Test verification method and system suitable for intelligent substation

Abstract

The invention discloses a test verification method and system suitable for an intelligent substation, and relates to the technical field of test verification, and the method comprises the following steps: collecting operation information in the substation, and obtaining an initial feature; performing multi-source information association analysis according to the initial features to obtain a function mapping relation; performing communication performance verification according to the function mapping relation to obtain a performance index; performing logic consistency verification according to the performance index to obtain a logic verification result; performing redundancy reliability detection according to a logic verification result to obtain a reliability index; and performing consistency evaluation according to the reliability index to obtain a whole-station verification result. According to the method, the mapping relation of each functional path in the range of the whole station is constructed, the one-sidedness problem caused by limited information sources is solved, multi-source information fusion and logic dependence quantification are realized, and the reliability, comprehensiveness and accuracy of test verification are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of test verification, in particular to a test verification method and system suitable for a smart substation. BACKGROUND

[0002] As an important part of the new generation of power systems, the smart substation integrates automatic control, digital communication, information collection and data processing and other technologies. Its operation relies on the coordinated work of a large number of functional paths, including protection, measurement and control, station control, communication and other subsystems. With the development of digitization and intelligence, the data generated by various devices and systems within the substation presents multi-source, high timeliness and complex correlation. In actual operation, testing and verifying the functions and performance of the smart substation not only needs to ensure the reliability of single devices, but also needs to ensure the overall executability of the whole station functional path under the conditions of redundancy switching, communication delay, logic consistency, etc.

[0003] In the prior art, there are deficiencies in multi-source information correlation analysis: the existing test verification method mainly based on a single type of data source (such as communication messages or device status) generates functional mapping through traditional logic analysis or rule matching when performing correlation analysis, and lacks comprehensive analysis of multi-source information. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a test verification method and system suitable for a smart substation to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions: In a first aspect, the present application provides a test verification method and system suitable for a smart substation, comprising the following steps: S1, collecting the running information in the substation to obtain initial features; S2, performing multi-source information correlation analysis according to the initial features to obtain functional mapping relationships; S3, performing communication performance verification according to the functional mapping relationships to obtain performance indicators; S4, performing logic consistency verification according to the performance indicators to obtain logic verification results; S5, performing redundancy reliability detection according to the logic verification results to obtain reliability indicators; S6, performing consistency evaluation according to the reliability indicators to obtain whole station verification results.

[0006] Further optimize the technical solution, the running information collection in step S1 includes: According to the hierarchical structure of the smart substation, the collection object is determined and the collection device is arranged, the running information in the smart substation is comprehensively collected and analyzed, and the initial features are obtained.

[0007] To further optimize this technical solution, the multi-source information correlation analysis in step S2 includes: Based on the initial features obtained, in a multi-source heterogeneous data environment, an association analysis model is used to identify the logical response relationship between events by utilizing time series features and event triggering patterns, thereby obtaining the functional mapping relationship.

[0008] To further optimize this technical solution, the correlation analysis model includes: in: :Signal and The strength of the correlation between them; :Signal and The event trigger interval; Expected trigger interval; Normalized time scale.

[0009] To further optimize this technical solution, the event triggering interval includes: in: :Signal timestamp; :Signal timestamp; The time interval between two events is calculated using their timestamps to determine the order of events and logical dependencies.

[0010] To further optimize this technical solution, the communication performance verification in step S3 includes: Based on the functional mapping relationship obtained in step S2, the communication behavior is mapped to the functional execution path, and the transmission performance of the communication link is quantitatively verified to obtain performance indicators.

[0011] To further optimize this technical solution, the logical consistency verification in step S4 includes: Based on the obtained performance indicators, a functional path logical consistency verification method is established, which directly associates the communication performance indicators with the functional mapping relationship to form a logical verification result.

