Power grid operation order intelligent mistake prevention generation method and system based on source compatibility principle

Abstract

The application discloses a kind of power grid operation ticket intelligent mistake-proof generation method and system based on source compatibility principle, it is related to electric power automation technical field, comprising the following steps: extracting first data from dispatching instruction, first data includes operation object and equipment initial state;Dynamic power grid model is constructed based on first data, and source attribute is marked to each device in model;Combining the dynamic power grid model after marking and pre-defined source compatibility constraint criterion, analysis is carried out through source compatibility rule engine, and the operation framework containing source matching requirement is generated;Operation sequence meeting source matching requirement is searched based on operation framework planning, and operation sequence is converted into specific operation item;Specific operation item is checked for source compatibility, and operation item is optimized according to the check result, generates final operation ticket.The present application is used to solve the problem that traditional mistake-proof system is insufficient in real-time matching of power supply and load, operation sequence checking is not complete and operation ticket generation lacks intelligent support.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system automation, and more particularly, to a power grid operation ticket intelligent anti-misoperation generation method and system based on source compatibility principle. BACKGROUND

[0002] In power grid operation scheduling, the operation ticket is a key means to ensure operation safety and prevent misoperation. With the expansion of the scale of the power system and the large number of access of new energy power sources, the topology structure and operation state of the power grid are increasingly complex, and the compilation and checking of the operation ticket are significantly improved. The traditional operation ticket mainly relies on manual experience generation and auditing, which can meet the basic operation requirements to a certain extent, but in the face of complex power grid environment and multi-source access conditions, the existing method has obvious deficiencies:

[0003] Lack of support for dynamic power grid model. The current method is usually based on static topology, and cannot effectively combine the operation state and source properties of the power grid equipment, cannot accurately reflect the real-time matching relationship between power sources and loads during operation, and cannot meet the requirements of modern power grid for dynamic adaptability; the operation sequence generation and checking are incomplete. The traditional operation ticket generation method can only give sequential operation steps, but lacks heuristic search and constraint-driven optimization process, especially in parallel operation, operation dependency and path validity checking, which leads to the risk of misoperation in the operation sequence. The conversion from the operation sequence to the specific operation item lacks intelligent support. The existing technology usually stops at the operation step level when generating the operation ticket, lacks further source compatibility checking and parallel correction mechanism, and thus still needs manual intervention and secondary auditing, which reduces the efficiency and reliability.

[0004] In view of the above problems, the present application provides a solution. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an intelligent anti-misoperation generation method and system for power grid operation ticket based on source compatibility principle, which solves the problems of the traditional anti-misoperation system in real-time matching of power sources and loads, incomplete operation sequence checking and lack of intelligent support for operation ticket generation through dynamic modeling, framework generation driven by source compatibility rule engine, heuristic search and parallel constraint checking.

[0006] To achieve the above purpose, the present application provides the following technical scheme:

[0007] The application discloses a power grid operation ticket intelligent mistake-proof generation method based on a source compatibility principle, and comprises the following steps: extracting first data from a dispatching instruction, wherein the first data comprises an operation object and a device initial state; constructing a dynamic power grid model based on the first data, and marking the source attribute of each device in the model; combining the marked dynamic power grid model and a predefined source compatibility constraint criterion, analyzing through a source compatibility rule engine, and generating an operation framework containing source matching requirements; searching for an operation sequence meeting the source matching requirements based on the operation framework, and converting the operation sequence into specific operation items; performing source compatibility checking on the specific operation items, optimizing the operation items according to the checking result, and generating a final operation ticket.

[0008] In a preferred embodiment, the source attribute marking of each device in the model comprises: constructing a source attribute marking knowledge base and continuously updating the same; traversing the dynamic power grid model, identifying and generating a target device set requiring source attribute marking according to model state changes; extracting real-time topological connection relationships, device type information and operating state information of the target device set, and constructing a multi-dimensional feature vector for representing device source characteristics; matching and calculating the multi-dimensional feature vector with the updated source attribute marking knowledge base, generating a source attribute marking result of each target device, and writing the marking result into the corresponding target device node in the dynamic power grid model.

[0009] In a preferred embodiment, the construction of the source attribute marking knowledge base and the continuous updating thereof specifically comprises: constructing an initial source attribute marking knowledge base based on historical device operating data, correcting and expanding the initial rules in combination with expert experience, forming a first source attribute marking knowledge base; dynamically optimizing and updating the rule set in the first source attribute marking knowledge base based on real-time collected device operating state data through an online learning mechanism, and forming a second source attribute marking knowledge base with stronger adaptability.

