Cross-region power quality data aggregation and optimal control method and device

Abstract

The application relates to a cross-regional power quality data aggregation and optimal control method and device. The method comprises the following steps: collecting various power parameters in the power system of different power grid regions in real time through a multi-protocol adaptive interface of an edge intelligent control terminal, performing data preprocessing, and obtaining power system collection data of each power grid region; obtaining a power data aggregation result by fusing the power system collection data of each power grid region and adding a region label of each power grid region to the fused data; performing power risk assessment on the power quality index of each power grid region determined according to the power data aggregation result and the power flow coherence index of each power grid region, and obtaining a power abnormality evaluation result; generating a control strategy based on the power abnormality evaluation result, and sending the control strategy to the power system of each power grid region; and the control strategy is a strategy for differentiating power optimization control of different power grid regions. The method can improve the power quality collaborative control efficiency.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for cross-regional power quality data aggregation and optimization control. Background Technology

[0002] As new power systems develop towards high-proportion renewable energy integration and multi-energy complementarity, the scale of cross-regional power grid interconnection continues to expand. Power quality monitoring and collaborative control face challenges such as high heterogeneity of data from different regional power grids and insufficient real-time response under the volatility of new energy sources and the randomness of loads.

[0003] Traditional technologies typically employ centralized cloud processing, which requires the transmission of massive amounts of raw data. This approach struggles to meet the demands in terms of data throughput and latency, making it inadequate for handling sudden power outages. Furthermore, traditional power quality monitoring is often limited to independent monitoring in a single region, analyzing only the power parameters of that region. This makes it difficult to develop collaborative control strategies and accurately identify the root causes of cross-regional anomalies, thus hindering the improvement of the overall power quality of the power grid. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for cross-regional power quality data aggregation and optimization control to address the aforementioned technical problems.

[0005] In a first aspect, this application provides a method for cross-regional power quality data aggregation and optimization control, applied to an edge intelligent control terminal, the method comprising:

[0006] Through the multi-protocol adaptation interface of the edge intelligent control terminal, various power parameters in the power system of different power grid areas are collected in real time and data preprocessed to obtain power system data for each power grid area.

[0007] By fusing power system data collected from each of the aforementioned power grid regions and adding regional labels for each power grid region to the fused data, a power data aggregation result is obtained; the regional labels are used to mark the data source.

[0008] Based on the power quality indicators of each power grid region determined by the power data aggregation results, a power risk assessment is performed with the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions.

[0009] A control strategy is generated based on the power anomaly assessment results, and the control strategy is sent to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions.

[0010] In one embodiment, the step of performing a power risk assessment on the power quality indicators of each power grid region determined based on the power data aggregation results and the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result includes:

[0011] The reference information corresponding to each of the different power quality indicators is obtained for comparison of power quality indicators, and the normal state information of the power flow coherence indicators is obtained for comparison of power flow coherence indicators.

[0012] If an anomaly is detected in the power quality index comparison results, and / or an anomaly is detected in the power flow coherence index comparison results, the power anomaly assessment result is determined to indicate the presence of a power anomaly.

[0013] In one embodiment, the method further includes:

[0014] Based on the power quality indicators and historical data of each power grid region, a power quality indicator change function is generated.

[0015] Based on the power quality index change function, the predicted power quality index for each of the power grid regions is obtained.

[0016] The predicted power quality indicators for each power grid region are compared with the reference information to obtain the power risk prediction results.

[0017] In one embodiment, the method further includes:

[0018] Based on the number of power transmission nodes in any power grid region, determine the number of power transmission paths connecting the any power grid region to other power grid regions;

[0019] Based on the specified time period and the number of power transmission paths, the power flow coherence index of any power grid region is calculated.

[0020] In one embodiment, the method further includes:

[0021] When an abnormal decrease in the power flow coherence index is detected in multiple power grid regions, a target power grid region is determined from the multiple power grid regions; the target power grid region has the largest decrease in the power flow coherence index compared to the normal state information.

[0022] By analyzing the abnormal states of each power transmission path in the target power grid area, abnormal source control processing is performed.

[0023] In one embodiment, the method further includes:

[0024] Using power flow coherence indices for each power grid region under different detection periods, a time series vector of power flow coherence indices for each power grid region is constructed.

[0025] The target power anomaly type is determined based on the similarity between the time series vector and the abnormal sample sequence vector;

[0026] The preset control strategy corresponding to the target power anomaly type is used as the power optimization control strategy.

