Online updated power quality data adaptive acquisition system and method

Abstract

The invention discloses an on-line updated power quality data adaptive acquisition system and method, and relates to the technical field of data acquisition, and the system comprises the steps: obtaining a risk alarm log of a molded case circuit breaker, and mining a periodic staggered fundamental wave sampling frequency; constructing a zero-crossing comparator; cooperating with the zero-crossing comparator and the metering device to carry out electric data acquisition to obtain acquired electric data; and transmitting the acquired electric data to a data center based on a communication interface, and performing electric energy quality evaluation and data updating. The technical problems that in the prior art, the sampling frequency of the electric data of the molded case circuit breaker lacks pertinence, the electric energy quality evaluation is not accurate and comprehensive enough, and the data transmission and processing efficiency is low are solved, and accurate sampling, efficient transmission and comprehensive electric energy quality evaluation of the electric data of the molded case circuit breaker are achieved. And the accuracy and reliability of operation monitoring of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data acquisition, and in particular to an online updated power quality data adaptive acquisition system and method. Background Art

[0002] During the operation of the power system, the molded case circuit breaker is a key protection and control device. The accurate collection and analysis of its electrical data is crucial to ensure the stable operation of the power system. Traditional technologies have many defects in the collection of electrical data of molded case circuit breakers. On the one hand, the sampling frequency often uses a fixed value, which fails to fully consider the complex working conditions and characteristics of different electrical data during the operation of the molded case circuit breaker. It is impossible to accurately capture the key information when power quality problems such as harmonics and voltage sag occur, resulting in inaccurate evaluation of power quality and difficulty in meeting the needs of modern power systems for refined management of power quality. On the other hand, the traditional data acquisition system lacks an efficient coordination mechanism with the subsequent data processing links, and the data transmission efficiency is low. In addition, it is difficult to quickly and accurately update and store a large amount of electrical data, which makes it difficult for operation and maintenance personnel to obtain the latest and effective power quality information in a timely manner, and it is impossible to discover potential power system failure hazards in a timely manner, which seriously affects the reliability of the power system.

[0003] The existing technology has technical problems such as the lack of specificity in the sampling frequency of electrical data of molded case circuit breakers, the inaccurate and comprehensive evaluation of power quality, and the low efficiency of data transmission and processing. Summary of the invention

[0004] The present application provides an online updated power quality data adaptive acquisition system and method, which is used to solve the technical problems in the prior art that the electrical data sampling frequency of molded case circuit breakers lacks specificity, the power quality assessment is not accurate and comprehensive, and the data transmission and processing efficiency is low.

[0005] In view of the above problems, the present application provides an online updated power quality data adaptive collection system and method.

[0006] The first aspect of the present application is an online updated power quality data adaptive acquisition system, the system comprising:

[0007] The module for acquiring the staggered fundamental sampling frequency is used to acquire the risk alarm log of the molded case circuit breaker and mine the periodic staggered fundamental sampling frequency, wherein there is a one-to-one correspondence between the electrical data and the staggered fundamental sampling frequency; the module for constructing a zero-crossing comparator is used to configure the first sampling layer based on the staggered fundamental sampling frequency, configure the second sampling layer based on the grid frequency fluctuation, and construct a zero-crossing comparator according to the first sampling layer and the second sampling layer, wherein the fundamental frequency is the zero point and the sampling frequency is adjusted with the zero-crossing amplitude; the module for acquiring collected electrical data is used to coordinate the zero-crossing comparator and the metering device to collect electrical data and acquire collected electrical data, wherein the zero-crossing comparator and the metering device are built in the molded case circuit breaker; the module for updating the data is used to transmit the collected electrical data to the data center based on the communication interface to perform power quality assessment and data update.

[0008] A second aspect of the present application provides an online updated power quality data adaptive collection method, the method comprising:

[0009] The risk alarm log of the molded case circuit breaker is obtained, and the periodic staggered fundamental sampling frequency is mined, wherein there is a one-to-one correspondence between the electrical data and the staggered fundamental sampling frequency; a first sampling layer is configured based on the staggered fundamental sampling frequency, and a second sampling layer is configured based on the power grid frequency fluctuation; a zero-crossing comparator is constructed according to the first sampling layer and the second sampling layer, wherein the fundamental frequency is taken as the zero point and the sampling frequency is adjusted with the zero-crossing amplitude; the zero-crossing comparator and the metering device are coordinated to collect electrical data and obtain the collected electrical data, wherein the zero-crossing comparator and the metering device are built in the molded case circuit breaker; based on the communication interface, the collected electrical data is transmitted to the data center to perform power quality assessment and data update.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Obtain the risk alarm log of the molded case circuit breaker and mine the periodic staggered fundamental sampling frequency; construct a zero-crossing comparator; coordinate the zero-crossing comparator with the metering device to collect electrical data and obtain the collected electrical data; based on the communication interface, transmit the collected electrical data to the data center for power quality assessment and data update. The accurate sampling, efficient transmission and comprehensive power quality assessment of the electrical data of the molded case circuit breaker are achieved, and the accuracy and reliability of the power system operation monitoring are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of the structure of an online updated power quality data adaptive acquisition system provided in an embodiment of the present application;

[0014] Figure 2 A flow chart of an online updated power quality data adaptive collection method provided in an embodiment of the present application.

