical area, a topological region, or a collection of multiple sub-regions. It represents the total extent of the current fault event's impact on the power grid.

[0037] Specifically, the main fault area (switch B, protection device B) and the sub-fault area (line AF) are integrated. The final fault area can be described as: the area with the grid-connected outlet switch and protection device of photovoltaic power station B as the core area, and associated with the transmission line AF from wind farm A to substation F. Step S132 realizes the division from discrete fault nodes to continuous fault areas by quantifying the distance between nodes and combining the physical location and functional attributes of the nodes. Distinguishing between the main and sub-areas helps operation and maintenance personnel to prioritize the handling of core issues while paying attention to secondary impacts, providing richer spatial information for subsequent fault event inference.

[0038] Therefore, by collecting the status of the grid connected to new energy sources, determining the first fault coefficient based on the location and morphology of the fault area, determining the second fault coefficient based on the location of the fault area and the status of the grid connected to new energy sources, and determining the corresponding fault event based on the mapping relationship between the first fault coefficient, the second fault coefficient, and the fault event, this approach takes into account the overall consideration of the mapping relationship between the first fault coefficient, the second fault coefficient, and the fault event, ensuring the accuracy of the corresponding fault event. At the same time, by introducing fault grid data, this approach takes into account the overall consideration of the location, morphology, and status of the grid connected to new energy sources, improving the accuracy of fault event detection.

[0039] At this time, the status of the grid connected to new energy sources is collected. The grid status is a comprehensive concept, which usually includes, but is not limited to: power status: active power (P), reactive power (Q), and power factor (PF) of each grid-connected area; voltage status: voltage amplitude, voltage deviation, and three-phase voltage balance of each key node; frequency status: grid frequency value and its stability; new energy output status: wind power prediction and actual output, photovoltaic power prediction and actual output, and the operating status of new energy power plants (such as the number of wind turbine shutdowns, photovoltaic panel shading, etc.); tie line status: power flow and power limitation status of tie lines with external grids or adjacent areas; protection and control status: the operating status of major protection devices and the operating status of automatic control systems (such as AGC, AVC). These data usually come from SCADA systems, PMUs (phasor measurement units), and new energy power plant monitoring systems, etc.; the collected frequency needs to meet real-time requirements.

[0040] The first fault coefficient is determined based on the location and shape of the fault area. The first fault coefficient primarily focuses on the characteristics of the fault area itself. Location: Where in the power grid is the fault area located? Is it near a power source, a load center, or an intermediate transmission link? For example, a fault near a power source affects the output of more renewable energy sources, while a fault near a load center directly affects power supply reliability. Shape: Is the fault area point-like (single device), line-like (a section of line), or area-like (a region composed of multiple devices)? What is the size and shape of the area? For example, a small area containing switches and protection devices indicates a fault at the equipment level; while a long-distance line area indicates a problem with the line itself. The first fault coefficient is usually a quantitative value (e.g., between 0 and 1), representing the severity or nature of the fault based on the characteristics of the area itself.

[0041] Specifically, the system analyzes the fault areas determined by S132; location analysis: the main fault area (switch B, protection device B) is located at the grid connection point of photovoltaic power station B, which is a key control node for the output power of photovoltaic power station; the sub-fault area (line AF) connects wind farm A and substation F, and is an important power transmission channel; area morphology analysis: the main fault area contains two adjacent key control devices, and its morphology is point-like / small area; the sub-fault area is line AF, and its morphology is linear / long area; according to the rules, the main fault area that is point-like / small area and contains key control devices has a higher first fault coefficient, for example, set to 0.8 (indicating that the problem in this area is serious or highly probable); the linear sub-fault area has a medium first fault coefficient, for example, set to 0.5 (indicating that there is also a problem with the line, but it is not as direct as the equipment problem).

[0042] The second fault coefficient is determined based on the location of the fault area and the state of the grid connected to the renewable energy source. This second fault coefficient focuses on the interaction between the fault area and the overall grid state. Location and state interaction: How much pressure does the fault area experience under the current grid state? For example, if line AF (the sub-fault area) is under high load (large power flow), even minor problems will be amplified, leading to protection activation. If photovoltaic power station B (near the main fault area) is operating at full capacity, its grid-connected switches and protection devices will experience greater current and voltage stress, increasing the probability of failure. Grid state background: Are there other anomalies in the current grid? For example, generally high / low voltage, frequency fluctuations, or anomalies reported in other areas can exacerbate or mask the symptoms of the fault area. The second fault coefficient is a quantitative value (e.g., between 0 and 1) representing the degree of fault impact or background relevance based on the overall grid state and the location of the fault area.

[0043] Specifically, the system analyzes the collected grid status and the fault location determined by S132; location and status interaction analysis: main fault area (PV power station B grid connection point): PV power station B is approaching full power generation (1500kW), and the current borne by switch B and protection device B is relatively large; the voltage of substation F bus is slightly high (508kV), which puts some pressure on the equipment at the grid connection point; sub-fault area (line AF): the power flow of line AF (2200A) is close to the rated current and is in a high load state; grid status background analysis: the grid frequency is normal, and there are no serious abnormality reports in other areas; since the equipment in the main fault area is operating at high output and slightly high voltage, and the line in the sub-fault area is in a high load state, both areas are under great pressure in the current state; therefore, the second fault coefficient can be set relatively high; for example, the second coefficient of the main fault area is set to 0.7, and the second coefficient of the sub-fault area is set to 0.6.

