A smart substation commissioning method based on CMS protocol

Abstract

This invention relates to the field of intelligent substation commissioning, and more particularly to an intelligent substation commissioning method based on the CMS protocol. The commissioning master station loads the system configuration description file, and through semantic pre-parsing, logical model parsing, and communication configuration parsing, generates the topology of all substation equipment. Subsequently, it collects equipment operating status data in real time, performs normalization processing, and constructs equipment operating status vectors. Based on these vectors, a dynamic state entropy flow collaborative algorithm is used to calculate the basic disturbance energy of a single device, and the coupling propagation strength between devices is introduced to generate a dynamic state entropy flow. This flow is compared with a set stability threshold range, and the CMS commissioning path and commissioning priority are adjusted in real time. This method solves the technical problems of low SCD configuration parsing efficiency, inability to uniformly model the communication and control relationships between devices, separation between operating status and protocol commissioning process, difficulty in identifying equipment anomaly propagation relationships, and the inability of fixed commissioning sequences to adapt to complex operating conditions.

Description

Technical Field

[0001] This invention relates to the field of intelligent substation commissioning, and in particular to an intelligent substation commissioning method based on the CMS protocol. Background Technology

[0002] With the continuous expansion of smart substation construction, digital communication methods based on the IEC 61850 standard have been widely applied in power system operation. Within smart substations, protection devices, measurement and control devices, merging units, intelligent terminals, and station control layer equipment rely on communication protocols to complete operational status interaction, control command transmission, and fault information reporting. With the advancement of the State Grid's independent and controllable technology system, the traditional MMS protocol is gradually evolving towards the CMS protocol. The CMS protocol is beginning to undertake business functions such as model access, operation monitoring, remote control, and operational information interaction in smart substations. Therefore, how to perform high-reliability debugging of the CMS protocol has become a key issue in the construction and operation of smart substations.

[0003] Existing CMS protocol debugging methods mostly rely on fixed test scripts or fixed message playback. These methods primarily verify the CMS service's responsiveness by pre-setting protocol messages and executing communication tests in a fixed order. However, smart substations are highly coupled real-time operating systems. Devices not only have communication relationships but also electrical linkages, protection linkages, and time synchronization dependencies. Therefore, simply relying on fixed message tests cannot accurately reflect the CMS protocol's operational status under complex operating conditions. Furthermore, existing CMS debugging systems typically use a recursive, layer-by-layer approach to read IED devices, logical nodes, and control block information when parsing SCD configuration files. When the substation is large, the number of logical nodes is high, or object references are complex, this can lead to repetitive traversal, low parsing efficiency, and unclear object relationships, hindering the rapid establishment of the mapping relationship between CMS services and operational data during subsequent debugging. In addition, existing CMS debugging systems only focus on the correctness of the protocol messages themselves, lacking a comprehensive analysis of the device's operational status. For example, when network links fluctuate, GOOSE communication is abnormal, time synchronization deviations increase, or protection actions malfunction, traditional commissioning systems continue to perform high-risk test operations such as remote control and setting value writing according to a fixed test sequence. This can easily lead to distorted commissioning results and, in severe cases, may cause equipment malfunctions or abnormal linkages. Furthermore, due to the lack of dynamic propagation relationship analysis capabilities between devices, when a device malfunctions, existing technologies struggle to identify the propagation process of the abnormal state across multiple bays and links, resulting in difficulties in fault location and low commissioning efficiency. Therefore, there is an urgent need to propose a smart substation commissioning method based on the CMS protocol to solve these problems. Summary of the Invention

[0004] This invention provides a smart substation commissioning method based on the CMS protocol to solve the technical problems existing in the CMS protocol commissioning process of existing smart substations, such as low SCD configuration parsing efficiency, inability to uniformly model the communication and control correlation between devices, separation between operating status and protocol commissioning process, difficulty in identifying the propagation relationship of device anomalies, and the inability of fixed commissioning sequence to adapt to complex operating conditions.

