A billing method for intelligent substation operation ticket system based on rule reasoning

Abstract

The present invention discloses a method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning, which relates to the technical field of issuing invoices for substation operation ticket systems, and specifically includes the following steps: analyzing the acquired operation status information of each device, predicting whether the voltage of each device will have a timing mismatch problem when switching, and dividing each device into a timing matching device and a timing mismatching device according to the prediction result; constructing an adjustment mechanism for dynamically adjusting the switching sequence and time of each device according to the division result of each device; in the application process of the adjustment mechanism, obtaining feedback information of each device in real time, analyzing the feedback information, and evaluating whether the adjustment degree of the adjustment mechanism to the switching sequence of each device meets the set timing requirements. The present invention solves the timing mismatch problem when the voltage of the intelligent substation is switched, and realizes the dynamic adjustment and optimization of the operation ticket system.

Description

Technical Field

[0001] The present invention relates to the technical field of invoicing of a transformer substation operation ticket system, and in particular to an invoicing method of an intelligent transformer substation operation ticket system based on rule reasoning. Background Art

[0002] The substation operation ticket system is a tool used to record and manage the steps that substation operators need to perform when operating equipment, ensuring that the operation process is strictly carried out in accordance with the regulations, reducing the risk of misoperation and ensuring the safe operation of the power system. Based on the traditional operation ticket, the intelligent substation operation ticket system combines data acquisition and automatic processing technology to realize the process of automatically generating operation tickets by analyzing real-time data such as equipment status and operating parameters, thereby improving the efficiency and accuracy of invoicing. The intelligent substation operation ticket system based on rule reasoning goes a step further on this basis. By establishing a rule library covering standard operations, exception handling and emergency processes, and using the rule reasoning engine to intelligently analyze the collected operation information, it can not only automatically generate operation tickets that meet the operation requirements, but also dynamically adjust the content of the operation ticket according to the real-time monitoring data during the operation. The special feature of this method is that it is more intelligent, and can realize the automatic reasoning generation, dynamic revision and rationality verification of operation tickets, greatly reducing human errors, while adapting to changes in the substation operating environment, and significantly improving the safety and management level of the power system.

[0003] The existing intelligent substation operation ticket system invoicing technology based on rule reasoning first obtains the operation information of the substation in real time through the data acquisition module, including basic data such as equipment status, operating parameters and maintenance records, and then performs intelligent rule reasoning analysis on these data based on the established rule base. The rule base covers specific rules for standard operating procedures, abnormal situation handling and emergency operations. Driven by the rule reasoning engine, the system can automatically identify the operation events that require the issuance of operation tickets, and generate operation ticket contents containing detailed operation steps, precautions, equipment information and operation conditions, while ensuring that these contents comply with operating procedures and safety requirements. If changes in the environment or equipment status are detected during the execution of the operation, the system will dynamically adjust the operation ticket according to the rule base, regenerate or modify the operation content to ensure the safety and effectiveness of the operation, and finally complete the entire invoicing process after manual review and confirmation.

[0004] The prior art has the following deficiencies:

[0005] When performing multi-level voltage conversion operations in smart substations, when different devices switch step by step according to the voltage level, there will be a timing mismatch problem caused by the difference in device feedback speed and switch execution time. Specifically, the voltage feedback of some devices is slow, or the switch execution speed is affected by the mechanical structure and electrical characteristics, which makes the voltage switching time of different devices inconsistent. The existing smart substation operation ticket system based on rule reasoning fails to capture the feedback speed differences between these devices in real time when reasoning and generating operation tickets, and cannot dynamically adjust the operation sequence of each device, and still relies on static standard operation procedures. This will cause the switching instructions of different devices in the operation ticket to be generated at a fixed time, ignoring the response differences in the actual environment. Since the smart substation operation ticket system fails to consider these device feedback differences during the process of generating the ticket, the generated operation ticket instructions may be inaccurate and the instruction sequence may be unreasonable, which will affect the operator's operation arrangement of the equipment and increase the risk of misoperation. In addition, due to these delay problems, the device switching timing may be disordered, resulting in a short-term overshoot or undervoltage phenomenon in some devices, which in turn affects the stability of the entire power grid, increases the risk of equipment damage, malfunction of protection devices or local power outages, and may eventually cause large-scale instability of the power grid.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning, specifically comprising the following steps:

[0009] When performing multi-level voltage conversion operations, determine the voltage conversion sequence and operation requirements of each device, and obtain the operating status information of each device in real time through the sensor network installed on each device;

[0010] Analyze the acquired operation status information of each device, predict whether the voltage of each device will have a timing mismatch problem when switching, and divide each device into a timing matching device and a timing mismatching device according to the prediction result;

[0011] According to the division results of each device, an adjustment mechanism is constructed to dynamically adjust the switching sequence and time of each device, and the adjustment mechanism is applied to the current operation ticket generation process;

[0012] During the application of the adjustment mechanism, feedback information from each device is obtained in real time, and the feedback information is analyzed to evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized according to the evaluation results;

[0013] Based on the long-term collected equipment feedback information and switching execution data, the adjustment mechanism is dynamically optimized and updated, so that the equipment switching sequence and time in the operation ticket can be optimized according to the data analysis results in long-term operation.

[0014] Preferably, the acquired operation status information of each device is analyzed to predict whether a timing mismatch problem will occur when the voltage of each device is switched, and the devices are divided into timing matching devices and timing mismatching devices according to the prediction results, which specifically includes the following steps:

[0015] Preprocessing the acquired operation status information of each device;

[0016] Extracting device feedback delay information and device switching stability information from the preprocessed operation status information of each device, and analyzing them after extraction to generate a dynamic feedback delay coefficient and a switching stability index for each device respectively;

[0017] The dynamic feedback delay coefficient and switching stability index of each device are generated to construct a timing matching prediction model, and the matching coefficient of each device is generated. The generated matching coefficient of each device is compared with the pre-set matching coefficient threshold of each device. According to the comparison result, it is predicted whether the voltage of each device will have a timing mismatch problem when switching, and according to the prediction result, each device is divided into a timing matched device and a timing mismatched device.

[0018] Preferably, the acquisition logic of the dynamic feedback delay coefficient and the switching stability index of each device is as follows:

[0019] Extract the device feedback delay information from the preprocessed operation status information of each device, including the instruction sending time series sent by the smart substation operation ticket system to each device at different time points in a period of time, the feedback completion time series of each device, the operating temperature of each device, and the expected response time of each device under standard operating conditions. The instruction sending time series sent by the smart substation operation ticket system to each device at different time points in a period of time, the feedback completion time series of each device, and the operating temperature of each device are respectively calculated by function according to the time series. , and To express, is a time point, and the time period is defined as , Indicates that within a period of time At this time, the intelligent substation operation ticket system sends The time sequence of commands sent by each device, Indicates that within a period of time Time point The feedback from each device completes the time series. Indicates that within a period of time Time point The expected response time of each device under standard operating conditions is calibrated as , Indicates The expected response time of a device under standard operating conditions, , is a positive integer;

[0020] Calculate the dynamic feedback delay coefficient of each device. The specific calculation formula is as follows:

[0021]

[0022] In the formula, For the Dynamic feedback delay coefficient of each device;

[0023] Extract the device switching stability information from the preprocessed operation status information of each device, specifically including the switching voltage deviation value of each device in the voltage switching process at different time points within a period of time, the load current value of each device in the operation process, the voltage ripple amplitude of each device when performing the voltage switching operation, and the target voltage of each device, and use the function to calculate the switching voltage deviation value of each device in the voltage switching process at different time points within a period of time, the load current value of each device in the operation process, and the voltage ripple amplitude of each device when performing the voltage switching operation. , and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The switching voltage deviation value of each device during the voltage switching process, Indicates a period of time Time point The load current of a device during operation, Indicates a period of time Time point The voltage ripple amplitude of each device when performing voltage switching operation, and the target voltage of each device is calibrated as , Indicates The target voltage for each device;

[0024] Calculate the switching stability index of each device. The specific calculation formula is as follows:

[0025]

[0026] In the formula, For the The switching stability index of a device.

