An intelligent verification method and system for virtual circuits of substation configuration files

Abstract

The present invention discloses an intelligent verification method and system for virtual circuits in a substation configuration file. The method includes: determining the virtual circuits to be verified and the corresponding device data according to the substation configuration file to be tested; obtaining the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuits to be verified according to the device data; detecting the virtual connection status between the auxiliary contacts of the switching devices and the input terminals of the protection devices according to the device data and the actual mapping relationship, and obtaining the virtual circuit detection result of the substation configuration file to be tested. The present invention can utilize the above actual mapping relationship and device data to realize the automatic detection of the virtual connection status between the auxiliary contacts of the switching devices and the input terminals of the protection devices, effectively avoid the situation of misdetection and missed detection, significantly improve the detection accuracy of the virtual circuits in the substation configuration file, and is applicable to different arrangements of input terminals.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to an intelligent verification method and system for virtual circuits of substation configuration files. Background Art

[0002] In the critical period when the substation automation system is moving towards in-depth intelligence and China is vigorously building a digital and intelligent strong power grid, digital technology has begun to be applied in the substation automation system. The virtual terminal circuit is a key component of the substation configuration file and also a core element for realizing the protection and control functions of the whole station. Its accuracy is directly related to the safe and stable operation of the substation and even the entire power grid. Therefore, it is crucial to ensure the accuracy of the virtual terminal circuit.

[0003] The automatic verification technology of virtual terminal circuits has broad application prospects in the business of intelligent substation engineering design, construction commissioning, operation and maintenance, etc. However, currently, in the detection of virtual circuits in substation configuration files, a series of operation modes have been formed to ensure the correctness of virtual terminal circuits. For example, relying on the professional skills of business personnel to view and review, using visual comparison tools to find version differences, carrying out actual commissioning and test verification, etc. But these traditional means have obvious defects. On the one hand, business personnel usually use the arrangement rules of input terminals summarized by operation experts to manually detect the virtual circuits of substation configuration files. However, there are differences in the arrangement methods of input terminals of protection devices from different manufacturers, resulting in the difficulty of applying the arrangement rules of input terminals summarized based on experience to all situations. And the high degree of manual participation also makes the work efficiency low, and it is difficult to comprehensively and accurately detect virtual circuit errors. On the other hand, although the relevant configuration technical standards of substation configuration files have been released, the integration and production links of substation configuration files of on-site intelligent substations are not standardized and errors occur frequently, resulting in difficulty in verifying virtual circuits by finding version differences. In addition, the static manual verification technology of virtual circuits is difficult and the troubleshooting process is complex. Repeated on-site test verification not only consumes a lot of time and resources, but also cannot fundamentally guarantee the accuracy and efficiency of virtual circuit detection. It can be seen that there are many drawbacks in the existing detection methods of virtual circuits in substation configuration files based on manual detection. Summary of the Invention

[0004] The present invention provides an intelligent verification method and system for virtual circuits of substation configuration files to solve the technical problem that the existing technology cannot accurately and effectively detect the virtual circuits of substation configuration files.

[0005] To solve the above technical problem, in the first aspect of the embodiments of the present invention, an intelligent verification method for virtual circuits of substation configuration files is provided, including:

[0006] Determine the virtual circuits to be verified and the device data corresponding to the virtual circuits to be verified according to the configuration file of the substation to be measured;

[0007] According to the device data, obtain the actual mapping relationship between the auxiliary contacts of each switching device in the virtual circuit to be verified and the input terminals of each protection device;

[0008] According to the device data and the actual mapping relationship, detect the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device, and obtain the virtual circuit detection result of the configuration file of the substation to be measured.

[0009] As a preferred solution, the step of obtaining the actual mapping relationship between the auxiliary contacts of each switching device in the virtual circuit to be verified and the input terminals of each protection device according to the device data specifically includes:

[0010] According to the device data, obtain the auxiliary contact state data of each switching device and the input terminal state data of each protection device;

[0011] According to the auxiliary contact state data and the input terminal state data, obtain the first state value of the auxiliary contact, the second state value of the input terminal, and the state change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device;

[0012] According to the state change time difference and the preset signal transmission delay, match the first state value and the second state value to obtain the actual mapping relationship between each auxiliary contact and each input terminal in the virtual circuit to be verified.

[0013] As a preferred solution, the step of detecting the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual circuit detection result of the configuration file of the substation to be measured specifically includes:

[0014] Based on the switching device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact state data of the switching device to be measured and the input terminal state data of the protection device to be measured from the device data;

[0015] According to the auxiliary contact state data and the input terminal state data, obtain the state change time difference between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured under each switching operation of the switching device to be measured;

[0016] When it is detected that the time difference of the state change is greater than a preset abnormal time threshold, it is determined that the virtual connection state between the auxiliary contact of the switch device to be measured and the input terminal of the protection device to be measured is a virtual connection abnormality;

[0017] Based on the auxiliary contacts of each switch device to be measured and the input terminals of the protection device to be measured whose virtual connection states are virtual connection abnormalities, the virtual loop detection result is obtained.

[0018] As a preferred solution, the method for detecting the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual loop detection result of the configuration file of the substation to be measured specifically includes:

[0019] Obtain the terminal physical position, terminal identifier, and contact function attribute of each input terminal from the device data;

[0020] Input the terminal physical position, the terminal identifier, and the contact function attribute into a preset machine learning model to obtain the standard arrangement rule of the input terminals output by the machine learning model; wherein, the machine learning model is obtained by training based on a preset prior arrangement data set of input terminals; the standard arrangement rule of the input terminals is used to indicate the standard mapping relationship between each auxiliary contact and each input terminal;

[0021] Convert the actual mapping relationship and the standard mapping relationship into a first feature vector and a second feature vector respectively, and calculate the Euclidean distance between the first feature vector and the second feature vector;

[0022] When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are virtual connection abnormalities in the virtual loop to be verified.

[0023] As a preferred solution, the method for detecting the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual loop detection result of the configuration file of the substation to be measured specifically includes:

[0024] Determine the connection relationship between each switch device and each protection device according to the actual mapping relationship;

[0025] Obtain the first physical position of each switch device and the second physical position of each protection device from the device data;

[0026] Generate a connection topology graph between each of the switch devices and each of the protection devices by using a graph theory algorithm according to the connection relationship, the first physical location, and the second physical location;

[0027] Use a depth - first search algorithm to search for a target connection path between the switch device and the protection device in the connection topology graph;

[0028] Obtain the first - state time - series data of the switch device and the second - state time - series data of the protection device in each of the target connection paths from the device data;

[0029] Calculate the state - change time - series correlation degree between the switch device and the protection device in each of the target connection paths by using a correlation - calculation method according to the first - state time - series data and the second - state time - series data;

[0030] Determine the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset corresponding relationship between the state - change time - series correlation degree and the connectivity probability;

[0031] Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual - connection abnormal probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switch device and the protection device;

[0032] When it is detected that the virtual - connection abnormal probability is greater than a preset probability threshold, determine that the virtual - connection state between the auxiliary contact of the switch device and the input - quantity terminal of the protection device in the target connection path is a virtual - connection abnormality;

[0033] Obtain the virtual - loop detection result according to each group of auxiliary contacts and input - quantity terminals with the virtual - connection state being a virtual - connection abnormality.

[0034] As a preferred solution, the step of using a depth - first search algorithm to search for a target connection path between the switch device and the protection device in the connection topology graph specifically includes:

[0035] Determine the physical distance between each node in the connection topology graph according to the first physical location and the second physical location; wherein, the node is the switch device or the protection device;

[0036] Determine the edge weight between each node according to the preset corresponding relationship between the physical distance and the edge weight;

[0037] Using the depth - first search algorithm, traverse the nodes in the connection topology graph in the order from large to small of the edge weights, and obtain several alternative connection paths between the switch device and the protection device;

[0038] According to the product of the edge weights between each pair of nodes in each alternative connection path, determine the path weight corresponding to each alternative connection path, and take the alternative connection path with the largest path weight as the target connection path.

[0039] As a preferred solution, after detecting the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device, the method further includes:

[0040] When detecting that the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is abnormal, obtain the attribute parameters of each target switch device from the device data;

[0041] According to the attribute parameters, generate the device feature identification values corresponding to each target switch device;

[0042] Use the decision - tree algorithm to normalize each device feature identification value, and according to the normalized device feature identification value, determine the virtual connection abnormal level corresponding to each target switch device according to the preset abnormal - level classification threshold.