[0005] The present invention provides a method for commissioning a smart substation based on the CMS protocol, comprising the following steps:

[0006] S1. The debugging master station loads the system configuration description file and obtains the object association mapping table, logical model association relationship, and communication path mapping relationship through semantic pre-parsing, logical model parsing, and communication configuration parsing stages. Based on the object association mapping table, logical model association relationship, and communication path mapping relationship, a full-station equipment topology structure including electrical connection relationship, communication connection relationship, control linkage relationship, and CMS service association relationship is generated. Then, the equipment operation status data is collected in real time, normalized, and a device operation status vector is constructed.

[0007] S2. Based on the device operating state vector, the basic disturbance energy of a single device is calculated through a dynamic state entropy flow collaborative algorithm, and the coupling propagation strength between devices is introduced to generate a dynamic state entropy flow. Based on the dynamic state entropy flow, it is compared with the set stable threshold range, and the CMS debugging path and debugging priority are adjusted in real time to realize dynamic adaptive closed-loop debugging of the CMS protocol debugging process.

[0008] Preferably, S1 specifically includes:

[0009] Based on the equipment operating status data, normalization processing is first performed to obtain voltage offset normalization factor, current fluctuation normalization factor, GOOSE link stability normalization factor, SV sampling stability normalization factor, protection action activity normalization factor, PTP time synchronization deviation normalization factor, load rate, bit error rate, and CMS service response anomaly normalization factor, which are then combined into the equipment operating status vector.

[0010] Preferably, S2 specifically includes:

[0011] In the dynamic state entropy flow cooperative algorithm, the time synchronization propagation amplification factor is calculated by multiplying the PTP time synchronization deviation normalization factor in the device operating state vector by the set synchronization deviation amplification factor and adding 1. The voltage offset normalization factor, current fluctuation normalization factor, load rate, and bit error rate in the device operating state vector are squared respectively. Then, the squares of the squares of the voltage offset normalization factor, current fluctuation normalization factor, load rate, and bit error rate are weighted, summed, and the square root is taken to calculate the operating disturbance. The operating disturbance is multiplied by the time synchronization propagation amplification factor to calculate the basic disturbance energy of a single device.

[0012] Preferably, S2 specifically includes:

[0013] In the dynamic state entropy flow coordination algorithm, the load rates of each device are summed and the CMS service response anomaly normalization factor in the device operation state vector is added. Then, a logarithmic operation with the natural number base is performed to obtain the service load coupling term. The inter-device link bandwidth utilization capacity, service priority factor, service access frequency, and network hop count are obtained. The square root of the network hop count plus 1 is used as the denominator, and the product of the inter-device link bandwidth utilization capacity, service priority factor, and service access frequency is used as the numerator to calculate the basic link coupling quantity.

[0014] Preferably, S2 specifically includes:

[0015] In the dynamic state entropy flow cooperative algorithm, the square root of the basic link coupling is used to obtain the propagation coupling release capability; the logarithmic operation is performed on 1 plus the service load coupling term to obtain the service load amplification term.

[0016] Preferably, S2 specifically includes:

[0017] In the dynamic state entropy flow collaborative algorithm, based on the CMS service response anomaly normalization factor, the degree of suppression of the propagation capability of CMS abnormal behavior by the abnormal state of the device is calculated through an exponential function; the product of the propagation coupling release capability and the business load amplification term is divided by the degree of suppression of the propagation capability of CMS abnormal behavior by the abnormal state of the device to calculate the coupling propagation strength between devices.

[0018] Preferably, S2 specifically includes:

[0019] In the dynamic state entropy flow cooperative algorithm, the basic disturbance energy of a single device is multiplied by the coupling propagation strength to obtain the disturbance propagation amount; the absolute value of the difference between the operating state vectors of the two devices is used to calculate the state difference; based on the state difference, the degree of increase in state complexity after propagation is calculated through logarithmic operation.