[0010] In a preferred embodiment, the combination of the marked dynamic power grid model and the predefined source compatibility constraint criterion, and the analysis through the source compatibility rule engine to generate an operation framework containing source matching requirements specifically comprises: integrating the device connection relationships in the dynamic power grid model, the device source attributes and the predefined source compatibility constraint criterion, forming a unified data set; pre-processing and feature extracting the data set to obtain a parameter set; inputting the parameter set into an energy balance calculator, establishing a constraint condition, and calculating the preliminary operation boundary between the power supply and the load; inputting the device connection relationships into a topological analysis unit for connectivity analysis, eliminating paths not meeting the energy matching condition, and obtaining topological connectivity logic; fusing the preliminary operation boundary and the topological connectivity logic, and generating an operation framework containing source matching requirements.

[0011] In a preferred embodiment, the source compatibility rules engine comprises: a topology analysis unit that analyzes the connection relationship of devices in the dynamic power grid model and outputs connectivity information; an energy balance calculator that calculates the preliminary operating boundary of the power supply and the load based on the parameter set, while considering the energy balance constraint, the voltage stability constraint and the frequency constraint.

[0012] In a preferred embodiment, the source matching requirement refers to the power supply-load matching condition defined in the operation framework, which is fused from the preliminary operating boundary generated by the energy balance calculator and the topology connectivity logic generated by the topology analysis unit.

[0013] In a preferred embodiment, the operation sequence is processed by the parallel constraint processing method to generate a specific operation item, specifically: the operation sequence is split into a plurality of subsequences according to the execution dependency relationship and parallel feasibility of the operation steps in the operation sequence; an independent constraint verification logic is configured for each subsequence, and the core content of the constraint verification logic is an electrical constraint condition; each constraint verification logic performs constraint checking on the operation steps of the respective subsequence in parallel, and outputs the subsequence that passes the checking; for the subsequence that fails the checking, a modified sequence is output after parallel correction according to a preset rule; the subsequence that passes the checking and the modified sequence are integrated according to the execution dependency relationship of the original operation sequence to generate a specific operation item containing an operation object and an operation type.

[0014] In a preferred embodiment, the operation sequence that satisfies the electrical constraint condition is generated in the state space by using a heuristic search algorithm, specifically: in the state space, the electrical constraint condition is embedded into the evaluation function and the pruning mechanism of the heuristic search algorithm to construct a configured complete heuristic search algorithm; based on the configured complete heuristic search algorithm, a state node corresponding to an initial state is started from, and state transitions corresponding to device operations are explored step by step to generate a plurality of candidate paths; the electrical constraint condition checking is performed on the candidate paths, and the paths that do not satisfy the condition are pruned and removed, and finally the operation sequence that satisfies the electrical constraint condition is obtained

[0015] In a preferred embodiment, the operation sequence is processed by the parallel constraint processing method to generate a specific operation item, specifically: the operation sequence is split into a plurality of subsequences according to the execution dependency relationship and parallel feasibility of the operation steps in the operation sequence; an independent constraint verification logic is configured for each subsequence, and the core content of the constraint verification logic is an electrical constraint condition; each constraint verification logic performs constraint checking on the operation steps of the respective subsequence in parallel, and outputs the subsequence that passes the checking; for the subsequence that fails the checking, a modified sequence is output after parallel correction according to a preset rule; the subsequence that passes the checking and the modified sequence are integrated according to the execution dependency relationship of the original operation sequence to generate a specific operation item containing an operation object and an operation type

[0016] In a second aspect, the application provides a power grid operation ticket intelligent error prevention generation system based on a source compatibility principle, comprising: a data extraction module configured to extract first data from a dispatching instruction, the first data including an operation object and an initial state of a device; a dynamic power grid modeling and source attribute labeling module configured to construct a dynamic power grid model based on the first data and label the source attributes of each device in the model; an operation framework generation module configured to analyze the labeled dynamic power grid model and predefined source compatibility constraint criteria through a source compatibility rule engine to generate an operation framework containing source matching requirements; an operation sequence planning module configured to search for an operation sequence that meets the source matching requirements based on the operation framework and convert the operation sequence into specific operation items; and an operation item optimization and source compatibility verification module configured to verify the source compatibility of the specific operation items, optimize the operation items according to the verification results, and generate a final operation ticket.