[0027] Preferably, the dynamic feedback delay coefficients of each device generated and switching stability index Build a time series matching prediction model and generate the matching coefficient of each device through weighted summation , and the matching coefficients of each device generated Matching coefficient thresholds with pre-set values ​​for each device Compare and predict whether the voltage of each device will have a timing mismatch problem when switching based on the comparison results, and divide each device into timing matching devices and timing mismatching devices based on the prediction results. The specific comparison analysis and division are as follows:

[0028] like , the voltage of the device will not have a timing mismatch problem when switching, then the device is classified as a timing matching device;

[0029] like , if the voltage of the device has a timing mismatch problem when switching, the device is classified as a timing mismatch device.

[0030] Preferably, according to the division results of each device, an adjustment mechanism for dynamically adjusting the switching order and time of each device is constructed, specifically: according to the division results of the timing matching devices and the timing non-matching devices, different switching order and time adjustment parameters are set respectively to form an adjustment mechanism; the adjustment mechanism is based on the matching coefficient of each device and the current switching timing state, and automatically determines the adjustment range of the switching order and the optimization method of the switching time through a pre-set adjustment rule;

[0031] Different timing adjustments are made to timing-matched devices and timing-mismatched devices respectively, specifically including: for timing-matched devices, the standard switching sequence and time parameters in the adjustment mechanism are used to keep the current switching sequence unchanged; for timing-mismatched devices, the dynamic switching sequence and time optimization parameters in the adjustment mechanism are used to adjust the switching sequence and time of the devices.

[0032] Preferably, during the application of the adjustment mechanism, feedback information from each device is obtained in real time, and the feedback information is analyzed to evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps:

[0033] During the application of the adjustment mechanism, feedback information from each device is obtained in real time and preprocessed after acquisition;

[0034] Extracting the timing execution feedback information and dynamic load response information from the pre-processed feedback information of each device, and analyzing them after extraction to generate the timing adjustment deviation coefficient and dynamic load impact index of each device respectively;

[0035] An adjustment evaluation model is constructed using the generated timing adjustment deviation coefficient and dynamic load impact index of each device to generate an adjustment coefficient for each device. The generated adjustment coefficient for each device is compared with the pre-set adjustment coefficient threshold for each device. The comparison result is used to evaluate whether the adjustment degree of the switching order of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized based on the evaluation result.

[0036] Preferably, the logic for obtaining the timing adjustment deviation coefficient and the dynamic load impact index of each device is as follows:

[0037] Extract the timing execution feedback information from the pre-processed feedback information of each device, including the actual execution time of each device performing voltage switching at different time points within a period of time, the voltage switching instruction time received by each device, and the total duration of the switching operation of each device, and use the function to calculate the actual execution time of each device performing voltage switching at different time points within a period of time and the voltage switching instruction time received by each device in accordance with the time series. and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The actual execution time of a device in voltage switching, Indicates a period of time Time point The voltage switching instruction time received by each device is calibrated as , Indicates The total duration of the device switching operation, , is a positive integer;

[0038] Calculate the timing adjustment deviation coefficient of each device. The specific calculation formula is as follows:

[0039]

[0040] In the formula, For the Timing adjustment deviation coefficient of each device;

[0041] Extract the dynamic load response information from the pre-processed feedback information of each device, including the actual load current value, voltage value and maximum load current value of each device at different time points in a period of time, and use the function to calculate the actual load current value and voltage value of each device at different time points in a period of time according to the time series. and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The actual load current value of each device, Indicates a period of time Time point The voltage value of each device is calibrated as , Indicates The maximum load current value of each device;

[0042] Calculate the dynamic load impact index of each device. The specific calculation formula is as follows:

[0043]

[0044] In the formula, For the The dynamic load impact index of each device, for a period of time Time point The rate of change of the voltage value of a device.

[0045] Preferably, the generated timing adjustment deviation coefficients of each device and dynamic load impact index Construct an adjustment evaluation model and generate the adjustment coefficients for each device through weighted summation , and the generated adjustment coefficients for each device Adjustment coefficient thresholds for each device that are set in advance Compare and evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements based on the comparison results, and optimize the adjustment mechanism based on the evaluation results. The specific comparison and analysis are as follows:

[0046] like , the degree of adjustment of the switching sequence of the device by the adjustment mechanism does not meet the set timing requirements, and the adjustment mechanism needs to be optimized, including: optimizing the switching sequence, adjusting the switching time window, resetting the threshold parameters and monitoring the feedback information in real time, and dynamically adjusting the switching sequence and time of the device;

[0047] like The degree of adjustment of the switching sequence of the device by the adjustment mechanism meets the set timing requirements, and there is no need to optimize the adjustment mechanism.

[0048] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0049] 1. The present invention realizes precise timing control in the multi-level voltage conversion operation of the smart substation by real-time acquisition of the operating status information of the equipment, especially the dynamic data of the equipment feedback delay and the switch execution time. Compared with the prior art, the present invention can not only dynamically capture the feedback speed differences between the equipment, but also generate the corresponding operation sequence and time adjustment mechanism according to the actual operating status of each equipment. This enables the operation ticket system to effectively avoid the timing disorder problem caused by the fixed time generation instructions in the traditional system, greatly improve the operation accuracy of the system, and reduce the risk of misoperation.

[0050] 2. The dynamic feedback delay coefficient and switching stability index in the present invention are calculated through complex mathematical models, which can accurately reflect the actual response of the device during the voltage conversion process. The adjustment evaluation model generated by weighted summation provides a more reasonable dynamic adjustment basis for the operation sequence and time of each device. In particular, during the operation of the equipment, the system can optimize the switching sequence and time window of the equipment in real time according to the load condition, feedback speed and response time of the equipment. This advantage ensures that the system can adapt to the complex operating conditions of the power system, ensure the stability of the power grid, and reduce the occurrence of equipment overload, undervoltage and other phenomena.

[0051] 3. By analyzing the long-term collected equipment feedback information and switching execution data, the present invention can gradually optimize the adjustment mechanism to ensure that the equipment switching sequence and time in the operation ticket can be optimized according to the actual operation of the equipment. This not only enhances the flexibility and robustness of the system, but also effectively extends the service life of the equipment, reduces the failure rate, and improves the overall operation efficiency. The system continuously improves the operation accuracy and efficiency through the self-learning function, fully demonstrating the advantages of intelligence and automation. The data collection and dynamic optimization mechanism in long-term operation enable the system to have the ability of continuous improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0053] Figure 1 The present invention is a flowchart of a method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0055] The present invention provides Figure 1 The method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning specifically includes the following steps:

[0056] When performing multi-level voltage conversion operations, determine the voltage conversion sequence and operation requirements of each device, and obtain the operating status information of each device in real time through the sensor network installed on each device;

[0057] Determining the voltage conversion sequence and operation requirements of each device can be achieved by analyzing the voltage level, load status and operation priority of the devices in the system. The specific method is to collect the historical operation data and current power load information of the power system through software, and dynamically analyze the voltage switching sequence of each device in combination with the pre-set rule reasoning algorithm. For example, based on the voltage level difference of each device and its position in the power network, the system can give priority to devices with lighter loads and faster feedback speeds for voltage switching operations, and generate the operation sequence of each device in the same way. At the same time, the operation requirements can be determined by analyzing the operating status of the equipment and the necessity of its voltage conversion. For example, some equipment may need to convert the voltage under certain conditions in priority due to its high load, thereby reducing the risk of overload during operation. The whole process optimizes and makes decisions on the voltage conversion requirements of multiple devices in real time through the rule reasoning model.