[0020] Preferably, S2 specifically includes:

[0021] In the dynamic state entropy flow cooperative algorithm, a protocol behavior stability factor is introduced based on the perturbation propagation amount, and the protocol stability constraint is calculated using the Sigmoid function. The dynamic state entropy flow is calculated by summing the product of the degree of increase in state complexity after propagation and the protocol stability constraint.

[0022] Preferably, S2 specifically includes:

[0023] Based on the average value and standard deviation of the dynamic state entropy flow during the long-term normal operation of the equipment, a stable threshold range is determined. When the dynamic state entropy flow of the equipment is less than or equal to the average value plus the standard deviation, it is determined to be in a stable operating state. When the dynamic state entropy flow of the equipment is greater than the average value plus the standard deviation but less than or equal to the average value plus twice the standard deviation, it is determined to be in a slightly disturbed state. When the dynamic state entropy flow of the equipment is greater than the average value plus twice the standard deviation, it is determined to be in a highly disturbed operating state.

[0024] The beneficial effects of the technical solution of the present invention are:

[0025] 1. By performing semantic pre-parsing on the system configuration description file and establishing a global semantic index relationship in advance, the parsing process is transformed from the traditional layer-by-layer recursive parsing method to an association-based parsing method based on global reference relationships. This effectively avoids the problem of repeatedly traversing nodes under complex nested structures, reduces the parsing complexity of large-scale intelligent substation SCD files, and improves parsing efficiency and stability. At the same time, by establishing an object association mapping table, a unified association relationship is formed between logical nodes, datasets, report control blocks, GSEControl control blocks, sampled value control blocks, and communication access nodes. This ensures that the correspondence between running data and communication services can be quickly located during subsequent debugging, improving data access efficiency and service call accuracy during CMS debugging.

[0026] 2. By normalizing multi-source operational data such as equipment voltage status, equipment current status, GOOSE communication status, sampled value communication status, network link status, time synchronization status, and CMS service status, and constructing equipment operation status vectors, unified modeling is achieved across different dimensions, refresh cycles, and data types. Compared to traditional debugging methods based solely on a single communication status, this invention can simultaneously integrate electrical operation status, network operation status, and protocol operation status, enabling the CMS debugging process to more realistically reflect the actual operating conditions of the smart substation, thereby improving the reliability of the debugging results.

[0027] 3. By introducing a dynamic state entropy flow collaborative algorithm, which continuously couples and calculates the basic disturbance energy of the equipment and the coupling propagation strength between equipment, it can effectively describe the dynamic propagation process of abnormal equipment states in the GOOSE link, sampled value link, and CMS service link. Compared with the traditional debugging method that only judges the abnormality of a single device, this invention can identify the propagation trend of abnormal states between multiple intervals, thereby improving the ability to identify abnormalities and predict faults in complex linkage fault scenarios. Especially in the case of abnormal protection actions, fluctuations in the GOOSE link, or increased time synchronization deviation, the system can identify potential operational risks in advance and prevent the abnormality from spreading further. Attached Figure Description

[0028] Figure 1 This is a flowchart of a smart substation commissioning method based on the CMS protocol according to the present invention. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] The following description, in conjunction with the accompanying drawings, details a specific scheme for a smart substation commissioning method based on the CMS protocol provided by this invention.

[0032] See attached document Figure 1 The diagram illustrates a flowchart of a smart substation commissioning method based on the CMS protocol, provided by an embodiment of the present invention. The method includes the following steps:

[0033] S1. The debugging master station loads the system configuration description file and obtains the object association mapping table, logical model association relationship, and communication path mapping relationship through semantic pre-parsing, logical model parsing, and communication configuration parsing. Based on the object association mapping table, logical model association relationship, and communication path mapping relationship, a full-station equipment topology structure including electrical connection relationship, communication connection relationship, control linkage relationship, and CMS service relationship is generated. Then, the equipment operation status data is collected in real time, normalized, and a device operation status vector is constructed.