[0043] As a preferred solution, the attribute parameters include the operation duration, maintenance interval, and operation environment temperature;

[0044] The step of generating the device feature identification values corresponding to each target switch device according to the attribute parameters specifically includes:

[0045] According to the preset abnormal - score comparison table, respectively obtain the first abnormal score corresponding to the operation duration, the second abnormal score corresponding to the maintenance interval, and the third abnormal score corresponding to the operation environment temperature of each target switch device; where the abnormal - score comparison table records the abnormal scores corresponding to different operation durations, maintenance intervals, and operation environment temperatures respectively;

[0046] Perform weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score to obtain the device feature identification values corresponding to each target switch device.

[0047] As a preferred solution, after determining the virtual connection abnormal level corresponding to each target switch device according to the preset abnormal - level classification threshold, the method further includes:

[0048] Adjust the exception level classification threshold according to the occurrence frequency of exceptions corresponding to each of the virtual connection exception levels.

[0049] In a second aspect of the embodiments of the present invention, an intelligent verification system for virtual circuits of a substation configuration file is provided, including:

[0050] A data acquisition module, configured to determine the virtual circuits to be verified and the device data corresponding to the virtual circuits to be verified according to the substation configuration file to be tested;

[0051] An actual mapping relationship determination module, configured to obtain the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuits to be verified according to the device data;

[0052] A virtual circuit detection module, configured to detect the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the substation configuration file to be tested.

[0053] As a preferred solution, the actual mapping relationship determination module is configured to obtain the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuits to be verified according to the device data, specifically including:

[0054] Obtain the auxiliary contact state data of each switching device and the input terminal state data of each protection device according to the device data;

[0055] According to the auxiliary contact state data and the input terminal state data, obtain the first state value of the auxiliary contact, the second state value of the input terminal, and the state change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device;

[0056] Match the first state value and the second state value according to the state change time difference and a preset signal transmission delay to obtain the actual mapping relationship between each auxiliary contact and each input terminal in the virtual circuits to be verified.

[0057] As a preferred solution, the virtual circuit detection module is configured to detect the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the substation configuration file to be tested, specifically including:

[0058] Based on the switch device under test and the protection device under test with the actual mapping relationship, obtain the auxiliary contact status data of the switch device under test and the input terminal status data of the protection device under test from the device data;

[0059] According to the auxiliary contact status data and the input terminal status data, obtain the state change time difference between the auxiliary contacts of the switch device under test and the input terminals of the protection device under test for each switch operation of the switch device under test;

[0060] When it is detected that the state change time difference is greater than the preset abnormal time threshold, determine that the virtual connection state between the auxiliary contacts of the switch device under test and the input terminals of the protection device under test is a virtual connection anomaly;

[0061] According to each set of auxiliary contacts of the switch device under test and the input terminals of the protection device under test with the virtual connection state being a virtual connection anomaly, obtain the virtual loop detection result.

[0062] As a preferred solution, the virtual loop detection module is used to detect the virtual connection state between the auxiliary contacts of the switch device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual loop detection result of the configuration file of the substation under test, specifically including:

[0063] Obtain the terminal physical position, terminal identifier, and contact function attribute of each input terminal from the device data;

[0064] Input the terminal physical position, the terminal identifier, and the contact function attribute into a preset machine learning model to obtain the standard arrangement rule of the input terminals output by the machine learning model; wherein, the machine learning model is obtained by training based on a preset prior arrangement data set of input terminals; the standard arrangement rule of the input terminals is used to indicate the standard mapping relationship between each auxiliary contact and each input terminal;

[0065] Convert the actual mapping relationship and the standard mapping relationship into a first feature vector and a second feature vector respectively, and calculate the Euclidean distance between the first feature vector and the second feature vector;

[0066] When it is detected that the Euclidean distance is greater than the preset Euclidean distance threshold, determine that there is at least one set of auxiliary contacts and input terminals with the virtual connection state being a virtual connection anomaly in the virtual loop to be verified.

[0067] As an optimal solution, the virtual circuit detection module is used to detect the virtual connection state between the auxiliary contacts of the switchgear and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the substation configuration file to be tested, specifically including:

[0068] Determine the connection relationship between each of the switchgears and each of the protection devices according to the actual mapping relationship;

[0069] Obtain the first physical position of each of the switchgears and the second physical position of each of the protection devices from the device data;

[0070] Generate a connection topology graph between each of the switchgears and each of the protection devices by using a graph theory algorithm according to the connection relationship, the first physical position and the second physical position;

[0071] Use the depth-first search algorithm to search for the target connection path between the switchgear and the protection device in the connection topology graph;

[0072] Obtain the first state time series data of the switchgear and the second state time series data of the protection device in each of the target connection paths from the device data;

[0073] Calculate the state change time series correlation degree between the switchgear and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data;

[0074] Determine the connectivity probability between the switchgear and the protection device in each of the target connection paths according to the corresponding relationship between the preset state change time series correlation degree and the connectivity probability;

[0075] Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection abnormal probability output by the Bayesian network; wherein, the path weight is determined based on the first physical position and the second physical position; the Bayesian network is trained based on the historical topology data between the switchgear and the protection device;

[0076] When it is detected that the virtual connection abnormal probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contacts of the switchgear and the input terminals of the protection device in the target connection path is a virtual connection abnormality;

[0077] Obtain the virtual circuit detection result according to each group of auxiliary contacts and input terminals whose virtual connection state is a virtual connection abnormality.

[0078] As a preferred solution, the virtual circuit detection module is used to search for the target connection path between the switching device and the protection device in the connection topology diagram by using the depth-first search algorithm, specifically including:

[0079] Determine the physical distance between each node in the connection topology diagram according to the first physical position and the second physical position; wherein, the node is the switching device or the protection device;

[0080] Determine the edge weight between each node according to the preset corresponding relationship between the physical distance and the edge weight;

[0081] Use the depth-first search algorithm to traverse the nodes in the connection topology diagram in the order of the edge weight from large to small, and obtain several alternative connection paths between the switching device and the protection device;

[0082] Determine the path weight corresponding to each alternative connection path according to the product of the edge weights between each node in each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0083] Compared with the prior art, the beneficial effect of the embodiment of the present invention is that by obtaining the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuit to be verified based on the device data corresponding to the virtual circuit to be verified, the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device can be automatically detected by using the actual mapping relationship and the device data. Compared with the manual detection method, the situation of misdetection and missed detection can be effectively avoided, the detection accuracy of the virtual circuit of the substation configuration file is significantly improved, and it is applicable to different arrangements of input terminals. Description of the Drawings

[0084] Figure 1 is a schematic flow chart of the intelligent verification method for the virtual circuit of the substation configuration file in the embodiment of the present invention;

[0085] Figure 2 is a schematic structural diagram of the intelligent verification system for the virtual circuit of the substation configuration file in the embodiment of the present invention. Detailed Embodiments

[0086] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0087] Substation configuration files involve complex connection relationships among numerous switchgears and protection devices. During the previous operation of substations, situations such as misoperation of bus protection occurred due to potential hidden dangers in virtual circuits. As an example, due to the virtual connection error between the main transformer branch protection tripping of bus differential protection and the incoming of main transformer protection failure tripping, when there was zero-sequence or negative-sequence overcurrent on the high-voltage side of the main transformer and the incoming of failure tripping was received, the main transformer failure tripping protection misoperated and tripped the three-side switches of the main transformer, resulting in power outages of some lines and having a great impact on the safe and stable operation of the power grid. With the development of intelligent substation technology, power grid companies in various regions have paid increasing attention to potential hidden dangers in virtual circuits. For example, State Grid has carried out inspections on potential hidden dangers in virtual circuits of already-operated intelligent substations in multiple provinces, and Southern Power Grid has put forward strict requirements for the quality of configuration files in the infrastructure construction link. Against this background, the virtual circuit intelligent verification method for substation configuration files proposed in the embodiments of the present invention has important practical significance.

[0088] When detecting virtual circuits in a substation configuration file of a substation, according to the virtual circuit intelligent verification method for substation configuration files proposed in the embodiments of the present invention, after obtaining device data and actual mapping relationships, the virtual connection status between the auxiliary contacts of switchgears and the incoming terminals of protection devices is detected. During the detection process, various complex situations that may occur in actual operation are fully considered, and through a rigorous intelligent detection process, the accuracy and reliability of virtual circuit detection are effectively improved, and the safe and stable operation of intelligent substations is effectively guaranteed.