[0058] In order to obtain the operating status information of each device in real time, smart sensors can be installed on each device to monitor the key parameters of the device such as voltage, current, temperature, switch status, etc. in real time. The sensor network can transmit data with the central control system through wireless communication (such as Wi-Fi, 5G or LoRa) or wired network (such as industrial Ethernet). The operating status of the equipment is continuously collected and sent to the system through sensors, and the system uses software to process and analyze these real-time data. In order to achieve efficient data collection and transmission, the system can collect the sensor data of each device into a data center through a distributed network architecture, and use software algorithms to dynamically analyze and process these data. There are many ways to obtain device status information, such as measuring voltage levels through voltage sensors, monitoring the load of equipment through current sensors, and preventing equipment from overheating through temperature sensors. Through real-time status feedback, the system can provide accurate device status basis for subsequent voltage conversion operations.

[0059] The purpose of this step is to solve the timing mismatch problem caused by the difference in device feedback speed and switch execution time during multi-level voltage conversion operations in smart substations. By determining the voltage conversion sequence and operation requirements of each device, it can be ensured that each device is switched according to its priority and actual operating conditions during the voltage conversion process, reducing the problem of operation timing disorder caused by fixed processes. At the same time, by installing a sensor network to obtain the operating status information of the device in real time, the feedback speed and switch execution status of each device can be monitored in time. This is crucial to solving the problem of inconsistent voltage conversion timing caused by different feedback speeds or switch execution time differences. The operating status data obtained in real time will provide a basis for the subsequent adjustment mechanism, ensuring that the system can dynamically adjust according to the current status of the device, thereby avoiding voltage overshoot or undervoltage. Through the combination of these two steps, the system can timely identify and respond to the complexity of multi-device voltage conversion, ensure that the dynamic generation of operation tickets matches the actual device status, and improve the stability and safety of power grid operation.

[0060] Analyze the acquired operation status information of each device, predict whether the voltage of each device will have a timing mismatch problem when switching, and divide each device into a timing matching device and a timing mismatching device according to the prediction result;

[0061] In this embodiment, the acquired operation status information of each device is analyzed to predict whether a timing mismatch problem will occur when the voltage of each device is switched, and each device is divided into a timing matching device and a timing mismatching device according to the prediction result, which specifically includes the following steps:

[0062] Preprocessing the acquired operation status information of each device;

[0063] The purpose of preprocessing the acquired operating status information of each device is to ensure the accuracy and consistency of the data and the effectiveness of subsequent analysis. Since the operating status information of the device may come from different devices, and there are differences in information format, acquisition frequency and data integrity, the data needs to be standardized and cleaned before analysis. First, the data may contain noise, outliers and missing values, so it is necessary to identify and filter out outliers through anomaly detection algorithms, and process missing data through interpolation or data filling algorithms. Secondly, since the acquisition frequency of device data may be different, data alignment is required to resample the status information of all devices according to a unified time series. After resampling, the operating status information of each device also needs to be normalized so that the parameters of different devices can be compared at the same level to avoid analysis deviations due to different units or levels. These preprocessing steps can be implemented through software, using algorithms such as data cleaning, normalization and time alignment to standardize the various types of information collected, laying a good data foundation for subsequent feature extraction and analysis.

[0064] Extracting device feedback delay information and device switching stability information from the preprocessed operation status information of each device, and analyzing them after extraction to generate a dynamic feedback delay coefficient and a switching stability index for each device respectively;

[0065] The dynamic feedback delay coefficient and switching stability index of each device are generated to construct a timing matching prediction model, and the matching coefficient of each device is generated. The generated matching coefficient of each device is compared with the pre-set matching coefficient threshold of each device. According to the comparison result, it is predicted whether the voltage of each device will have a timing mismatch problem when switching, and according to the prediction result, each device is divided into a timing matched device and a timing mismatched device.

[0066] The "pre-set matching coefficient thresholds for each device" can be determined through historical data analysis, device performance evaluation, and differences in device operating environments. Specifically, the software first collects historical operating data of each device under different operating conditions, including key parameters such as feedback delay, switching stability, and voltage fluctuations. Then, using statistical analysis methods, cluster analysis or regression analysis is performed on these historical data to find the matching coefficient range of each device during normal operation and identify the critical points where the device may cause timing mismatches under different operating environments. Through these analyses, matching coefficient distribution graphs for the devices can be generated, and the software can automatically generate thresholds based on these distribution graphs to ensure that the devices do not exceed these thresholds under normal operating conditions. In addition, the thresholds of different devices can be dynamically adjusted by considering factors such as the type of device, operating environment (such as temperature, load conditions, etc.). Finally, the software will generate an adaptive matching coefficient threshold for each device based on these analysis results, thereby ensuring that the system can accurately predict the timing matching of each device.

[0067] In this embodiment, the logic for obtaining the dynamic feedback delay coefficient and the switching stability index of each device is as follows:

[0068] Extract the device feedback delay information from the preprocessed operation status information of each device, including the instruction sending time series sent by the smart substation operation ticket system to each device at different time points in a period of time, the feedback completion time series of each device, the operating temperature of each device, and the expected response time of each device under standard operating conditions. The instruction sending time series sent by the smart substation operation ticket system to each device at different time points in a period of time, the feedback completion time series of each device, and the operating temperature of each device are respectively calculated by function according to the time series. , and To express, is a time point, and the time period is defined as , Indicates that within a period of time At this time, the intelligent substation operation ticket system sends The time sequence of commands sent by each device, Indicates that within a period of time Time point The feedback from each device completes the time series. Indicates that within a period of time Time point The expected response time of each device under standard operating conditions is calibrated as , Indicates The expected response time of a device under standard operating conditions, , is a positive integer;

[0069] The extraction of equipment feedback delay information can be automatically completed by the data acquisition module in the smart substation operation ticket system. Specifically, when sending instructions, the smart substation operation ticket system will automatically record the time when each voltage switching instruction is sent to each device to form an instruction sending time sequence. These data can be obtained from the operation ticket system in real time through the device communication protocol. At the same time, the status feedback module inside the device will automatically send a feedback signal after the voltage switching is completed, and the timestamp of the feedback signal will be recorded by the operation ticket system to form a feedback completion time sequence. The operating temperature data of the equipment can be monitored in real time through the temperature sensor installed on the equipment and transmitted to the operation ticket system through the sensor network of the equipment. The standard response time of the equipment is usually set by the manufacturer and stored in the operation database of the equipment. The operation ticket system can directly call these standard parameters when executing commands. This information can be connected to the operation ticket system through a data interface or a remote communication module (such as the IEC61850 protocol), so as to automatically collect and record the status data of each device at different time points.

[0070] Instruction sending time series: This refers to each time point recorded when the operation ticket system sends a voltage switching instruction to the device. This data is automatically generated by the operation ticket system and collected in real time through the device communication module. This time series is used to accurately track the time of each instruction sending, ensuring that the time difference between the instruction issuance and the device feedback can be accurately calculated.

[0071] Feedback completion time series: This is the timestamp of the feedback signal returned by the device after completing the voltage switching operation. The feedback signal is automatically sent by the device's control system, and the feedback time is recorded by the operation ticket system. This time series reflects the actual time required for the device to complete the instruction and is an important basis for measuring the device's responsiveness.

[0072] Operating temperature of each device: This data is collected in real time by the temperature sensor built into the device. Temperature changes will affect the response speed of the device. Temperature data is transmitted to the operation ticket system through the device sensor network and updated in real time. Since the feedback speed of the device may slow down in a high temperature environment, temperature is an important factor affecting feedback delay.

[0073] Expected response time: The standard response time is the expected response time of the device under normal operating conditions, which is set by the manufacturer and recorded in the device's parameter database. The operation ticket system can directly call these standard parameters when executing instructions to evaluate the feedback performance of the device. The standard response time is used to compare the actual feedback time with the ideal time to help determine whether the delay exceeds expectations.