[0034] At the start of the smart substation commissioning phase, the commissioning master station first obtains the system configuration description file of the substation to be commissioned and loads it into the SCD parsing engine. The SCD parsing engine does not directly read the file content layer by layer according to the XML node order. Instead, it first performs semantic pre-parsing processing on the entire system configuration description file. Specifically, it extracts the reference relationships between IED device nodes, AccessPoint communication access nodes, LogicalDevice logical device nodes, LogicalNode logical nodes, DataObject data object nodes, DataAttribute data attribute nodes, and control block nodes such as ReportControlBlock, GSEControl, and sampled value control blocks, establishing a global semantic index relationship. This facilitates the pre-establishment of data and communication dependencies between nodes, avoiding repeated traversal due to nested object references during subsequent parsing, thereby improving parsing efficiency and stability. Furthermore, based on the global semantic index relationship, it establishes an object association mapping table, forming a unified association relationship between logical nodes, datasets, report control blocks, GSEControl control blocks, sampled value control blocks, and communication access nodes.

[0035] After semantic pre-parsing, the logical model parsing stage begins, yielding the logical model relationships. Specifically, firstly, IED device nodes are parsed, and a corresponding IED device running entity object is generated for each IED device node. This running entity object uniformly includes device identification information, communication access information, a running status cache, a control service cache, and a communication status cache. Next, AccessPoint communication access nodes are parsed, and a communication access entity object is established based on the network parameter information in the communication access node, including network address information, communication protocol configuration parameters, and network running attribute information. This establishes a one-to-one correspondence between the IED device running entity object and the communication access entity object, thus establishing the communication access relationship. Further, LogicalDevice logical device nodes are parsed, and logical device running objects are established based on the functional type of the LogicalDevice, making protection functions, measurement and control functions, and waveform recording functions independent logical device running domains. Finally, LogicalNode logical nodes are parsed, and corresponding data status monitoring relationships and control relationships are established based on the logical node type. Behavioral and communication service relationships enable logical nodes to form a unified mapping with subsequent CMS service debugging processes. Furthermore, DataObject and DataAttribute nodes are parsed. During parsing, a hierarchical runtime data mapping structure is established based on the logical node to which the data object belongs and the data object to which the data attribute belongs. Simultaneously, data status indexes, data update time indexes, and communication service association indexes are established within this hierarchical runtime data mapping structure, allowing direct association with the corresponding runtime data during subsequent CMS service execution. Further, ReportControlBlock, GSEControl, and SampledValueControl blocks (i.e., sampled value control blocks) are parsed, and triggering relationships between control blocks and runtime data are established based on dataset reference relationships within these control blocks. This reflects the control linkage relationship, enabling report upload behavior, GOOSE linkage behavior, and sampled value transmission behavior to form a unified mapping structure with specific runtime data.

[0036] After completing the logical model parsing phase, the communication configuration parsing phase begins. First, the SubNetwork sub-network nodes in the Communication area are parsed, and network operation domain objects are established based on different sub-networks. The SubNetwork sub-network nodes themselves do not directly correspond to IED device nodes, LogicalDevice logical device nodes, or LogicalNode logical nodes; instead, they are indirectly associated with the previously parsed AccessPoint communication access nodes through ConnectedAP communication connection nodes. Further, ConnectedAP communication connection nodes are parsed, and device communication associations are established based on the IED device identifier, communication access identifier, and network address information within the ConnectedAP communication connection nodes. Each ConnectedAP communication connection node corresponds to one AccessPoint communication access node for one IED device. During the establishment of device communication associations, communication path mapping relationships are further established based on the GSEControl control block, SampledValueControl control block, and CMS communication address. This ensures that the GOOSE communication path, sampled value transmission path, and CMS service communication path between devices form a unified communication link structure. The CMS communication address is determined based on the device information to which the CMS service object belongs. Furthermore, based on the object association mapping table obtained from semantic pre-parsing, the logical model association relationship obtained from the logical model parsing stage, and the communication path mapping relationship obtained from the communication configuration parsing stage, the topology of the entire station's equipment is generated.