[0089] Please refer to Figure 1 , the first aspect of the embodiments of the present invention provides a virtual circuit intelligent verification method for substation configuration files, including the following steps S1 to S3:

[0090] Step S1, determine the virtual circuits to be verified and the device data corresponding to the virtual circuits to be verified according to the substation configuration file to be tested;

[0091] Step S2, according to the device data, obtain the actual mapping relationships between the auxiliary contacts of each switchgear and the incoming terminals of each protection device in the virtual circuits to be verified;

[0092] Step S3, according to the device data and the actual mapping relationships, detect the virtual connection status between the auxiliary contacts of the switchgears and the incoming terminals of the protection devices, and obtain the virtual circuit detection result of the substation configuration file to be tested.

[0093] Specifically, the substation configuration file, i.e., the SCD file (Substation Configuration Description), defines the mapping relationship between the position signals of the switching equipment and the input terminals of the protection device. In order to accurately and effectively detect the virtual circuits in the substation configuration file, in this embodiment, first, based on the substation configuration file to be tested, the virtual circuits to be verified are determined and the corresponding device data is obtained. Since the embodiments of the present invention mainly detect the virtual connection state between the auxiliary contacts of the switching equipment and the input terminals of the protection device, the device data obtained includes switching equipment data and protection device data. It should be noted that the input quantity refers to the signal input of the protection device, which collects the position information of on-site switches, disconnectors, etc. It is the input potential determined according to the contact state. When the contact is closed, a low potential is input, and when the contact is open, a high potential is input. The switching equipment in this embodiment includes circuit breakers, disconnectors, etc. The type of the switching equipment is not specifically limited in this embodiment.

[0094] Furthermore, since the state change data of the auxiliary contacts of each switching equipment and the input terminals of each protection device at different times can be determined based on the device data, considering that the state changes of the auxiliary contacts and the input terminals with a mapping relationship should be synchronized within the allowable signal transmission delay, this embodiment can clarify the actual mapping relationship between the auxiliary contacts of each switching equipment and the input terminals of each protection device.

[0095] Furthermore, since the actual mapping relationship reflects the virtual connection relationship between the auxiliary contacts and the input terminals, combined with relevant device data, such as the physical position of the terminals, the state values of each auxiliary contact and the input terminal at different times, the physical positions of the switching equipment and the protection device, etc., it is possible to judge whether there are auxiliary contacts and input terminals with abnormal virtual connections in the virtual circuits to be verified, thereby realizing the detection of the virtual circuits in the substation configuration file to be tested.

[0096] When actually applying the intelligent verification method proposed in the embodiments of the present invention to detect the virtual circuits in the substation configuration file, the complex and changeable actual situation on site is fully considered. There are many non-standard aspects in the integration and production links of the substation configuration file at the current intelligent substation site, which makes the error rate of the virtual circuits remain high and difficult to troubleshoot. For example, in a certain actual project, the virtual terminal semantics of some IED devices (Intelligent Electronic Device) in the substation configuration file are different due to different device manufacturers, which brings great difficulties to accurately analyzing the internal information of the substation configuration file.

[0097] To solve these problems, in the process of detecting the virtual connection state, when obtaining the relevant information of the input terminal, in view of the non-standard and incomplete information in the substation configuration file, this embodiment of the present invention adopts a technical path combining configuration file parsing, decoupling, reconstruction and human-computer interaction to deeply explore and accurately understand information such as the physical location of the terminal, the terminal identifier, and the contact function attributes. Taking a 220 kV substation as an example, when processing its substation configuration file, through this technical path, the complex primary and secondary equipment configurations and their associated relationships are successfully sorted out, and the relevant attributes of each input terminal can be accurately identified, laying a solid foundation for subsequent detection.

[0098] In the link of determining the standard arrangement rule of the input terminals, this embodiment of the present invention can utilize a machine learning model trained based on a preset prior arrangement data set of input terminals, fully considering the differences in equipment from different manufacturers. A large amount of data is collected from actual operation records, covering the arrangement of input terminals under different voltage levels and wiring methods, to construct a rich prior arrangement data set. In view of the differences in the contact arrangement methods of switch equipment from different manufacturers, such as the different arrangements of Manufacturer A and Manufacturer B, the machine learning model can effectively learn and output accurate standard arrangement rules, providing a reliable basis for judging whether the actual mapping relationship is abnormal.

[0099] Throughout the detection process, the basic requirements of the verification basis are always followed. The standard virtual circuit knowledge relied on is rich and diverse, capable of adapting to various complex verification objects, whether it is a substation of different voltage levels or a complex wiring method with multiple design forms, and can accurately respond. At the same time, through the unique identifier verification basis, it is ensured that the appropriate basis can be quickly and accurately matched during verification, greatly improving the accuracy and efficiency of detection.

[0100] In addition, it can be considered to introduce the idea of constructing a standardized model in the virtual circuit detection of the substation configuration file. Based on artificial intelligence and big data technologies, the equipment data in the substation configuration file is deeply analyzed. By collecting a large number of substation configuration files of different types of substations, including equipment parameters, virtual circuit connection relationships, etc., a standardized model covering the entire process of virtual circuit detection is constructed. This model clarifies the input data for virtual circuit detection, such as equipment data and actual mapping relationships; stipulates key detection steps, such as data preprocessing, mapping relationship matching, and anomaly judgment; and determines the output results, such as virtual circuit detection results and anomaly reports. Through the standardized model, the virtual circuit detection process is unified, reducing detection errors caused by human factors and improving detection efficiency. At the same time, a simulation verification and adaptive adjustment mechanism is used to dynamically optimize the model according to the actual situation of different substations. For example, when encountering new equipment types or wiring methods, the model can automatically adjust parameters and detection logic to ensure accurate detection of the virtual circuit state and meet the virtual circuit detection requirements of substation configuration files under different scenarios.

[0101] In addition, through the intelligent configuration generation method of the test template, modular test templates can be designed for the virtual loop detection of the substation configuration file. Among them, the test template is a standardized file used to describe the specific test process and steps, usually including input excitation quantities, judgment criteria, test steps, key judgment nodes, etc., and is customized according to different device configuration parameters and virtual loop detection requirements. The virtual loop detection process is decomposed into multiple combinable small modules, such as data acquisition module, mapping relationship analysis module, anomaly detection module, etc. Using an automated template generation tool, according to the device configuration parameters and virtual loop detection requirements in the substation configuration file, an adapted detection script can be quickly generated. The template supports dynamic loading and adjustment. When the substation configuration file changes, such as new devices are added or the virtual loop connection relationship is modified, the template can update the detection content in real time. In addition, the template system integrates the function of automatically generating reports, fills the key data in the detection process into the report template in real time, and ensures the accuracy and traceability of the report through version control. In this way, when detecting the virtual loop of the substation configuration file, the detection preparation time can be greatly shortened, and the detection efficiency and flexibility can be improved.

[0102] In an optional embodiment, the embodiment of the present invention supports automatically instantiating into a specific virtual loop detection outline after importing the substation configuration file on the basis of the standard test template. The system has the ability to automatically adjust the test template according to the device configuration in the substation configuration file, and automatically match the input and output signal points, function configurations, protection settings and test requirements of each device in the virtual loop. When detecting the virtual loop of the substation configuration file of a newly built substation, after the system reads the substation configuration file, it quickly identifies the switch device and protection device information therein, combines the standard test template with the actual device configuration, and automatically adjusts the test parameters and process. For example, according to the arrangement method and function definition of the input terminal of different protection devices in the substation configuration file, the acquisition and judgment methods of the input terminal state in the detection process are automatically adjusted to achieve "one-key" quick configuration and flexible adjustment, so that the detection process highly matches the actual on-site requirements and improves the detection accuracy.