[0074] Calculate the dynamic feedback delay coefficient of each device. The specific calculation formula is as follows:

[0075]

[0076] In the formula, For the Dynamic feedback delay coefficient of each device;

[0077] This calculation formula can quantify the deviation of the actual feedback time of the device under different conditions from the expected standard response time. The integral part of the formula Reflects the feedback completion time of the device within a specific time period The time when the command is sent The difference between the feedback delay and the delay between the voltage switching and the temperature correction coefficient is used to measure the time it takes for the device to complete the voltage switching after receiving the instruction from the operation ticket system. Multiply by 1 to take into account the device's operating temperature, as changes in temperature have a direct impact on the device's response speed. The higher the temperature, the more delayed the device may be. The integral sign means that this is a cumulative calculation of the response delay over the entire time period, ensuring that you are analyzing the average performance of the device over a period of time, rather than the instantaneous situation at a single point in time. Finally, divide this result by the device's standard response time , the standard response time is the expected performance of the device under ideal conditions. This ratio can clearly show whether the feedback delay of the device is lower or higher than expected, so as to judge whether the response of the device meets the requirements. This calculation method can fully reflect the actual operation of the device, taking into account the time difference, temperature change and expected response standard, making the calculated dynamic feedback delay coefficient more accurate and of practical application value.

[0078] No. Dynamic feedback delay coefficient for each device The size of directly reflects the difference between the actual feedback time of the device during the voltage switching process and its expected standard response time. The larger the dynamic feedback delay coefficient, the greater the delay in the device when executing the switching instruction, especially when the feedback speed is affected by external factors such as temperature, the delay problem will be more obvious. When the coefficient exceeds a certain pre-set threshold, it indicates that the feedback response time of the device is significantly slower than the standard response time, which may cause the timing mismatch between the execution order of the device's switching instructions and other devices. Specifically, if the feedback delay of a device is large, and other devices complete the switching operation as expected, this will cause an imbalance in the switching timing of different devices in the multi-level voltage conversion operation, and then cause voltage overshoot or undervoltage and other phenomena. Therefore, by calculating the dynamic feedback delay coefficient, it is possible to effectively predict whether the response of the device will cause a timing mismatch problem, thereby providing a dynamic adjustment basis for the smart substation operation ticket system.

[0079] Extract the device switching stability information from the preprocessed operation status information of each device, specifically including the switching voltage deviation value of each device in the voltage switching process at different time points within a period of time, the load current value of each device in the operation process, the voltage ripple amplitude of each device when performing the voltage switching operation, and the target voltage of each device, and use the function to calculate the switching voltage deviation value of each device in the voltage switching process at different time points within a period of time, the load current value of each device in the operation process, and the voltage ripple amplitude of each device when performing the voltage switching operation. , and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The switching voltage deviation value of each device during the voltage switching process, Indicates a period of time Time point The load current of a device during operation, Indicates a period of time Time point The voltage ripple amplitude of each device when performing voltage switching operation, and the target voltage of each device is calibrated as , Indicates The target voltage for each device;

[0080] The extraction of equipment switching stability information can be achieved by combining the operation ticket system of the smart substation with the built-in sensor network and data acquisition module of the equipment. During the voltage switching process, the operation ticket system can automatically record the target voltage and switching voltage deviation value of each device. The target voltage is usually generated by the operation ticket system and is based on the preset operating parameters. The switching voltage deviation value is formed by the voltage sensor inside the equipment to monitor the difference between the actual output voltage and the target voltage in real time to form voltage deviation data. The load current value is collected in real time by the current sensor in the equipment to record the current data consumed by the equipment during the voltage switching process. The voltage ripple amplitude is extracted by the voltage monitoring module inside the equipment to record the voltage fluctuations during the switching process. All this information is transmitted to the operation ticket system through the sensor network of the equipment, and is collected and analyzed in real time in the system to generate data for calculating the switching stability index.

[0081] Switching voltage deviation value: This is the difference between the actual output voltage and the target voltage obtained by the built-in voltage sensor of the device. This data is used to reflect the accuracy of the voltage execution of the device during the switching process. The smaller the deviation, the higher the switching stability of the device. Real-time monitoring of voltage deviation helps identify potential problems in device switching and ensures that voltage switching meets expectations.

[0082] Load current value: The load current of the device is collected in real time through the current sensor of the device. This data reflects the load that the device bears during the voltage switching process. Load changes will affect the stability of the device during switching. If the load current fluctuates greatly, it means that the device is under great pressure during the switching process, which may lead to unstable switching.

[0083] Voltage ripple amplitude: This is the voltage fluctuation recorded by the voltage monitoring module when the device performs voltage switching. Voltage ripple reflects the voltage fluctuation amplitude in the internal circuit of the device and is an important data for evaluating the stability of the device during voltage switching. The larger the ripple amplitude, the more unstable the voltage switching of the device.

[0084] Target voltage: The target voltage is the ideal voltage value set by the operation ticket system for each device, representing the voltage level that the device is expected to reach during the voltage switching process. This value is usually generated based on the requirements or preset parameters in the operation ticket and used as a reference value for voltage deviation. The target voltage provides a basis for judging the accuracy of device switching voltage.

[0085] Calculate the switching stability index of each device. The specific calculation formula is as follows:

[0086]

[0087] In the formula, For the The switching stability index of a device.

[0088] This calculation formula can comprehensively measure the operating stability of the equipment during the voltage switching process. The denominator of the formula It reflects the main unstable factors of the equipment during voltage switching, including voltage deviation, load current and voltage ripple. These factors are in the time period The integral value in the figure represents the stability impact factor accumulated over a period of time. The actual voltage of the device and the target voltage The larger the difference between the two, the less accurate the device voltage switching is; the load current It reflects the current load on the device during voltage switching. The greater the fluctuation, the more unstable the device operation. This indicates the fluctuation of the voltage inside the device. Large fluctuations mean that the electrical environment of the device is unstable. By accumulating and integrating these factors, the formula comprehensively evaluates the switching stability of the device. Represents the target voltage, which is used as the standard value of stability and compared with the cumulative instability factors in the denominator to generate the switching stability index of the device. This calculation method can effectively capture the comprehensive performance of the device during the voltage switching process affected by factors such as voltage, load, and internal fluctuations. The higher the index, the better the switching stability of the device, and the lower the index, the greater the instability of the switching process of the device.

[0089] No. Switching stability index of each device The size directly reflects the ability of the device to maintain voltage accuracy, load stability and internal voltage fluctuation control during the voltage switching process. The higher the switching stability index, the closer the actual output voltage of the device during the switching process is to the target voltage, the smaller the load current fluctuation and voltage ripple are, and the overall operating state is more stable. Such a device is easier to maintain consistent switching timing with other devices in multi-level voltage switching operations, thereby reducing the risk of timing mismatch. On the contrary, if the switching stability index of a device is low, it means that the device has a large voltage deviation, load fluctuation or internal voltage instability during the voltage switching process, which will cause the device to show a large response difference during the execution of the switching instruction, thereby increasing the probability of timing mismatch with other devices. Therefore, the switching stability index can be used as an important indicator to evaluate whether the device will have a timing mismatch problem during the switching process, and is used to guide the smart substation operation ticket system to dynamically adjust the operation sequence of the equipment.

[0090] In this embodiment, the dynamic feedback delay coefficients of each device are generated and switching stability index Build a time series matching prediction model and generate the matching coefficient of each device through weighted summation , and the matching coefficients of each device generated Matching coefficient thresholds with pre-set values ​​for each device Compare and predict whether the voltage of each device will have a timing mismatch problem when switching based on the comparison results, and divide each device into timing matching devices and timing mismatching devices based on the prediction results. The specific comparison analysis and division are as follows:

[0091] like , the voltage of the device will not have a timing mismatch problem when switching, then the device is classified as a timing matching device;

[0092] This situation means that the dynamic feedback delay coefficient and switching stability index of the device indicate that the device is relatively stable during the voltage switching process, and the switching sequence can maintain good timing synchronization with other devices, and will not cause timing mismatch problems. Therefore, the device can be classified as a "timing matching device", and the operation ticket system can send switching instructions according to the original switching order and time without additional timing adjustments. This division is automatically performed through the operation ticket system. After the system collects and analyzes the real-time data of the device, the predicted matching coefficient of each device is compared with the set threshold. If it is lower than the threshold, the system automatically classifies it as a timing matching device. The impact is that it can reduce additional computing burdens, improve the operating efficiency of the system, and ensure the smooth progress of the power grid switching process.