[0037] The overall station equipment topology is not a single network connection structure, but a multi-layered coupled topology that simultaneously includes electrical connections, communication connections, control linkages, and CMS service associations. Electrical connections are obtained by parsing the substation structure information in the system configuration description file and are used to describe the electrical connection status between bay equipment and bus equipment. Communication connections describe the communication link status between switches and IED devices. Control linkages describe the linkage between protection actions and circuit breaker controls. CMS service associations describe the calling relationship between CMS services and operational data. After generating this multi-layered coupled topology, a topology status cache is further established based on the equipment index relationship, enabling subsequent operational status data to be directly mapped to the corresponding topology nodes.

[0038] After establishing the overall equipment topology, the equipment operation status data collection process is initiated based on the established communication access relationships and communication path mapping relationships. Specifically, equipment operation status data is collected in real time through the CMS communication interface, GOOSE monitoring interface, sampled value monitoring interface, network management interface, and time synchronization interface. The equipment operation status data includes equipment voltage status, equipment current status, GOOSE communication status, sampled value communication status, network link status, time synchronization status, and CMS service status, etc.

[0039] To standardize the units of measurement for equipment operating status data, the collected equipment operating status data is normalized and then quantified using the equipment... For an object, its running state is constructed into a running state vector, specifically expressed as: , in, Indicates device The operating state vector is used to uniformly describe the first state in a smart substation. The overall operating status of each device at the current operating moment; It is equipment The voltage offset normalization factor is used to describe the voltage state of the equipment, that is, the degree of deviation of the real-time operating voltage of the equipment from the rated reference voltage. ,in, It is equipment The real-time operating voltage is derived from equipment operating status data. It is the rated reference voltage corresponding to the voltage level of the equipment. , These represent the maximum and minimum permissible operating voltages, respectively. The rated reference voltage and the maximum and minimum permissible operating voltages are derived from the equipment instruction manual. It is equipment The current fluctuation normalization factor is used to describe the current state of the equipment, that is, the degree of fluctuation in the equipment load. ,in It is equipment The real-time operating current is derived from the equipment's operating status data. It is the average current within a preset sliding window (e.g., 100) by technicians based on application requirements. This indicates the rated current of the equipment, which is derived from the equipment's instruction manual. It is equipment The GOOSE link stability normalization factor is used to reflect the stability of GOOSE communication status. It is obtained by accumulating outliers such as GOOSE packet loss rate and GOOSE retransmission frequency, and then normalizing them using the Sigmoid function. Outliers such as GOOSE packet loss rate and GOOSE retransmission frequency are derived from device operating status data. It is equipment The SV sampling stability normalization factor is used to describe the degree of fluctuation of the sampled value. It is obtained by calculating the variance of the sampled value within a sliding window (such as 100) preset by technicians according to application requirements, and then normalizing it by the maximum value method. The sampled value refers to the real-time electrical quantity sampling data carried by the SV (Sampled Value) message in the process layer of the smart substation, which comes from the equipment operating status data. It is equipment The protection action activity normalization factor is obtained by counting the number of protection actions within a detection cycle (e.g., 2) preset by technicians according to application requirements, and comparing it with the maximum allowable action frequency. The maximum allowable action frequency is derived from the equipment instruction manual. It is equipment The PTP time synchronization deviation normalization factor is obtained by taking the absolute value of the difference between the current clock of the device and the master clock, and then dividing it by the maximum allowable time synchronization deviation threshold preset according to the operation requirements of the smart substation, the protection action time limit requirements, the real-time requirements of the GOOSE link, and the SV sampling synchronization accuracy requirements. It is equipment The network load normalization factor, i.e. load rate, is determined by normalizing the ratio of switch port traffic to the maximum port throughput. Switch port traffic is derived from device operating status data, and the maximum port throughput is derived from the device user manual. It is equipment The fiber optic error normalization factor, i.e. the bit error rate, is determined by calculating the ratio of the number of errors per unit time to the maximum permissible number of errors. The maximum permissible number of errors is derived from the equipment instruction manual. It is equipment The CMS service response anomaly normalization factor is obtained by summing up the statistically obtained CMS rejection count, timeout count, and anomaly return count, and then normalizing it using the maximum value method.