[0103] In an alternative embodiment, intelligent analysis and human-machine collaboration technologies are introduced during the virtual circuit detection process. By using intelligent analysis technologies and real-time data collection means, the status data of the auxiliary contacts of switchgear and the input terminals of protection devices are monitored and automatically analyzed in real time. An abnormal detection model is established. Through learning a large amount of historical data, the status change pattern and time threshold of the virtual circuit under normal conditions are determined. When the detected time difference of status change or other abnormal indicators exceed the preset range, the intelligent diagnosis engine quickly issues an alarm and gives a preliminary abnormal diagnosis conclusion. At the same time, the judgment experience of power industry experts on the virtual circuits of substation configuration files is integrated to build a knowledge base. In case of complex abnormal situations, operators can refer to the information in the knowledge base, combine their own professional judgments, and work in collaboration with the intelligent system to jointly determine the abnormal situations of the virtual circuits and the treatment plans. In addition, the user interface of the detection system is optimized to make its operation more convenient, and a remote collaboration function is added to facilitate experts and technicians in different regions to jointly participate in the detection and analysis of the virtual circuits of substation configuration files, improving the accuracy and reliability of detection.

[0104] The intelligent verification method for virtual circuits in substation configuration files provided by the embodiment of the present invention can obtain the actual mapping relationship between the auxiliary contacts of each switchgear and the input terminals of each protection device in the virtual circuit to be verified based on the device data corresponding to the virtual circuit to be verified. By using this actual mapping relationship and device data, it can realize the automatic detection of the virtual connection status between the auxiliary contacts of switchgear and the input terminals of protection devices. Compared with the manual detection method, it effectively avoids the situations of misdetection and missed detection, significantly improves the detection accuracy of the virtual circuits in substation configuration files, and is applicable to different arrangements of input terminals.

[0105] As a preferred solution, the step of obtaining the actual mapping relationship between the auxiliary contacts of each switchgear and the input terminals of each protection device in the virtual circuit to be verified according to the device data specifically includes:

[0106] Obtain the status data of the auxiliary contacts of each switchgear and the status data of the input terminals of each protection device according to the device data;

[0107] According to the status data of the auxiliary contacts and the status data of the input terminals, obtain the first status value of the auxiliary contacts, the second status value of the input terminals, and the time difference of status change between the auxiliary contacts and the input terminals under each switch operation of the switchgear.

[0108] Match the first state value and the second state value according to the time difference of the state change and the preset signal transmission delay, so as to obtain the actual mapping relationship between each of the auxiliary contacts and each of the input terminals in the virtual loop to be verified.

[0109] Specifically, for the auxiliary contact status data and the input terminal status data, the status values of the auxiliary contacts and the input terminals at different times are usually obtained at a fixed time interval of 5 milliseconds to clarify the status time sequence data of each auxiliary contact and input terminal. It can be understood that the switchgear includes at least 2 auxiliary contacts and may experience an opening operation or a closing operation during each switch operation. When experiencing a closing operation, the normally open contact changes from the open state to the closed state, and the normally closed contact changes from the closed state to the open state. Exemplarily, the open state can be represented by the numerical value "0", and the closed state can be represented by the numerical value "1". Other numerical representation methods can also be used, which are not specifically limited in this embodiment. The status data of the input terminal reflects the opening and closing position of the corresponding auxiliary contact. Therefore, the auxiliary contact status data and the input terminal status data will show a strong correlation. That is, when the auxiliary contact changes its state, the corresponding input terminal also changes its state accordingly, and the time difference between the two state change moments is within the allowable signal transmission delay, such as 10 milliseconds. Therefore, in this embodiment, obtaining the first state value of the auxiliary contact, the second state value of the input terminal, and the time difference of the state change between the auxiliary contact and the input terminal during each switch operation of the switchgear is transformed into the time difference between the moment when it changes to the first state value and the moment when it changes to the second state value.

[0110] Further, match the first state value and the second state value when the time difference of the state change is within the signal transmission delay. Exemplarily, assume that the open state is represented by the numerical value "0", and the closed state is represented by the numerical value "1", and the signal transmission delay is 10 milliseconds. The auxiliary contact changes to the open state at a certain moment, that is, the first state value is 0 at this time. Check whether there is a second state value that synchronously changes and has a value of 0 within 10 milliseconds after this moment. If so, it is determined that the first state value and the second state value match. To ensure the accuracy of the actual mapping relationship, it is necessary to match the first state value and the second state value for each switch operation. According to the number of successful matches and the total number of matches, the matching success rate can be determined, and the auxiliary contact and the input terminal with the highest matching success rate are selected, indicating that they have a mapping relationship.

[0111] In an alternative embodiment, the embodiment of the present invention can also construct a standardized detection process. Through the standardized test case generation technology, the process generally includes: (1) establishing a fixed value list library, that is, establishing a fixed value list library according to the regulations of the fixed value detection range and error requirements in the standard; (2) establishing a basic function template library, that is, establishing a template for setting analog quantities during a fault, a template for setting fault time, and a template for gradual change setting associated with the fixed value, as well as templates for the output state of analog quantities before and after a fault, input / output templates for switch quantities, and templates for delays before and after a fault that are not related to the fixed value, etc., for use when establishing test type templates; (3) generating a test type template library, that is, generating a common test type template library according to the fixed value and combining the test methods encapsulated in the program, such as a virtual circuit test template; (4) generating test instances, thereby constructing a standardized process for detecting virtual circuits in substation configuration files. When detecting virtual circuits in substation configuration files, a standardized virtual circuit detection model is established based on the substation configuration file data of a large number of different types of substations. Just like establishing a fixed value list library, a key information library for substation configuration files is established, including device data, virtual circuit connection relationships, etc. According to the virtual circuit detection methods and principles, standard detection steps and specifications are formulated. For example, the frequency and range of data acquisition are specified, and the basis and threshold for judging the virtual connection state are clarified. When actually detecting the virtual circuits in the substation configuration file of a certain 220 kV substation, according to the standardized process, first extract the device data and actual mapping relationships from the substation configuration file, and then, based on the standard judgment rules, detect the virtual connection state one by one to ensure the consistency and accuracy of the detection process and avoid detection errors caused by human factors.

[0112] As a preferred solution, detecting the virtual connection state between the auxiliary contact of the switching device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual circuit detection result of the substation configuration file to be measured specifically includes:

[0113] Based on the switching device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact state data of the switching device to be measured and the input terminal state data of the protection device to be measured from the device data;

[0114] According to the auxiliary contact state data and the input terminal state data, obtain the difference in the state change time between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured for each switching operation of the switching device to be measured;

[0115] When it is detected that the difference in the state change time is greater than the preset abnormal time threshold, determine that the virtual connection state between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured is a virtual connection anomaly;

[0116] Obtain the virtual loop detection result according to the auxiliary contacts of the switchgear under test with virtual connection anomaly in each group and the input terminals of the protection device under test.

[0117] Specifically, since the actual mapping relationship between the auxiliary contacts of each switchgear and the input terminals of each protection device is clear, for the switchgear under test and the protection device under test with an actual mapping relationship, obtain the corresponding auxiliary contact status data and input terminal status data respectively. At this time, the obtained auxiliary contact status data and input terminal status data should show a strong correlation when the virtual connection is normal, that is, when the status of the auxiliary contact changes, the corresponding input terminal also changes its status, and the time difference between the status change times of the two is within the allowable signal transmission delay. The virtual connection anomaly is mainly manifested as the asynchronous change of the status of the input terminal and the status of the auxiliary contact, that is, the status change time of the input terminal lags behind the status change time of the auxiliary contact, and the lag time exceeds the preset abnormal time threshold, such as 20 milliseconds. At this time, it is determined that the virtual connection status between the auxiliary contact of the switchgear under test and the input terminal of the protection device under test is a virtual connection anomaly.

[0118] As a preferred solution, the method for detecting the virtual connection status between the auxiliary contacts of the switchgear and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual loop detection result of the substation configuration file under test specifically includes:

[0119] Obtain the terminal physical position, terminal identifier, and contact function attribute of each of the input terminals from the device data;

[0120] Input the terminal physical position, the terminal identifier, and the contact function attribute into a preset machine learning model to obtain the standard arrangement rule of the input terminals output by the machine learning model; wherein, the machine learning model is obtained by training based on a preset prior arrangement data set of input terminals; the standard arrangement rule of the input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the input terminals;

[0121] Convert the actual mapping relationship and the standard mapping relationship into a first feature vector and a second feature vector respectively, and calculate the Euclidean distance between the first feature vector and the second feature vector;

[0122] When it is detected that the Euclidean distance is greater than the preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals with the virtual connection status of virtual connection anomaly in the virtual loop to be verified.

