A multifunctional measurement and control integrated method for power system

Abstract

The application relates to the technical field of power equipment operation state measurement and control, and discloses a multifunctional measurement and control integrated method for a power system. The method collects current detection data, voltage detection data and temperature state data, pre-processes the collected data, performs time correlation analysis on different source measurement and control data through a multistage source data fusion module, judges whether the data time deviation meets the fusion condition, samples and adjusts the abnormal data source when the data does not meet the fusion requirement, and performs fusion judgment again according to the re-collected data. Further, state evaluation is performed according to the fused data, different state parameter weights are calculated through an optimal weighting module, the control priority is determined, and the power equipment operation state measurement and control are realized by combining data calibration and control output processes. The application improves the time consistency in the multi-source measurement and control data fusion process and enhances the reliability of the equipment state analysis and control process.

Description

Technical Field

[0001] This invention relates to the field of power equipment operation status measurement and control technology, specifically a multi-functional measurement and control integrated method for power systems. Background Technology

[0002] With the development of power equipment operation status monitoring technology, the measurement and control devices in the power system usually need to collect and analyze a variety of status parameters during equipment operation. Among them, current detection data, voltage detection data and temperature status data are important parameters reflecting the operation of power equipment. However, in actual operation, due to the differences in detection methods, sensing response characteristics and sampling periods corresponding to different detection parameters, it is difficult to keep the acquisition time of each data source completely synchronized.

[0003] Current detection data can usually quickly reflect changes in equipment load, and its sampling frequency is relatively high. However, temperature status data is affected by the thermal inertia of the equipment and the heat transfer process, and its status changes relatively slowly. Its acquisition period is usually different from that of current detection data. Therefore, different types of measurement and control data acquired within the same analysis period may not correspond to the same actual operating state of the equipment. If data collected at different times are directly fused, it is easy to cause deviations in the time correspondence between the data, making the fusion result unable to accurately reflect the current state of the equipment. Therefore, it is urgent to develop a multi-functional measurement and control integration method for power systems to solve these practical problems. Summary of the Invention

[0004] This invention aims to solve the problem that in the existing power equipment monitoring and control process, due to the differences in sampling periods and acquisition times of monitoring and control data from different sources, it is difficult to accurately correspond the operating status of the same equipment when multi-source monitoring and control data are fused and processed.

[0005] Specifically, this invention analyzes the time relationship of measurement and control data from different sources, determines whether the data sources meet the fusion conditions before data fusion, and adjusts the corresponding data sources when abnormal data time deviations are detected, so as to improve the time consistency during the fusion of multi-source measurement and control data.

[0006] To achieve the above objectives, the present invention provides a multi-functional measurement and control integration method for power systems, comprising the following steps:

[0007] First, a multi-source data acquisition module is used to acquire various types of measurement and control data during the operation of the power equipment. The measurement and control data includes current detection data, voltage detection data, and temperature status data. Different types of measurement and control data are used to reflect the different operating status characteristics of the equipment.

[0008] Subsequently, the acquired multi-source measurement and control data is input into the data preprocessing module to perform basic processing on the raw measurement and control data in order to reduce noise interference and data deviations during the acquisition process and obtain preprocessed data that meets the requirements of subsequent processing.

[0009] The pre-processed measurement and control data from different sources are input into the multi-level source data fusion module. The time label extraction unit obtains the acquisition time information corresponding to each data source, and the time deviation calculation unit calculates the time deviation between different data sources. Based on the time deviation results, the data from different sources are time-matched and grouped.

[0010] When the temporal relationship between multiple data sources meets the preset fusion conditions, the fusion condition judgment unit determines that the corresponding data meets the fusion conditions, and performs fusion processing on the data within the same time-related range to obtain the fused measurement and control data.

[0011] When a large time deviation or data anomaly is detected between different data sources, the corresponding abnormal data source is determined by the abnormal data source identification and marking unit, and the sampling process of the corresponding data source is adjusted by the sampling adjustment request generation unit, so that the abnormal data source re-collects measurement and control data, and the re-collected data participates in time matching and fusion judgment again.