[0012] To further optimize this technical solution, the redundancy reliability detection in step S5 includes: Based on the logical verification results obtained in step S4, the redundancy reliability of the verified functional paths is verified and quantified through dual-network redundancy switching detection to obtain redundancy reliability indicators.

[0013] To further optimize this technical solution, the consistency assessment in step S6 includes: Based on the reliability indicators obtained in step S5, a station-wide consistency analysis is performed. The redundant reliability indicators of the functional paths are integrated into a comprehensive indicator for the entire station, and the station-wide verification results are obtained, thereby achieving a quantitative evaluation from the functional paths to the entire station.

[0014] This technical solution has been further optimized, including the following functional modules: The system includes an information acquisition module, a correlation analysis module, a communication verification module, a logic verification module, a redundancy detection module, and a site-wide evaluation module.

[0015] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a test and verification method and system applicable to intelligent substations as described in the first aspect of the present invention.

[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a test and verification method and system applicable to intelligent substations as described in the first aspect of the present invention.

[0017] Compared with existing technologies, the present invention provides a testing and verification method and system suitable for intelligent substations, which has the following beneficial effects: This testing and verification method and system applicable to smart substations constructs a mapping relationship for each functional path across the entire substation through an association analysis model. This solves the problem of limited information sources leading to one-sidedness, realizes multi-source information fusion and logical dependency quantification, and improves the reliability, comprehensiveness, and accuracy of testing and verification. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Fig. 1 This is a flowchart illustrating a testing and verification method for smart substations proposed in this invention. Fig. 2This is a flowchart illustrating the correlation analysis model of a testing and verification method for smart substations proposed in this invention. Fig. 3 This is a schematic diagram of a test and verification system for smart substations proposed in this invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0023] Example 1: Reference Figs. 1-2 This is the first embodiment of the present invention, which provides a testing and verification method suitable for intelligent substations, including the following steps: S1. Collect operational information within the substation to obtain initial characteristics.

[0024] In this embodiment, the collection of operational information includes: During the testing and verification of smart substations, all subsequent steps are based on real operational data. Because smart substations have diverse data types, including analog sampling signals, electrical status variables, communication messages, logic trigger signals, clock synchronization information, etc., these data have different sources, different timing sequences, and different formats. If these data are not collected and standardized before the test begins, it will be impossible to accurately establish communication mapping relationships and logical dependency chains in the subsequent analysis phase.

[0025] Based on the hierarchical structure of the intelligent substation, the data collection targets are identified and data collection equipment is deployed. The operational information within the intelligent substation is comprehensively collected and analyzed to obtain initial characteristics, thereby providing input basis for substation verification.

[0026] The implementation steps include: Determine the scope of data collection: Based on the hierarchical structure of the smart substation, identify the data collection objects, including process layer equipment (such as merging unit MU, intelligent terminal IT), bay layer equipment (such as protection and control devices, fault recorders), and station control layer equipment (such as monitoring host, communication management unit, time synchronization device). The scope of data collection should cover the communication messages and electrical quantity outputs of the above-mentioned equipment to ensure the comprehensiveness of the characteristic data. Acquiring real-time operating signals: In the station network, various devices are connected via the IEC 61850 interface to acquire signals, including SV messages (representing voltage and current sample values, used to reflect the operating status of primary equipment), GOOSE messages (representing real-time trigger signals of protection and control logic), MMS messages (representing equipment status monitoring and measurement and control quantity transmission), and PTP clock signals (providing a unified time reference for the entire station), etc. Execution signal parsing and structured representation: Standard parsing of the above message content, extraction of key parameters, including voltage and current sampling values, message identifier and timestamp, message delay and packet loss records, time synchronization deviation values, etc. All data are recorded in a unified structure to ensure that the output results of different devices have a consistent data format; Time reference alignment: Due to slight differences in the sampling periods of each device, time calibration is required based on the PTP master clock. All sampled data are reordered with the master clock as a reference so that each signal corresponding to the same moment can be correctly matched. Generate initial features: Through the above parsing and alignment, a complete set of initial features is obtained, including electrical operating features (such as voltage and current), communication features (such as message delay, message ID, and trigger status), and time features (such as clock deviation and sampling alignment results).