[0015] Description of the reference numerals: time-shifted fundamental wave sampling frequency acquisition module 10 , zero-crossing comparator construction module 20 , collected electrical data acquisition module 30 , collected electrical data acquisition module 40 . DETAILED DESCRIPTION

[0016] The present application provides an online updated power quality data adaptive acquisition system and method, which is used to solve the technical problems in the prior art that the electrical data sampling frequency of molded case circuit breakers lacks specificity, the power quality assessment is not accurate and comprehensive, and the data transmission and processing efficiency is low.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides an online updated power quality data adaptive acquisition system, the system comprising:

[0019] The staggered fundamental wave sampling frequency acquisition module 10 is used to obtain the risk alarm log of the molded case circuit breaker and mine the periodic staggered fundamental wave sampling frequency, wherein there is a one-to-one correspondence between the electrical data and the staggered fundamental wave sampling frequency.

[0020] Specifically, the risk alarm log of the molded case circuit breaker is obtained, and then the event is clustered to determine N groups (N≥1) of alarm events. Then, these alarm events are traversed, and the relevant electrical data analysis is performed for each group of events to determine the fundamental sampling frequency, thereby obtaining N groups of fundamental sampling frequencies. After that, these N groups of frequencies are integrated. In this process, for each group of fundamental sampling frequencies, a group of alarm events is first extracted and the relevant electrical data is determined. The critical alarm state of the group of alarm events is set as the fundamental scenario, and then the periodic sampling frequency of the electrical data in this scenario is determined as the fundamental sampling frequency (and the synchronization frequency of the electrical data). Then, the N groups of fundamental sampling frequencies are integrated, and the frequency clustering is performed according to the attribution of the electrical data to obtain M frequency groups corresponding to the electrical data one by one. Then the first frequency group of the first electrical data is extracted, and the minimum frequency in the group is taken as the fundamental sampling frequency of the electrical data. In this way, the M frequency groups are traversed to determine the M fundamental sampling frequencies, and finally the mapping of the M fundamental sampling frequencies and the electrical data is established and integrated to determine the staggered fundamental sampling frequency, so as to ensure that each electrical data corresponds to a fundamental sampling frequency.

[0021] The zero-crossing comparator construction module 20 configures the first sampling layer based on the staggered fundamental sampling frequency, configures the second sampling layer based on the grid frequency fluctuation, and constructs a zero-crossing comparator based on the first sampling layer and the second sampling layer, wherein the fundamental frequency is taken as the zero point and the sampling frequency is adjusted with the zero-crossing amplitude.

[0022] Specifically, the first sampling layer is carefully configured according to the staggered fundamental sampling frequency determined by the staggered fundamental sampling frequency acquisition module. This process requires the relevant parameters of the staggered fundamental sampling frequency to be accurately integrated into the settings of the first sampling layer to ensure that it can sample the electrical data as expected. At the same time, the power grid frequency fluctuation is also closely monitored, and the second sampling layer is configured based on this. The acquisition and analysis of power grid frequency fluctuation information is a key step, and its dynamic change characteristics must be accurately grasped. After completing the configuration of the first sampling layer and the second sampling layer, a zero-crossing comparator is constructed based on the characteristics and parameters of the two sampling layers. In this comparator, the fundamental frequency is explicitly used as the zero point, which is a crucial benchmark. By using the zero-crossing scale to flexibly adjust the sampling frequency, it can adapt to different electrical data acquisition needs, ensure that the collected electrical data has high accuracy and effectiveness, and better reflect the actual operating status of the power grid.

[0023] The electrical data acquisition module 30 is used to cooperate with the zero-crossing comparator and the metering device to collect electrical data and obtain the collected electrical data, wherein the zero-crossing comparator and the metering device are built in the molded case circuit breaker.

[0024] Specifically, the acquisition module 30 for collecting electrical data undertakes the important task of coordinating the zero-crossing comparator and the metering device to collect electrical data. Since the zero-crossing comparator and the metering device are both built into the molded case circuit breaker, it is necessary to first establish an effective communication and cooperation mechanism with the zero-crossing comparator. The zero-crossing comparator issues corresponding instructions and signals according to the rules and parameters set by it, guiding the key elements such as the start, stop and frequency of the acquisition process. At the same time, it works closely with the metering device, which is responsible for the actual measurement and recording of the electrical data. It has high-precision measurement capabilities and can accurately obtain various parameters of the electrical data. The acquisition module 30 for collecting electrical data plays a bridging and coordinating role between the two, ensuring the smooth progress of the entire acquisition process, and finally obtaining the collected electrical data that meets the requirements, which will provide a basis for subsequent power quality assessment and analysis.

[0025] The data updating module 40 transmits the collected electrical data to the data center based on the communication interface to perform power quality assessment and data updating.

[0026] Specifically, the data update module 40 transmits the collected electrical data acquired by the collected electrical data acquisition module to the data center accurately based on a stable and reliable communication interface. The communication interface ensures the integrity and accuracy of the data transmission process to avoid data loss or errors. In the data center, a comprehensive power quality assessment will be carried out on the received collected electrical data. This assessment process covers an in-depth analysis of multiple key parameters of the electrical data, such as voltage, current, power factor, etc., to determine whether the power quality meets the standards. At the same time, the relevant data is updated based on the assessment results. This update not only includes the correction and improvement of existing historical data records in the database, but may also involve the generation of new reports and statistical information, thereby providing accurate and reliable data support for subsequent further analysis and decision-making.