[0044] The corresponding fault event is determined based on the first fault coefficient, the second fault coefficient, and the fault event mapping relationship. The fault event mapping relationship is a predefined rule base or knowledge base that associates different combinations of coefficients, fault area characteristics, and common fault event types. For example: High first coefficient + High second coefficient + Equipment area + Instantaneous overcurrent protection action > indicates a short circuit fault in the equipment; Medium first coefficient + High second coefficient + Line area + Overcurrent protection action > indicates a line overload or partial short circuit; Low first coefficient + Low second coefficient + Larger area + Multiple minor protection alarms > indicates minor interference or insulation degradation.

[0045] Specifically, the system inputs the first fault coefficient, the second fault coefficient, the fault area information (location, shape, and included node functions) determined in S132, and the protection action type recorded in S121 / S122 into the fault event mapping relationship for matching.

[0046] Input: Main fault area: First coefficient = 0.8, Second coefficient = 0.7, Shape = point (equipment), Includes nodes = switches, protection devices, Protection action = instantaneous trip; Sub-fault area: First coefficient = 0.5, Second coefficient = 0.6, Shape = linear, Includes nodes = lines, Protection action = (associated or independent).

[0047] Examine the coefficient combination (0.8, 0.7) and characteristics of the main fault area; in the mapping relationship, the high first coefficient (high equipment problem) + high second coefficient (current state exacerbates the problem) + point-like equipment area + instantaneous overcurrent protection action strongly points to a serious fault at the equipment level; combined with the fact that the protection device B is an instantaneous overcurrent action and the reclosing is unsuccessful, the fault event is a phase A ground short circuit fault, which occurs in the switch B itself or its adjacent connection part.

[0048] The coefficient combination (0.5, 0.6) of the sub-fault area (line AF) is relatively low. Although the line is also under high load (second coefficient contribution), its first coefficient (the nature of the line problem itself) and shape (linear) make it more affected by the main fault or have secondary problems. Under the current inference, the main fault event has been identified, and the line problem is a secondary correlation or subsequent investigation item.

[0049] The main fault event ultimately determined by the system is: a phase A ground fault occurred near grid-connected switch B of photovoltaic power station B. This conclusion is based on key information such as the high severity coefficient and high state correlation coefficient of the fault area, as well as the action of instantaneous overcurrent protection. Step S133 combines the inherent characteristics of the fault area (first fault coefficient) and the external environment (second fault coefficient) and uses preset mapping rules to link the located fault area with the specific fault event type, providing more accurate fault diagnosis results for operation and maintenance decisions. This greatly improves the pertinence and efficiency of fault handling.

[0050] refer to Figure 5 In step S14, the specific steps are as follows: S141: Collect fault events, determine multiple sub-fault items based on the detection of fault events, determine the corresponding item content based on the identification of multiple sub-fault items, and mark the corresponding faulty components based on the parsing of each item content; S142: Collect the power supply path of the new energy parallel grid, determine the first maintenance sequence of multiple faulty components according to the content of each project and the power supply path of the new energy parallel grid, determine the second maintenance sequence of multiple faulty components according to the corresponding faulty components and the power supply path of the new energy parallel grid, and determine the fault maintenance sequence of multiple faulty components based on the first maintenance sequence and the second maintenance sequence. S143: Collect multiple working data from each faulty component, determine the corresponding fault type based on the identification of the multiple working data of the faulty component, and determine the fault maintenance plan based on the functional priority, corresponding fault maintenance sequence and fault type of multiple faulty components.

[0051] In the embodiments of this application, a fault event is collected, and a final confirmed fault event description is obtained from the previous step (S133). This description should be as specific and clear as possible. For example, it is not just "short circuit", but "a specific type of short circuit in a specific location and a specific phase". This information is the basis for subsequent fault decomposition.

[0052] Based on the nature, location, and impact of the fault event, it is broken down into a series of smaller, more manageable, and inspectable "sub-projects." These sub-projects are usually key links or aspects involved in the fault investigation or handling process. For example, for a short circuit fault, it is necessary to check the short circuit point itself, the cause of the short circuit, and the related equipment affected. For each sub-fault project, it is necessary to further clarify the specific inspection or operation content, which is equivalent to creating a "task list" or "inspection points" for each sub-project. The content should be specific, actionable, and guide subsequent operations.

[0053] Each sub-fault item and its specific content should be ultimately assigned to a specific physical component in the power grid. This helps subsequent steps (such as S142 to determine the maintenance sequence) and allows maintenance personnel to clearly identify the objects that need to be operated. The component markings should be clear, unique, and correspond to the actual equipment.

[0054] Furthermore, the power supply path of the new energy parallel grid is collected, and the first maintenance sequence of multiple faulty components is determined according to the content of each project and the power supply path of the new energy parallel grid. The second maintenance sequence of multiple faulty components is determined according to the corresponding faulty components and the power supply path of the new energy parallel grid. The fault maintenance sequence of multiple faulty components is determined based on the first maintenance sequence and the second maintenance sequence, which takes into account the overall consideration of the first maintenance sequence and the second maintenance sequence and ensures the accuracy of the fault maintenance sequence of multiple faulty components.

[0055] At this point, the power supply path of the new energy grid is collected to obtain the current grid topology information, especially the part related to the fault area. The power supply path describes how electrical energy flows from the power source (such as photovoltaic power station B) to the load or grid. This usually includes: physical connection relationship: which devices are connected together by lines; switch status: which switches are closed and which are open (although they were activated during the fault); grid operation mode: for example, whether it is islanded or grid-connected, whether there is a backup path, etc. This information is the basis for determining the maintenance sequence, because it reveals the dependencies between devices and the scope of fault propagation / impact.

[0056] The focus is on prioritizing based on the fault itself. It considers which component is most likely the root cause of the fault, or which component's inspection can most quickly identify the fault point, based on the fault symptoms (such as the A-phase ground short circuit identified by S133) and the inspection items decomposed from S141. Furthermore, inspecting switch contacts is more directly related to short-circuit faults than inspecting the surrounding environment. In the power supply path, the fault point usually affects its downstream components. Therefore, inspecting components closest to the fault initiation point (switch B) and most relevant to the fault symptoms has a higher priority. The first maintenance sequence is determined based on fault diagnosis logic, with the aim of quickly locating and confirming the fault source.