[0012] Based on the fused measurement and control data, the current operating status of power equipment is analyzed through the status evaluation module to obtain the corresponding status evaluation results.

[0013] Furthermore, based on the status evaluation results, different status parameters are analyzed through the optimization and weighting module. The corresponding weights are determined according to the degree of change, abnormality, and status impact of each status parameter. The main parameters affecting the equipment operating status and control priorities are determined based on the weight ranking results.

[0014] The preferred weighting module obtains parameter features from the state evaluation results through the state parameter feature extraction unit, obtains corresponding evaluation indicators through the change degree calculation unit, the anomaly degree calculation unit, and the state influence degree calculation unit, and calculates the weight of each state parameter through the state parameter weight synthesis unit.

[0015] Finally, a control strategy is generated based on the determined control priority. Before the control command is output, the execution data is verified by the data calibration module. When the control execution conditions are met, the control command is output and the corresponding control operation is completed by the execution module. When the execution conditions are not met, the data backtracking module returns to the previous data processing process to reprocess the relevant data.

[0016] This invention provides a multi-functional measurement and control integration method for power systems. Compared with existing technologies, it has the following advantages:

[0017] 1. This invention addresses the problem of inconsistent data acquisition times from different sources during the measurement and control of power equipment. By introducing time tag acquisition and time deviation judgment processes during the fusion of multi-source measurement and control data, time correlation analysis is performed on the data involved in the fusion, enabling different detection parameters to be matched based on similar time states. This reduces data correspondence deviations caused by differences in sampling periods and improves the matching degree between the fusion results of multi-source measurement and control data and the actual operating state of the equipment.

[0018] 2. In the process of multi-level source data fusion, this invention does not directly fuse all the collected data. Instead, when a large time deviation of the data is detected or the fusion conditions are not met, abnormal data sources are identified, and the corresponding sampling process is adjusted to reacquire the data. This allows the abnormal data to be processed, preventing the abnormal data from continuously affecting subsequent status analysis and improving the stability of the measurement and control data processing process.

[0019] 3. This invention evaluates the status based on the fused measurement and control data, and analyzes the importance of different status parameters through a preferred weighting method. This enables the control process to determine the control priority based on the degree of influence of different parameters on the equipment's operating status. At the same time, combined with the data calibration process before control, it reduces control deviations caused by differences in parameter weights and data anomalies, thereby improving the pertinence and reliability of the power equipment measurement and control process. Attached Figure Description

[0020] Figure 1 This is an overall structural block diagram of a multi-functional measurement and control integration method for power systems according to the present invention;

[0021] Figure 2 This is a schematic diagram of the internal structure of the multi-level source data fusion module of the present invention;

[0022] Figure 3 This is a schematic diagram of the internal structure of the preferred weighting module of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] First embodiment;

[0025] like Figures 1 to 3As shown in the figure, this embodiment provides a multi-functional measurement and control integration method for power systems, which is used to collect, process, evaluate the status of current, voltage and temperature during the operation of power equipment and output control data. The method is executed in the following order: data acquisition and preprocessing, multi-level source data fusion, status evaluation and optimization weighting, control output and data backtracking.

[0026] S1. Data Acquisition and Preprocessing

[0027] In this embodiment, the multi-source data acquisition module receives measurement and control data output from the current detection data source, the voltage detection data source, and the temperature status data source, respectively. When each data source outputs measurement and control data, it also provides the acquisition time information corresponding to the measurement and control data. The multi-source data acquisition module sends the acquired measurement and control data to the data preprocessing module.

[0028] Specifically, the data preprocessing module filters, preliminarily screens and calibrates the input data, and maintains the correlation between the acquisition time information and the corresponding data. The preprocessed current detection data, voltage detection data and temperature status data serve as the input to the multi-level source data fusion module. Data preprocessing only performs basic organization on the original measurement and control data and does not change the original sampling period of each data source.

[0029] S2, Multi-level source data fusion process

[0030] like Figure 2 As shown, the multi-level source data fusion module includes a time tag extraction unit, a time deviation calculation unit, a time matching grouping unit, a fusion condition judgment unit, a data source reliability determination unit, a same-group data fusion unit, and a fusion result output unit. It also includes an abnormal data source identification and marking unit corresponding to the abnormal handling branch, a sampling adjustment request generation unit, and a corresponding data source resampling unit.