[0027] In a possible implementation, the time-staggered fundamental wave sampling frequency acquisition module 10 includes:

[0028] Event clustering is performed on the risk alarm log to determine N groups of alarm events, where N is a positive integer greater than or equal to 1.

[0029] The N groups of alarm events are traversed, event-related electrical data analysis and fundamental wave sampling frequency determination are performed, and N groups of fundamental wave sampling frequencies are obtained.

[0030] The N groups of fundamental wave sampling frequencies are integrated to determine the staggered fundamental wave sampling frequency, wherein each electrical data corresponds to a fundamental wave sampling frequency.

[0031] Among them, the fundamental wave sampling frequency is obtained by:

[0032] A set of alarm events is extracted, and electrical data related to the events is determined as a set of electrical data.

[0033] The critical alarm state of a group of alarm events is taken as a fundamental wave scenario, and the periodic sampling frequency of the group of electrical data under the fundamental wave scenario is determined as a group of fundamental wave sampling frequencies, wherein the group of fundamental wave sampling frequencies is an electrical data synchronization frequency.

[0034] Specifically, event clustering of risk alarm logs is a key step, which aims to extract valuable information from complex log data. By adopting a suitable clustering algorithm, such as the K-Means algorithm based on distance measurement, the alarm events in the log are analyzed and classified. According to various characteristics of the alarm event, such as alarm time, alarm type, changes in related electrical parameters, etc., similar alarm events are clustered together to determine N groups of alarm events, where N is a positive integer greater than or equal to 1. This process can better understand the distribution pattern and internal laws of alarm events, and provide a basis for subsequent analysis and processing.

[0035] After completing the clustering of alarm events and determining N groups of alarm events, each group of alarm events needs to be analyzed and processed in detail. First, for each group of alarm events, extract the electrical data related to the event, which contains various parameter information such as voltage, current, and power. Then, conduct an in-depth analysis of these electrical data, including observing their changing trends over time, analyzing the correlation between different parameters, and identifying possible periodic characteristics. Based on these analysis results, further determine the fundamental wave sampling frequency corresponding to each group of alarm events. Since each group of alarm events has its own unique electrical data characteristics and changing rules, a specific fundamental wave sampling frequency can be obtained for each group, and finally N groups of fundamental wave sampling frequencies are obtained.

[0036] In the process of integrating N groups of fundamental sampling frequencies to determine the staggered fundamental sampling frequencies, the key is to clarify that each electrical data corresponds to a suitable fundamental sampling frequency. To obtain the fundamental sampling frequency, specific steps must be followed.

[0037] Extract a group of alarm events from all alarm events. This step requires clear classification and identification capabilities for alarm events in order to accurately select a representative group. After selecting a group of alarm events, it is necessary to dig deep into the electrical data associated with the events. These electrical data cover multiple aspects such as voltage, current, and power. Together, they constitute a group of electrical data, which is the basis for subsequent analysis. Next, the critical alarm state of this group of alarm events is determined as the fundamental wave scenario. This fundamental wave scenario is an important reference standard. It represents an idealized state in which the changes in electrical data have certain regularity. However, the actual scenario is often different from the fundamental wave scenario, which requires flexible adjustment of the sampling frequency according to the differences between the actual scenario and the fundamental wave scenario. The staggered sampling mentioned here is a core concept, which emphasizes that for different electrical data, an adaptive setting method should be adopted instead of synchronous sampling. Each electrical data has its own period standard. When this standard is met, the electrical data will automatically trigger sampling. This adaptive sampling method can cleverly avoid the large amount of data collection and processing problems that may occur during synchronous sampling, and improve the efficiency and accuracy of data collection. Finally, in the fundamental wave scenario, the periodic sampling frequency of a set of electrical data is determined through a detailed analysis. This frequency can reflect the periodic change law of the electrical data over time in the fundamental wave scenario, so it is defined as a set of fundamental wave sampling frequencies, and it is the synchronization frequency of the electrical data. The fundamental wave sampling frequency obtained in this way provides important basic data for the subsequent integration of N sets of frequencies to determine the staggered fundamental wave sampling frequency.

[0038] In a possible implementation, the time-staggered fundamental wave sampling frequency acquisition module 10 includes:

[0039] The N groups of fundamental wave sampling frequencies are integrated, and frequency clustering is performed based on the attribution of the electrical data to determine M frequency groups, wherein the M frequency groups correspond to the electrical data one by one.

[0040] A first frequency group of the first electrical data is extracted, and the minimum frequency in the group is taken as the fundamental wave sampling frequency of the first electrical data.

[0041] The M frequency groups are traversed to determine M fundamental wave sampling frequencies.

[0042] A mapping between the M fundamental wave sampling frequencies and electrical data is established and integrated as the staggered fundamental wave sampling frequency.

[0043] Specifically, after determining the N groups of fundamental sampling frequencies, these frequencies need to be further processed. The first is the integration operation, which integrates these N groups of fundamental sampling frequencies to form a set containing all frequency information. Then, frequency clustering is performed based on the important criterion of electrical data attribution. During the clustering process, the electrical data features corresponding to each fundamental sampling frequency are analyzed, and the frequencies are grouped according to the similarity of these features. In this way, M frequency groups are finally determined. In this process, it is necessary to ensure that each frequency group corresponds one-to-one to specific electrical data, that is, one frequency group corresponds to only one electrical data, and one electrical data corresponds to only one frequency group. This one-to-one correspondence is the key basis for the subsequent accurate determination of the fundamental sampling frequency of each electrical data, and it also provides a guarantee for the precise operation of the entire system.