[0027] Secondly, this application also provides a cross-regional power quality data aggregation and optimization control device, applied to an edge intelligent control terminal, the device comprising:

[0028] The power data acquisition module is used to collect various power parameters in the power system of different power grid areas in real time through the multi-protocol adapter interface of the edge intelligent control terminal, perform data preprocessing, and obtain the power system acquisition data of each power grid area.

[0029] The power data aggregation module is used to fuse power system data collected from each of the power grid regions and add regional labels of each of the power grid regions to the fused data to obtain power data aggregation results; the regional labels are used to mark the data source.

[0030] The power anomaly assessment module is used to assess power risk based on the power quality indicators of each power grid region determined by the power data aggregation results and the power flow coherence indicators of each power grid region, and to obtain the power anomaly assessment results; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions;

[0031] A control strategy generation module is used to generate a control strategy based on the power anomaly assessment results and send the control strategy to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0033] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0034] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0035] The aforementioned cross-regional power quality data aggregation and optimization control method, device, computer equipment, computer-readable storage medium, and computer program product, through the multi-protocol adaptation interface of the edge intelligent control terminal, collect various power parameters in the power systems of different power grid regions in real time, perform data preprocessing, and obtain power system data for each power grid region. Then, by fusing the power system data collected from each power grid region and adding the regional labels of each power grid region to the fused data, a power data aggregation result is obtained. The regional labels are used to mark the data source. Based on the power quality indicators of each power grid region determined by the power data aggregation result, a power risk assessment is performed with the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result. The power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions. Then, a control strategy is generated based on the power anomaly assessment result and sent to the power systems of each power grid region. This control strategy is a strategy for differentiated power optimization control for different power grid regions. Thus, based on multi-protocol adaptation and edge-side data aggregation, unified collection and correlation analysis of heterogeneous power data across regions are realized, which can accurately identify power anomalies coupled between regions. Combined with dynamically updated power flow coherence indicators, differentiated control strategies can be generated, which can improve the real-time performance and accuracy of cross-regional power grid power quality collaborative control. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating a cross-regional power quality data aggregation and optimization control method in one embodiment;

[0038] Figure 2 This is a schematic diagram of the structure of an edge intelligent control terminal in one embodiment;

[0039] Figure 3 This is a schematic diagram of the power quality data aggregation and optimization control process in one embodiment;

[0040] Figure 4This is a schematic diagram illustrating the relationship between the power phase angle and reactive power and active power in one embodiment;

[0041] Figure 5 This is a schematic diagram of a control process based on power flow coherence index in one embodiment;

[0042] Figure 6 This is a flowchart illustrating a cross-regional power quality data aggregation and optimization control method in another embodiment;

[0043] Figure 7 This is a structural block diagram of a cross-regional power quality data aggregation and optimization control device in one embodiment;

[0044] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0046] In one exemplary embodiment, such as Figure 1 As shown, a method for cross-regional power quality data aggregation and optimization control is provided, applied to an edge intelligent control terminal. In this embodiment, the method includes steps 101 to 104. Wherein:

[0047] Step 101: Through the multi-protocol adaptation interface of the edge intelligent control terminal, various power parameters in the power system of different power grid areas are collected in real time and the data is preprocessed to obtain the power system data collected for each power grid area.

[0048] As an example, the edge intelligent control terminal can be configured with an interface module compatible with multiple communication protocols, namely a multi-protocol adapter interface. It can directly establish a connection with power equipment (such as sensors, smart meters, relay protection devices, etc.) that use different communication standards in various power grid areas. Without additional protocol conversion equipment, it can obtain various power parameters such as voltage, current, power, and harmonics in real time, which can ensure that the edge terminal can access cross-regional heterogeneous power data in a unified and efficient manner.

[0049] In practical applications, such as Figure 2 As shown, an edge intelligent control terminal may include a data acquisition module, a data aggregation module, an edge computing module, an optimization control module, and a communication interaction module. Figure 3As shown, the data acquisition module can be used to collect various power parameters in the power system of different power grid areas in real time. For example, the data acquisition module may include a multi-channel sampling unit and a data preprocessing unit. The multi-channel sampling unit can be used to collect various power parameters in each power system, and the data preprocessing unit can be used to perform data preprocessing such as filtering and noise reduction on the collected data.