[0017] As can be seen from the above technical solutions, the application introduces a dynamic power grid model and source attribute labeling to solve the problem that traditional methods only rely on static topology and cannot reflect the real-time matching relationship between power sources and loads; the source compatibility rule engine is combined with heuristic search to solve the problem of lack of constraint-driven optimization and complete verification in the operation sequence generation process; parallel constraint processing and source compatibility verification are used to solve the problem of dependence on manual intervention and lack of intelligent support in operation ticket generation. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 FIG. 1 is a flowchart of a power grid operation ticket intelligent error prevention generation method based on a source compatibility principle according to the application.

[0019] Figure 2 FIG. 2 is a structural diagram of a power grid operation ticket intelligent error prevention generation system based on a source compatibility principle according to the application.

[0020] Figure 3 FIG. 3 is a source attribute labeling flowchart. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0022] Embodiment 1, Figure 1 A power grid operation ticket intelligent error prevention generation method based on a source compatibility principle is provided, comprising the following steps:

[0023] S1, extracting first data from a dispatching instruction, the first data including an operation object and an initial state of a device;

[0024] S2, constructing a dynamic power grid model based on the first data, and marking a source attribute of each device in the model;

[0025] S3, combining the marked dynamic power grid model and a predefined source compatibility constraint criterion, analyzing through a source compatibility rule engine, and generating an operation framework including a source matching requirement;

[0026] S4, searching for an operation sequence meeting the source matching requirement based on the operation framework, and converting the operation sequence into a specific operation item;

[0027] S5, performing source compatibility verification on the specific operation item, optimizing the operation item according to a verification result, and generating a final operation ticket.

[0028] It can be seen from the above technical solution that the dynamic power grid model and the source attribute marking are introduced, the problem that a traditional method only relies on a static topology and cannot reflect a real-time matching relationship between a power source and a load is solved, the source compatibility rule engine is combined with heuristic search, the problems of lack of constraint-driven optimization and complete verification in an operation sequence generation process are solved, parallel constraint processing and source compatibility verification are performed, and the problem that operation ticket generation relies on manual intervention and lacks intelligent support is solved.

[0029] S1, extracting first data from a dispatching instruction, the first data including an operation object and an initial state of a device.

[0030] In the embodiment, the dispatching instruction is a structured message issued by a power grid dispatching system, and includes identification information of the operation object and operation parameters of the device.

[0031] The structured message is parsed, and the operation object and the initial state of the device are extracted to form the first data. The data extraction process can be implemented by using existing data parsing technology, and does not belong to the key content of the application.

[0032] S2, constructing a dynamic power grid model based on the first data, and marking a source attribute of each device in the model;

[0033] The dynamic power grid model is constructed based on the first data, specifically as follows:

[0034] Identifying upstream and downstream devices having an electrical connection relationship with the operation object, and obtaining corresponding physical connection information from a power grid topology database;

[0035] Constructing a graph structure model taking the devices as nodes and the physical connection information as edges, and attaching the initial state of each device to the nodes as attribute information of the nodes.

[0036] In the embodiment, the source attribute labeling of each device in the model comprises:

[0037] The source attribute labeling knowledge base is constructed and continuously updated;

[0038] The dynamic power grid model is traversed, and a target device set requiring source attribute labeling is identified and generated according to model state changes;

[0039] Real-time topological connection relationships, device type information and operating state information of the target device set are extracted, and a multi-dimensional feature vector for representing source features of the device is constructed;

[0040] The multi-dimensional feature vector is matched and calculated with the updated source attribute labeling knowledge base, a source attribute labeling result of each target device is generated, and the labeling result is written into the corresponding target device node in the dynamic power grid model.

[0041] Specifically,

[0042] The specific process of the source attribute labeling of each device in the model is as shown in Figure 3

[0043] In the embodiment, the source attribute labeling knowledge base is constructed and continuously updated, and specifically comprises:

[0044] An initial source attribute labeling knowledge base is constructed based on historical device operating data, and the initial rules are corrected and expanded in combination with expert experience to form a first source attribute labeling knowledge base;

[0045] Based on real-time collected device operating state data, the rule set in the first source attribute labeling knowledge base is dynamically optimized and updated through an online learning mechanism to form a second source attribute labeling knowledge base with stronger adaptability.