[0093] like , if the voltage of the device has a timing mismatch problem when switching, the device is classified as a timing mismatch device.

[0094] This situation means that there may be large feedback delays, load fluctuations or voltage ripple problems during the voltage switching process of the device. The device cannot maintain the consistency of the switching timing with other devices, and there is a risk of timing mismatch. At this time, the device will be classified as a "timing mismatch device". The operation ticket system will dynamically adjust the switching order and time of the device according to the status information of the device to avoid the occurrence of switching timing mismatch. This division is automatically completed by the system. When the matching coefficient of the device exceeds the preset threshold, the system triggers the automatic adjustment mechanism to correct the switching time and sequence in the operation ticket in real time to ensure that the voltage switching operation of all devices in the system proceeds smoothly. The impact is to ensure the sequentiality and stability of voltage switching, and avoid grid instability or equipment failure problems that may be caused by timing mismatch.

[0095] To "generate dynamic feedback delay coefficients for each device and switching stability index To build a timing matching prediction model, we first need to calculate the two key parameters of the device, namely the dynamic feedback delay coefficient and switching stability index As input variables. The core goal of the timing matching prediction model is to use these two parameters to predict whether a device may have a timing mismatch with other devices during voltage switching. These two parameters are combined into a matching coefficient by weighted summation. , the formula can be expressed as: ;in, and are the weight coefficients of the dynamic feedback delay coefficient and the switching stability index respectively. The selection of the weight coefficient depends on the importance of these two parameters to the timing matching of the devices in different voltage switching operations. For example, if the feedback delay of the device in the system has a greater impact on the switching timing, the dynamic feedback delay coefficient can be given Assign higher weight , and if the stability of the device is more critical, switch the stability index Will get higher weight These weight coefficients are usually determined by historical data or empirical formulas and are obtained through multiple simulations or actual operation optimization to ensure that reasonable prediction results can be obtained under different equipment and operating environments. Finally, the matching coefficient of the equipment is generated through weighted summation. , used to evaluate the timing matching of the device during voltage switching.

[0096] According to the division results of each device, an adjustment mechanism is constructed to dynamically adjust the switching sequence and time of each device, and the adjustment mechanism is applied to the current operation ticket generation process;

[0097] In this embodiment, according to the division results of each device, an adjustment mechanism for dynamically adjusting the switching order and time of each device is constructed, specifically: according to the division results of the timing matching device and the timing non-matching device, different switching order and time adjustment parameters are set respectively to form an adjustment mechanism; the adjustment mechanism is based on the matching coefficient of each device and the current switching timing state, and automatically determines the adjustment range of the switching order and the optimization method of the switching time through a pre-set adjustment rule;

[0098] In order to achieve "building an adjustment mechanism for dynamically adjusting the switching order and time of each device based on the division results of each device", it can be realized through the software module of the intelligent substation operation ticket system. First, the system will calculate the dynamic feedback delay coefficient and switching stability index of each device according to the real-time operation data of the device, and generate the matching coefficient of the device through weighted summation. Next, the system automatically divides the device into a timing matching device or a timing mismatching device according to the comparison of the matching coefficient with the preset timing threshold. For timing matching devices, the system will set the standard switching order and time parameters to maintain the current switching instructions unchanged. For timing mismatching devices, the system will call the pre-set adjustment rules according to the size of its matching coefficient to dynamically adjust the switching order and time of the device. This process analyzes the current operating status of the device (such as load current, feedback delay, etc.) and optimizes the switching timing of the device in combination with the actual power grid operation requirements. Through the software algorithm, the system can adaptively determine the switching order adjustment amplitude and time optimization method to ensure that the timing between devices is consistent during the voltage switching process. This dynamic adjustment mechanism optimizes the operation sequence through real-time monitoring and data analysis, avoids timing mismatch problems, and ensures the stability and safety of power grid operation.

[0099] Different timing adjustments are made to timing-matched devices and timing-mismatched devices respectively, specifically including: for timing-matched devices, the standard switching sequence and time parameters in the adjustment mechanism are used to keep the current switching sequence unchanged; for timing-mismatched devices, the dynamic switching sequence and time optimization parameters in the adjustment mechanism are used to adjust the switching sequence and time of the devices.

[0100] In order to achieve "different timing adjustments for timing matching devices and timing mismatching devices respectively", the software module in the smart substation operation ticket system can be implemented in the following way. First, the system monitors the operating status data of the equipment in real time, calculates the dynamic feedback delay coefficient and switching stability index of each device, and then generates the matching coefficient for each device. For timing matching devices (i.e., their matching coefficient is lower than the preset timing threshold), the system can use the preset standard switching sequence and time parameters to keep the switching instructions of these devices unchanged, because the timing switching of these devices is relatively stable, meets the needs of the system, and does not require additional adjustment. This operation is directly applied to the operation ticket generation through the preset parameters in the equipment's operation database.

[0101] For timing mismatched devices (matching coefficient is higher than the timing threshold), the system will call the preset dynamic adjustment algorithm, first by analyzing the feedback delay, switching voltage deviation and other operating status data of the device, combined with the system's grid operation requirements, to calculate the optimized switching order and time parameters in real time. This adjustment algorithm takes into account the dynamic response characteristics of the device and the switching process of other devices to ensure that the switching time of the mismatched device is consistent with other devices, avoiding voltage fluctuations and grid instability caused by timing mismatch. The software system dynamically updates the switching order and time parameters of these devices by comprehensively considering the status of the device, the priority of the operation sequence, and the safety of the grid operation, and applies them to the operation ticket generation process. This dynamic adjustment method ensures the reliability and flexibility of grid operation and prevents timing inconsistencies caused by differences in the physical characteristics of the equipment.

[0102] During the application of the adjustment mechanism, feedback information from each device is obtained in real time, and the feedback information is analyzed to evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized according to the evaluation results;

[0103] In this embodiment, during the application of the adjustment mechanism, feedback information of each device is obtained in real time, and the feedback information is analyzed to evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps:

[0104] During the application of the adjustment mechanism, feedback information from each device is obtained in real time and preprocessed after acquisition;

[0105] During the application of the adjustment mechanism, the smart substation operation ticket system obtains feedback information from each device in real time through the built-in sensor network and data acquisition module of the device. Each device is equipped with a condition monitoring sensor that can continuously monitor key parameters such as the switching execution time, load current, voltage fluctuation, etc. The data of these sensors are transmitted to the operation ticket system through the communication interface of the device (such as a communication protocol based on the IEC 61850 standard). The system receives this feedback information in real time and uses a dedicated software module for data storage and processing. In actual operation, the information feedback from the device status will be continuously updated in the form of a time series to ensure that the operation ticket system can accurately record the operating status of the device at every moment, thereby providing support for the dynamic adjustment mechanism. Through a network monitoring module or a wireless data transmission module (such as 5G or Ethernet), the system can remotely collect and update the feedback data of the device to ensure real-time and data integrity.

[0106] Feedback information preprocessing is necessary because the raw data directly obtained from the device may contain noise, packet loss or duplicate data, which will affect the accuracy of subsequent analysis. The main purpose of preprocessing is to clean, smooth and format these data to ensure the quality and consistency of the data. In the preprocessing process, data denoising is first required to filter out the instantaneous outliers or noise information that may be generated by the sensor. Next, the time alignment of the data is performed, because the feedback information of different devices may have inconsistent timestamps due to the acquisition frequency or network transmission delay. The data of different devices need to be unified on the same time axis through interpolation algorithms or synchronization algorithms. In addition, preprocessing also includes unit conversion and data format standardization, which unifies the feedback information of the device into a format that can be directly used by the system (such as standard voltage and current units). Through these preprocessing steps, the operation ticket system can ensure that subsequent analysis and parameter calculations are based on high-quality and consistent data, thereby improving the accuracy and reliability of the adjustment mechanism.