[0040] S2. Based on the device operating state vector, the basic disturbance energy of a single device is calculated through a dynamic state entropy flow collaborative algorithm, and the coupling propagation strength between devices is introduced to generate a dynamic state entropy flow. Based on the dynamic state entropy flow, it is compared with the set stable threshold range, and the CMS debugging path and debugging priority are adjusted in real time to realize dynamic adaptive closed-loop debugging of the CMS protocol debugging process.

[0041] There are dynamic influence relationships between devices. For example, when a protection device malfunctions, it not only affects that device itself but also impacts multiple bays along the GOOSE link. To address the problem that traditional commissioning systems cannot reflect the dynamic influence relationships between devices, a dynamic state entropy flow collaborative algorithm is introduced. The specific implementation process is as follows:

[0042] Since voltage deviation, current fluctuation, network load, and bit error rate all affect the operational stability of the device, the basic disturbance energy of a single device is first constructed, and the calculation formula is as follows: , in, Indicates device The basic disturbance energy is used to describe the overall degree to which the current operating state of the equipment deviates from the normal stable state; , , , This represents the perturbation weight coefficients, which are summed to 1. They are determined using the existing Transformer attention mechanism, with 8 attention heads and 64 dimensions. This is the synchronization deviation amplification factor, used to represent the amplification capability of time synchronization anomalies to operational disturbances. It is determined based on the specific application scenario; for example, in low-sensitivity stations, it is taken as... Ordinary station pick-up High real-time station acquisition ; This represents the combined energy of multiple sources of operational disturbances in the equipment, i.e., operational disturbances; It is the time synchronization propagation amplification factor, used to describe the extent to which time synchronization anomalies amplify operational disturbances.

[0043] After completing the basic disturbance energy calculation for a single device, since device disturbances do not exist in isolation but propagate along the communication link, a device coupling propagation model is further constructed to calculate the coupling propagation strength between devices. The calculation formula is as follows: , in, Indicates device and equipment The coupling propagation strength between them; It is a basic link coupling quantity used to represent the device. With equipment Between them, the basic propagation capability is formed by communication link capacity, service access frequency, and link priority. ,in, This indicates the bandwidth utilization capability of the link between devices, describing the effective bandwidth capacity of the current link between devices that can be used for CMS service propagation. This represents the service priority factor, which describes the scheduling priority level of CMS services in the current link. This indicates the frequency of business access, describing the number of CMS service calls between devices. This represents the network hop count, which describes the number of switches or routing nodes between devices. , , , All data were obtained from the substation's historical database; This is a business load coupling term, which refers to the business pressure formed between devices due to network load and abnormal CMS behavior. It is calculated by adding the load rates of each device and adding a CMS service response anomaly normalization factor. Then perform logarithmic operations with the base of the natural number to obtain it; It is equipment CMS service response anomaly normalization factor; It is equipment CMS service response anomaly normalization factor; Indicates the propagation coupling release capability; This represents the business load amplification item, used to describe the business propagation complexity caused by the combined effects of network load and abnormal CMS behavior; It represents the degree to which abnormal equipment conditions suppress the propagation of abnormal CMS behavior.

[0044] Obtaining the basic disturbance energy of a single device and coupling propagation strength Then, the output of the previous stage is used as the input of the next stage, thus forming a continuous coupled computation. First, the basic disturbance energy of the device is... With coupling propagation strength Multiplying these yields the perturbation propagation amount; subsequently, a protocol behavior stability factor is introduced. The protocol stability constraints are constructed using the Sigmoid function; finally, the complexity of state changes is described using the logarithm of state differences, resulting in the dynamic state entropy flow, calculated as follows: , in, Indicates device The dynamic state entropy flow; This represents the total number of all devices; The protocol behavior stability factor is used to represent the stability of the device's CMS service. It is calculated by subtracting the protocol anomaly rate from 1. The protocol anomaly rate is the ratio of the total number of statistically obtained abnormal events to the total number of communications. For example, if there are 20 timeouts, 10 rejections, and 20 parsing errors in 1000 CMS service interactions, the protocol anomaly rate is 0.05. Indicates device The running state vector; Indicates device The running state vector; Indicates the amount of disturbance propagation; This indicates that the protocol stability constraint is a device The resulting operational disturbances affect the equipment. Effective propagation energy after propagation; Indicates state differences; It refers to the degree of increase in the complexity of the state formed after propagation.