[0027] S2. Perform multi-source information association analysis based on the initial features to obtain the functional mapping relationship.

[0028] In this embodiment, the multi-source information correlation analysis includes: In smart substations, the behavior of a single device often fails to represent the overall operational logic. For example, the formation of a tripping action may involve a series of processes, including current sampling anomalies, protection device judgment, and control device execution. Although these events are distributed across different levels (process level, bay level, and station control level), they have a strict causal dependency in time. Traditional testing and verification often only verify the correctness of each individual link, lacking cross-link logical correlation analysis, which makes it impossible to accurately determine whether the entire functional chain truly executes as expected.

[0029] Based on the initial characteristics obtained, in a multi-source heterogeneous data environment, an association analysis model is used to identify the logical response relationship between events by utilizing time series characteristics and event triggering patterns, thereby obtaining the functional mapping relationship and determining the causal dependency relationship. This enhances the analytical capability and improves the accuracy and traceability of substation testing and verification.

[0030] Specific implementation methods include: Feature type identification and grouping: From the initial feature set obtained from S1, the events are classified according to their signal source and attributes, including electrical operation features (such as voltage and current), communication features (such as message delay, message ID, and trigger status), and time features (such as clock deviation and sampling alignment results), providing a basic framework for subsequent time series correlation analysis. Time series organization and event sorting: Based on the timestamp information of each event, different types of events are arranged in chronological order to form a unified time series stream, and duplicate sampling points and empty time slots are eliminated to ensure the continuity of the time axis and the absence of event conflicts. The start and end times of all events use a unified master clock reference to ensure the time comparability of cross-device data and avoid logical association errors caused by timing deviations. Temporal correlation calculation and causal identification: Using a correlation analysis model, the correlation between signals of two events is calculated. By calculating the response interval between adjacent events, if the time interval between two events meets the expectation and the response direction conforms to the logical relationship, it is determined that there is a strong correlation between the two. Functional Unit Construction and Link Formation: Based on the identified highly correlated signal groups, they are grouped into the same functional unit (e.g., "tripping functional unit", "synchronization detection functional unit", "splitting functional unit", etc.). Within each functional unit, a directed link of "input signal - processing signal - output signal" is further established in chronological order. The link is marked, including the function name, start signal, execution signal, and terminal signal, thereby transforming discrete events into an analyzable functional structure and forming a complete functional mapping path. Functional mapping relationship output: The link information of all functional units is summarized to obtain a set of functional mapping relationships. Each functional mapping relationship includes the functional unit name, participating events, event order, and logical dependency strength, so that substation testing and verification can trace the source and impact path of each signal at the functional level.

[0031] Furthermore, the association analysis model includes: in: :Signal and The correlation strength between two event signals is used to determine the logical dependency strength between them. The larger the value, the stronger the correlation, thus representing a complete functional link. :Signal and The event trigger interval is used to compare with the expected trigger interval to determine the logical dependency between events. The closer it is to the expected trigger interval, the stronger the logical relationship between the two events. Expected trigger interval, which is the expected trigger interval between events set according to the function type, is used to measure whether the actual triggering meets the design. For example, the expected trigger interval of events in the protection action link can be set to 5ms. Normalized time scale, used to control the decay rate, can be set according to experience or function type, usually set to 1~5ms.

[0032] Furthermore, the event triggering interval includes: in: :Signal The timestamp is used to determine which event is triggered before the other event. :Signal timestamp; The time interval between two events is calculated using their timestamps to determine the order of events and logical dependencies.

[0033] This model describes how to determine the logical dependencies and correlation strength between events by analyzing the response intervals of events.