[0057] Prioritization focuses on the perspective of grid operation and restoration; it considers: which component repair will restore power most quickly, minimize the impact on other users, or create conditions for subsequent inspections; the location of the component in the power supply path; for example, repairing a critical switch will restore power to a large area more effectively than repairing a section of the end of a cable; whether there are bypasses or backup paths available; and determining a second maintenance sequence based on the grid operation strategy, with the aim of optimizing overall operational efficiency and restoration speed.

[0058] The first and second maintenance orders obtained from the first two steps are combined to obtain the final, single maintenance order. Usually, the two orders are combined according to certain rules or weights. For example: Rule 1 (conservative priority): Prioritize the items that are at the beginning of both orders; if they are only at the beginning of one order, they are adjusted according to their relative position in the other order; Rule 2 (diagnosis priority): Prioritize the items that are at the beginning of the first maintenance order (diagnosis logic) because these items are the most direct in finding the source of the fault; the second maintenance order (running logic) is used for fine-tuning within or after the diagnosis order; Rule 3 (weighted average): Assign weights to the two orders and calculate the overall ranking.

[0059] Therefore, multiple operational data points are collected from each faulty component. The corresponding fault type is determined based on the identification of these multiple operational data points. A fault maintenance plan is then determined based on the functional priorities, corresponding fault maintenance sequences, and fault types of multiple faulty components. This comprehensive approach, which considers the functional priorities, corresponding fault maintenance sequences, and fault types of multiple faulty components, ensures the accuracy of the fault maintenance plan.

[0060] At this time, multiple operational data are collected from each faulty component. Data related to its operating status and performance are collected from each faulty component to be maintained (such as the A-phase contact, A-phase insulation, and connecting cable of switch B). This data includes: real-time monitoring data: such as current, voltage, temperature, partial discharge signal, vibration, oil level (if it is an oil switch); historical operating data: operating trends over a period of time, such as load changes and temperature change curves; protection device records: waveforms at the time of the fault, protection action information, fault phase, fault current value, and fault occurrence time; maintenance records: the component's past maintenance history, replacement records, and test reports; and descriptions of fault phenomena: such as smoke, odor, discoloration, and deformation observed on-site. The data can be collected automatically from SCADA, protection devices, and online monitoring systems, or manually from on-site inspection records.

[0061] By utilizing the collected data, combined with the characteristics of the faulty component and the fault phenomena, logical reasoning and pattern matching are performed to determine the specific cause and nature of the fault. This usually requires relying on an expert knowledge base, fault diagnosis model, or rule base. Fault types include: mechanical faults: such as contact wear, jamming, and operating mechanism malfunction; electrical faults: such as overheating, arc erosion, insulation breakdown, poor contact, short circuit, and grounding; environmental factors: such as dirt, humidity, small animal intrusion, and excessively high / low temperatures; and aging faults: performance degradation or failure caused by material deterioration. Determining the fault type is a prerequisite for developing an effective maintenance plan.

[0062] Based on all the preceding information, generate a detailed and actionable maintenance plan. Considerations include: Functional priority: Some components (such as main switches and protection devices) are more important than auxiliary equipment, and their failures have a wider impact; Fault maintenance sequence: This determines the order of inspection and repair; Fault type: This determines the specific repair measures required; for example, cleaning contacts, tightening bolts, replacing parts, or addressing insulation issues. The plan typically includes: Maintenance task list: Specific operating steps for each faulty component; Required resources: Personnel, tools, spare parts, safety equipment, etc.; Safety measures: Power outage area, voltage testing, grounding wire installation, setting up safety barriers, etc.; Estimated work hours: The time required for each task; Quality standards: Acceptance standards after maintenance completion; Responsible person: Clearly define who is responsible for each task; Contingency plan: Response measures if a more serious problem is discovered than expected.

[0063] The system not only accurately diagnosed the fault type (poor contact of phase BA switch leading to overheating and short circuit), but also generated a detailed maintenance plan based on the high priority of the component, maintenance sequence, and specific fault nature. This plan guides on-site personnel on how to complete maintenance work safely and efficiently, including specific operating steps, required resources, safety precautions, quality standards, and estimated time. This makes the entire process from fault diagnosis to repair more systematic and scientific, minimizing the impact of faults on the operation of the new energy parallel grid.

[0064] refer to Figure 6 In step S15, the specific steps are as follows: S151: Collect the fault maintenance plan, and build a corresponding intelligent maintenance script based on the detection of the fault maintenance plan. Trigger the maintenance of multiple faulty components by executing the intelligent maintenance script, so that multiple faulty components are maintained in sequence, and mark the maintenance progress of each faulty component. S152: Based on the detection of multiple faulty components, the component types of multiple faulty components are determined, and the power supply efficiency of the new energy parallel grid is collected. At this time, the first control coefficient is determined according to the component types of multiple faulty components and the power supply efficiency of the new energy parallel grid. S153: Determine the second control coefficient based on the maintenance progress of multiple faulty components and the power supply efficiency of the new energy parallel grid, determine the autonomous control system based on the first control coefficient, the second control coefficient and the autonomous control mapping relationship, and trigger the autonomous control of the maintenance progress of multiple faulty components.

[0065] In the embodiments of this application, the fault maintenance plan is collected. This plan includes a list of faulty components that need to be repaired, the specific operation steps to be performed for each component, the operation sequence, the required tools and spare parts, safety precautions, quality acceptance standards, and the estimated execution time. The system needs to accurately extract the maintenance plan that needs to be executed from the database, file system, or plan management module.