[0046] First, historical device operating data of each device in the power grid including power output, load change, switch state and fault record are collected, and the collected data are preprocessed, the preprocessing including data cleaning, outlier rejection, normalization processing and statistical analysis, to provide basic data for rule generation;

[0047] The voltage, current, power factor, load response characteristic parameters recorded in the historical device operating data are statistically analyzed to identify their stable intervals, abnormal intervals and change trends, and in combination with expert experience knowledge or preset threshold conditions, high-frequency appearing feature modes are extracted to form a first source attribute labeling knowledge base;

[0048] ​Further, real-time device state data is continuously acquired during power grid operation; and through an online learning mechanism, the threshold, weight and rule in the source attribute labeling knowledge base are dynamically adjusted, realizing adaptive updating of the first source attribute labeling knowledge base with the evolution of the power grid operation state, forming a second source attribute labeling knowledge base;

[0049] Subsequently, all devices in the dynamic power grid model are traversed to screen out target devices requiring source attribute labeling, forming a target device set; for each device in the target device set, its topological connection relationship, device type and real-time operation state are extracted, and the above information is integrated and coded into a multi-dimensional feature vector as an input for rule matching calculation;

[0050] Finally, the constructed multi-dimensional feature vector is matched with the second source attribute labeling knowledge base for calculation:

[0051] The multi-dimensional feature vector of the target device is analyzed by components, and is compared with the rule conditions in the second source attribute labeling knowledge base item by item; if the feature vector meets the threshold condition in a certain rule item, it is determined that the device has the corresponding source attribute. For the case where there are multiple candidate rule items, similarity calculation (such as Euclidean distance or cosine similarity) can be further used for discrimination, and the rule item with the highest similarity is selected as the final labeling result, and the result is written into the corresponding target device.

[0052] Figure 3 The above flowchart shows the overall processing path and logical relationship.

[0053] S3, combined with the labeled dynamic power grid model and the pre-defined source compatibility constraint criteria, analyzes through a source compatibility rule engine to generate an operation framework containing source matching requirements, including:

[0054] Integrate the device connection relationship in the dynamic power grid model, the device source attribute and the pre-defined source compatibility constraint criteria to form a unified data set;

[0055] Preprocess and extract features from the data set to obtain a parameter set;

[0056] Input the parameter set into an energy balance calculator to establish a constraint condition and calculate the preliminary operation boundary between the power supply and the load;

[0057] Input the device connection relationship into a topological analysis unit for connectivity analysis to eliminate paths that do not meet the energy matching condition to obtain topological connectivity logic;

[0058] Fuse the preliminary operation boundary and the topological connectivity logic to generate an operation framework containing source matching requirements.

[0059] Specifically:

[0060] First, the device connection relationship in the dynamic power grid model, the device source attribute, and the pre-defined source compatibility constraint criteria are integrated to construct a unified data set. The data set reflects the topology between devices, records the power supply attribute of each device, and contains the compatibility constraint conditions between the power supply and the load, providing complete input for subsequent calculation;

[0061] Second, normalization and feature extraction processing are performed on the data set to form a parameter set. Normalization is used to eliminate the influence of different dimensions, and feature extraction is used to retain key parameters that have a decisive effect on energy distribution and system stability, such as voltage amplitude, power factor, and rated capacity;

[0062] Then, the parameter set is input into the energy balance calculator built-in the source compatibility rule engine. The energy balance calculator calculates the parameter set formed by the integration of the dynamic power grid model and the constraint criteria under the limitation of the source compatibility constraint criteria, to obtain the preliminary operation boundary between the power supply and the load, which is used to describe the feasible interval of power supply output and load demand under the constraint conditions;

[0063] On this basis, in combination with the topology analysis unit, the connectivity of the device connection relationship is analyzed, and links that do not meet the energy matching conditions are removed. For example, links whose power output is insufficient to support the load demand in the path, or links whose voltage drop exceeds the allowed range, will be determined as unusable paths, thereby forming the topology connectivity logic.

[0064] Finally, the preliminary operation boundary and the topology connectivity logic are fused to generate an operation framework containing source matching requirements. The operation framework provides a basis for power supply dispatching and load distribution, ensuring that the dispatching strategy meets the constraint requirements in terms of energy supply and demand balance, topology reachability, and power stability.

[0065] In the embodiment, the source compatibility rule engine includes:

[0066] a topology analysis unit for analyzing the device connection relationship in the dynamic power grid model and outputting connectivity information;

[0067] an energy balance calculator for calculating the preliminary operation boundary of the power supply and the load based on the parameter set, while considering the energy balance constraint, the voltage stability constraint, and the frequency constraint.

[0068] The topology analysis unit is used to analyze the device connection relationship in the dynamic power grid model and output connectivity information. The specific implementation is as follows:

[0069] The connection relationship matrix and device number information of each device in the dynamic power grid model are input.