[0031] The preprocessed measurement and control data from various sources first enter the time tag extraction unit. The time tag extraction unit extracts the time tag from the acquisition time information carried in the data and sets the time tag as the first time tag. The time stamp of each data source is denoted as , will the The time stamp of each data source is denoted as The time stamps are associated one-to-one with the corresponding current, voltage, or temperature data. The time deviation calculation unit receives the time stamps from each data source and calculates the time deviation between any two data sources to be matched. The calculation method is as follows:

[0032] ;

[0033] in:

[0034] Indicates the first The data source and the first Time skew between data sources;

[0035] Indicates the first The collection time stamps of each data source;

[0036] Indicates the first The collection time stamps of each data source;

[0037] The time matching grouping unit divides data within the same time correlation range into a data group to be fused based on the time deviation calculation results. For the same current or voltage sampling time, data with the closest time label and meeting the time consistency threshold can be selected from the temperature data to participate in the grouping, thereby avoiding the combination of data from different sources only according to the order of data arrival.

[0038] The fusion condition judgment unit compares the time deviation of the data within each group with a preset time consistency threshold. By comparison, the data is deemed to meet the time fusion condition when the following formula is satisfied:

[0039] ;

[0040] If the corresponding data sources are deemed to meet the time consistency requirements, they can proceed to the subsequent fusion processing.

[0041] in, The preset time consistency threshold can be set according to the type of device under test, the sampling period of each detection parameter, and the rate of change of state. For a data set to be fused containing multiple data sources, time deviation verification needs to be performed separately for each data source; if any set of time deviations exceeds... At that time, the data group to be merged will not directly enter the data fusion unit of the same group.

[0042] When the data group to be fused meets the fusion conditions, the data source reliability determination unit determines the range of data to be fused based on whether the data is complete, whether it is within the valid value range of the corresponding detection parameter, and whether there are abnormal markers in the preprocessing stage. Subsequently, the same group data fusion unit combines the current, voltage, and temperature status data in the same time-related group to form fused data with a common time reference, and sends it to the data consistency verification module by the fusion result output unit. This fusion process is used to establish the time correspondence between different measurement and control parameters without changing the physical meaning of each detection parameter.

[0043] When the fusion condition judgment unit determines that a certain data does not meet the fusion condition, the abnormal data source identification and marking unit determines the data source that needs to be resampled based on the time deviation exceeding the threshold and its corresponding time label, and sets an abnormal mark in the data record corresponding to the data source. The sampling adjustment request generation unit generates a sampling adjustment request based on the original sampling period of the abnormal data source and the time range of the current data to be matched. The request is used to shorten the sampling period of the corresponding data source, adjust the sampling time, or trigger a resampling.

[0044] The corresponding data source resampling unit obtains new measurement and control data according to the sampling adjustment request. The re-acquired data is returned to the time tag extraction unit, which sequentially re-executes time tag extraction, time deviation calculation, time matching grouping, and fusion condition judgment. If the re-acquired data meets the fusion conditions, it enters the data source reliability determination unit and the same group data fusion unit. If it still does not meet the fusion conditions, the anomaly mark is retained, and a sampling adjustment request is generated again or the fusion processing of the group of data is terminated. This forms a feedback path of anomaly identification, sampling adjustment, and re-judgment.

[0045] After the fusion results are output to the data consistency verification module, the data consistency verification module verifies the time correspondence, data integrity, and anomaly marking status of the fused data. Data that passes the verification enters the parallel data processing module, which organizes the data according to the current, voltage, and temperature parameter types and then sends it to the status evaluation module. If the verification results indicate that the corresponding data source needs to be adjusted, the data enters the sampling adjustment module. The sampling adjustment module adjusts the sampling strategy of the abnormal data source and sends the adjusted data that meets the evaluation requirements to the status evaluation module. Data that does not meet the evaluation requirements does not participate in this status evaluation.