[0045] The dynamic state entropy flow is calculated. Subsequently, instead of waiting for all devices to complete a unified state analysis before debugging, the dynamic state entropy stream result of the current device can be directly used as a real-time input parameter for the CMS debugging path generation module, thereby dynamically adjusting the test order and test strategy for the CMS service corresponding to the current device. Specifically, during debugging execution, the device is first... Dynamic state entropy flow Compared to a stability threshold range, which is based on the average dynamic state entropy flow of equipment during long-term normal operation in the substation's historical database, this comparison is made with other values. and standard deviation Confirmed, when When, it is determined to be in a stable operating state; when When, it is judged as a state of slight disturbance; when When a high-disturbance operating state is detected, it indicates that increased operational disturbances, communication link fluctuations, or increased time synchronization deviations have occurred in the current equipment's bay area. Subsequently, the CMS debugging and scheduling module immediately reduces the execution priority of control-related CMS services, including high-risk operations such as remote circuit breaker operation, setting value writing, and report control block modification. Simultaneously, it automatically increases the execution priority of status query-related CMS services, including low-intrusion debugging behaviors such as obtaining logical node status, reading data object values, reading report buffer status, and querying log status. Furthermore, it further reduces the CMS control message sending frequency and increases the sampling frequency of equipment operating status data, prioritizing the monitoring of operating status trends in this area to avoid further fluctuations in equipment operating status due to high-frequency control testing. When the equipment... Dynamic state entropy flow Gradually recover to within the stable threshold range, that is Then, control-related CMS services are gradually restored, thereby realizing a dynamic closed-loop debugging process of "status analysis - debugging scheduling - status re-evaluation". In this way, the CMS protocol debugging process can dynamically change the test path according to the real-time operating status of the smart substation, enabling the debugging process to have the ability to adapt to the operating status, and avoiding the problems of malfunction or debugging distortion caused by the traditional fixed debugging sequence under complex operating conditions.

[0046] The data collected in S1 and S2, as well as the intermediate data calculated, are stored in the substation's historical database.

[0047] In summary, a smart substation commissioning method based on the CMS protocol has been developed.

[0048] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0049] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0050] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for commissioning a smart substation based on the CMS protocol, characterized in that, Includes the following steps: S1. The debugging master station loads the system configuration description file and obtains the object association mapping table, logical model association relationship, and communication path mapping relationship through semantic pre-parsing, logical model parsing, and communication configuration parsing stages. Based on the object association mapping table, logical model association relationship, and communication path mapping relationship, a full-station equipment topology structure including electrical connection relationship, communication connection relationship, control linkage relationship, and CMS service association relationship is generated. Then, the equipment operation status data is collected in real time, normalized, and a device operation status vector is constructed. S2. Based on the device operating state vector, the basic disturbance energy of a single device is calculated through a dynamic state entropy flow collaborative algorithm, and the coupling propagation strength between devices is introduced to generate a dynamic state entropy flow; Based on the dynamic state entropy flow, it is compared with the set stable threshold range to adjust the CMS debugging path and debugging priority in real time, so as to realize dynamic adaptive closed-loop debugging of the CMS protocol debugging process.

2. The intelligent substation commissioning method based on the CMS protocol according to claim 1, characterized in that, S1 specifically includes: Based on the equipment operating status data, normalization processing is first performed to obtain voltage offset normalization factor, current fluctuation normalization factor, GOOSE link stability normalization factor, SV sampling stability normalization factor, protection action activity normalization factor, PTP time synchronization deviation normalization factor, load rate, bit error rate, and CMS service response anomaly normalization factor, which are then combined into the equipment operating status vector.