[0066] The collected maintenance plans are transformed into a more structured sequence of operations that is easier for automated systems or to guide manual execution. Intelligent maintenance scripts not only include task steps but also: trigger conditions: the execution of a step depends on the result of the previous step (e.g., replacing contacts is only performed when the contact resistance measurement exceeds the limit); checkpoint / verification steps: checkpoints are set after critical operations, such as confirming that contacts are clean, the replacement spare parts are of the correct type, and insulation tests are passed; resource allocation instructions: instructing automated systems or personnel to prepare or retrieve the necessary tools and spare parts; safety confirmation instructions: requiring safety confirmation before performing high-risk operations (e.g., confirming that the grounding wire is properly installed); communication interfaces: the script may include interfaces with monitoring systems, databases, or other subsystems for data uploading, status updates, or receiving instructions; and decision logic: determining which branch path to execute based on real-time detection data (such as measured resistance values).

[0067] The system begins to execute the steps defined in the intelligent maintenance script, which involves: instruction issuance: sending operation instructions to the operators (manual) or automated equipment (such as robots or automatic testing equipment) performing the maintenance; status monitoring: monitoring the status during the execution process in real time, such as operator feedback, signals returned by the equipment, and readings of measuring instruments; and process control: controlling the flow of operations according to the script's logic, including sequential execution, conditional branching, and looping (if multiple attempts are required).

[0068] If the maintenance plan involves multiple faulty components (for example, in addition to switch B, there is also a problem with a voltage transformer TV1 on the same line), the system needs to execute the corresponding intelligent maintenance script for each component in the order determined by S142; at the same time, the system needs to record and update the maintenance status and progress of each faulty component in real time.

[0069] Ensure that high-priority components are processed first, followed by low-priority components. For each component, record its current maintenance step (e.g., "Cleaning contacts", "Insulation test completed", "Waiting for replacement parts"), as well as the overall completion percentage. This can be obtained through operator feedback, signals from automated equipment, or automatic calculations by the system. This progress information can be displayed in real time on the monitoring screen, operator's workstation, or maintenance management system interface, making it easy for managers and operators to understand the overall situation.

[0070] The system successfully transformed the detailed maintenance plan for "phase-to-ground short circuit of switch BA" into an intelligent operation script. This script not only contains clear steps but also incorporates logic for inspection, decision-making, and safety confirmation. During the execution phase, the system guides the operator step by step according to the script instructions to complete all operations from power outage testing to final power restoration, and updates the maintenance progress of switch B in real time. If other faulty components (such as TV1) need to be repaired later, the system will also start the corresponding maintenance scripts in a predetermined order and continuously track the maintenance status of all components. This ensures that the maintenance work is carried out in an orderly and standardized manner and provides basic data for subsequent progress monitoring and dynamic adjustment (S152, S153).

[0071] Specifically, assuming we are processing the maintenance plan mentioned in S143 regarding "a phase-to-ground short-circuit fault occurred at grid-connected switch B of photovoltaic power station B"; the system will extract the following content from the maintenance plan database: Plan ID: PM_20250722_001; Associated Fault Event: BA phase-to-ground short circuit of grid-connected switch B in photovoltaic power station; Faulty Component: Switch B (Model: XX-1000A), specifically the A phase contact and connection parts; Maintenance Sequence: 1. Power off and test for voltage; 2. Disassemble the BA phase casing of switch; 3. Check for contact erosion; 4. Clean the contact surface; 5. Measure the contact resistance; 6. If the resistance exceeds the standard (>50μΩ), replace the A phase contact; 7. Reassemble switch B; 8. Perform insulation resistance test; 9. Restore power; 10. Record maintenance results.

[0072] Required resources: high voltage detector, insulating gloves, insulating boots, wrench set, contact resistance tester, new contact spare parts (model matching), insulation resistance tester, multimeter; Safety procedures: implement work permit system, set up safety fences, confirm that the upstream and downstream switches are disconnected and grounded, and conduct safety briefing before operation; Estimated time: 4 hours.

[0073] The system transforms the aforementioned maintenance plan into an intelligent maintenance script. The system initiates the "Maintenance_Switch B_A Phase_Script"; first, the system sends the instruction "Execute Step 1: Power Off and Verify" to the operator responsible for the area (or their mobile terminal); after the operator executes the instruction, they provide feedback "Power Off Confirmed" and "Grounding Wire Installed" by scanning a code or clicking the confirmation button; upon receiving confirmation, the system automatically triggers the next instruction "Execute Step 2: Disassemble Switch BA Phase Housing"; this process repeats until the script is completed; if automated equipment is involved (e.g., a robotic arm for replacing contacts), the system directly sends precise movement and operation instructions to the robotic arm.

[0074] Assume the maintenance plan also includes repairing "the insulation performance of voltage transformer TV1 has deteriorated"; S142 determines to repair switch B first, then TV1; the system first executes the maintenance script for switch B (as described above), and marks the progress of the "switch B" component in the maintenance management system, for example: initial state: not started (0%); when executing to step 5: measuring contact resistance (40%); when executing to step 9: restoring power (90%); after execution: completed (100%); after the maintenance of switch B is completed and confirmed to be normal, the system automatically switches and starts executing the intelligent maintenance script for TV1 (assuming the script for TV1 has also been built); at the same time, the maintenance management system interface will display the status of the two components: switch B: completed; TV1: executing step X: ... (e.g., measuring insulation resistance) (Y%); the manager can clearly see which component is being repaired, what step it has reached, and which components are still to be repaired through this interface.

[0075] Furthermore, based on the detection of multiple faulty components, the component types of multiple faulty components are determined, and the power supply efficiency of the new energy parallel grid is collected. At this time, the first control coefficient is determined according to the component types of multiple faulty components and the power supply efficiency of the new energy parallel grid, which is compatible with the overall consideration of the detection of multiple faulty components and ensures the accuracy of the component types of multiple faulty components.