[0034] Traditional testing and verification methods (such as manual configuration comparison and static logic verification) can mostly only verify the logical sequence in the configuration file, and cannot reflect the dynamic triggering deviation in actual operation. They are also difficult to process multi-source data at the same time, and lack comprehensive analysis of multi-source information and quantitative indicators of correlation strength. In contrast, this model constructs the mapping relationship of each functional path across the entire site through multi-source information correlation analysis, which solves the problem of one-sidedness caused by limited information sources, realizes multi-source information fusion and logical dependency quantification, and improves the reliability, comprehensiveness, accuracy and traceability of testing and verification.

[0035] The steps for using this model include: Data acquisition: Based on the initial features obtained in S1, the time information of the corresponding events is obtained according to the signal source and attribute classification and arranged in chronological order; Parameter calculation: Based on the obtained data, calculate the event trigger interval between events. This is used to determine the logical dependencies between events; Correlation analysis: Based on the calculated event trigger interval Compare it with the expected trigger interval Compare the two, calculate the difference in interval between them, and normalize the time scale. Normalization is performed to obtain the correlation strength between signals. The correlation is compared with the correlation threshold to determine whether there is a strong correlation. The threshold can be set based on experience or actual situation. For example, when the correlation strength is greater than or equal to 0.95, the event is marked as a strong correlation and included in the same functional unit and component functional mapping path. Otherwise, the two are considered to be weakly correlated or have no direct causal relationship.

[0036] S3. Verify communication performance based on the functional mapping relationship to obtain performance indicators.

[0037] In this embodiment, the communication performance verification includes: In smart substations, information exchange between equipment is achieved through the IEC 61850 communication network. The communication performance varies significantly at different levels (process level, bay level, and station control level), and any communication anomaly may lead to malfunctions in protection systems, delays in measurement and control, or dispatching failures. Therefore, communication performance verification is a core component of the testing and validation system.

[0038] Based on the functional mapping relationship obtained in step S2, the communication behavior is mapped to the functional execution path, the transmission performance of the communication link is quantitatively verified, the communication performance is quantified, and performance indicators are obtained, thereby providing a real-time, stable and reliable objective basis for measuring the communication performance of substations.

[0039] The steps for verifying communication performance include: Communication Link Extraction: In a smart substation, functional paths may span the process layer, bay layer, and station control layer, with each layer using different communication protocols and transmission paths. Based on the functional mapping relationship obtained in S2, the communication paths involved in each functional unit are identified. For the functional path "input device - intermediate processing - output device", the corresponding communication interface information is extracted, such as GOOSE message channel, SV (Sampled Value) sampling channel, MMS management message channel, etc. By parsing the communication configuration file (such as SCD file) and actual link monitoring data, a communication path list is established. Each path includes source device, target device, protocol type, and port information. Communication event capture: Deploy a message monitoring device on the mirror port of the network switch. Utilize the mirror port function of the switch to completely copy the message flow without affecting the actual business. Copy the transmission messages in the real network to the monitoring device, thereby capturing the communication messages involved in all functional paths in real time. Record the sending timestamp, receiving timestamp, message sequence number and identifier of each message. Communication performance index calculation: The captured messages are statistically analyzed to extract communication performance parameters, including transmission delay (the propagation time of a message from the source device to the target device, used to evaluate real-time performance), jitter (the fluctuation range of delay between consecutive messages, reflecting communication stability), packet loss rate (the ratio of the expected number of messages to the actual number of messages received, indicating the reliability of the messages), time synchronization deviation (by comparing the PTP master-slave clock difference, reflecting the time consistency of the entire station), etc. These performance indicators are statistically summarized by functional path to form a performance index set; Performance threshold comparison and verification: The calculated performance indicators are compared and verified with standard requirements (e.g., industry standards or typical engineering experience). For example, the GOOSE message latency should be less than or equal to 3ms, the SV message latency should be less than or equal to 4ms, the packet loss rate should be less than 0.01%, and the synchronization deviation should be less than or equal to 1μs. When the performance indicators meet the standard range, the communication link is judged to be qualified. If it exceeds the standard range, it is marked as a link with abnormal performance, so as to realize the quantitative and range-based verification of the communication performance of the entire station. Performance results output: The performance parameters of each functional path are organized and output in a structured manner according to the functional unit. Each functional unit corresponds to a set of indicators, resulting in a set of communication performance indicators. Each indicator can be traced back to a specific functional path, a specific message, and a specific device, which is beneficial for problem localization during operation and maintenance and verification.