3. The intelligent substation commissioning method based on the CMS protocol according to claim 1, characterized in that, S2 specifically includes: In the dynamic state entropy flow cooperative algorithm, the time synchronization propagation amplification factor is calculated by multiplying the PTP time synchronization deviation normalization factor in the device operating state vector with the set synchronization deviation amplification factor by 1. The voltage offset normalization factor, current fluctuation normalization factor, load rate, and bit error rate in the device operating state vector are squared respectively. Then, the squares of the squares of the voltage offset normalization factor, current fluctuation normalization factor, load rate, and bit error rate are weighted, summed, and the square root is taken to calculate the operating disturbance. The operating disturbance is multiplied by the time synchronization propagation amplification factor to calculate the basic disturbance energy of a single device.

4. The intelligent substation commissioning method based on the CMS protocol according to claim 3, characterized in that, S2 specifically includes: In the dynamic state entropy flow coordination algorithm, the load rates of each device are summed and the CMS service response anomaly normalization factor in the device operation state vector is added. Then, a logarithmic operation with the natural number base is performed to obtain the service load coupling term. The inter-device link bandwidth utilization capacity, service priority factor, service access frequency, and network hop count are obtained. The square root of the network hop count plus 1 is used as the denominator, and the product of the inter-device link bandwidth utilization capacity, service priority factor, and service access frequency is used as the numerator to calculate the basic link coupling quantity.

5. The intelligent substation commissioning method based on the CMS protocol according to claim 4, characterized in that, S2 specifically includes: In the dynamic state entropy flow cooperative algorithm, the square root of the basic link coupling is used to obtain the propagation coupling release capability; the logarithmic operation is performed on 1 plus the service load coupling term to obtain the service load amplification term.

6. The intelligent substation commissioning method based on the CMS protocol according to claim 5, characterized in that, S2 specifically includes: In the dynamic state entropy flow collaborative algorithm, based on the CMS service response anomaly normalization factor, the degree of suppression of the propagation capability of CMS abnormal behavior by the abnormal state of the device is calculated through an exponential function; the product of the propagation coupling release capability and the business load amplification term is divided by the degree of suppression of the propagation capability of CMS abnormal behavior by the abnormal state of the device to calculate the coupling propagation strength between devices.

7. The intelligent substation commissioning method based on the CMS protocol according to claim 6, characterized in that, S2 specifically includes: In the dynamic state entropy flow cooperative algorithm, the basic disturbance energy of a single device is multiplied by the coupling propagation strength to obtain the disturbance propagation amount; the absolute value of the difference between the operating state vectors of the two devices is used to calculate the state difference; based on the state difference, the degree of increase in state complexity after propagation is calculated through logarithmic operation.

8. The intelligent substation commissioning method based on the CMS protocol according to claim 7, characterized in that, S2 specifically includes: In the dynamic state entropy flow cooperative algorithm, a protocol behavior stability factor is introduced based on the perturbation propagation amount, and the protocol stability constraint is calculated using the Sigmoid function. The dynamic state entropy flow is calculated by summing the product of the degree of increase in state complexity after propagation and the protocol stability constraint.

9. A method for commissioning a smart substation based on the CMS protocol according to claim 8, characterized in that, S2 specifically includes: Based on the average value and standard deviation of the dynamic state entropy flow during the long-term normal operation of the equipment, a stable threshold range is determined. When the dynamic state entropy flow of the equipment is less than or equal to the average value plus the standard deviation, it is determined to be in a stable operating state. When the dynamic state entropy flow of the equipment is greater than the average value plus the standard deviation but less than or equal to the average value plus twice the standard deviation, it is determined to be in a slightly disturbed state. When the dynamic state entropy flow of the equipment is greater than the average value plus twice the standard deviation, it is determined to be in a highly disturbed operating state.

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