[0050] Step 102: By fusing the power system data collected from each of the power grid regions and adding the regional labels of each of the power grid regions to the fused data, the power data aggregation result is obtained.

[0051] Among them, region labels can be used to mark the data source.

[0052] In specific implementations, such as Figure 3 As shown, the data aggregation module can be used to aggregate and label various collected power parameters. For example, the data aggregation module may include a synchronization scheduling unit, a data fusion unit, and a regional labeling unit. The synchronization scheduling unit can be used to time-align data from different regions, the data fusion unit can be used to merge redundant information and generate data in a standardized format, and the regional labeling unit can be used to label the data source.

[0053] Step 103: Based on the power quality indicators of each power grid region determined by the power data aggregation results, and the power flow coherence indicators of each power grid region, perform a power risk assessment to obtain a power anomaly assessment result.

[0054] Among them, the power flow coherence index can be dynamically updated by the edge intelligent control terminal based on real-time communication between power grid areas, and is used to characterize the power transmission coherence coefficient between the corresponding power grid area and other power grid areas.

[0055] As an example, power quality indicators may include, but are not limited to, voltage deviation, harmonic content, and current imbalance.

[0056] In one example, such as Figure 3 As shown, the edge computing module can be used to analyze the power quality of each power grid area based on the aggregated data (i.e., the power data aggregation result) to determine the possibility of power anomalies. At the same time, it can also determine the possibility of power anomalies based on the power coherence. Specifically, the edge computing module can include a power quality assessment unit and a risk judgment unit. The power quality assessment unit can be used to calculate the power quality index of each power grid area based on the aggregated data. The risk judgment unit can be used to determine the possibility of power anomalies based on the power quality index and the power coherence of each power grid area.

[0057] Step 104: Generate a control strategy based on the power anomaly assessment results, and send the control strategy to the power systems of each of the power grid regions.

[0058] Among them, the control strategy can be a strategy for differentiated power optimization control of different power grid areas.

[0059] In one example, such as Figure 3 As shown, the optimization control module can be used to generate control strategies based on the calculation results of the edge computing module (i.e., power anomaly assessment results), and the communication interaction module can be used to send the control strategies to various power systems.

[0060] For example, the optimization control module may include a control strategy generation model, which can be trained using a deep learning model. By inputting power quality indicators and corresponding analysis results into the control strategy generation model, and / or inputting power flow coherence indicators into the control strategy generation model, the corresponding control strategy can be automatically output. The communication interaction module may include an instruction sending unit and a user interaction unit. The instruction sending unit can be used to convert the control strategy into control instructions and send them to various power devices. The user interaction unit can be used to display the analysis results of the edge computing module and the control strategy generated by the optimization control module.

[0061] Compared to traditional methods, the technical solution in this embodiment monitors power anomalies in various power grid areas by using power quality indicators, power flow coherence indicators, and coherence coefficients. By considering the mutual influence between different areas and combining power quality indicators to judge power anomalies, it helps to improve the comprehensiveness of anomaly detection.

[0062] In the aforementioned cross-regional power quality data aggregation and optimization control method, various power parameters in the power systems of different power grid regions are collected in real time through the multi-protocol adaptation interface of the edge intelligent control terminal. Data preprocessing is performed to obtain power system data for each power grid region. Then, the collected power system data from each power grid region is fused, and regional labels for each power grid region are added to the fused data to obtain power data aggregation results. Based on the power quality indicators of each power grid region determined by the power data aggregation results, power risk assessment is performed along with power flow coherence indicators of each power grid region to obtain power anomaly assessment results. Subsequently, a control strategy is generated based on the power anomaly assessment results and sent to the power systems of each power grid region. This control strategy is a differentiated power optimization control strategy for different power grid regions. Thus, based on multi-protocol adaptation and edge-side data aggregation, unified collection and correlation analysis of cross-regional heterogeneous power data is achieved. This enables accurate identification of power anomalies coupled between regions. Combined with dynamically updated power flow coherence indicators to generate differentiated control strategies, the real-time performance and accuracy of cross-regional power grid power quality collaborative control are improved.

[0063] In an exemplary embodiment, the step of performing a power risk assessment on the power quality indicators of each power grid region determined based on the power data aggregation results and the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result may include the following steps:

[0064] The reference information corresponding to each of the different power quality indicators is obtained for power quality indicator comparison, and the normal state information of the power flow coherence indicators is obtained for power flow coherence indicator comparison; if an abnormality is detected in the power quality indicator comparison result, and / or an abnormality is detected in the power flow coherence indicator comparison result, the power anomaly assessment result is determined to be that there is a power anomaly.