[0040] S4. Perform logical consistency verification based on performance indicators to obtain the logical verification results.

[0041] In this embodiment, the logical consistency verification includes: In smart substations, protection, measurement and control, and automation functions are all driven by communication networks. Their correct execution depends not only on the correctness of the logical structure of the functional chain, but also on whether the communication performance meets the real-time and reliability requirements of the function execution. In actual operation, the execution of the functional chain requires both logic and performance to be satisfied. Therefore, simply verifying the logic or communication cannot guarantee the executability of the function; logical consistency verification is necessary.

[0042] Based on the obtained performance indicators, a functional path logic consistency verification method is established, which directly links communication performance indicators with functional mapping relationships to form quantifiable and traceable logic verification results. This enables quantitative analysis of logic execution and site-wide logic consistency verification, thereby improving the ability to identify and locate risks.

[0043] The steps for verifying the method include: Functional path node mapping: Based on the functional mapping relationship of S2, each functional path is split into nodes (input-processing-output), and each node is marked with the corresponding communication link and related performance indicators. The latency, jitter, packet loss rate and synchronization deviation data of the corresponding link are extracted from the performance indicator set of S3. Each functional path is formed into a node list to ensure that the node order is consistent with the functional mapping. Node execution feasibility assessment: For each functional node, check whether the communication performance meets the execution conditions, such as latency not exceeding the maximum response time allowed by the functional path, packet loss rate being lower than the functional path tolerance threshold, jitter and synchronization deviation meeting design requirements, etc. If a node meets all conditions, it is determined that the node logic is executable; if any indicator is not met, it is determined that the node may cause logical abnormalities and is not executable. Overall path consistency analysis: The executable results of all nodes on each functional path are summarized and analyzed. If all nodes are executable, the functional path consistency is judged as "normal". If there are nodes that are not executable, they are judged as "abnormal". The specific nodes and their corresponding performance indicators are recorded. The consistency score of the functional path is based on the proportion of executable nodes to the total number of nodes, which is used to quantify the logical consistency of the functional path. Abnormal node location and identification: For functional chains that are determined to be abnormal, further analyze the causes of abnormal nodes. For example, if the latency exceeds the standard, it is identified as "transmission delay risk"; if the packet loss rate exceeds the standard, it is identified as "message loss risk"; if the synchronization deviation is abnormal, it is identified as "time asynchrony risk". Record the location and impact range of abnormal nodes in the functional path to provide targeted guidance for operation and maintenance. Logical verification result output: Output the logical consistency judgment result of each functional path to form the final logical verification result set. The logical verification result includes the functional path name, execution feasibility of each node, logical consistency score of the path, abnormal nodes and abnormal types (if they exist).

[0044] S5. Based on the logic verification results, perform redundancy reliability testing to obtain reliability indicators.

[0045] In this embodiment, the redundancy reliability detection includes: The functional paths of intelligent substations not only rely on single devices or single paths, but also on redundant designs (such as primary and backup networks, dual-channel protection, and redundant switches) to ensure high availability. This allows them to maintain functionality under partial faults or anomalies. Even if logic verification results show that the chain consistency is normal, if redundancy switching is not timely or fails, protection malfunctions or control lags may still occur under abnormal conditions. Therefore, redundancy reliability testing is necessary to verify the availability of redundancy mechanisms and ensure fault tolerance.