[0123] Specifically, the arrangement of the input terminals of the substation has a fixed pattern, which represents the connection rule with the auxiliary contacts of the switchgear. In the actual operation records, the terminal numbers are recorded according to the physical positions of the terminals. For example, the switch off-position terminals are located at positions 1 to 4, and the switch on-position terminals are located at positions 5 to 8. Each switchgear is configured with 4 normally open contacts and 4 normally closed contacts. The normally open contacts record the off-position state, and the normally closed contacts record the on-position state. The input terminal identifier adopts a combination of letters and numbers. For example, KA01 represents the normally open contact of the A-phase switch off-position, and KB02 represents the normally closed contact of the B-phase switch on-position. The contact function attributes are distinguished by the terminal identifier. There are differences in the contact arrangements of switchgears from different manufacturers. For example, for the switchgear of manufacturer A, the normally open contacts are configured at the physical positions 1 to 4 of the terminals, and the normally closed contacts are configured at the physical positions 5 to 8 of the terminals. While for the switchgear of manufacturer B, an alternating arrangement is adopted, that is, the normally open contacts are configured at the odd-numbered physical positions of the terminals, and the normally closed contacts are configured at the even-numbered physical positions of the terminals. As a result, the arrangements of the input terminals also vary. By statistically analyzing the distribution frequencies of the input terminals with different contact function attributes at the physical positions of the terminals, the occurrence probabilities of different contact function attributes at each physical position of the terminals are calculated. When the occurrence probability of a certain contact function attribute at a specific physical position of the terminal exceeds 80%, it is determined that this physical position of the terminal is the standard position of such input terminals. Based on this, in this embodiment, based on the arrangement rules of the input terminals of different manufacturers summarized by operation experts, they are converted into a prior arrangement dataset of input terminals, and the machine learning model is trained so that the machine learning model can learn the standard positions of the input terminals with different contact function attributes. It can be understood that the prior arrangement dataset of input terminals contains the terminal physical position labels, terminal identifier labels, and contact function attribute labels corresponding to each arrangement rule of the input terminals. Inputting the terminal physical positions, terminal identifiers, and contact function attributes of each input terminal in the device data corresponding to the virtual loop to be verified into the trained machine learning model can obtain the corresponding standard arrangement rule of the input terminals, which reflects the standard mapping relationship between the current auxiliary contacts and each input terminal.

[0124] Further, for the convenience of comparison, the actual mapping relationship and the standard mapping relationship are respectively converted into a first feature vector and a second feature vector. Exemplarily, the connection relationship between different auxiliary contacts and the input terminals can be encoded to respectively form the first feature vector and the second feature vector, and then the Euclidean distance between the two feature vectors is calculated. It can be understood that the greater the Euclidean distance, the smaller the similarity between the two feature vectors, that is, there is a virtual connection anomaly in the actual mapping relationship. In this embodiment, when it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, such as 0.8, it is determined that there is at least one group of auxiliary contacts and input terminals with virtual connection anomalies in the virtual loop to be verified. Exemplarily, when the Euclidean distance is in the range of 0.8 to 0.9, it indicates that the positions of the same type of contacts are incorrect, and when the Euclidean distance is greater than 0.9, it indicates that misconnections occur between different types of contacts.

[0125] In an alternative embodiment, the embodiment of the present invention can also implement compatibility detection for virtual loops of substation configuration files of different manufacturers and different models of equipment. Analyze the format differences of substation configuration files of equipment from different manufacturers, and develop a general parsing interface so that the detection system can read and process substation configuration files in various formats. Taking a certain regional power grid as an example, which contains equipment from multiple manufacturers, through a unified parsing interface, virtual loop detection can be performed on substation configuration files of equipment from different manufacturers at the same time. During the detection process, just like controlling different testers, according to the equipment type and the characteristics of the substation configuration file, the detection parameters and methods are automatically adjusted to achieve efficient detection of virtual loops of multiple devices and improve the applicability of the detection system.

[0126] As a preferred solution, detecting the virtual connection state between the auxiliary contacts of the switchgear and the input terminals of the protection device according to the device data and the actual mapping relationship to obtain the virtual loop detection result of the substation configuration file to be measured specifically includes:

[0127] According to the actual mapping relationship, determine the connection relationship between each of the switchgears and each of the protection devices;

[0128] Obtain the first physical position of each of the switchgears and the second physical position of each of the protection devices from the device data;

[0129] According to the connection relationship, the first physical position and the second physical position, use a graph theory algorithm to generate a connection topology graph between each of the switchgears and each of the protection devices;

[0130] Use a depth-first search algorithm to search for the target connection path between the switchgear and the protection device in the connection topology graph;

[0131] Obtain the first state time series data of the switching device and the second state time series data of the protection device in each of the target connection paths from the device data;

[0132] According to the first state time series data and the second state time series data, calculate the state change time series correlation degree between the switching device and the protection device in each of the target connection paths by using a correlation calculation method;

[0133] According to the preset corresponding relationship between the state change time series correlation degree and the connectivity probability, determine the connectivity probability between the switching device and the protection device in each of the target connection paths;

[0134] Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection abnormal probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switching device and the protection device;

[0135] When it is detected that the virtual connection abnormal probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contact of the switching device and the input terminal of the protection device in the target connection path is a virtual connection abnormality;

[0136] Obtain the virtual loop detection result according to each group of auxiliary contacts and input terminals with the virtual connection state being a virtual connection abnormality.

[0137] Specifically, in this embodiment, each switching device and each protection device are regarded as several nodes, and the connection relationship between the switching device and the protection device is regarded as an edge. A corresponding connection topology graph is generated through a graph theory algorithm. Further, since there may be multiple connection paths between the switching device and the protection device with a connection relationship, and the longer the connection path, the greater the signal transmission delay usually is, resulting in unstable virtual connections. To be able to determine the optimal connection path between the switching device and the protection device, this embodiment uses a depth-first search algorithm to search for the target connection path between the switching device and the protection device in the connection topology graph. After determining the target connection path, the first state time series data of the switching device and the second state time series data of the protection device in each target connection path are obtained, and the correlation is calculated. It can be understood that the state change time series correlation can reflect the connectivity probability between the switching device and the protection device in the target connection path, that is, the probability that there is a path between these two nodes in the connection topology graph. Exemplarily, this embodiment presets the corresponding relationship between the state change time series correlation and the connectivity probability. For example, when the state change time series correlation is higher than 0.9, the connectivity probability takes a value of 0.9; when the state change time series correlation is between 0.5 and 0.9, the connectivity probability takes a value of 0.6; when the state change time series correlation is lower than 0.5, the connectivity probability takes a value of 0.3. To be able to analyze the virtual connection abnormality probability between the switching device and the protection device in the target connection path, this embodiment uses a pre-trained Bayesian network. It can be understood that this Bayesian network is trained using the historical topology data between the switching device and the protection device. The historical topology data includes the path weight label of the connection path between the switching device and the protection device, the connectivity probability between the switching device and the protection device, and the virtual connection status label between the switching device and the protection device. The path weight in this embodiment is determined based on the above-mentioned first physical location and second physical location. The farther the switching device and the protection device are apart, the smaller the path weight is, indicating a higher signal transmission delay, and the lower the virtual connection abnormality probability between the two. Inputting the above-obtained connectivity probability and the path weight corresponding to the target connection path into the trained Bayesian network can obtain the virtual connection abnormality probability. When it is greater than a preset probability threshold, such as 0.8, it is determined that the virtual connection status between the auxiliary contact of the switching device and the input terminal of the protection device in the current target connection path is a virtual connection abnormality.

[0138] As a preferred solution, the step of using a depth-first search algorithm to search for the target connection path between the switching device and the protection device in the connection topology graph specifically includes:

[0139] According to the first physical location and the second physical location, determine the physical distance between each node in the connection topology graph; wherein, the node is the switching device or the protection device;

[0140] Determine the edge weights between each of the nodes according to the preset correspondence between the physical distance and the edge weight;

[0141] Using the depth - first search algorithm, traverse the nodes in the connection topology graph in the order of the edge weights from large to small, and obtain several alternative connection paths between the switchgear and the protection device;

[0142] Determine the path weight corresponding to each of the alternative connection paths according to the product of the edge weights between the nodes in each of the alternative connection paths, and use the alternative connection path with the largest path weight as the target connection path.