[0046] S3, Optimization and Weighting Process

[0047] The status evaluation module receives the fused data that has passed the consistency check, generates status evaluation results according to the current, voltage, and temperature status parameters, and inputs these status evaluation results into the optimization and weighting module, such as... Figure 3 As shown, the preferred weighting module includes a state parameter feature extraction unit, a change degree calculation unit, an anomaly degree calculation unit, a state influence degree determination unit, a feature index normalization unit, a state parameter weight synthesis unit, a weight sorting unit, a primary and secondary influencing factor determination unit, a control priority mapping unit, and a control strategy output unit.

[0048] The state parameter feature extraction unit extracts the current value, normal operation reference value, allowable range, and changes in previous and subsequent time periods for each state parameter from the state evaluation results. The degree of change calculation unit determines the degree of change based on the change in the current state parameter relative to the normal operation reference value. The anomaly calculation unit determines the anomaly level based on the degree to which the current state parameters deviate from the allowable range. The state impact determination unit determines the state impact level based on the correspondence between the state parameter and the equipment operating status evaluation items. .

[0049] Feature index normalization units respectively , and Dimensional processing is performed to allow evaluation indices for different state parameters to be compared within a unified range. The state parameter weight synthesis unit calculates the weight corresponding to the th state parameter based on the normalized degree of change, anomaly, and state influence. The calculation formula is:

[0050]

[0051] in:

[0052] Indicates the first The weight values ​​corresponding to each state parameter;

[0053] Indicates the first The degree of change of each state parameter;

[0054] Indicates the first The degree of abnormality of each state parameter;

[0055] Indicates the first The degree of influence of each state parameter on the state;

[0056] This represents the corresponding adjustment coefficient, used to adjust the influence ratio of different evaluation indicators in the weight calculation process;

[0057] This represents the number of state parameters participating in this state assessment. The sum of all weights after normalization of the denominator is 1.

[0058] The weighted sorting unit sorts according to each state parameter. The parameters are sorted from largest to smallest. The primary and secondary influencing factors determination unit identifies the state parameters that rank first and meet the preset control judgment conditions as primary influencing factors, and the remaining state parameters participating in the evaluation as secondary influencing factors. The control priority mapping unit determines the control order based on the state type corresponding to the primary influencing factors, and sends the state parameters, weight ranking results, and control order to the control strategy output unit. The weight calculation results are used for control decision-making here and are not output as independent data analysis results.

[0059] S4, Control Output and Data Backtracking

[0060] The control strategy output by the optimization and weighting module enters the data calibration module. Before the control is executed, the data calibration module re-verifies the fused data, status evaluation results and anomaly markers on which the control strategy is based. When the above data meets the execution conditions, the control command output module generates control commands according to the control priority and sends the control commands to the execution module, which then performs control on the corresponding power equipment.

[0061] When the data calibration module determines that the data does not meet the execution conditions, it does not output the current control command, but instead sends the relevant data to the data backtracking module. The data backtracking module locates the data that needs to be reprocessed based on the anomaly marker or calibration result, and returns to the data preprocessing module to re-execute the preprocessing, multi-level source data fusion, data consistency verification, status evaluation and optimization weighting process.

[0062] Second embodiment;

[0063] Multi-source measurement and control data fusion based on 10kV distribution line pole-mounted vacuum circuit breaker;

[0064] This embodiment takes the operation status monitoring of a 10kV distribution line pole-mounted vacuum circuit breaker as an example to illustrate the method of Embodiment 1. The module connection relationship and data processing order used in this embodiment are the same as those in Embodiment 1.

[0065] During the operation of the pole-mounted vacuum circuit breaker, the multi-source data acquisition module receives current detection signals, voltage detection signals, and temperature status signals. The current detection signal is used to reflect changes in line load, the voltage detection signal is used to reflect the line operating status, and the temperature status signal is used to reflect the temperature rise of the circuit breaker contacts and conductive connection parts.

[0066] In this embodiment, the current sampling period is set to 20ms, the voltage sampling period is set to 20ms, and the temperature sampling period is set to 500ms. The data preprocessing module performs basic filtering, preliminary screening of outliers, and calibration on the three types of data, and sends the preprocessed data and its acquisition time information to the multi-level source data fusion module.