[0065] In practical applications, power quality indicators can be compared with their corresponding reference values ​​(i.e., reference information) to obtain power quality analysis results. If the power quality indicator value exceeds the range of the reference value, a power anomaly is determined, and / or, if the coherence of power in different regions is abnormal, a power anomaly is also determined. This enables the accurate detection of single or cross-regional power anomalies, contributing to a comprehensive and accurate assessment of power anomalies.

[0066] In one exemplary embodiment, the following steps may also be included:

[0067] Based on the power quality indicators and historical data of each power grid region, a power quality indicator change function is generated; based on the power quality indicator change function, the predicted power quality indicators of each power grid region are obtained; the predicted power quality indicators of each power grid region are compared with the reference information to obtain the power risk prediction results.

[0068] In an optional embodiment, the risk prediction unit can also obtain a power quality index change function over time (i.e., a power quality index change function) by fitting historical power quality indicators of each power grid area with current power quality indicators and time. This allows for the prediction of power quality indicators at a future point in time. Furthermore, risk prediction can be achieved by comparing the power quality indicators at each future point in time with their corresponding reference values. For example, the current and voltage values ​​of each power grid area at each future point in time can be obtained through fitting. Thus, by constructing a power quality index change function to achieve trend prediction and comparing it with reference information, potential power risks in each power grid area can be identified in advance, improving risk prediction capabilities.

[0069] In one exemplary embodiment, the following steps may also be included:

[0070] Based on the number of power transmission nodes in any power grid region, the number of power transmission paths connecting the power grid region to other power grid regions is determined; based on the specified detection time period and the number of power transmission paths, the power flow coherence index of the power grid region is calculated.

[0071] In practical implementation, besides judging the probability of power anomalies based on power quality indicators, the probability can also be judged based on power flow coherence indicators. The power flow coherence indicators for a specific power grid area over a certain time period can be calculated as follows:

[0072]

[0073] in, This is the power flow coherence index for a certain power grid area during the time period from t1 to t2 (i.e., the specified time period for detection), where N is the number of power transmission paths in the power grid area that are connected to other power grid areas.

[0074] For example, each power grid area can contain several power transmission nodes, which are connected by power transmission paths. The power transmission path involved in N is the power transmission path connecting two nodes located in different power grid areas.

[0075] In one example, Let be the coherence coefficient of the nth power transmission path during the time interval t1 to t2, where t1 is the start time and t2 is the end time. Let X be the power phase angle of the nth power transmission path at time t, and let X be the number of power transmission paths (excluding the nth power transmission path) connected to nodes outside the power grid region connected to the nth power transmission path. When X=0, its corresponding... It is also 0. The power phase angle at time t for the xth power transmission path (excluding the nth power transmission path) connected to a node outside the power grid region connected to the nth power transmission path; Let be the reactive power of the nth power transmission path at time t. Let be the active power of the nth power transmission path at time t. Let be the reactive power of the x-th power transmission path at time t. Let be the active power of the x-th power transmission path at time t.

[0076] In yet another example, such as Figure 4 As shown, it is a graph showing the relationship between the power phase angle of the nth power transmission path at time t, the reactive power of the nth power transmission path at time t, and the active power of the nth power transmission path at time t.

[0077] Optionally, the active power and reactive power of each power transmission path at each time point can be calculated using voltage and current values; the time interval between t1 and t2 can be set according to the required detection frequency and detection accuracy. For example, the larger the time interval, the lower the detection frequency and the higher the detection accuracy.

[0078] By setting The mean coherence coefficient of the power transmission path connecting each power grid region to other power grid regions can be calculated, serving as a power flow coherence index. It can be used to obtain the correlation between two power transmission paths within a specified time period t1 to t2. For example, the smaller the difference in power phase angle and the larger the cosine value, the higher the correlation between the two paths.

[0079] In this embodiment, by combining the number of power transmission nodes in a power grid area with the dynamic changes in transmission paths within a specified time period, the correlation strength of power transmission between the power grid area and other power grid areas can be accurately quantified, which can effectively improve the accuracy of cross-regional power risk assessment.