[0107] Extracting the timing execution feedback information and dynamic load response information from the pre-processed feedback information of each device, and analyzing them after extraction to generate the timing adjustment deviation coefficient and dynamic load impact index of each device respectively;

[0108] An adjustment evaluation model is constructed using the generated timing adjustment deviation coefficient and dynamic load impact index of each device to generate an adjustment coefficient for each device. The generated adjustment coefficient for each device is compared with the pre-set adjustment coefficient threshold for each device. The comparison result is used to evaluate whether the adjustment degree of the switching order of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized based on the evaluation result.

[0109] The "pre-set adjustment coefficient thresholds for each device" can be determined by analyzing the historical operation data of the device and the actual operation requirements. First, the operation ticket system will conduct a large-scale statistical analysis of the historical operation data of each device to extract the fluctuation range of the dynamic feedback delay coefficient and the switching stability index of the device under different operating conditions. Through the data analysis module in the software system, the historical data can be used to generate the adjustment coefficient distribution of the device under normal operating conditions, and the upper and lower limits of the device adjustment coefficient can be calculated through a mathematical model. For example, using a clustering algorithm or regression analysis based on device performance, the system can identify the adjustment coefficient pattern of each device under different load, voltage and temperature conditions. Then, the system calculates the average adjustment coefficient of the device based on this data and sets a suitable threshold range to reflect the adjustment coefficient that the device can accept under normal or optimized conditions. To improve accuracy, the operation ticket system can also further optimize the threshold setting through simulated operation tests. Finally, the software automatically applies these adjustment coefficient thresholds to the real-time monitoring of each device to dynamically evaluate the adjustment effect of the device switching sequence.

[0110] In this embodiment, the logic for obtaining the timing adjustment deviation coefficient and dynamic load impact index of each device is as follows:

[0111] Extract the timing execution feedback information from the pre-processed feedback information of each device, including the actual execution time of each device performing voltage switching at different time points within a period of time, the voltage switching instruction time received by each device, and the total duration of the switching operation of each device, and use the function to calculate the actual execution time of each device performing voltage switching at different time points within a period of time and the voltage switching instruction time received by each device in accordance with the time series. and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The actual execution time of a device in voltage switching, Indicates a period of time Time point The voltage switching instruction time received by each device is calibrated as , Indicates The total duration of the device switching operation, , is a positive integer;

[0112] In the smart substation operation ticket system, the extraction of timing execution feedback information depends on the built-in sensor network and real-time data acquisition module of the device. Each device is equipped with a time synchronization system and a status monitoring sensor, which can accurately record the instruction time received by the device during the voltage switching process, the actual execution time, and the total duration of the switching operation. First, the system communicates with the control module of the device through the device's communication interface (such as the IEC 61850 protocol or the IoT-based device communication protocol) to obtain the timing feedback information of each device in real time. The system records the timestamp when the switching instruction is issued, and records the time when the device actually completes the switching operation by monitoring the execution status of the device. After the switching operation is completed, the system extracts the total duration by comparing the instruction time and the execution time. These data are automatically stored in the device operation database through the operation ticket system to ensure that the information can be called and analyzed at any time.

[0113] Actual execution time: The actual execution time of the device is obtained through the execution status feedback signal of the device. Each device will send a feedback signal when switching voltage, marking the start and end of the operation. The system monitors these signals in real time through the device's status monitoring sensor, and records the time points when the device operation starts and ends, extracting the actual execution time. This data can be synchronized with the communication module of the device control system to ensure the accuracy of the record and the consistency of the timestamp.

[0114] Receiving command time: The command time is obtained through the switching command timestamp issued by the operation ticket system. When the system sends a voltage switching command to the device, the operation ticket system automatically generates a timestamp when the command is issued and records it in the system log. This time is ensured to be accurate through the system's built-in time synchronization function.

[0115] Total duration: The total duration of the device switching is calculated by comparing the time when the device receives the instruction and the time when the execution is actually completed. The system automatically extracts the total duration based on the device's operating status information, the switching signal, and the timestamp of the instruction for subsequent evaluation and analysis.

[0116] Calculate the timing adjustment deviation coefficient of each device. The specific calculation formula is as follows:

[0117]

[0118] In the formula, For the Timing adjustment deviation coefficient of each device;

[0119] This formula calculates the timing deviation of the device in the switching operation by integrating, reflecting the difference between the device execution time and the instruction time. The timing deviation over a period of time is accumulated to quantify the deviation of the device during the entire operation process; through normalization , so that the deviation is independent of the device operation time, ensuring that the deviations of different devices are comparable. Absolute value processing is used in the formula to ensure that the timing deviation, whether leading or lagging, can be accurately recorded and evaluated, thus providing a reliable evaluation standard for adjusting the device timing.

[0120] No. Timing adjustment skew factor for each device The size of directly reflects the degree of deviation of the device from the instruction time when executing the switching operation. The larger the value, the greater the deviation between the actual execution time of the device and the expected instruction time, indicating that the timing mismatch of the device is high and greater adjustments are required to meet the set timing requirements. Small, indicating that the execution time of the device is close to the instruction time, the timing match is good, and the adjustment mechanism has little effect or does not need to be adjusted. The system can determine whether the adjustment mechanism effectively optimizes the switching sequence of the devices and ensure that all devices can successfully complete the operation according to the expected timing during the voltage switching process.

[0121] Extract the dynamic load response information from the pre-processed feedback information of each device, including the actual load current value, voltage value and maximum load current value of each device at different time points in a period of time, and use the function to calculate the actual load current value and voltage value of each device at different time points in a period of time according to the time series. and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The actual load current value of each device, Indicates a period of time Time point The voltage value of each device is calibrated as , Indicates The maximum load current value of each device;

[0122] Extracting dynamic load response information of each device depends on the device sensor network and data acquisition system in the smart substation. Each device is equipped with current sensors and voltage sensors, which can monitor the actual load current and voltage of the device in real time during the voltage switching process. The system communicates with these sensors through communication protocols (such as IEC 61850 standard or industrial protocols such as Modbus) to collect these data in real time. The operation ticket system automatically triggers data collection through the operation control module of the device. When the voltage switching command is issued, the system will continuously collect current and voltage feedback data at different time points (for example, at millisecond time intervals) and store these data in the system database for subsequent analysis. The maximum load current value of the device is usually obtained through the device nameplate information or device specification document, and can also be recorded in real time by sensors monitoring the historical operation data of the device.

[0123] Actual load current value: obtained through the current sensors in the equipment, which directly measure the real-time current value of the equipment. The sensors transmit the measured current data to the operation ticket system through the communication module of the equipment, and the system records the load current of the equipment at each time point in a time series.

[0124] Voltage value: obtained through the voltage sensor in the equipment. The voltage sensor monitors the voltage change of the equipment in real time, especially during the voltage switching process, the sensor can detect the voltage fluctuation. The operation ticket system obtains voltage feedback from the sensor through the monitoring module and stores the voltage data for subsequent calculation of the voltage change rate.

[0125] Maximum load current value: The maximum load current value of the device is usually recorded on the device nameplate or obtained through the device's technical specification document. In addition, the system can also record the changes in the maximum load current value based on the device's historical operating data to ensure that the maximum load current value can still be accurately obtained under extreme load conditions.

[0126] Calculate the dynamic load impact index of each device. The specific calculation formula is as follows:

[0127]

[0128] In the formula, For the The dynamic load impact index of each device, for a period of time Time point The rate of change of the voltage value of a device.

[0129] This formula calculates the actual load current of the device by and voltage change rate The product of and integration is used to evaluate the dynamic load response capability of the device during voltage switching. , can accumulate the mutual influence of equipment load current and voltage changes over a period of time, and reflect the dynamic response of equipment load changes during voltage switching. Normalization in the formula It is used to ensure that the index is compared with the maximum load current of the device, so that the calculated load impact index is applicable to devices with different load capacities. This calculation method can accurately capture the dynamic relationship between the load and voltage of the device during the voltage switching process, which helps to evaluate the stability and adaptability of the device in dynamic switching, and then provide a basis for the switching adjustment of the system.