[0076] At this point, during the maintenance process, faulty components that are currently being maintained or have been maintained are identified and classified. The system needs to know what type of equipment these components are, such as transformers, circuit breakers, inverters, lines, protection devices, or other types of equipment. This classification helps to understand the different impacts of different types of component faults on power grid performance. The classification can be based on a predefined component type library or equipment tag information.

[0077] The system monitors and acquires real-time power supply efficiency data for the entire renewable energy grid (or critical areas affected by faults). Power supply efficiency can be measured by various indicators, such as: energy conversion efficiency (e.g., the ratio of the actual output power of a photovoltaic power plant to its theoretical maximum output power (based on current light intensity and other conditions); system loss rate (the proportion of total losses generated by the power grid during transmission and distribution to the total input / output power); power factor (an indicator that measures the effectiveness of energy utilization); and voltage / frequency stability (the degree to which the grid voltage and frequency deviate from their nominal values, which also indirectly reflects the power supply quality). The system needs to acquire this real-time data from SCADA systems, energy management systems (EMS), smart meters, or other sensors.

[0078] The system needs to establish a model or rule base that associates the types of faulty components and their impact on power supply efficiency with a "first control coefficient," which represents the "pressure" or "influence weight" of the current fault state on the power grid's power supply efficiency.

[0079] The determination method may include: rule base / matching table: pre-defining the first control coefficient corresponding to different component types under a specific power supply efficiency range; for example, when the "high voltage circuit breaker" fault is "system loss rate > 10%", the coefficient is "high"; when the "current transformer" fault is "system loss rate > 5%", the coefficient is "medium"; table lookup interpolation: based on the component type and power supply efficiency value, the coefficient is obtained by looking up or interpolating in a pre-calculated two-dimensional table. This coefficient will be used to guide the next step of S153's autonomous control strategy.

[0080] Specifically, suppose that during the execution of S151, we are dealing with two faulty components: Faulty component A: Switch B (grid-connected switch of photovoltaic power station B) - component type: high-voltage circuit breaker; Faulty component B: TV1 (current transformer on a section of line connecting switch B and the internal combiner box of photovoltaic power station B) - component type: current transformer; The system confirms that these two components belong to the two categories of "high-voltage circuit breaker" and "current transformer" respectively by reading information from equipment tags, maintenance records or maintenance plans.

[0081] During the maintenance of switch B and TV1, the system continuously collects power grid efficiency data. Assume that the power efficiency index is selected as "system loss rate". Before the maintenance begins (during the fault), the system loss rate spikes to 15% due to the short circuit fault. When the maintenance is halfway through (for example, switch B has been dealt with and TV1 is being dealt with), the impact of the fault has been partially eliminated, and the system loss rate drops to 8%. When the maintenance is nearing its end, the system loss rate further decreases to 5% (close to the normal level). The system records these data in real time.

[0082] Suppose we use a rule base plus simple weighting method to determine the first control coefficient: Component type weight: The weight of "high voltage circuit breaker" is set to 0.7 and the weight of "current transformer" is set to 0.3 in advance. This is because circuit breaker failures usually have a greater impact and require a higher weight.

[0083] Power supply efficiency impact assessment: When the system loss rate > 10%, the impact level is "high" with a corresponding impact value of 1.0; when 5% < system loss rate <= 10%, the impact level is "medium" with a corresponding impact value of 0.5; when the system loss rate <= 5%, the impact level is "low" with a corresponding impact value of 0.1.

[0084] Initial maintenance phase: System loss rate 15% (>10%), impact level "high" (1.0); First control coefficient = (0.7 switch B impact) + (0.3 TV1 impact) = (0.7 + 1.0) + (0.3 + 1.0) = 0.7 + 0.3 = 1.0 (maximum pressure); Mid-term maintenance: System loss rate 8% (5% < 8% <= 10%), impact level "medium" (0.5); first control coefficient = (0.7 0.5) + (0.3 0.5) = 0.35 + 0.15 = 0.5 (medium pressure).

[0085] Late maintenance: System loss rate 5% (<=5%), impact level "low" (0.1); First control coefficient = (0.7 0.1) + (0.3 0.1) = 0.07 + 0.03 = 0.1 (low pressure). This first control coefficient (1.0, 0.5, 0.1) quantifies the pressure level caused by the decline in power grid efficiency due to the failure of these specific components at different maintenance stages.

[0086] During the maintenance of switch B and TV1, the system continuously monitors the power supply efficiency of the power grid (system loss rate). Simultaneously, the system clearly identifies the types of components being processed (high-voltage circuit breakers and current transformers). Based on the importance of each component and the degree of deterioration in current power supply efficiency, the system dynamically calculates a "first adjustment coefficient." This coefficient gradually decreases from 1.0 (initial maintenance phase, greatest impact) to 0.1 (late maintenance phase, least impact), clearly reflecting the gradual reduction in the negative impact of the fault on the power grid's power supply efficiency as maintenance progresses. This coefficient will serve as input for the next step, S153, to determine how to adjust the power grid operation to optimize the maintenance process and power grid performance.

[0087] Therefore, a second control coefficient is determined based on the maintenance progress of multiple faulty components and the power supply efficiency of the new energy parallel grid. An autonomous control system is determined based on the mapping relationship between the first control coefficient, the second control coefficient, and the autonomous control system, and the autonomous control of the maintenance progress of multiple faulty components is triggered. This system takes into account the overall consideration of the first control coefficient, the second control coefficient, and the autonomous control mapping relationship, ensuring the accuracy of the autonomous control system. At the same time, a fault maintenance plan is introduced, realizing the overall consideration of the maintenance progress of multiple faulty components, the component types of multiple faulty components, and the power supply efficiency of the new energy parallel grid, thereby realizing the autonomous control of the maintenance progress of multiple faulty components and ensuring the autonomous maintenance of multiple faulty components.