[0067] The time consistency threshold is set as follows: =100ms;

[0068] In a specific data set to be fused, the time label for the current detection data is: =1000ms;

[0069] The time stamp of the temperature status data that matches the current detection data is: =1080ms;

[0070] The time deviation calculation unit calculates the time deviation between the current detection data and the temperature status data:

[0071] ;

[0072] Further calculations: ;

[0073] Since 80ms < 100ms, that is: ;

[0074] Therefore, the current detection data and temperature status data meet the time fusion condition. The time matching grouping unit divides the two into the same time association group, and together with the voltage detection data that meets the time fusion condition, they enter the data source reliability determination unit and the same group data fusion unit.

[0075] When the time stamp of the temperature status data changes to: ;

[0076] The time deviation between the current detection data and the temperature status data is:

[0077] ;

[0078] at this time: If 200ms > 100ms, the fusion condition judgment unit determines that the temperature state data does not meet the fusion requirements for this time.

[0079] The abnormal data source identification and marking unit identifies the temperature data source as an abnormal data source based on the time deviation exceeding the threshold and its corresponding data source, and sets an abnormal mark for the temperature status data.

[0080] The sampling adjustment request generation unit generates a sampling adjustment request for the temperature data source. In this embodiment, the temperature sampling period is adjusted from 500ms to 100ms, and the temperature status data is re-acquired. This adjustment is performed for the current time matching process and does not change the 20ms sampling period of the current data and voltage data.

[0081] The newly acquired temperature status data carries a new time label and is returned to the time label extraction unit, where time deviation calculation, time matching grouping, and fusion condition judgment are performed again.

[0082] When the time deviation between the new temperature status data and the corresponding current and voltage data is no greater than 100ms, the temperature status data enters the same group of data fusion units; when it still exceeds 100ms, the abnormality mark is retained, and sampling adjustment continues or the fusion of the group is terminated.

[0083] After the merged current, voltage, and temperature status data are verified for data consistency, the data of normal branches are organized by the parallel data processing module according to parameter type and input into the status evaluation module; the data that needs to be adjusted is processed by the sampling and adjustment module and then input into the status evaluation module.

[0084] The condition evaluation module generates corresponding condition evaluation results based on line load, voltage operating status, and temperature rise of circuit breaker contacts and conductive connection parts.

[0085] Furthermore, in the aforementioned condition evaluation process for pole-mounted vacuum circuit breakers, when the degree of change and abnormality of the current parameters relative to the normal operating state is high, while the degree of change of the temperature parameters is small, the preferred weighting module calculates the corresponding values ​​for the current parameters and temperature parameters respectively. , , The corresponding weights are obtained according to the weight calculation formula in Example 1.

[0086] At this point, the weight of the current parameter is higher than that of the temperature parameter. The weight sorting unit determines the current parameter as the state parameter with the highest ranking, and the primary and secondary influencing factor determination unit determines the current parameter as the primary influencing factor.

[0087] The control priority mapping unit determines the control priority based on the line operating status corresponding to the current anomaly, so that the control strategy is adjusted first for the current anomaly status; the temperature parameter continues to participate in the status monitoring as a secondary influencing factor.

[0088] Before the control strategy is output, the data calibration module verifies the data it is based on. If the execution conditions are met, the control command is output. If the execution conditions are not met, the data backtracking module returns to the data preprocessing process.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-functional measurement and control integration method for power systems, characterized in that, This includes acquiring multi-source measurement and control data during the operation of power equipment, including current detection data, voltage detection data, and temperature status data; The multi-source measurement and control data is preprocessed, and the processed measurement and control data is input into the multi-level source data fusion module; The multi-level source data fusion module performs time correlation analysis and fusion judgment on measurement and control data from different sources. When different data sources meet the fusion conditions, the corresponding data are fused to obtain the fusion result. When the fusion conditions are not met, the abnormal data source is sampled and adjusted, and the fusion judgment is performed again based on the re-collected data; Based on the fused data, the equipment operating status is evaluated to obtain the status evaluation results; Based on the state evaluation results, the weights of different state parameters are calculated by the optimization and weighting module to determine the influence priority of different state parameters and generate a control strategy. The data corresponding to the control strategy is calibrated, and when the execution conditions are met, a control command is output to control the power equipment.