[0044] After frequency clustering has been completed based on the attribution of electrical data and M frequency groups have been determined, specific operations are carried out for the first electrical data. First, the first frequency group corresponding to the first electrical data is accurately extracted from all frequency groups. This extraction process is based on the one-to-one correspondence between the frequency group and the electrical data established previously. Then, all frequency values ​​in the extracted first frequency group are compared and analyzed. Among these frequency values, the minimum frequency is selected as the fundamental sampling frequency of the first electrical data. The reason for selecting the minimum frequency is based on a comprehensive consideration of multiple factors such as the accuracy, stability and overall system performance of electrical data acquisition. In this way, a specific fundamental sampling frequency is determined for the first electrical data, providing an accurate frequency basis for subsequent operations such as power quality assessment and data acquisition.

[0045] After determining the M frequency groups, it is necessary to perform a traversal operation on each frequency group. Starting from the first frequency group, a fundamental sampling frequency is determined for each frequency group. In this process, for each frequency group, it is necessary to analyze the characteristics of the frequency within the group, the relationship with the corresponding electrical data, and other related factors. By performing such processing on each frequency group, M fundamental sampling frequencies can be determined in the end. These fundamental sampling frequencies will provide key basic data for the subsequent establishment of a mapping relationship with electrical data and the determination of the staggered fundamental sampling frequency.

[0046] After determining the M fundamental sampling frequencies, we will start to establish the mapping relationship between them and the electrical data. This process requires accurately matching each electrical data with the corresponding fundamental sampling frequency to ensure one-to-one correspondence and accuracy. Through in-depth analysis of the characteristics, sources, and role of electrical data in the system, combined with the acquisition background and applicable conditions of the fundamental sampling frequency, the precise mapping of the two is completed. Once all the mapping relationships are established, these mapping relationships are integrated to form a complete, staggered fundamental sampling frequency system that is closely adapted to each electrical data. This system can fully take into account the individual needs of different electrical data. In actual operation, the corresponding fundamental sampling frequency is automatically called according to different electrical data conditions, thereby providing an accurate and reliable frequency basis for the power quality assessment, data acquisition, and subsequent analysis and processing of the entire system, ensuring the efficient and stable operation of the system.

[0047] In a possible implementation, the electrical data acquisition module 30 includes:

[0048] The power quality data of the upper acquisition node is called and transmitted to the first sampling layer of the zero-crossing comparator.

[0049] The power quality data is checked against the fundamental wave scenario to determine a fundamental wave scenario deviation.

[0050] Based on the fundamental wave scene deviation, a sampling adjustment ratio is determined.

[0051] Based on the sampling adjustment ratio, the fundamental wave sampling frequency of each electrical data in the staggered fundamental wave sampling frequency is proportionally adjusted to determine a lower-level acquisition node, wherein the lower-level acquisition node corresponds to the electrical data one-to-one.

[0052] Specifically, first, the data collection process is started to obtain power quality data from the upper collection node. These data will be completely transmitted to the first sampling layer of the zero-crossing comparator to provide raw materials for subsequent processing. Then, the proofreading phase is entered to carefully compare the received power quality data with the preset fundamental scenario data. For each data point, the difference between it and the corresponding point in the fundamental scenario is calculated, and these differences are comprehensively processed to determine the fundamental scenario deviation. The square of the difference of each data point is first calculated, and then all the squares of the differences are summed and squared to obtain a value that can quantify the degree of deviation between the two. This value is the fundamental scenario deviation.

[0053] After obtaining the fundamental scene deviation, the sampling adjustment ratio is determined according to the system's pre-set rules. This is a functional relationship based on experience or verified by a large number of experiments. For example, if the deviation is small, the sampling adjustment ratio is also relatively small; as the deviation increases, the sampling adjustment ratio increases, and the increase may follow a linear or nonlinear rule, so as to flexibly adjust the subsequent sampling frequency.

[0054] Then, based on the determined sampling adjustment ratio, the fundamental sampling frequency corresponding to each electrical data in the existing staggered fundamental sampling frequency is adjusted, and the original fundamental sampling frequency is multiplied by a coefficient related to the sampling adjustment ratio to obtain a new fundamental sampling frequency, so that the sampling frequency can better adapt to the actual situation of the power quality data.

[0055] In determining the sampling periodic nodes, based on the data of the first sampling layer, by calculating the difference between the real-time power quality data and the fundamental wave scenario data, the fundamental wave periodic frequency is adjusted using this difference. For example, if the difference shows that the current power quality data fluctuates greatly and deviates greatly from the fundamental wave scenario, the fundamental wave periodic frequency is appropriately shortened, otherwise it is appropriately extended. Through such adjustments, a relatively large sampling range, i.e., the sampling periodic nodes, is determined so that enough power quality data can be obtained within this range, thereby being able to fully reflect the static characteristics and dynamic fluctuations of the electric data, and provide rich information for subsequent in-depth analysis.

[0056] After the periodic node determined based on the first sampling layer is determined, the actual sampling operation begins at the node. Each time zone originally has a preset baseline sampling number, which is the amount of data planned to be collected under normal power grid conditions. However, the system will monitor the stability of the power grid frequency in real time. Once it is found that the power grid frequency fluctuates greatly and exceeds the preset stable range, in order to reduce the measurement error caused by frequency fluctuations, the number of samples will be increased, such as using high-speed sampling to increase the sampling data points in the time zone, so as to more accurately capture the changes in power quality data. Through the above series of rigorous and orderly steps, the lower-level acquisition nodes corresponding to each electrical data are finally determined. These lower-level acquisition nodes will collect power quality data according to the adjusted sampling frequency and sampling strategy, providing a solid and reliable data foundation and strong support for the subsequent power quality assessment, fault diagnosis and optimization control of the entire system.