[0130] No. Dynamic load impact index of each device The size reflects the load response of the device during voltage switching. The larger the value, the greater the fluctuation of load current and voltage during voltage switching. This means that the load dynamic response of the device is more intense and there may be high instability. It is necessary to make greater adjustments to the device switching sequence and time to ensure that the set timing requirements are met. If the load current and voltage of the device are relatively stable, it means that the device's response to the switching operation is relatively stable, and the adjustment mechanism requires less adjustment or even no adjustment. The size of directly determines whether the device switching sequence needs to be adjusted to ensure the overall stability and timing requirements of the system.

[0131] In this embodiment, the generated timing adjustment deviation coefficients of each device are and dynamic load impact index Construct an adjustment evaluation model and generate the adjustment coefficients for each device through weighted summation , and the generated adjustment coefficients for each device Adjustment coefficient thresholds for each device that are set in advance Compare and evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements based on the comparison results, and optimize the adjustment mechanism based on the evaluation results. The specific comparison and analysis are as follows:

[0132] like , the degree of adjustment of the switching sequence of the device by the adjustment mechanism does not meet the set timing requirements, and the adjustment mechanism needs to be optimized, including: optimizing the switching sequence, adjusting the switching time window, resetting the threshold parameters and monitoring the feedback information in real time, and dynamically adjusting the switching sequence and time of the device;

[0133] This situation means that the degree of adjustment of the switching sequence of the device does not meet the set timing requirements, which may cause inaccurate switching time of the device, thus affecting the overall operation stability of the system. In order to ensure that the timing requirements are met, the adjustment mechanism needs to be optimized. The specific methods include: optimizing the switching sequence and adjusting the time window of the device according to the latest feedback information, resetting the timing matching threshold and adjustment parameters of each device, and ensuring that the device can switch in the expected time sequence. The optimization process collects the feedback information of each device in real time through software, and uses algorithms to calculate the switching timing deviation of each device, and dynamically adjusts the switching time window, such as extending or shortening the switching time of a specific device. In addition, it is also necessary to continuously monitor the operating status of the device and adjust the switching sequence in real time according to the load condition and dynamic response data of the device to ensure the dynamic balance of the entire system.

[0134] like The degree of adjustment of the switching sequence of the device by the adjustment mechanism meets the set timing requirements, and there is no need to optimize the adjustment mechanism.

[0135] This situation indicates that the switching sequence adjustment of the equipment has met the timing requirements of the system, the equipment operation status is stable, and the switching time is within the expected range, so no further adjustment mechanism is required. At this time, the system can continue to monitor the feedback information of the equipment, but no adjustment is made. In order to ensure the stability of subsequent operation, the software will maintain the existing adjustment parameters and time window settings, and collect the status information of each device during operation in real time to avoid unnecessary adjustment operations, thereby improving the efficiency of the system. Through continuous analysis of equipment operation data and automated self-diagnosis functions, the system can ensure that intervention is minimized and the efficient operation of the system is maintained while the timing requirements of the equipment are met.

[0136] "The generated timing adjustment deviation coefficients for each device and dynamic load impact index The adjustment evaluation model is constructed by combining these two parameters to reflect the timing and load conditions of the equipment and generate the adjustment coefficient of each equipment by weighted summation. Specifically, first calculate the and , and then set different weight coefficients for these two parameters according to the different requirements of the system for timing accuracy and load stability. Generally, if the system has higher requirements for timing, the timing adjustment deviation coefficient The weight coefficient is large, and the dynamic load influence index The weight coefficient is relatively small; on the contrary, if the system runs with a large load and has high requirements for load stability, then The weight coefficient of increases. and satisfy , the calculation formula of the adjustment coefficient is: By weighted summation, the system can comprehensively consider the timing and load conditions of the equipment and generate an adjustment coefficient for each device. , thereby determining whether the switching sequence and time of the equipment need to be further optimized.

[0137] Based on the long-term collected equipment feedback information and switching execution data, the adjustment mechanism is dynamically optimized and updated, so that the equipment switching sequence and time in the operation ticket can be optimized according to the data analysis results in long-term operation.

[0138] Long-term collection of equipment feedback information and switching execution data can be achieved through intelligent sensor networks and data acquisition systems. The sensors installed inside each device can monitor the key operating parameters of the equipment such as current, voltage, temperature, etc. in real time, and record the specific switching execution time and load changes during the switching operation. The data collection process is automatically triggered by the software system, and the operating data of the equipment will be recorded in the system in chronological order to form a complete time series data. Through industrial communication protocols (such as IEC 61850 or Modbus), the operation ticket system maintains continuous communication with the equipment, and collects the feedback information and switching data of the equipment on a regular or on-demand basis. Long-term monitoring ensures that the system can obtain enough equipment operating status information, thereby providing a reliable basis for subsequent analysis.

[0139] The collected device feedback information and switching data need to be processed by the data analysis module. The system can use advanced algorithms to analyze the historical operation data of the equipment and identify timing deviations or load fluctuations that occur during the equipment switching process. The specific analysis process includes statistical analysis of parameters such as the switching time, load response, and feedback delay of each device, and finding out the long-term trends and potential problems of equipment operation through time series analysis methods. For example, whether the switching time of the equipment shows a trend of gradual increase in long-term operation, or whether the load response gradually deviates from the standard. Through the data processing algorithms and statistical tools in the software, the system can discover potential optimization opportunities in the execution process of equipment switching.

[0140] After acquiring and analyzing the data of the equipment in long-term operation, the system can dynamically optimize and update the adjustment mechanism based on the analysis results. This process is achieved by adjusting the switching order and time of the equipment in real time, ensuring that each device can be optimized according to the actual timing requirements and load response in long-term operation. For example, the system can automatically adjust the switching time window of the equipment, extend the switching time for equipment with fluctuating switching execution time, or adjust the switching order of the equipment based on load feedback information. The software can continuously learn the operating mode of the equipment through machine learning algorithms, and dynamically adjust the parameters of the adjustment mechanism, so that the responsiveness and efficiency of the system are continuously improved.

[0141] The purpose of dynamic optimization and update adjustment mechanism is to ensure that the equipment can adapt to dynamic changes in load and timing requirements in long-term operation. The operating environment of the equipment may change over time, such as load increase, equipment aging, or changes in external power conditions, so it is necessary to timely discover problems and make adjustments through data analysis. Through long-term collected data and dynamic optimization mechanism, the system can predict potential problems in advance, reduce timing mismatches or load instability during equipment switching, and ensure stable operation of the system. Long-term dynamic optimization can not only improve the efficiency of the operation ticket system, but also extend the service life of the equipment, reduce the system failure rate, and ensure the safety and reliability of power grid operation.

[0142] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0143] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0144] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0145] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0146] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0149] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning, characterized in that: The specific steps include: When performing multi-level voltage conversion operations, determine the voltage conversion sequence and operation requirements of each device, and obtain the operating status information of each device in real time through the sensor network installed on each device; Analyze the acquired operation status information of each device, predict whether the voltage of each device will have a timing mismatch problem when switching, and divide each device into a timing matching device and a timing mismatching device according to the prediction result; According to the division results of each device, an adjustment mechanism is constructed to dynamically adjust the switching sequence and time of each device, and the adjustment mechanism is applied to the current operation ticket generation process; During the application of the adjustment mechanism, feedback information from each device is obtained in real time, and the feedback information is analyzed to evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized according to the evaluation results; Based on the long-term collected equipment feedback information and switching execution data, the adjustment mechanism is dynamically optimized and updated, so that the equipment switching sequence and time in the operation ticket can be optimized according to the data analysis results in long-term operation.