[0143] Specifically, since there may be direct paths and indirect paths between the switchgear and the protection device with a connection relationship. For example, a certain circuit breaker is indirectly connected to the protection device through a disconnector. Therefore, in this embodiment, it is necessary to obtain the physical distance between each node, which can be the physical distance between two switchgears or the physical distance between the switchgear and the protection device with a connection relationship. This embodiment does not make specific limitations here. Based on the preset different correspondences between the physical distance and the edge weight, such as when the switchgear and the protection device are installed in the same cabinet, the physical distance is less than 10 meters, and the edge weight of the connection is taken as 0.8, indicating the most reliable connection relationship; when the switchgear and the protection device are installed in adjacent cabinets, the physical distance is in the range of 10 meters to 50 meters, and the edge weight of the connection is taken as 0.6; when the switchgear and the protection device are installed in different places, the physical distance exceeds 50 meters, and the edge weight of the connection is taken as 0.4. Further, in the process of the depth - optimization search algorithm, traverse the nodes in the connection topology graph in the order of the edge weights from large to small to obtain the connection path with the shortest distance as much as possible, so as to obtain several alternative connection paths between the switchgear and the protection device. Since the alternative connection paths may include two or more nodes, for example, a certain circuit breaker has two alternative connection paths. One is directly connected to the protection device with a connection relationship, and at this time the path weight is the edge weight between the circuit breaker and the protection device; the other is indirectly connected to the protection device with a connection relationship through a disconnector, and at this time the path weight is the product of the edge weights between the circuit breaker and the disconnector and the edge weights between the disconnector and the protection device. Sort the path weights corresponding to each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0144] As a preferred solution, after detecting the virtual connection state between the auxiliary contact of the switchgear and the input terminal of the protection device, the method further includes:

[0145] When the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is detected as a virtual connection anomaly, obtain the attribute parameters of each of the target switch devices from the device data;

[0146] Generate the device characteristic identification values corresponding to each of the target switch devices according to the attribute parameters;

[0147] Use the decision tree algorithm to normalize each of the device characteristic identification values, and determine the virtual connection anomaly level corresponding to each of the target switch devices according to the normalized device characteristic identification values and the preset anomaly level classification threshold.

[0148] Specifically, after detecting the existence of a virtual connection anomaly in this embodiment, the virtual connection anomaly level is further identified to facilitate implementing different countermeasures for different virtual connection anomaly levels. First, it is necessary to obtain the attribute parameters of each target switch device with a virtual connection anomaly. It can be understood that the attribute parameters include the running duration, maintenance interval, and operating environment temperature, etc. These attribute parameters have a certain impact on the virtual connection anomaly. For example, as the running duration and maintenance interval increase, the virtual connection anomaly degree of the current target switch device will increase. And as the operating environment temperature increases, it will affect the contact resistance and signal transmission delay of the target switch device, and at the same time will exacerbate the contact wear, making the mechanism action speed slower. For example, for every 10-degree increase in the operating environment temperature, the contact resistance of the auxiliary contact increases by 10%, the signal transmission delay increases by 5 milliseconds, the contact wear is exacerbated when operating in a high-temperature environment above 35 degrees, and the mechanism action speed becomes slower. Therefore, in this embodiment, based on these attribute parameters, the device characteristic identification values corresponding to each target switch device are generated to characterize the overall device condition of each target switch device.

[0149] Furthermore, use the decision tree algorithm to normalize each of the device characteristic identification values. It can be understood that based on the maximum and minimum values of the device characteristic identification values, each of the device characteristic identification values can be normalized, mapping each device characteristic identification value to the range between 0 and 1, and then determining the corresponding virtual connection anomaly level according to the preset anomaly level classification threshold. Exemplarily, the anomaly levels in this embodiment include minor anomaly, moderate anomaly, and severe anomaly, and the anomaly level classification thresholds include 0.3 and 0.7, that is, when the normalized device characteristic identification value is in the range of 0 to 0.3, it is a minor anomaly, when it is in the range of 0.3 to 0.7, it is a moderate anomaly, and when it exceeds 0.7, it is a severe anomaly.

[0150] As a preferred solution, the attribute parameters include the running duration, maintenance interval, and operating environment temperature;

[0151] Generating the device characteristic identification values corresponding to each of the target switch devices according to the attribute parameters specifically includes:

[0152] According to a preset abnormal score comparison table, respectively obtaining a first abnormal score corresponding to the operation duration of each target switch device, a second abnormal score corresponding to the maintenance interval, and a third abnormal score corresponding to the operation environment temperature; wherein, different operation durations, maintenance intervals, and operation environment temperatures and their corresponding abnormal scores are recorded in the abnormal score comparison table.

[0153] Performing weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score to obtain the device characteristic identification values corresponding to each of the target switch devices.

[0154] Specifically, in order to effectively obtain the device characteristic identification values corresponding to each target switch device, this embodiment presets an abnormal score comparison table to clarify the abnormal scores corresponding to different operation durations, maintenance intervals, and operation environment temperatures. For example, the abnormal score corresponding to an operation duration of 5 to 10 years is 0.4, the abnormal score corresponding to an operation duration of 10 to 15 years is 0.6, and the abnormal score corresponding to an operation duration of more than 15 years is 0.8. The abnormal score corresponding to a maintenance interval of 6 to 12 months is 0.4, the abnormal score corresponding to a maintenance interval of 12 to 18 months is 0.6, and the abnormal score corresponding to a maintenance interval of more than 18 months is 0.8. This embodiment will not elaborate further here. Performing weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score obtains the corresponding device characteristic identification values. The weight corresponding to each abnormal score can be set according to the influence degree of the current attribute parameter on the virtual connection abnormality. For example, if the operation duration has a higher influence degree on the virtual connection abnormality, the weights of the first abnormal score, the second abnormal score, and the third abnormal score are set to 0.4, 0.3, and 0.3 respectively. This embodiment does not make specific limitations here.

[0155] As a preferred solution, after determining the virtual connection abnormality levels corresponding to each of the target switch devices according to the preset abnormal level classification threshold, the method further includes:

[0156] Adjusting the abnormal level classification threshold according to the abnormal occurrence frequency corresponding to each virtual connection abnormality level.

[0157] Specifically, since the frequency of occurrence of exceptions corresponding to each virtual connection exception level can reflect the deterioration degree of the current virtual connection, in order to process devices with virtual connection exceptions of different degrees in a timely manner, in this embodiment, based on the frequency of occurrence of exceptions corresponding to each virtual connection exception level, the current exception level classification threshold is adjusted, so that when evaluating the virtual connection exception level next time, a dynamic evaluation of the virtual connection exception level based on the frequency of occurrence of exceptions can be achieved based on the adjusted exception level classification threshold. Exemplarily, the virtual connection exception levels in this embodiment include minor exceptions, moderate exceptions, and severe exceptions, and the corresponding exception level classification thresholds include a first classification threshold and a second classification threshold. If minor exceptions occur frequently, it indicates that the virtual connection begins to deteriorate; if moderate exceptions occur frequently, it indicates that the virtual connection has deteriorated; if severe exceptions occur frequently, it indicates that the virtual connection is about to fail. In order to be able to process in a timely manner before the virtual connection deteriorates or even fails, this embodiment needs to adjust the exception level classification threshold. For example, if the occurrence frequency of minor exceptions exceeds 10 times, the first classification threshold is lowered by 0.1, indicating that the deterioration degree of the current virtual connection is enhanced, and the judgment range of moderate exceptions needs to be correspondingly expanded, so as to be able to avoid the complete deterioration of the virtual connection in a timely manner. If the occurrence frequency of moderate exceptions exceeds 5 times, the second classification threshold is lowered by 0.2. If the occurrence frequency of severe exceptions exceeds 3 times, the second classification threshold is lowered by 0.3, indicating that there is a greater risk of failure in the current virtual connection, and the judgment range of severe exceptions needs to be correspondingly expanded, so as to be able to avoid the complete failure of the virtual connection in a timely manner, greatly improving the operation safety of the substation automation system.

[0158] Please refer to Figure 2 , the second aspect of the embodiment of the present invention provides a virtual circuit intelligent verification system 100 for a substation configuration file, including:

[0159] A data acquisition module 11, configured to determine a virtual circuit to be verified and device data corresponding to the virtual circuit to be verified according to the substation configuration file to be tested;

[0160] An actual mapping relationship determination module 12, configured to obtain an actual mapping relationship between auxiliary contacts of each switching device and input terminals of each protection device in the virtual circuit to be verified according to the device data;

[0161] A virtual circuit detection module 13, configured to detect a virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain a virtual circuit detection result of the substation configuration file to be tested.