[0057] In a possible implementation, the electrical data acquisition module 30 includes:

[0058] In combination with the second sampling layer, sampling triggering of the electrical data and adjustment of the second sampling frequency are performed according to the lower-level acquisition node to obtain the collected electrical data.

[0059] The triggering of sampling of electrical data and adjusting the second sampling frequency include:

[0060] When the lower sampling node of the first electrical data is reached, the grid frequency fluctuation is collected.

[0061] Based on the grid frequency fluctuation, the second sampling frequency in the sampling time zone corresponding to the lower sampling node is adjusted to determine the second adjusted sampling frequency, wherein the greater the grid frequency fluctuation, the greater the number of time zone samplings.

[0062] Based on the second adjusted sampling frequency, the metering device is controlled to perform first electrical data collection.

[0063] Specifically, when entering the electrical data collection link, the work is carried out in close connection with the second sampling layer in the system. First, the sampling action of electrical data is triggered in a timely manner based on the key information provided by the previously accurately determined lower-level collection node. In this process, the adjustment of the second sampling frequency plays a key role. Once it is determined that the lower-level sampling node of specific electrical data has been reached, the real-time monitoring and data collection of the power grid frequency fluctuation condition is immediately started. This is because the power grid frequency fluctuation condition is directly related to the degree of change in the power quality, which is an important basis for the subsequent reasonable adjustment of the sampling frequency. Based on the acquired power grid frequency fluctuation data, the second sampling frequency in the sampling time zone corresponding to the lower-level sampling node is quickly adjusted flexibly and accurately. For example, if the power grid frequency fluctuation is significantly increased, this indicates that the power quality has changed dramatically during this period. In order to be able to fully and accurately capture these subtle changes, the number of samples in this time zone is automatically increased, the sampling frequency is increased, and the most suitable second adjustment sampling frequency is determined. Finally, based on the determined second adjustment sampling frequency, precise control instructions are sent to the metering device responsible for data collection, so that it can efficiently perform the electrical data collection task according to the new sampling frequency, thereby successfully acquiring the collected electrical data that meets the requirements, providing a solid data foundation for the stable operation of the entire power system and subsequent in-depth analysis.

[0064] When the system detects that it has reached the lower sampling node of the first electrical data, it quickly starts the collection of power grid frequency fluctuations. In this process, the sliding window averaging method is used to collect power grid frequency values ​​at fixed intervals (such as 10 milliseconds) within a time window of a specific duration (such as 100 milliseconds). In this way, the total duration is divided by the interval duration to determine the number of frequency values ​​collected in this window, and these collected values ​​will be used for subsequent calculations. During the calculation, the average frequency in the window is first calculated, and then the frequency standard deviation is calculated to accurately quantify the power grid frequency fluctuations.

[0065] Then, according to the obtained quantitative index of power grid frequency fluctuation, the second sampling frequency in the sampling time zone corresponding to the lower sampling node is adjusted. Here, the established rules are followed: if the standard deviation of frequency fluctuation is less than the preset first threshold (such as 0.1 Hz), the original second sampling frequency is kept unchanged, because the power grid is relatively stable at this time, and there is no need to increase the sampling density; when the standard deviation is greater than the threshold and less than the second threshold (such as 0.5 Hz), a linear adjustment algorithm is used, and the new second adjustment sampling frequency is equal to the original frequency multiplied by a linear growth coefficient related to the standard deviation. The sampling frequency can be moderately increased to cope with a certain degree of fluctuation; once the standard deviation exceeds the second threshold, the exponential adjustment algorithm is enabled to allow the second adjustment sampling frequency to increase exponentially, ensuring that enough data can be collected when the power grid fluctuates greatly, and ensuring the integrity and accuracy of the data.

[0066] Finally, according to the determined second adjustment sampling frequency, the system sends precise control instructions to the metering device. After receiving the instructions, the metering device strictly collects the first electrical data in accordance with the time interval corresponding to the new sampling frequency, thereby ensuring the quality of the collected electrical data is reliable, providing solid data support for subsequent power system-related analysis and operations, and ensuring the stable operation and efficient management of the entire power system.

[0067] In a possible implementation, the data updating module 40 includes:

[0068] The collected electrical data is timestamped and transmitted to the data center based on the communication interface.

[0069] The collected power data is traversed, static power quality assessment and dynamic power quality assessment are performed, and a first power quality and a second power quality are determined.

[0070] The first power quality and the second power quality are used as power quality data.

[0071] Specifically, when the electrical data is successfully collected, the timestamp identification program is quickly started, and the corresponding collection timestamp will be accurately attached to each collected electrical data. This timestamp is generated based on a high-precision clock system, which can be accurate to milliseconds or even microseconds to ensure the accurate recording of the data collection moment, clearly mark the time point when the data is generated, and provide a key time dimension reference for the subsequent data traceability, analysis, and synchronization comparison with other data. After completing the timestamp identification, call the pre-configured communication interface, which has high-speed, stable and reliable data transmission capabilities and can adapt to different network environments and data transmission protocols. The system packages the collected electrical data with timestamps and quickly pushes the data to the data center through the communication interface according to the established communication protocol format. During the transmission process, the communication interface will monitor the data transmission status in real time to ensure that the data arrives at the data center intact and without error, realize the efficient flow of data from the collection end to the storage end, and lay a solid foundation for subsequent centralized management and in-depth analysis.