[0070] The reachability and path connectivity of each node in the network are analyzed through graph theory methods such as connected component extraction or shortest path calculation.

[0071] Output connectivity information, including reachable links from each power source to the load and their corresponding connection status identifiers, for subsequent power source and load matching calculations.

[0072] The energy balance calculator is used to calculate the preliminary operating boundary between the power source and the load based on a parameter set, under the constraints of source compatibility criteria.

[0073] Energy balance constraints:

[0074]

[0075] in, For the first Power supply capacity, For the first Each load power, For network losses;

[0076] Voltage stability constraints:

[0077]

[0078] in, For the first Node voltage, and These are the minimum and maximum allowable voltage values, respectively;

[0079] Frequency constraints:

[0080]

[0081] in For system frequency, For the rated frequency, The maximum allowable deviation;

[0082] In this embodiment, the source matching requirement refers to the power-load matching conditions defined in the operation framework, which are formed by the fusion of the preliminary operation boundary generated by the energy balance calculator and the topology connectivity logic generated by the topology analysis unit.

[0083] It should be noted that the predefined source compatibility constraint criteria refer to the set of operating conditions that must be followed to ensure the safe and stable operation of the power grid when matching and scheduling power sources and loads. Specifically, these include:

[0084] Energy balance constraint: The total power output should be balanced with the total load demand and network losses;

[0085] Voltage stability constraint: The voltage at each node should be within the allowable deviation range;

[0086] Frequency constraint: the system operating frequency shall be maintained within the allowable deviation range of the rated value.

[0087] It should be noted that the device source attribute refers to a set of basic parameters for characterizing the supply and demand characteristics of the power supply device or load device in the grid operation. The device source attribute includes:

[0088] For power supply devices: rated capacity, output upper and lower limits, node voltage level, regulation capacity;

[0089] For load devices: rated demand power, demand fluctuation range, interruptibility.

[0090] S4, search for an operation sequence that meets the source matching requirements based on the operation framework, and convert the operation sequence into specific operation items, specifically:

[0091] Convert the source matching requirements in the operation framework into electrical constraint conditions;

[0092] Based on the electrical constraint conditions, filter the device connection relationships in the dynamic grid model, and construct a state space containing the device connection relationships;

[0093] In the state space, a heuristic search algorithm is used to generate an operation sequence that meets the electrical constraint conditions, and the operation sequence contains continuous operation steps from the initial state to the target state;

[0094] The operation sequence is processed by a parallel constraint processing method to generate specific operation items.

[0095] The conversion of the source matching requirements in the operation framework into electrical constraint conditions is specifically:

[0096] First, extract two types of key constraint information in the source matching requirements:

[0097] One is the source attribute constraint parameter, which corresponds to the preliminary operation boundary generated by the energy balance calculator, including power, voltage, frequency, and other quantitative constraints;

[0098] The other is the topological connectivity constraint parameter, which corresponds to the topological logical rules generated by the topological analysis unit, including path validity, operation dependency, and device type matching.

[0099] According to different constraint information, standardization conversion processing is performed:

[0100] For power, voltage, frequency, and other quantitative requirements, convert them into numerical interval constraints;

[0101] For logical requirements such as path validity and device dependency, convert them into logical condition expressions;

[0102] For device type matching relationships, convert them into type constraints.

[0103] Thus, the abstract matching requirement becomes a set of constraint rules that can be directly processed by a computer, i.e., electrical constraint conditions.

[0104] Based on the electrical constraint conditions, the connection relationship of the devices in the dynamic power grid model is screened, and a state space containing the connection relationship of the devices is constructed:

[0105] First, all the devices and their connection relationships are extracted from the dynamic power grid model to form an original network;

[0106] Then, the electrical constraint conditions are applied to the original network, and lines that do not meet the numerical interval requirements of power, voltage, and frequency are removed, and connections that do not have a valid power supply path are removed, and logical constraints such as operation dependency and device type matching are introduced in the state space, so that illegal connections cannot occur.

[0107] On this basis, the device operating state that meets the constraints is defined as a state node, and the device operation that can be legally executed under the constraint conditions is defined as a state transition, and then a state space containing only valid connection relationships and feasible state evolution paths is constructed.

[0108] In this embodiment, a heuristic search algorithm is used in the state space to generate an operation sequence that meets the electrical constraint conditions, specifically:

[0109] In the state space, the electrical constraint conditions are embedded in the evaluation function and pruning mechanism of the heuristic search algorithm to construct a fully configured heuristic search algorithm.