[0162] As a preferred solution, the actual mapping relationship determination module 12 is configured to obtain an actual mapping relationship between auxiliary contacts of each switching device and input terminals of each protection device in the virtual circuit to be verified according to the device data, specifically including:

[0163] Obtain the auxiliary contact status data of each of the switching devices and the status data of the input terminals of each of the protection devices according to the device data;

[0164] According to the auxiliary contact status data and the status data of the input terminals, obtain the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device;

[0165] Match the first status value and the second status value according to the status change time difference and a preset signal transmission delay to obtain the actual mapping relationship between each of the auxiliary contacts and each of the input terminals in the virtual circuit to be verified.

[0166] As a preferred solution, the virtual circuit detection module 13 is used to detect the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including:

[0167] Based on the switching device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact status data of the switching device to be measured and the status data of the input terminals of the protection device to be measured from the device data;

[0168] According to the auxiliary contact status data and the status data of the input terminals, obtain the status change time difference between the auxiliary contacts of the switching device to be measured and the input terminals of the protection device to be measured under each switching operation of the switching device to be measured;

[0169] When it is detected that the status change time difference is greater than a preset abnormal time threshold, determine that the virtual connection status between the auxiliary contacts of the switching device to be measured and the input terminals of the protection device to be measured is abnormally virtual-connected;

[0170] Obtain the virtual circuit detection result according to each set of auxiliary contacts of the switching device to be measured and the input terminals of the protection device to be measured with the virtual connection status being abnormally virtual-connected.

[0171] As a preferred solution, the virtual circuit detection module 13 is used to detect the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including:

[0172] Acquire the terminal physical position, terminal identifier and contact function attribute of each of the binary input terminals from the device data;

[0173] Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals;

[0174] Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector;

[0175] When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

[0176] As a preferred solution, the virtual circuit detection module 13 is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the substation configuration file to be tested, specifically including:

[0177] Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices;

[0178] Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data;

[0179] Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position;

[0180] Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm;

[0181] Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data;

[0182] Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data;

[0183] Determine the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability;

[0184] Input the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection anomaly probability output by the Bayesian network; wherein, the path weight is determined based on the first physical location and the second physical location; the Bayesian network is trained based on the historical topology data between the switch device and the protection device;

[0185] When it is detected that the virtual connection anomaly probability is greater than a preset probability threshold, determine that the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device in the target connection path is a virtual connection anomaly;

[0186] Obtain the virtual loop detection result according to each group of auxiliary contacts and input terminals whose virtual connection state is a virtual connection anomaly.

[0187] As a preferred solution, the virtual loop detection module 13 is used to search for the target connection path between the switch device and the protection device in the connection topology diagram by using the depth - first search algorithm, specifically including:

[0188] Determine the physical distance between each node in the connection topology diagram according to the first physical location and the second physical location; wherein, the node is the switch device or the protection device;

[0189] Determine the edge weight between each node according to the preset correspondence between the physical distance and the edge weight;

[0190] Use the depth - first search algorithm to traverse the nodes in the connection topology diagram in the order of the edge weight from large to small to obtain several alternative connection paths between the switch device and the protection device;

[0191] Determine the path weight corresponding to each alternative connection path according to the product of the edge weights between each node in each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

[0192] As a preferred solution, the virtual loop detection module 13 is further used for:

[0193] When it is detected that the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is a virtual connection anomaly, obtain the attribute parameters of each of the target switch devices from the device data;

[0194] Generate the device characteristic identification values corresponding to each of the target switch devices according to the attribute parameters;

[0195] Use a decision tree algorithm to normalize each of the device characteristic identification values, and determine the virtual connection anomaly levels corresponding to each of the target switch devices according to the normalized device characteristic identification values and the preset anomaly level classification thresholds.

[0196] As a preferred solution, the attribute parameters include the operation duration, the maintenance interval, and the operation environment temperature;

[0197] The virtual loop detection module 13 is used to generate the device characteristic identification values corresponding to each of the target switch devices according to the attribute parameters, specifically including:

[0198] According to a preset anomaly score comparison table, respectively obtain the first anomaly score corresponding to the operation duration of each of the target switch devices, the second anomaly score corresponding to the maintenance interval, and the third anomaly score corresponding to the operation environment temperature; wherein, different operation durations, maintenance intervals, and operation environment temperatures and their corresponding anomaly scores are recorded in the anomaly score comparison table;

[0199] Perform a weighted sum of the first anomaly score, the second anomaly score, and the third anomaly score to obtain the device characteristic identification values corresponding to each of the target switch devices.

[0200] As a preferred solution, the virtual loop detection module 13 is further used for:

[0201] Adjust the anomaly level classification threshold according to the anomaly occurrence frequency corresponding to each type of virtual connection anomaly level.

[0202] In an alternative embodiment, the embodiment of the present invention can introduce intelligent analysis and real-time data acquisition technologies in virtual loop verification. By establishing an anomaly detection model and an intelligent diagnosis engine, real-time monitoring and automatic analysis are performed on the status data of the auxiliary contacts of the switch device and the input terminals of the protection device. Integrate the judgment experience of power industry experts on the virtual loops of substation configuration files to build a knowledge base. When an abnormal change in the virtual loop status is detected, the intelligent diagnosis engine quickly analyzes and combines the information in the knowledge base to give accurate diagnosis conclusions and treatment suggestions. During the virtual loop verification process of the substation configuration file of a certain substation, the intelligent system collects data in real time. When it is found that the time difference of the status change of a certain virtual loop exceeds the normal range, the system quickly judges that there may be a connection failure, and retrieves the treatment methods of similar cases from the knowledge base to provide reference for operation and maintenance personnel, improving the accuracy and reliability of virtual loop verification.

[0203] The virtual loop intelligent verification system 100 for substation configuration files provided by the embodiments of the present invention can obtain the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual loop to be verified based on the device data corresponding to the virtual loop to be verified. By using this actual mapping relationship and device data, it can realize the automatic detection of the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device. Compared with the manual detection method, it effectively avoids the situation of misdetection and undetected, significantly improves the detection accuracy of the virtual loop of the substation configuration file, and is applicable to different arrangements of input terminals.

[0204] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art of the present technology, several improvements and refinements can be made without departing from the principle of the present invention, and these improvements and refinements are also regarded as the protection scope of the present invention.

Claims

1. An intelligent verification method for virtual circuits of substation configuration files, characterized in that Including: Determine the virtual circuit to be verified and the device data corresponding to the virtual circuit to be verified according to the configuration file of the substation to be measured; According to the device data, obtain the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuit to be verified; According to the device data and the actual mapping relationship, detect the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device, and obtain the virtual circuit detection result of the configuration file of the substation to be measured; Among them, the step of obtaining the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuit to be verified according to the device data specifically includes: According to the device data, obtain the auxiliary contact state data of each switching device and the input terminal state data of each protection device; According to the auxiliary contact state data and the input terminal state data, obtain the first state value of the auxiliary contact, the second state value of the input terminal, and the state change time difference between the auxiliary contact and the input terminal under each switching action of the switching device; According to the state change time difference and the preset signal transmission delay, match the first state value and the second state value to obtain the actual mapping relationship between each auxiliary contact and each input terminal in the virtual circuit to be verified.

2. The intelligent verification method for virtual circuits of substation configuration files according to claim 1, wherein The step of detecting the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual circuit detection result of the configuration file of the substation to be measured specifically includes: Based on the switching device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact state data of the switching device to be measured and the input terminal state data of the protection device to be measured from the device data; According to the auxiliary contact state data and the input terminal state data, obtain the state change time difference between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured under each switching action of the switching device to be measured; When it is detected that the state change time difference is greater than the preset abnormal time threshold, determine that the virtual connection state between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured is a virtual connection abnormality; According to each group of auxiliary contacts of the switching device to be measured and the input terminals of the protection device to be measured with the virtual connection state being a virtual connection abnormality, obtain the virtual circuit detection result.