[0046] Based on the logic verification results obtained in step S4, the redundancy reliability of the verified functional paths is verified and quantified through dual-network redundancy switching detection to obtain redundancy reliability indicators. This achieves functional-level redundancy reliability quantification, ensuring that the functional paths can still be executed normally in the event of a fault or network switching, thereby improving the high availability and operational reliability of the intelligent substation communication and control system.

[0047] Furthermore, the dual-network redundancy handover detection includes: Function chain mapping to redundant channels: Using the logic verification results output by S4, the nodes of each functional logic are mapped to the communication channels of the main channel and the backup channel, and the specific message binding and communication interface of each node in the main channel and the backup channel are determined to ensure that the nodes are traceable during the switching process; Controllable switching trigger: By introducing a primary / backup network switching event through network testing equipment or control simulation device, the switching time of the primary / backup channel and its impact on the functional path are measured. During the switching process, node communication packets are captured, and latency, packet loss rate and synchronization deviation are recorded to simulate real network faults or switching scenarios and ensure that the test results are close to the actual operating environment. Node switching executability analysis: Analyze the communication performance of each functional path node during the switching process, including whether the node can receive messages during the switching process, whether the message latency and packet loss rate are still within the allowable range, etc. Mark non-executable nodes (i.e. nodes whose indicators exceed the range) as "switching risk" and associate them with switching time and communication indicators to realize node-level reliability judgment under redundant switching state. Reliability index calculation: Based on the primary and backup channel switching time and the communication delay of the node after switching, the redundancy reliability index of the node is calculated. The redundancy reliability index of all nodes on the functional path is summed and averaged to obtain the redundancy reliability index of the functional path, which reflects the comprehensive level of switching performance and logic execution feasibility. If the reliability index is greater than or equal to 0.95, the functional path is determined to be reliable under redundancy switching. If the reliability index is less than 0.95, the risk node of the path and the reason are recorded. Reliability index output: The reliability index, switching time, abnormal nodes and abnormal types of each functional path under redundancy switching are output in a structured manner to achieve traceable and quantitative redundancy reliability evaluation.

[0048] Furthermore, the calculation of the reliability index includes: in: :node The redundancy reliability index is used to comprehensively evaluate the reliability of nodes under primary / standby switching conditions. :node The executability metric indicates whether a node can still execute logically during redundancy switching. The metric is 0 for an unexecutable node and 1 otherwise. Primary / backup channel switching time: This refers to the time required for the smart substation network to switch from the primary channel to the backup channel. :node The communication latency of nodes after switching reflects whether the nodes can process logic in a timely manner after the switch. :node The maximum allowable response time represents the maximum tolerable response time of a node when executing a logical action. It can be set through product technical specifications, actual needs, etc. For example, the maximum allowable response time of a node in the fast trip function is 3~5ms, and in the control function it is 10~20ms. Based on the primary / backup channel switching time and the communication latency of the node after switching, combined with the node's executability index, the redundancy reliability index of the node is calculated.

[0049] S6. Conduct a consistency assessment based on reliability indicators to obtain the site-wide verification results.

[0050] In this embodiment, the consistency assessment includes: The intelligent substation contains multiple functional paths, and there are complex logical dependencies between these paths. The reliability index of the functional paths obtained by S5 cannot directly indicate whether the functional paths of the entire station can maintain consistency and coordination under coupled conditions. Therefore, a consistency assessment is required.

[0051] Based on the reliability indicators obtained in step S5, a station-wide consistency analysis is performed. The redundancy reliability indicators of the functional paths are integrated into a comprehensive indicator for the entire station, and the station-wide verification results are obtained. This enables quantitative evaluation from the functional paths to the entire station, ensuring that the global functions are still executed logically under conditions of redundancy switching, communication delay, or abnormalities, thereby achieving high availability and secure operation of the entire station.