[0110] Based on the fully configured heuristic search algorithm, starting from the state node corresponding to the initial state, the state transition corresponding to the device operation is gradually explored to generate a number of candidate paths.

[0111] The electrical constraint conditions are checked on the candidate paths, and the paths that do not meet the conditions are removed through pruning processing, and finally an operation sequence that meets the electrical constraint conditions is obtained.

[0112] In the state space, the electrical constraint conditions are embedded in the evaluation function and pruning mechanism of the heuristic search algorithm to construct a fully configured heuristic search algorithm, specifically:

[0113] The source attribute constraint parameters are extracted from the electrical constraint conditions And the topological connectivity constraint parameters Based on the source attribute constraint parameters and the topological connectivity constraint parameters, an evaluation function containing constraint weights is constructed; the evaluation function is:

[0114]

[0115] In the formula, is the operation cost (basic cost); and is the constraint weight (priority guarantee source matching and topology compliance), which is dynamically adjusted based on the priority of the source matching requirement in the operation framework; is a constant term used to realize global policy preference, for example, preferentially selecting a path with fewer operation steps, and the coefficient can be configured according to the overall scheduling strategy;

[0116] Further, hierarchical pruning logic is designed for the two types of constraints:

[0117] Source attribute hard constraint pruning: when an operation in the path violates the source type incompatibility or power limit, the path search is immediately terminated;

[0118] Topology connectivity soft constraint pruning: when the path violates a non-core topology rule, for example, when the path redundancy slightly exceeds the threshold, it is marked as a low-priority path and is only retained when there is no better solution, and the path that satisfies the topology constraint is preferentially expanded;

[0119] Based on the evaluation function and pruning mechanism of the embedded heuristic search algorithm described above, a configuration complete heuristic search algorithm is generated.

[0120] The configuration complete heuristic search algorithm starts from the state node corresponding to the initial state, step by step explores the state transition corresponding to the device operation, and generates a number of candidate paths, specifically:

[0121] Based on the configuration complete heuristic search algorithm, the initial state is explored step by step; in the state space, the running state of each device can be abstracted as a state node, and each operation action (such as opening, closing, switching) can be abstracted as a state transition;

[0122] The heuristic search algorithm starts from the initial state node, step by step expands the state transition path according to the constraint conditions and the evaluation function, thereby generating a number of candidate paths.

[0123] The candidate paths are subjected to electrical constraint condition checking, and the paths that do not meet the conditions are removed through pruning processing, and finally the operation sequence that satisfies the electrical constraint condition is obtained, specifically:

[0124] Each item of running parameter and topology relationship involved in the candidate path is checked item by item, for example, whether the power, voltage, and frequency of each node in the path are within the allowed interval, whether the supply and demand matching requirements of the power supply and load are met; at the same time, the topology connectivity is also verified to ensure that the power supply path is effective and does not violate the operation dependency relationship or type matching rule between devices;

[0125] ​For the candidate paths that do not meet the electrical constraint conditions, the pruning mechanism is used to eliminate them;

[0126] Through the constraint checking and pruning mechanism, the final candidate path set is the operation sequence that meets the electrical constraint conditions, which contains continuous operation steps from the initial state to the target state.

[0127] In this embodiment, the operation sequence is processed by the parallel constraint processing method to generate specific operation items, specifically:

[0128] According to the execution dependency relationship and parallel feasibility of the operation steps in the operation sequence, the operation sequence is split into several sub-sequences;

[0129] Each sub-sequence is configured with independent constraint verification logic, and the core content of the constraint verification logic is the electrical constraint condition;

[0130] Each constraint verification logic performs constraint checking on the respective sub-sequence operation steps in parallel, and outputs the sub-sequences that pass the checking;

[0131] For the sub-sequences that do not pass the checking, the modified sequences are output after being modified in parallel according to the preset rules;

[0132] The sub-sequences that pass the checking and the modified sequences are integrated according to the execution dependency relationship of the original operation sequence to generate specific operation items containing operation objects and operation types.