3. The intelligent verification method for virtual circuits of substation configuration files according to claim 1, characterized in that The step of detecting the virtual connection state between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtaining the virtual circuit detection result of the configuration file of the substation to be measured specifically includes: Obtain the terminal physical position, terminal identifier, and contact function attribute of each input terminal from the device data; Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals; Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

4. The intelligent verification method for virtual circuits of substation configuration files according to claim 1, characterized in that The detecting, according to the device data and the actual mapping relationship, the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device to obtain the virtual circuit detection result of the configuration file of the substation to be tested specifically includes: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determining the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability; Inputting the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection abnormality probability output by the Bayesian network; wherein the path weight is determined based on the first physical location and the second physical location; and the Bayesian network is obtained by training based on historical topology data between the switch device and the protection device; When it is detected that the probability of the virtual connection abnormality is greater than a preset probability threshold, the virtual connection state between the auxiliary contact of the switch device in the target connection path and the input terminal of the protection device is determined to be a virtual connection abnormality; The virtual circuit detection result is obtained according to each group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connection.

5. The intelligent verification method for virtual circuits of substation configuration files according to claim 4, characterized in that The step of searching for the target connection path between the switch device and the protection device in the connection topology diagram by using the depth - first search algorithm specifically includes: Determine the physical distances between the nodes in the connection topology diagram according to the first physical location and the second physical location; wherein, the nodes are the switch device or the protection device; Determine the edge weights between the nodes according to the preset correspondence between the physical distances and the edge weights; Use the depth - first search algorithm to traverse the nodes in the connection topology diagram in the order of the edge weights from large to small, and obtain several alternative connection paths between the switch device and the protection device; Determine the path weight corresponding to each alternative connection path according to the product of the edge weights between the nodes in each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

6. The intelligent verification method for virtual circuits of substation configuration files according to claim 1, characterized in that After the method detects the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device, it further includes: When detecting that the virtual connection state between the auxiliary contact of any target switch device and the input terminal of any target protection device is abnormal virtual connection, obtain the attribute parameters of each target switch device from the device data; Generate the device feature identification values corresponding to each target switch device according to the attribute parameters; Use the decision tree algorithm to normalize each device feature identification value, and determine the virtual connection abnormal level corresponding to each target switch device according to the normalized device feature identification value and the preset abnormal level classification threshold.

7. The intelligent verification method for virtual circuits of substation configuration files according to claim 6, characterized in that, The attribute parameters include the running duration, the maintenance interval, and the running environment temperature; The step of generating the device feature identification values corresponding to each target switch device according to the attribute parameters specifically includes: According to the preset abnormal score comparison table, respectively obtain the first abnormal score corresponding to the running duration, the second abnormal score corresponding to the maintenance interval, and the third abnormal score corresponding to the running environment temperature of each target switch device; wherein, the abnormal score comparison table records the abnormal scores corresponding to different running durations, maintenance intervals, and running environment temperatures; Perform weighted summation on the first abnormal score, the second abnormal score, and the third abnormal score to obtain the device feature identification values corresponding to each target switch device.

8. The intelligent verification method for virtual circuits of substation configuration files according to claim 6, wherein After the method determines the virtual connection abnormal level corresponding to each target switch device according to the preset abnormal level classification threshold, it further includes: Adjust the abnormal level classification threshold according to the abnormal occurrence frequency corresponding to each virtual connection abnormal level.

9. An intelligent verification system for virtual circuits of substation configuration files, characterized in that, It includes: A data acquisition module, configured to determine the virtual circuit to be verified and the device data corresponding to the virtual circuit to be verified according to the configuration file of the substation to be measured; An actual mapping relationship determination module, configured to obtain the actual mapping relationship between the auxiliary contacts of the switch devices and the input terminals of the protection devices in the virtual circuit to be verified according to the device data; The virtual circuit detection module is used to detect the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured; Among them, the actual mapping relationship determination module is used to obtain the actual mapping relationship between the auxiliary contacts of each switching device and the input terminals of each protection device in the virtual circuit to be verified according to the device data, specifically including: According to the device data, obtain the auxiliary contact status data of each switching device and the input terminal status data of each protection device; According to the auxiliary contact status data and the input terminal status data, obtain the first status value of the auxiliary contact, the second status value of the input terminal, and the status change time difference between the auxiliary contact and the input terminal under each switching operation of the switching device; According to the status change time difference and the preset signal transmission delay, match the first status value and the second status value to obtain the actual mapping relationship between each auxiliary contact and each input terminal in the virtual circuit to be verified.

10. The virtual loop intelligent checking system for substation configuration files according to claim 9, characterized in that, The virtual circuit detection module is used to detect the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including: Based on the switching device to be measured and the protection device to be measured with the actual mapping relationship, obtain the auxiliary contact status data of the switching device to be measured and the input terminal status data of the protection device to be measured from the device data; According to the auxiliary contact status data and the input terminal status data, obtain the status change time difference between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured under each switching operation of the switching device to be measured; When it is detected that the status change time difference is greater than the preset abnormal time threshold, determine that the virtual connection status between the auxiliary contact of the switching device to be measured and the input terminal of the protection device to be measured is abnormally virtual connected; According to each group of auxiliary contacts of the switching device to be measured and the input terminals of the protection device to be measured with the virtual connection status being abnormally virtual connected, obtain the virtual circuit detection result.

11. The virtual loop intelligent verification system for substation configuration files according to claim 9, characterized in that The virtual circuit detection module is used to detect the virtual connection status between the auxiliary contacts of the switching device and the input terminals of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be measured, specifically including: Obtain the terminal physical position, terminal identifier, and contact function attribute of each input terminal from the device data; Inputting the terminal physical position, the terminal identifier and the contact function attribute into a preset machine learning model, obtaining the standard arrangement rule of the binary input terminals output by the machine learning model; wherein the machine learning model is obtained by training based on a preset binary input terminal prior arrangement data set; the standard arrangement rule of the binary input terminals is used to indicate the standard mapping relationship between each of the auxiliary contacts and each of the binary input terminals; Converting the actual mapping relationship and the standard mapping relationship into a first eigenvector and a second eigenvector respectively, and calculating the Euclidean distance between the first eigenvector and the second eigenvector; When it is detected that the Euclidean distance is greater than a preset Euclidean distance threshold, it is determined that there is at least one group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connections in the virtual circuit to be checked.

12. The virtual loop intelligent verification system for substation configuration files according to claim 9, characterized in that The virtual circuit detection module is used to detect the virtual connection state between the auxiliary contact of the switch device and the input terminal of the protection device according to the device data and the actual mapping relationship, and obtain the virtual circuit detection result of the configuration file of the substation to be tested, specifically including: Determining, according to the actual mapping relationship, a connection relationship between each of the switch devices and each of the protection devices; Acquire a first physical position of each of the switch devices and a second physical position of each of the protection devices from the device data; Generate a connection topology diagram between each of the switch devices and each of the protection devices using a graph theory algorithm according to the connection relationship, the first physical position, and the second physical position; Searching for a target connection path between the switch device and the protection device in the connection topology graph using a depth-first search algorithm; Acquire first state timing data of a switch device and second state timing data of a protection device in each of the target connection paths from the device data; Calculate the state change time series correlation between the switch device and the protection device in each of the target connection paths by using a correlation calculation method according to the first state time series data and the second state time series data; Determining the connectivity probability between the switch device and the protection device in each of the target connection paths according to the preset correspondence between the state change timing correlation and the connectivity probability; Inputting the path weight corresponding to the target connection path and the connectivity probability into a preset Bayesian network to obtain the virtual connection abnormality probability output by the Bayesian network; wherein the path weight is determined based on the first physical location and the second physical location; and the Bayesian network is obtained by training based on historical topology data between the switch device and the protection device; When it is detected that the probability of the virtual connection abnormality is greater than a preset probability threshold, the virtual connection state between the auxiliary contact of the switch device in the target connection path and the input terminal of the protection device is determined to be a virtual connection abnormality; The virtual circuit detection result is obtained according to each group of auxiliary contacts and input terminals whose virtual connection states are abnormal virtual connection.

13. The virtual loop intelligent verification system for substation configuration files according to claim 12, characterized in that The virtual circuit detection module is used to search for the target connection path between the switch device and the protection device in the connection topology diagram by using the depth-first search algorithm, and specifically includes: Determine the physical distances between the nodes in the connection topology diagram according to the first physical location and the second physical location; wherein, the nodes are the switch device or the protection device; Determine the edge weights between the nodes according to the preset correspondence between the physical distance and the edge weight; Use the depth-first search algorithm to traverse the nodes in the connection topology diagram in the order of the edge weights from large to small, and obtain several alternative connection paths between the switch device and the protection device; Determine the path weight corresponding to each alternative connection path according to the product of the edge weights between the nodes in each alternative connection path, and use the alternative connection path with the largest path weight as the target connection path.

Images