[0088] At this point, the second control coefficient is determined based on the maintenance progress of multiple faulty components and the power supply efficiency of the new energy parallel grid. The maintenance progress of multiple faulty components refers to the completion status of multiple ongoing maintenance tasks (for previously identified faulty components); for example, whether the maintenance of a certain component has just begun, is halfway done, or is nearing completion; this can be obtained through reports from maintenance personnel, automatic system timing, or sensor data (such as disassembly / installation signals); the maintenance progress can be quantified as a percentage or stage (e.g., in preparation, in disassembly, in replacement, in testing, completed); the power supply efficiency of the new energy parallel grid reflects the current overall power supply performance of the grid, such as loss rate, voltage stability, frequency stability, power output capacity, etc. This indicator is constantly changing and is affected by faults and the grid operating status.

[0089] Based on these two pieces of information, the system calculates a "second control coefficient," which reflects the immediate impact of maintenance progress on grid power efficiency and the grid's capacity to withstand the current maintenance progress. For example, if maintenance progress is slow and grid power efficiency continues to deteriorate, a higher second control coefficient indicates that maintenance needs to be accelerated. If maintenance progress is fast but grid power efficiency is already very poor, a higher second control coefficient also indicates that critical power supply needs to be restored first, while non-critical maintenance should be temporarily slowed down. If maintenance progress is moderate and grid power efficiency is acceptable, a lower second control coefficient indicates that the current maintenance pace is appropriate.

[0090] The calculation methods can be: simple threshold method: set a maintenance progress percentage threshold and a power supply efficiency threshold, and directly determine the coefficient level (such as high, medium, low) according to the combination; weighted calculation method: set weights for maintenance progress and power supply efficiency respectively, and calculate a comprehensive score based on their current values. This score is the second control coefficient.

[0091] The system combines the "first control coefficient" calculated in S152 and the "second control coefficient" calculated in sub-step 1 of S153, and refers to a predefined "autonomous control mapping relationship" table or rule base to determine a specific "autonomous control system." This system describes the control strategy and objectives that the system will adopt. The "autonomous control mapping relationship" is a decision table that defines what control actions the system should take under different combinations of the first and second control coefficients. For example: (high first coefficient, high second coefficient) > Control system: urgently accelerate critical maintenance and start backup power; (high first coefficient, low second coefficient) > Control system: maintain the current maintenance speed, but closely monitor the grid status; (low first coefficient, high second coefficient) > Control system: prioritize the completion of the current maintenance, and then assess whether the subsequent plan needs to be adjusted; (low first coefficient, low second coefficient) > Control system: perform maintenance according to the original plan, without special control.

[0092] Based on the "autonomous control system" determined in the previous step, the system automatically or semi-automatically adjusts the maintenance progress of multiple faulty components in progress. This includes: adjusting resource allocation: allocating more maintenance personnel or equipment to the maintenance of critical components (such as switch B); changing the maintenance sequence: if the system requires optimized sequence, the system suggests or automatically postpones the maintenance of a non-critical component to prioritize the critical component; adjusting maintenance strategies: for example, increasing the degree of parallelism for tasks that can be processed in parallel; or adopting more efficient maintenance methods; and interacting with maintenance personnel: the system can notify maintenance personnel of the current control strategy through alarms, prompts, or task reassignment interfaces, guiding them to adjust their work pace.

[0093] Specifically, assume two faulty components are under maintenance: switch B (high-voltage circuit breaker) and TV1 (current transformer); maintenance progress: switch B: maintenance is 50% complete, expected to take another 2 hours to complete; TV1: maintenance has just started, is 10% complete, expected to take another 1.5 hours to complete; overall maintenance progress: can be taken as the average ((50%+10%) / 2=30%), or weighted according to the importance of the components (assuming switch B is more important, weight 0.7, TV1 weight 0.3, then weighted progress = 0.750%+0.310%=35%+3%=38%); power supply efficiency of the new energy parallel grid: the current system loss rate is 8% (between 5% and 10%, which is moderately deteriorating).

[0094] The second control coefficient is calculated according to the preset rules: Rule 1: If the weighted maintenance progress is <30% and the power supply efficiency deterioration level is "medium" (5% < loss rate < 10%), then the second control coefficient = 0.6; Rule 2: If the weighted maintenance progress is >=30% and the power supply efficiency deterioration level is "medium", then the second control coefficient = 0.4; Application rule: Our weighted maintenance progress is 38% (>30%), and the power supply efficiency deterioration level is "medium", so according to rule 2, the second control coefficient = 0.4. This coefficient indicates that the impact of the current maintenance progress on the grid efficiency is at a low to medium level, and the grid can still bear it.

[0095] First control coefficient = 0.5 (from example S152, assuming we are currently in the middle of maintenance); Second control coefficient = 0.4 (just calculated); Looking up the "Autonomous Control Mapping Relationship": Assume the table has the following rules: If the first coefficient >= 0.7 and the second coefficient >= 0.6, then the control system = "Emergency Mode: Accelerate maintenance at full speed, sacrificing non-critical loads if necessary"; If the first coefficient >= 0.5 and the second coefficient >= 0.3, then the control system = "Enhanced Mode: Appropriately accelerate the maintenance of critical components, optimize the maintenance sequence of non-critical components"; If the first coefficient < 0.5 and the second coefficient < 0.3, then the control system = "Regular Mode: Maintain according to plan, focusing on efficiency"; In other cases, the control system = "Observation Mode: Maintain the current pace, enhance monitoring"; Applying the rule: Our first coefficient is 0.5 (>= 0.5), and the second coefficient is 0.4 (>= 0.3), which conforms to the second rule; Therefore, the determined autonomous control system = "Enhanced Mode: Appropriately accelerate the maintenance of critical components, optimize the maintenance sequence of non-critical components".