[0133] Specifically:

[0134] First, according to the execution dependency relationship between the operation steps in the operation sequence and the parallel feasibility between devices, the operation sequence is split into several independent or partially independent sub-sequences. For example, if two operations act on different electrical islands and have no dependency relationship, they can be divided into parallel sub-sequences; if a certain operation must depend on the state of the previous device, it is kept in the same sub-sequence;

[0135] Then, each sub-sequence is configured with independent constraint verification logic. The core content of the verification logic is the electrical constraint condition, including numerical constraints such as power, voltage, and frequency, as well as logical constraints such as topology connectivity and device type matching. Each constraint verification logic can run synchronously and check each sub-sequence operation step one by one, and output the sub-sequences that pass the checking;

[0136] For the sub-sequences that do not pass the checking, they can be processed according to the preset parallel modification rules. The modification rules can include: reordering the operation sequence to make it comply with the dependency relationship; replacing some incompatible operation objects or devices; or inserting additional compensation operations (such as inserting a voltage recovery operation before closing) if necessary, so that the sub-sequences meet the constraint conditions. The modified sub-sequences are re-added to the overall sequence;

[0137] Finally, according to the execution dependency in the original operation sequence, the verified sub-sequences and the corrected sub-sequences are integrated to obtain operation items containing specific operation objects and operation types.

[0138] S5, source compatibility verification is performed on the specific operation items, and the operation items are optimized according to the verification result to generate a final operation ticket.

[0139] In the embodiment, the source compatibility verification of the specific operation items refers to the review and correction of the operation items at the execution level after the operation sequence generated based on the operation framework is converted into specific operation items, so as to ensure that the finally generated operation ticket has the executability and safety in the actual power grid operation environment. Specifically, the source compatibility verification includes:

[0140] First, the source compatibility verification is performed on the operation items one by one. The source compatibility verification not only verifies whether the operation items meet the power, voltage, frequency and other energy balance constraints under steady-state conditions, but also further checks the safety of the operation steps in the transition process, such as transient voltage drop caused by parallel operation, transient frequency fluctuation caused by operation object switching and other problems.

[0141] Secondly, for the operation items involving parallel execution of multiple devices, re-verification needs to be performed in combination with the dependency relationship and topological constraints to determine whether islanding, circulating current or illegal connection will occur. For operation items with risks, the operation sequence is adjusted according to the preset correction rules, such as modifying the operation sequence, inserting a compensation step or replacing the operation object, so as to ensure that each step always meets the source compatibility requirements in the dynamic process.

[0142] Finally, the operation items that pass the verification or are corrected are integrated into a final operation ticket to clearly define the operation objects, operation methods and execution sequence, so as to ensure that the actual dispatching process meets the requirements of power grid operation safety and error prevention.

[0143] Embodiment 2, Figure 2 The application provides an intelligent error prevention generation system for a power grid operation ticket based on a source compatibility principle, which comprises:

[0144] A data extraction module is configured to extract first data from a dispatching instruction, wherein the first data comprises operation objects and initial states of devices.

[0145] A dynamic power grid modeling and source attribute labeling module is configured to construct a dynamic power grid model based on the first data and label source attributes of devices in the model.

[0146] An operation framework generation module is configured to analyze the labeled dynamic power grid model and predefined source compatibility constraint criteria through a source compatibility rule engine to generate an operation framework comprising source matching requirements.

[0147] An operation sequence planning module is configured to search for an operation sequence that meets the source matching requirement based on the operation framework, and convert the operation sequence into specific operation items.

[0148] An operation item optimization and source compatibility checking module is configured to perform source compatibility checking on the specific operation items, optimize the operation items according to the checking result, and generate a final operation ticket.

[0149] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product.

[0150] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0151] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0152] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0153] Finally, the above is merely a preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligently preventing errors in generating power grid operation tickets based on the principle of source compatibility, characterized in that, Includes the following steps: Extract the first data from the scheduling instructions. The first data includes the operation object and the initial state of the device. A dynamic power grid model is constructed based on the first data, and the source attributes of each device in the model are labeled. By combining the labeled dynamic power grid model and predefined source compatibility constraint criteria, the source compatibility rule engine is used for analysis to generate an operational framework that includes source matching requirements. Based on the operation framework, search for and plan the operation sequence that meets the source matching requirements, and transform the operation sequence into specific operation items; Perform source compatibility verification on specific operation items, optimize the operation items based on the verification results, and generate the final operation ticket; The steps for generating the operation framework are as follows: The device connection relationships, device source attributes, and predefined source compatibility constraints in the dynamic power grid model are integrated to form a unified dataset; The dataset is preprocessed and features are extracted to obtain a parameter set. Quantitative constraints including power, voltage and frequency are established to obtain the preliminary operating boundary between the power supply and the load. The device connection relationship is input into the topology analysis unit for connectivity analysis, and paths that do not meet the energy matching conditions are eliminated to obtain the topology connectivity logic; By integrating the initial operation boundary and topological connectivity logic, an operation framework containing source matching requirements is generated. Source matching requirements refer to the power-load matching conditions defined in the operating framework, which are formed by the fusion of the initial operating boundary generated by the energy balance calculator and the topology connectivity logic generated by the topology analysis unit.