[0072] Traversing the collected electrical data, for static power quality assessment, the established standards and specifications are mainly used to calculate the effective values ​​of voltage and current and harmonic content through specific formulas to measure its stability. The root mean square algorithm is used to calculate the effective value, and the fast Fourier transform is used to analyze the harmonics. On this basis, a comprehensive judgment is made to determine the first power quality that reflects the power quality in a stable state. At the same time, for dynamic power quality assessment, appropriate monitoring windows and thresholds are set to capture the characteristic parameters of events such as voltage sags, swells, fluctuations, and flickers, such as recording the duration and amplitude changes of transient events. These dynamic changes are integrated and evaluated to determine the second power quality that characterizes the quality of electricity during dynamic changes. Through these two evaluation methods, the different aspects of power quality can be fully and accurately grasped, providing a key basis for subsequent power system optimization and operation decisions.

[0073] After the determination of the first power quality and the second power quality is completed, the next key step is to integrate the two as complete power quality data. The first power quality is based on the static characteristic analysis of the electric data. It covers key indicators such as the effective value of voltage and current and harmonic content. These indicators reflect the power quality of the power system under stable operation and provide a basic quality reference for the long-term stable operation of the system. The second power quality is determined by monitoring and analyzing dynamic events such as voltage sag, swell, fluctuation and flicker. The parameters such as the duration and amplitude change of transient events included in it reflect the power quality performance of the power system when dealing with instantaneous interference and dynamic load changes. The organic combination of these two aspects can form a comprehensive and accurate picture of the power quality of the power system. It is of vital importance for the daily operation and maintenance of the power system, troubleshooting, future optimization and upgrading, and planning decisions, and provides solid data support and decision-making basis for the efficient and reliable operation of the power system.

[0074] In a possible implementation, the data updating module 40 includes:

[0075] Determine the fundamental power quality in the fundamental scenario.

[0076] Based on the fundamental power quality, a risk assessment is performed on the power quality data to determine a risk assessment result.

[0077] If the risk assessment result is yes, an electric energy alarm message is generated to issue an alarm.

[0078] If the risk assessment result is no, the data storage is updated.

[0079] Specifically, the key parameters of the fundamental wave, such as voltage, current, and frequency, are accurately measured, and based on strict power quality standards, the deviation range of the effective value of the fundamental wave voltage, the proportion of harmonic content of the fundamental wave current, the frequency stability and other indicators are calculated, thereby comprehensively and accurately describing the actual situation of the fundamental wave power quality.

[0080] After clarifying the fundamental power quality in the fundamental scenario, the risk assessment process of power quality data is immediately started. First, the various parameters in the power quality data, such as voltage amplitude, current distortion rate, frequency stability, and three-phase imbalance, are carefully compared with the standard reference interval established based on the fundamental power quality. For example, for the voltage amplitude, if it deviates from the normal fluctuation range of the fundamental voltage amplitude to a certain extent, the corresponding risk score will be added according to the pre-set risk weight; similarly, when the current distortion rate exceeds the harmonic range allowed by the fundamental current, the risk score will also be increased according to the established rules. At the same time, considering factors such as the duration of these parameter anomalies and the frequency of anomalies, quantitative calculations are performed through a comprehensive risk assessment algorithm. When the accumulated risk score exceeds the preset risk threshold, the risk assessment result is determined to be yes, which means that there is a high risk in the power quality; conversely, if the risk score does not reach the threshold, the risk assessment result is determined to be no, indicating that the current power quality is in a relatively safe and stable state, so as to accurately determine the risk assessment result of the power quality data and provide a key basis for subsequent decision-making.

[0081] After completing the risk assessment of power quality data and determining the risk assessment results, different operation processes are executed according to the assessment results. If the risk assessment result is yes, it means that the current power quality is not good and there are potential risks, which may cause damage to the stable operation of the power system and various types of electrical equipment. At this time, the alarm mechanism is quickly activated to automatically generate detailed and accurate power alarm information, which covers key content such as the specific values ​​of abnormal parameters, the time point when the abnormality occurs, and the scope of possible impact. At the same time, this information is promptly conveyed to relevant personnel through a variety of alarm methods, such as popping up a striking warning pop-up window on the display screen of the monitoring center, highlighting the alarm content with bright colors and flashing effects; sending SMS notifications containing alarm details to the staff responsible for operation and maintenance to ensure that the information is obtained at the first time.

[0082] On the contrary, if the risk assessment result is negative, it indicates that the current power quality is in a normal and stable state and meets the established safety standards. In this case, the data storage and update operations are carried out in an orderly manner, and the power quality data collected this time is accurately archived in the historical database according to specific data structures and storage rules. These data will serve as an important basis for subsequent power quality analysis, trend prediction, and power system performance evaluation, providing strong data support for ensuring the continuous and stable operation of the power system, helping power operation and maintenance personnel to promptly discover potential problems and take preventive measures to ensure the reliability and safety of the entire power supply network.