[0052] The specific steps for implementing consensus assessment include: Functional chain reliability summary: The redundancy reliability indicators of each functional path output by S5 are summarized to form a system-wide functional path reliability set, providing basic data for system-wide analysis and ensuring that the reliability information of each path in the system is complete and traceable. Consistency index calculation: Combine the reliability index of each functional path with the path weight (which can be set according to importance, such as the path of protection function has a higher weight than the path of monitoring function), use the weighted summation method to calculate the overall site consistency index, and normalize it to quantify the overall site reliability level and reflect the impact of different functional paths on overall consistency. Risk area identification: Based on the redundancy and reliability indicators of each functional path output by S5, paths with risky nodes are screened out. The functional paths with risks are aggregated according to functional type, station location, and communication topology to identify risk areas, thereby realizing full-site risk visualization and providing a reference for subsequent operation and maintenance and optimization. The generation of the whole-station verification conclusion is based on the calculated whole-station consistency index and a threshold (such as 0.95). For example, when the index is higher than the threshold, it indicates that the whole-station consistency is good. When the index is lower than the threshold, it indicates that there is a risk in the substation and the risk functional path and risk area need to be focused on. The whole-station verification conclusion is obtained, including the consistency index, risk functional path, risk node and risk area identification results, thereby forming operable and traceable engineering data to support operation and maintenance, optimization and safety verification, forming a whole-station closed-loop verification.

[0053] Example 2: Reference Fig. 3 This is the second embodiment of the present invention, which provides a test and verification system suitable for intelligent substations, including the following functional modules: Information acquisition module: Collects operational information of various equipment within the station, including data on protection, measurement and control, communication, and scheduling, and generates initial features to provide basic data for subsequent analysis; Association Analysis Module: Performs multi-source data cross-association on the initial features of S1, establishes a full-site functional mapping relationship, clarifies the logical dependencies and interaction relationships between functional units, and forms functional paths; Communication verification module: Uses the functional mapping relationship obtained from S2 to verify communication performance and quantifies the communication indicators of each functional path node under switching and abnormal conditions; Logic verification module: Performs logical consistency verification on the performance index set output by S3, determines whether each functional chain maintains logical correctness under redundancy switching and abnormal conditions, and generates logical verification results; Redundancy detection module: Uses the logical verification results of S4 to perform redundancy reliability detection on the functional chain, quantifies node-level and path-level reliability indicators, identifies abnormal nodes, and provides data for site-wide analysis; The whole-site evaluation module uses the chain reliability index obtained from S5 to perform consistency evaluation, outputs the whole-site verification conclusion, and realizes the whole-site closed-loop verification.

[0054] Example 3: In practical applications, this method can be applied to the full-station operation verification of smart substations to ensure the logical consistency and reliability of functional units such as protection, measurement and control, communication and dispatch within the station under conditions of redundancy switching, abnormal events and dynamic loads. The following is an illustration using a smart substation connected to an UHV transmission line as a typical scenario.

[0055] In this scenario, the status data of the station's monitoring and control devices, protection equipment, communication nodes, and scheduling terminals are first comprehensively acquired through operational information collection, forming an initial feature set including indicators such as node response time, communication latency, and message integrity. Subsequently, the initial features are cross-processed through multi-source information correlation analysis to establish logical mapping relationships between functional units, clarifying the dependencies and data interaction paths between devices, and providing a data foundation for communication performance verification. Under conditions of full-station network switching and load fluctuations, communication performance verification is conducted, detecting communication latency, packet loss rate, and synchronization deviation at each functional path node, forming a quantified set of performance indicators.