2. The intelligent anti-mistake generation method for power grid operation tickets based on the source compatibility principle according to claim 1, characterized in that, The process of annotating the source attributes of each device in the model includes: Build and continuously update a source attribute annotation knowledge base; Traverse the dynamic power grid model, identify and generate the target device set that needs to perform source attribute annotation based on the changes in the model state; Extract the real-time topology connections, device type information, and operating status information of the target device set, and construct a multi-dimensional feature vector to characterize the source features of the devices; The multidimensional feature vectors are matched and calculated with the updated source attribute annotation knowledge base to generate source attribute annotation results for each target device, and the annotation results are written into the corresponding target device node in the dynamic power grid model.

3. The intelligent anti-mistake generation method for power grid operation tickets based on the source compatibility principle according to claim 2, characterized in that, The construction and continuous updating of the source attribute annotation knowledge base specifically includes: An initial source attribute annotation knowledge base is constructed based on historical equipment operation data. The initial rules are then modified and expanded by combining expert experience to form the first source attribute annotation knowledge base. Based on real-time collected equipment operation status data, the rule set in the first source attribute annotation knowledge base is dynamically optimized and updated through an online learning mechanism to form a more adaptable second source attribute annotation knowledge base.

4. The intelligent anti-mistake generation method for power grid operation tickets based on the source compatibility principle according to claim 3, characterized in that, The operation sequence that meets the source matching requirements is searched and planned based on the operation framework, and the operation sequence is transformed into specific operation items, specifically as follows: Transform the source matching requirements in the operational framework into electrical constraints; Based on electrical constraints, the device connection relationships in the dynamic power grid model are screened, and a state space containing the device connection relationships is constructed. A heuristic search algorithm is used in the state space to generate an operation sequence that satisfies electrical constraints. The operation sequence contains continuous operation steps from the initial state to the target state. The operation sequence is processed using a parallel constraint processing method to generate specific operation items.

5. The intelligent anti-mistake generation method for power grid operation tickets based on the source compatibility principle according to claim 4, characterized in that, The heuristic search algorithm used in the state space to generate an operation sequence that satisfies the electrical constraints is as follows: In the state space, electrical constraints are embedded into the evaluation function and pruning mechanism of the heuristic search algorithm to construct a fully configured heuristic search algorithm. Based on a fully configured heuristic search algorithm, starting from the state node corresponding to the initial state, the system explores the state transitions corresponding to device operations step by step, generating several candidate paths. Electrical constraints are checked on the candidate paths, and paths that do not meet the conditions are removed through pruning, finally obtaining the operation sequence that meets the electrical constraints.

6. The intelligent anti-mistake generation method for power grid operation tickets based on the source compatibility principle according to claim 5, characterized in that, The process of processing the operation sequence using a parallel constraint processing method to generate specific operation items is as follows: Based on the execution dependencies and parallel feasibility of the operation steps in the operation sequence, the operation sequence is divided into several sub-sequences; Each subsequence is configured with independent constraint verification logic, the core of which is electrical constraint conditions; Each constraint verification logic performs constraint verification on its respective subsequence operation steps in parallel and outputs the subsequence that passes the verification. For subsequences that fail the verification, the corrected sequence is output after parallel correction according to preset rules. Integrate the verified subsequences and corrected sequences according to the execution dependencies of the original operation sequence to generate specific operation items containing the operation object and operation type.

7. A system using the intelligent anti-mistake generation method for power grid operation tickets based on the source compatibility principle as described in any one of claims 1-6, comprising: The data extraction module is used to extract first data from the scheduling instructions. The first data includes the operation object and the initial state of the device. The dynamic power grid modeling and source attribute annotation module is used to construct a dynamic power grid model based on the first data and to annotate the source attributes of each device in the model. The operation framework generation module is used to combine the labeled dynamic power grid model and predefined source compatibility constraint criteria, and analyze them through the source compatibility rule engine to generate an operation framework that includes source matching requirements. The operation sequence planning module is used to search and plan operation sequences that meet the source matching requirements based on the operation framework, and to transform the operation sequences into specific operation items. The operation item optimization and source compatibility verification module is used to perform source compatibility verification on specific operation items, optimize the operation items based on the verification results, and generate the final operation ticket.

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