[0083] Embodiment 2, based on the same inventive concept as an online updated power quality data adaptive acquisition system in the above embodiment, such as Figure 2As shown, the present application provides an online updated power quality data adaptive acquisition method, and the method in the embodiment of the present application and the system embodiment are based on the same inventive concept. The method includes:

[0084] Step S100: Obtain the risk alarm log of the molded case circuit breaker and mine the periodic off-time fundamental wave sampling frequency, wherein there is a one-to-one correspondence between the electrical data and the off-time fundamental wave sampling frequency.

[0085] Step S200: configuring a first sampling layer based on the staggered fundamental wave sampling frequency, configuring a second sampling layer based on the grid frequency fluctuation, and constructing a zero-crossing comparator based on the first sampling layer and the second sampling layer, wherein the fundamental wave frequency is used as the zero point and the sampling frequency is adjusted with the zero-crossing amplitude.

[0086] Step S300: Collecting electrical data in cooperation with the zero-crossing comparator and the metering device to obtain collected electrical data, wherein the zero-crossing comparator and the metering device are built into the molded case circuit breaker.

[0087] Step S400: Based on the communication interface, the collected electrical data is transmitted to the data center to perform power quality assessment and data update.

[0088] Furthermore, step S100 also includes:

[0089] Step S110: performing event clustering on the risk alarm log to determine N groups of alarm events, where N is a positive integer greater than or equal to 1.

[0090] Step S120: traverse the N groups of alarm events, perform event-related electrical data analysis and fundamental wave sampling frequency determination, and obtain N groups of fundamental wave sampling frequencies.

[0091] Step S130: Integrate the N groups of fundamental wave sampling frequencies to determine the staggered fundamental wave sampling frequency, wherein each electrical data corresponds to a fundamental wave sampling frequency.

[0092] Among them, the fundamental wave sampling frequency is obtained by:

[0093] A group of alarm events are extracted, and electrical data related to the events are determined as a group of electrical data.

[0094] The critical alarm state of a group of alarm events is taken as a fundamental wave scenario, and the periodic sampling frequency of the group of electrical data under the fundamental wave scenario is determined as a group of fundamental wave sampling frequencies, wherein the group of fundamental wave sampling frequencies is an electrical data synchronization frequency.

[0095] Furthermore, step S100 also includes:

[0096] Step S140: Integrate the N groups of fundamental wave sampling frequencies, perform frequency clustering based on the attribution of the electrical data, and determine M frequency groups, wherein the M frequency groups correspond one-to-one to the electrical data.

[0097] Step S150: extracting a first frequency group of first electrical data, and taking the minimum frequency in the group as the fundamental wave sampling frequency of the first electrical data.

[0098] Step S160: traverse the M frequency groups to determine M fundamental wave sampling frequencies.

[0099] Step S170: Establish a mapping between the M fundamental wave sampling frequencies and electrical data, and integrate them as the staggered fundamental wave sampling frequencies.

[0100] Furthermore, step S300 also includes:

[0101] Step S310: calling the power quality data of the upper acquisition node and transmitting it to the first sampling layer of the zero-crossing comparator.

[0102] Step S320: calibrate the power quality data and the fundamental wave scenario to determine the fundamental wave scenario deviation.

[0103] Step S330: Determine a sampling adjustment ratio based on the fundamental wave scene deviation.

[0104] Step S340: Based on the sampling adjustment ratio, the fundamental wave sampling frequency of each electrical data in the staggered fundamental wave sampling frequency is proportionally adjusted to determine a lower-level acquisition node, wherein the lower-level acquisition node corresponds to the electrical data one-to-one.

[0105] Furthermore, step S340 further includes:

[0106] Step S341: combining the second sampling layer and according to the lower-level acquisition node, performing sampling triggering of the electrical data and adjusting the second sampling frequency to obtain the acquired electrical data.

[0107] Step S342: wherein the triggering of sampling of electrical data and the adjustment of the second sampling frequency include:

[0108] When the lower sampling node of the first electrical data is reached, the grid frequency fluctuation is collected.

[0109] Based on the grid frequency fluctuation, the second sampling frequency in the sampling time zone corresponding to the lower sampling node is adjusted to determine the second adjusted sampling frequency, wherein the greater the grid frequency fluctuation, the greater the number of time zone samplings.

[0110] Based on the second adjusted sampling frequency, the metering device is controlled to perform first electrical data collection.

[0111] Furthermore, step S400 further includes:

[0112] Step S410: marking the collected electrical data with a collection timestamp, and transmitting the collected electrical data to the data center based on the communication interface.

[0113] Step S420: traverse the collected power data, perform static power quality assessment and dynamic power quality assessment, and determine the first power quality and the second power quality.

[0114] Step S430: taking the first power quality and the second power quality as power quality data.

[0115] Furthermore, step S400 further includes:

[0116] Step S440: Determine the fundamental wave power quality in the fundamental wave scenario.

[0117] Step S450: Based on the fundamental power quality, a risk assessment is performed on the power quality data to determine a risk assessment result.

[0118] Step S460: If the risk assessment result is yes, generate power alarm information for warning.

[0119] Step S470: If the risk assessment result is no, update the data storage.