2. The integrated method for multi-functional measurement and control of a power system according to claim 1, characterized in that: The multi-level source data fusion module includes a time tag extraction unit, a time deviation calculation unit, a time matching grouping unit, and a fusion condition judgment unit, wherein: The time tag extraction unit is used to obtain the acquisition time information corresponding to different measurement and control data; The time deviation calculation unit is used to calculate the time deviation between different data sources; The time matching grouping unit is used to group data that meet the time correlation requirements according to the time deviation results; The fusion condition judgment unit is used to determine whether the grouped data meets the fusion processing conditions.

3. The integrated method for multi-functional measurement and control of a power system according to claim 2, characterized in that: The time deviation calculation unit uses the following formula to calculate the time deviation between different data sources: Formula code: ； in: Indicates the first The data source and the first Time skew between data sources; Indicates the first The collection time tags corresponding to each data source; Indicates the first The collection time tags corresponding to each data source; when: At that time, determine whether the corresponding data source meets the time consistency requirements; in: This indicates the preset time consistency threshold.

4. The integrated method for multi-functional measurement and control of a power system according to claim 2, characterized in that: The multi-level source data fusion module also includes an abnormal data source identification and marking unit, a sampling adjustment request generation unit, and a corresponding data source resampling unit; When the fusion condition judgment unit determines that the data does not meet the fusion conditions, the abnormal data source is identified by the abnormal data source identification and marking unit, and the sampling parameters of the corresponding data source are adjusted by the sampling adjustment request generation unit so that the corresponding data source re-collects the measurement and control data. The re-acquired monitoring and control data is returned to the time stamp extraction unit for further fusion and judgment.

5. The integrated method for multi-functional measurement and control of a power system according to claim 2, characterized in that: After the fusion conditions are met, the multi-level source data fusion module determines the data quality status of the data participating in the fusion through the data source credibility determination unit, and fuses the measurement and control data in the same time group through the same group data fusion unit, and outputs the fusion result.

6. The integrated method for multi-functional measurement and control of a power system according to claim 1, characterized in that: The preferred weighting module includes a state parameter feature extraction unit, a change degree calculation unit, an anomaly degree calculation unit, a state influence degree calculation unit, a state parameter weight synthesis unit, and a weight sorting unit; in: The state parameter feature extraction unit is used to extract the corresponding measurement and control parameter features from the state evaluation results; The degree of change calculation unit is used to calculate the degree of change of each state parameter relative to the normal operating state; The anomaly degree calculation unit is used to calculate the degree of abnormal deviation of each state parameter; The state influence degree calculation unit is used to determine the degree of influence of each state parameter on the equipment operation state evaluation results.

7. The integrated method for multi-functional measurement and control of a power system according to claim 6, characterized in that: The state parameter weighting synthesis unit calculates the corresponding weights based on the degree of change, anomaly, and impact of different state parameters, using the following formula: in: Indicates the first Each state parameter corresponds to a weight value; Indicates the first The degree of change of each state parameter; Indicates the first The degree of abnormality of each state parameter; Indicates the first The degree of influence of each state parameter on the state; This indicates the corresponding adjustment coefficient.

8. The integrated method for multi-functional measurement and control of a power system according to claim 1, characterized in that: The weight sorting unit sorts the parameters according to their corresponding weight values ​​and determines the primary and secondary influencing parameters. The control priority mapping unit establishes the correspondence between state parameters and control strategies based on the sorting results, and generates control priorities.

9. The integrated method for multi-functional measurement and control of a power system according to claim 1, characterized in that: The data calibration module is used to re-verify the fused data and status evaluation results before the control command is output, in order to determine whether the current data meets the control execution requirements.

10. A multi-functional measurement and control integration method for a power system according to claim 9, characterized in that: When the data calibration results do not meet the control execution requirements, the data backtracking module returns to the data preprocessing process, reprocesses the corresponding measurement and control data, and re-executes the data fusion, status evaluation, and control strategy generation processes.

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