2. According to the rule-based reasoning method for issuing invoices for smart substation operation ticket system according to claim 1, it is characterized in that: The acquired operation status information of each device is analyzed to predict whether a timing mismatch problem will occur when the voltage of each device is switched, and each device is divided into a timing matching device and a timing mismatching device according to the prediction result, specifically including the following steps: Preprocessing the acquired operation status information of each device; Extracting device feedback delay information and device switching stability information from the preprocessed operation status information of each device, and analyzing them after extraction to generate a dynamic feedback delay coefficient and a switching stability index for each device respectively; The dynamic feedback delay coefficient and switching stability index of each device are generated to construct a timing matching prediction model, and the matching coefficient of each device is generated. The generated matching coefficient of each device is compared with the pre-set matching coefficient threshold of each device. According to the comparison result, it is predicted whether the voltage of each device will have a timing mismatch problem when switching, and according to the prediction result, each device is divided into a timing matched device and a timing mismatched device.

3. According to the rule-based reasoning method for issuing invoices for smart substation operation ticket system in claim 2, it is characterized in that: The logic for obtaining the dynamic feedback delay coefficient and switching stability index of each device is as follows: Extract the device feedback delay information from the preprocessed operation status information of each device, including the instruction sending time series sent by the smart substation operation ticket system to each device at different time points in a period of time, the feedback completion time series of each device, the operating temperature of each device, and the expected response time of each device under standard operating conditions. The instruction sending time series sent by the smart substation operation ticket system to each device at different time points in a period of time, the feedback completion time series of each device, and the operating temperature of each device are respectively calculated by function according to the time series. , and To express, is a time point, and the time period is defined as , Indicates that within a period of time At this time, the intelligent substation operation ticket system sends The time sequence of commands sent by each device, Indicates that within a period of time Time point The feedback from each device completes the time series. Indicates that within a period of time Time point The expected response time of each device under standard operating conditions is calibrated as , Indicates The expected response time of a device under standard operating conditions, , is a positive integer; Calculate the dynamic feedback delay coefficient of each device. The specific calculation formula is as follows: In the formula, For the Dynamic feedback delay coefficient of each device; Extract the device switching stability information from the preprocessed operation status information of each device, specifically including the switching voltage deviation value of each device in the voltage switching process at different time points within a period of time, the load current value of each device in the operation process, the voltage ripple amplitude of each device when performing the voltage switching operation, and the target voltage of each device, and use the function to calculate the switching voltage deviation value of each device in the voltage switching process at different time points within a period of time, the load current value of each device in the operation process, and the voltage ripple amplitude of each device when performing the voltage switching operation. , and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The switching voltage deviation value of each device during the voltage switching process, Indicates a period of time Time point The load current of a device during operation, Indicates a period of time Time point The voltage ripple amplitude of each device when performing voltage switching operation, and the target voltage of each device is calibrated as , Indicates The target voltage for each device; Calculate the switching stability index of each device. The specific calculation formula is as follows: In the formula, For the The switching stability index of a device.

4. According to the rule-based reasoning-based smart substation operation ticket system ticketing method of claim 3, it is characterized in that: The dynamic feedback delay coefficients of each device will be generated and switching stability index Build a time series matching prediction model and generate the matching coefficient of each device through weighted summation , and the matching coefficients of each device generated Matching coefficient thresholds with pre-set values ​​for each device Compare and predict whether the voltage of each device will have a timing mismatch problem when switching based on the comparison results, and divide each device into timing matching devices and timing mismatching devices based on the prediction results. The specific comparison analysis and division are as follows: like , the voltage of the device will not have a timing mismatch problem when switching, then the device is classified as a timing matching device; like , if the voltage of the device has a timing mismatch problem when switching, the device is classified as a timing mismatch device.

5. According to the rule-based reasoning-based smart substation operation ticket system ticketing method of claim 4, it is characterized in that: According to the division results of each device, an adjustment mechanism for dynamically adjusting the switching order and time of each device is constructed, specifically: according to the division results of timing matching devices and timing non-matching devices, different switching order and time adjustment parameters are set respectively to form an adjustment mechanism; the adjustment mechanism is based on the matching coefficient of each device and the current switching timing state, and automatically determines the adjustment range of the switching order and the optimization method of the switching time through pre-set adjustment rules; Different timing adjustments are performed on timing matching devices and timing mismatching devices respectively, specifically including: for timing matching devices, using the standard switching sequence and time parameters in the adjustment mechanism to keep the current switching sequence unchanged; For timing mismatched devices, use the dynamic switching order and time optimization parameters in the adjustment mechanism to adjust the switching order and time of the devices.

6. The method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning according to claim 5 is characterized in that: During the application of the adjustment mechanism, feedback information from each device is obtained in real time, and the feedback information is analyzed to evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized according to the evaluation results, which specifically includes the following steps: During the application of the adjustment mechanism, feedback information from each device is obtained in real time and preprocessed after acquisition; Extracting the timing execution feedback information and dynamic load response information from the preprocessed feedback information of each device, and analyzing them after extraction to generate the timing adjustment deviation coefficient and dynamic load impact index of each device respectively; An adjustment evaluation model is constructed using the generated timing adjustment deviation coefficient and dynamic load impact index of each device to generate an adjustment coefficient for each device. The generated adjustment coefficient for each device is compared with the pre-set adjustment coefficient threshold for each device. The comparison result is used to evaluate whether the adjustment degree of the switching order of each device by the adjustment mechanism meets the set timing requirements, and the adjustment mechanism is optimized based on the evaluation result.

7. The method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning according to claim 6 is characterized in that: The logic for obtaining the timing adjustment deviation coefficient and dynamic load impact index of each device is as follows: Extract the timing execution feedback information from the pre-processed feedback information of each device, including the actual execution time of each device performing voltage switching at different time points within a period of time, the voltage switching instruction time received by each device, and the total duration of the switching operation of each device, and use the function to calculate the actual execution time of each device performing voltage switching at different time points within a period of time and the voltage switching instruction time received by each device in accordance with the time series. and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The actual execution time of a device in voltage switching, Indicates a period of time Time point The voltage switching instruction time received by each device is calibrated as , Indicates The total duration of the device switching operation, , is a positive integer; Calculate the timing adjustment deviation coefficient of each device. The specific calculation formula is as follows: In the formula, For the Timing adjustment deviation coefficient of each device; Extract the dynamic load response information from the pre-processed feedback information of each device, including the actual load current value, voltage value and maximum load current value of each device at different time points in a period of time, and use the function to calculate the actual load current value and voltage value of each device at different time points in a period of time according to the time series. and To express, is a time point, and the time period is defined as , Indicates a period of time Time point The actual load current value of each device, Indicates a period of time Time point The voltage value of each device is calibrated as , Indicates The maximum load current value of each device; Calculate the dynamic load impact index of each device. The specific calculation formula is as follows: In the formula, For the The dynamic load impact index of each device, for a period of time Time point The rate of change of the voltage value of a device.

8. The method for issuing invoices for an intelligent substation operation ticket system based on rule reasoning according to claim 7 is characterized in that: The generated timing adjustment deviation coefficients of each device and dynamic load impact index Construct an adjustment evaluation model and generate the adjustment coefficients for each device through weighted summation , and the generated adjustment coefficients for each device Adjustment coefficient thresholds for each device that are set in advance Compare and evaluate whether the adjustment degree of the switching sequence of each device by the adjustment mechanism meets the set timing requirements based on the comparison results, and optimize the adjustment mechanism based on the evaluation results. The specific comparison and analysis are as follows: like , the degree of adjustment of the switching sequence of the device by the adjustment mechanism does not meet the set timing requirements, and the adjustment mechanism needs to be optimized, including: optimizing the switching sequence, adjusting the switching time window, resetting the threshold parameters and monitoring the feedback information in real time, and dynamically adjusting the switching sequence and time of the device; like The degree of adjustment of the switching sequence of the device by the adjustment mechanism meets the set timing requirements, and there is no need to optimize the adjustment mechanism.

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