[0096] The established autonomous control system is "Enhanced Mode: Appropriately accelerate the maintenance of critical components and optimize the maintenance sequence of non-critical components"; Triggered control: The system sends a notification to the maintenance team responsible for switch B (high-voltage circuit breaker, critical component): "Based on the real-time status of the power grid, please appropriately accelerate the maintenance progress of switch B and prioritize the restoration of its function;" The system will also suggest allocating additional tools or manpower support; The system sends a notification to the maintenance team responsible for TV1 (current transformer, relatively non-critical component): "The current power grid efficiency has deteriorated moderately. Please continue the maintenance of TV1 according to the original plan, but temporarily do not prioritize it, and ensure that the maintenance of switch B is completed first;" The system lowers the priority of TV1 maintenance in the task management system.

[0097] Maintenance team A (switch B) will work harder or speed up due to enhanced instructions or additional support; maintenance team B (TV1) will maintain its original speed or slow down slightly to ensure the critical path (switch B) remains open. Through this regulation, the system attempts to maximize the speed of critical fault repair within the grid's capacity, thereby improving the grid's power supply efficiency more quickly.

[0098] Please see Figure 7 , Figure 7 This is a schematic diagram of the structural composition of a fault detection system for a new energy parallel grid based on grid data, as described in this embodiment of the invention. The fault detection system for the new energy parallel grid based on grid data includes: The power grid data combination module 21 is used to determine multiple power grid data based on the detection of multiple grid-connected areas in the new energy parallel power grid, and to determine the combination of power grid data of each grid-connected area according to the multiple power grid data and the location of each grid-connected area; The fault grid path module 22 is used to determine the corresponding fault grid data based on the filtering of various grid data combinations, and to determine the corresponding fault grid path based on the tracing of the fault grid data. The fault event module 23 is used to determine multiple fault nodes based on each fault grid path, determine the fault area of ​​the new energy parallel grid based on the multiple fault nodes, and determine the corresponding fault event based on the location, shape and status of the fault area and the new energy parallel grid. The fault maintenance plan module 24 is used to determine the fault maintenance sequence of multiple faulty components based on the project content of the fault event, the corresponding faulty components and the power supply path of the new energy parallel grid, and to determine the fault maintenance plan according to the fault maintenance sequence and fault type of multiple faulty components. The fault progress module 25 is used to trigger the maintenance of multiple faulty components according to the fault maintenance plan, and to autonomously control the maintenance progress of multiple faulty components based on the maintenance progress of multiple faulty components, the component types of multiple faulty components and the power supply efficiency of the new energy parallel grid.

[0099] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A fault detection method for a new energy parallel power grid based on power grid data, characterized in that, include: Multiple grid data are determined based on the detection of multiple grid-connected areas in the new energy parallel grid, and the combination of grid data for each grid-connected area is determined based on the multiple grid data and the location of each grid-connected area; The corresponding faulty power grid data is determined by filtering the various power grid data combinations, and the corresponding faulty power grid path is determined by tracing the faulty power grid data. Multiple fault nodes are identified based on each faulty power grid path. Fault areas of the new energy parallel power grid are determined based on the multiple fault nodes. Corresponding fault events are determined based on the location, regional shape, and state of the new energy parallel power grid. Based on the project content of the fault event, the corresponding faulty component and the power supply path of the new energy parallel grid, determine the fault maintenance sequence of multiple faulty components, and determine the fault maintenance plan according to the fault maintenance sequence and fault type of multiple faulty components. The maintenance plan triggers the maintenance of multiple faulty components. Based on the maintenance progress of multiple faulty components, the component types of multiple faulty components, and the power supply efficiency of the new energy grid, the maintenance progress of multiple faulty components is autonomously controlled.

2. The fault detection method for new energy parallel power grids based on power grid data according to claim 1, characterized in that, The method involves determining multiple grid data points based on the detection of multiple grid-connected areas in a new energy parallel grid, and determining the combination of grid data points for each grid-connected area based on the multiple grid data points and the location of each grid-connected area, including: Locate the grid of new energy parallel connection, collect the distribution map of the new energy parallel connection grid, determine multiple grid connection areas based on the detection of the distribution map of the new energy parallel connection grid, and mark the location of each grid connection area; detect multiple grid connection areas, and determine multiple grid data based on the detection of multiple grid connection areas; Each grid-connected area is matched with multiple corresponding grid data; within each grid-connected area, the combination of grid data for each grid-connected area is determined based on the multiple grid data, the location of each grid-connected area, and the corresponding grid-connected function.

3. The fault detection method for new energy parallel power grids based on power grid data according to claim 1, characterized in that, The process of determining the corresponding faulty power grid data based on the filtering of various power grid data combinations, and determining the corresponding faulty power grid path based on the tracing of the faulty power grid data, includes: Collect data combinations from various power grids and filter them. Based on the filtering of the data combinations, determine multiple power grid data at different levels. Based on the fault detection of multiple power grid data at different levels, determine the corresponding faulty power grid data. At this time, the multiple power grid data at different levels adopt the corresponding fault detection mode for fault detection. Collect multiple faulty power grid data and mark the fault level of the faulty power grid data. Determine the corresponding tracing method based on the multiple faulty power grid data and the corresponding fault level. Trace each faulty power grid data along the corresponding tracing method and mark the corresponding tracing node. Construct the corresponding faulty power grid path based on the multiple tracing nodes.