[0120] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0122] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An online updated power quality data adaptive acquisition system, characterized in that: The system comprises: The module for acquiring the time-staggered fundamental wave sampling frequency is used to acquire the risk alarm log of the molded case circuit breaker and mine the periodic time-staggered fundamental wave sampling frequency, wherein there is a one-to-one correspondence between the electrical data and the time-staggered fundamental wave sampling frequency; A zero-crossing comparator construction module is configured to configure a first sampling layer based on the staggered fundamental wave sampling frequency, configure a second sampling layer based on the power grid frequency fluctuation, and construct a zero-crossing comparator based on the first sampling layer and the second sampling layer, wherein the fundamental wave frequency is used as the zero point and the sampling frequency is adjusted with the zero-crossing amplitude; An electrical data acquisition module is used to collect electrical data in cooperation with the zero-crossing comparator and the metering device to acquire collected electrical data, wherein the zero-crossing comparator and the metering device are built into the molded case circuit breaker; The data update module transmits the collected electrical data to the data center based on the communication interface to perform power quality assessment and data update.

2. The online updated power quality data adaptive acquisition system according to claim 1, characterized in that: The staggered fundamental wave sampling frequency acquisition module comprises: Performing event clustering on the risk alarm log to determine N groups of alarm events, where N is a positive integer greater than or equal to 1; Traversing the N groups of alarm events, performing event-related electrical data analysis and fundamental wave sampling frequency determination, and obtaining N groups of fundamental wave sampling frequencies; Integrate the N groups of fundamental wave sampling frequencies to determine the staggered fundamental wave sampling frequency, wherein each electrical data corresponds to a fundamental wave sampling frequency; Among them, the fundamental wave sampling frequency is obtained by: Extract a group of alarm events and determine electrical data related to the events as a group of electrical data; The critical alarm state of a group of alarm events is taken as a fundamental wave scenario, and the periodic sampling frequency of the group of electrical data under the fundamental wave scenario is determined as a group of fundamental wave sampling frequencies, wherein the group of fundamental wave sampling frequencies is an electrical data synchronization frequency.

3. The online updated power quality data adaptive acquisition system according to claim 2, characterized in that: The staggered fundamental wave sampling frequency acquisition module also includes: Integrate the N groups of fundamental wave sampling frequencies, perform frequency clustering based on the attribution of the electrical data, and determine M frequency groups, wherein the M frequency groups correspond one-to-one to the electrical data; Extracting a first frequency group of first electrical data, and taking the minimum frequency in the group as the fundamental wave sampling frequency of the first electrical data; Traversing the M frequency groups to determine M fundamental wave sampling frequencies; A mapping between the M fundamental wave sampling frequencies and electrical data is established and integrated as the staggered fundamental wave sampling frequency.

4. The online updated power quality data adaptive acquisition system according to claim 2, characterized in that: The electrical data acquisition module comprises: Calling the power quality data of the upper acquisition node and transmitting it to the first sampling layer of the zero-crossing comparator; Verifying the power quality data and the fundamental wave scenario to determine the fundamental wave scenario deviation; Determining a sampling adjustment ratio based on the fundamental wave scene deviation; Based on the sampling adjustment ratio, the fundamental wave sampling frequency of each electrical data in the staggered fundamental wave sampling frequency is proportionally adjusted to determine a lower-level acquisition node, wherein the lower-level acquisition node corresponds to the electrical data one-to-one.

5. The online updated power quality data adaptive acquisition system according to claim 4, characterized in that: The electrical data acquisition module also includes: In combination with the second sampling layer, according to the lower-level acquisition node, the sampling trigger of the electrical data and the second sampling frequency adjustment are performed to obtain the collected electrical data; The triggering of sampling of electrical data and adjusting the second sampling frequency include: When reaching the lower sampling node of the first electrical data, collecting the power grid frequency fluctuation; Based on the grid frequency fluctuation, the second sampling frequency in the sampling time zone corresponding to the lower sampling node is adjusted to determine the second adjusted sampling frequency, wherein the greater the grid frequency fluctuation, the greater the number of time zone samplings; Based on the second adjusted sampling frequency, the metering device is controlled to perform first electrical data collection.

6. The online updated power quality data adaptive acquisition system according to claim 1, characterized in that: The data updating module comprises: Performing a collection timestamp on the collected electrical data, and transmitting the collected electrical data to the data center based on the communication interface; Traversing the collected power data, performing static power quality assessment and dynamic power quality assessment, and determining a first power quality and a second power quality; The first power quality and the second power quality are used as power quality data.

7. The online updated power quality data adaptive acquisition system according to claim 6, characterized in that: The data updating module also includes: Determine the fundamental power quality in fundamental wave scenarios; Based on the fundamental power quality, performing a risk assessment on the power quality data to determine a risk assessment result; If the risk assessment result is yes, generating electric energy alarm information for warning; If the risk assessment result is no, the data storage is updated.

8. An online updated power quality data adaptive acquisition method, characterized in that: The method is implemented by an online updated power quality data adaptive acquisition system according to any one of claims 1 to 7, comprising: Obtain the risk alarm log of the molded case circuit breaker and mine the periodic off-time fundamental wave sampling frequency, where there is a one-to-one correspondence between the electrical data and the off-time fundamental wave sampling frequency; A first sampling layer is configured based on the staggered fundamental wave sampling frequency, a second sampling layer is configured based on the power grid frequency fluctuation, and a zero-crossing comparator is constructed according to the first sampling layer and the second sampling layer, wherein the fundamental wave frequency is used as the zero point and the sampling frequency is adjusted according to the zero-crossing amplitude; Cooperating with the zero-crossing comparator and the metering device to collect electrical data and acquire collected electrical data, wherein the zero-crossing comparator and the metering device are built into the molded case circuit breaker; Based on the communication interface, the collected electrical data is transmitted to the data center for power quality assessment and data update.

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