[0056] Based on a performance metric set, logical consistency checks are performed on all functional paths across the entire site to ensure that each logic executes without anomalies under redundancy switching conditions, while also identifying potential logical risk nodes. Subsequently, based on the logic check results, redundancy detection is performed, quantifying the redundancy reliability of each functional path and key nodes, generating path-level and node-level reliability metrics, and outputting risk node information. Finally, all path reliability data is aggregated, consistency metrics are calculated, risk areas that may affect the overall site functionality are identified, and a structured overall site verification conclusion is generated, including consistency metrics, risky functional paths, risky nodes, and risky area identification results, providing direct reference for operation and maintenance and security assurance.

[0057] Example 4: This embodiment also provides a computer device applicable to a test and verification method and system for intelligent substations, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the test and verification method and system for intelligent substations as proposed in the above embodiment.

[0058] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a testing and verification method and system suitable for smart substations as proposed in the above embodiments.

[0059] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0060] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0062] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0063] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A test and verification method suitable for intelligent substations, characterized in that, Includes the following steps: S1. Collect operational information within the substation to obtain initial characteristics; S2. Perform multi-source information association analysis based on the initial features to obtain the functional mapping relationship; S3. Verify communication performance based on the functional mapping relationship to obtain performance indicators; S4. Perform logical consistency verification based on performance indicators to obtain the logical verification results; S5. Perform redundancy reliability testing based on the logic verification results to obtain reliability indicators; S6. Conduct a consistency assessment based on reliability indicators to obtain the site-wide verification results.

2. The test and verification method for intelligent substations according to claim 1, characterized in that, The operational information collection in step S1 includes: Based on the hierarchical structure of the intelligent substation, the data collection targets are identified and data collection equipment is deployed to comprehensively collect and analyze the operational information within the intelligent substation, thereby obtaining initial characteristics.

3. The test and verification method for intelligent substations according to claim 1, characterized in that, The multi-source information correlation analysis in step S2 includes: Based on the initial features obtained, in a multi-source heterogeneous data environment, an association analysis model is used to identify the logical response relationship between events by utilizing time series features and event triggering patterns, thereby obtaining the functional mapping relationship.

4. The test and verification method for intelligent substations according to claim 3, characterized in that, The correlation analysis model includes: in: :Signal and The strength of the correlation between them; :Signal and The event trigger interval; Expected trigger interval; Normalized time scale.

5. The test and verification method for intelligent substations according to claim 4, characterized in that, The event triggering interval includes: in: :Signal timestamp; :Signal timestamp; The time interval between two events is calculated using their timestamps to determine the order of events and logical dependencies.

6. The test and verification method for intelligent substations according to claim 1, characterized in that, The communication performance verification in step S3 includes: Based on the functional mapping relationship obtained in step S2, the communication behavior is mapped to the functional execution path, and the transmission performance of the communication link is quantitatively verified to obtain performance indicators.

7. The test and verification method for intelligent substations according to claim 1, characterized in that, The logical consistency verification in step S4 includes: Based on the obtained performance indicators, a functional path logical consistency verification method is established, which directly associates the communication performance indicators with the functional mapping relationship to form a logical verification result.

8. The test and verification method for intelligent substations according to claim 1, characterized in that, The redundancy reliability detection in step S5 includes: Based on the logical verification results obtained in step S4, the redundancy reliability of the verified functional paths is verified and quantified through dual-network redundancy switching detection to obtain redundancy reliability indicators.

9. A test and verification method for intelligent substations according to claim 1, characterized in that, The consistency assessment in step S6 includes: Based on the reliability indicators obtained in step S5, a station-wide consistency analysis is performed. The redundant reliability indicators of the functional paths are integrated into a comprehensive indicator for the entire station, and the station-wide verification results are obtained, thereby achieving a quantitative evaluation from the functional paths to the entire station.

10. A test and verification system for intelligent substations according to claim 1, constructed based on a test and verification method for intelligent substations according to any one of claims 1-9, characterized in that, Includes the following functional modules: The system includes an information acquisition module, a correlation analysis module, a communication verification module, a logic verification module, a redundancy detection module, and a site-wide evaluation module.

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