4. The fault detection method for new energy parallel power grids based on power grid data according to claim 1, characterized in that, The process involves identifying multiple fault nodes based on various faulty power grid paths, determining fault regions in the renewable energy grid based on these fault nodes, and identifying corresponding fault events based on the location, morphology, and state of the renewable energy grid. This includes: Collect data on each faulty power grid path, identify multiple faulty nodes based on the detection of each faulty power grid path, and assign different working functions to each faulty node. The distance between two adjacent fault nodes is collected. Based on the node location, corresponding work function of each fault node and the distance between two adjacent fault nodes, the main fault area and multiple sub-fault areas of the new energy parallel grid are determined. The fault area of ​​the new energy parallel grid is determined based on the main fault area and multiple sub-fault areas of the new energy parallel grid.

5. The fault detection method for new energy parallel power grids based on power grid data according to claim 4, characterized in that, The process of determining multiple fault nodes based on each faulty power grid path, determining the fault region of the renewable energy grid based on the multiple fault nodes, and determining the corresponding fault event based on the location, shape, and state of the renewable energy grid also includes: The status of the grid connected to new energy sources is collected. A first fault coefficient is determined based on the location and shape of the fault area. A second fault coefficient is determined based on the location of the fault area and the status of the grid connected to new energy sources. The corresponding fault event is determined based on the mapping relationship between the first fault coefficient, the second fault coefficient, and the fault event.

6. The fault detection method for new energy parallel power grids based on power grid data according to claim 1, characterized in that, The project content based on the fault event, the corresponding faulty component, and the power supply path of the new energy parallel grid determine the fault maintenance sequence of multiple faulty components. Based on the fault maintenance sequence and fault type of multiple faulty components, a fault maintenance plan is determined, including: Collect fault events, determine multiple sub-fault items based on the detection of fault events, determine the corresponding item content based on the identification of multiple sub-fault items, and mark the corresponding faulty components based on the parsing of each item content; Collect the power supply path of the new energy parallel grid, determine the first maintenance sequence of multiple faulty components based on the content of each project and the power supply path of the new energy parallel grid, determine the second maintenance sequence of multiple faulty components based on the corresponding faulty components and the power supply path of the new energy parallel grid, and determine the fault maintenance sequence of multiple faulty components based on the first maintenance sequence and the second maintenance sequence.

7. The fault detection method for new energy parallel power grids based on power grid data according to claim 6, characterized in that, The method of determining the fault maintenance sequence of multiple faulty components based on the project content of the fault event, the corresponding faulty component, and the power supply path of the new energy parallel grid, and determining the fault maintenance plan based on the fault maintenance sequence and fault type of multiple faulty components, also includes: In each faulty component, multiple operational data are collected. Based on the identification of these multiple operational data, the corresponding fault type is determined. Based on the functional priority of multiple faulty components, the corresponding fault maintenance sequence, and the fault type, a fault maintenance plan is determined.

8. The fault detection method for new energy parallel power grids based on power grid data according to claim 1, characterized in that, The autonomous control of triggering maintenance of multiple faulty components according to the fault maintenance plan, and triggering maintenance progress of multiple faulty components based on maintenance progress, component type, and power supply efficiency of the new energy grid, includes: The fault maintenance plan is collected, and a corresponding intelligent maintenance script is constructed based on the detection of the fault maintenance plan. The execution of the intelligent maintenance script triggers the maintenance of multiple faulty components, so that the multiple faulty components are maintained in sequence, and the maintenance progress of each faulty component is marked.

9. The fault detection method for new energy parallel power grids based on power grid data according to claim 8, characterized in that, The method of triggering maintenance of multiple faulty components according to the fault maintenance plan, and autonomously controlling the maintenance progress of multiple faulty components based on their maintenance progress, component types, and power supply efficiency of the renewable energy grid, further includes: Based on the detection of multiple faulty components, the component types of multiple faulty components are determined, and the power supply efficiency of the new energy parallel grid is collected. At this time, the first control coefficient is determined according to the component types of multiple faulty components and the power supply efficiency of the new energy parallel grid. The second control coefficient is determined based on the maintenance progress of multiple faulty components and the power supply efficiency of the new energy grid. The autonomous control system is determined based on the first control coefficient, the second control coefficient and the autonomous control mapping relationship, and the autonomous control of the maintenance progress of multiple faulty components is triggered.

10. A fault detection system for a new energy parallel power grid based on power grid data, characterized in that, The fault detection system for new energy parallel grids based on grid data is applied to the fault detection method for new energy parallel grids based on grid data as described in any one of claims 1-9, wherein the fault detection system for new energy parallel grids based on grid data includes: The power grid data combination module is used to determine multiple power grid data based on the detection of multiple grid-connected areas in the new energy parallel power grid, and to determine the combination of power grid data for each grid-connected area based on the multiple power grid data and the location of each grid-connected area; The fault grid path module is used to determine the corresponding fault grid data based on the filtering of various grid data combinations, and to determine the corresponding fault grid path based on the tracing of the fault grid data. The fault event module is used to determine multiple fault nodes based on each faulty power grid path, determine the fault area of ​​the new energy parallel grid based on the multiple fault nodes, and determine the corresponding fault event based on the location, shape and status of the fault area and the new energy parallel grid. The fault maintenance plan module is used to determine the fault maintenance sequence of multiple faulty components based on the project content of the fault event, the corresponding faulty components and the power supply path of the new energy parallel grid, and to determine the fault maintenance plan according to the fault maintenance sequence and fault type of multiple faulty components. The fault progress module is used to trigger the maintenance of multiple faulty components according to the fault maintenance plan. It can autonomously control the maintenance progress of multiple faulty components based on the maintenance progress of multiple faulty components, the component types of multiple faulty components, and the power supply efficiency of the new energy grid.

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