[0080] In one exemplary embodiment, the following steps may also be included:

[0081] When an abnormal decrease in the power flow coherence index is detected in multiple power grid regions, a target power grid region is determined from the multiple power grid regions; the power flow coherence index of the target power grid region has the largest decrease compared to the normal state information; by analyzing the abnormal state of each power transmission path in the target power grid region, abnormal source control processing is performed.

[0082] As an example, the power flow coherence index (i.e., normal state information) under normal conditions can be calculated using historical data on power quality under normal conditions and the average value of the calculated data can be obtained.

[0083] In practical applications, by obtaining the power flow coherence index of each power grid region, the power coherence status of each power grid region can be obtained. If the power flow coherence index of a certain power grid region drops rapidly compared to the normal state during a certain period, it can be determined that the power grid region has an anomaly during this period, and the greater the drop, the more severe the anomaly. If the power flow coherence index of multiple power grid regions drops simultaneously during a certain period, the power grid region with the largest drop (i.e., the target power grid region) is most likely to be the source of the anomaly in other power grid regions and needs to be dealt with first. Moreover, the power transmission path with the largest drop in coherence coefficient in the power grid region is most likely to be the source of the anomaly in that region.

[0084] In this embodiment, by setting power flow coherence index and coherence coefficient, the source of the anomaly can be determined based on the decrease in parameter values ​​in different power grid areas and their different transmission paths. This helps to regulate the source of power anomalies in a timely manner and enhances the system's ability to predict potential power anomalies.

[0085] In one exemplary embodiment, the following steps may also be included:

[0086] Using power flow coherence indices for each power grid region under different detection periods, a time series vector of power flow coherence indices for each power grid region is constructed; the target power anomaly type is determined based on the similarity between the time series vector and the anomaly sample sequence vector; and the preset control strategy corresponding to the target power anomaly type is used as the power optimization control strategy.

[0087] In one alternative embodiment, such as Figure 5 As shown, the following steps can be taken to regulate the power system based on the power flow coherence index:

[0088] S1: Obtain power flow coherence indices for different power grid regions at different detection periods. By organizing and analyzing the power flow coherence indices for each power grid region at different detection periods, a time series vector of the power flow coherence indices for each power grid region can be constructed. Optionally, the duration of the detection period can be set according to actual needs, such as a detection period of 5 minutes / time.

[0089] S2: The time series vector can be compared with the template library. Based on the similarity algorithm, the similarity between the time series vector and the sample sequence vector (i.e., abnormal sample sequence vector) corresponding to various anomaly types stored in the template library is matched. Then, the anomaly type corresponding to the highest similarity matching result can be used as the matched anomaly type (i.e., the target power anomaly type).

[0090] For example, similarity can be obtained in the following way:

[0091] Assume the time series vector X is The sample sequence vector Y is The local distance D(i,j) between the i-th element of X and the j-th element of Y is:

[0092]

[0093] Where e is the natural constant, point x Let X be the number of power transmission nodes contained in the region corresponding to X, and line x Let X be the number of power transmission paths contained in the region, point y Let Y be the number of power transmission nodes contained in the region, liney Let Y be the number of power transmission paths contained in the region, vpoint be the average number of power transmission nodes contained in each region, and vline be the average number of power transmission paths contained in each region. Let X be the value of the i-th element (e.g., the power flow coherence index). The element value of the j-th element of Y (such as the power flow coherence index).

[0094] For example, the obtained D(i,j) values ​​can be substituted into the DTW (Dynamic Time Warping) algorithm, and the output of this algorithm is the similarity. Since the input of the DTW algorithm is the Euclidean distance between two elements, the similarity only considers the element values ​​in this case. However, the power flow coherence index is actually highly correlated with the number of power transmission paths and the number of power transmission nodes. If only the element values ​​are considered, the error will be large. Therefore, by introducing the number of nodes and the number of paths, and setting an exponential function, the larger the difference in the number of nodes (or the number of paths), the closer the exponent is to 0, and the smaller D(i,j) is, thus better reflecting the similarity between the two power grid regions.

[0095] For example, if the similarity between the time series vector and all sample sequence vectors is lower than the similarity threshold, it can be determined that no corresponding matching type has been found. This similarity threshold can be set according to the required precision.

[0096] S3: Output the corresponding power anomaly type based on the matching type. If no matching type is found, it can be determined that there is no power anomaly.

[0097] S4: Based on the identified power anomaly type, output the corresponding control strategy. The control strategy for different power anomaly types can be set based on historical control information.

[0098] In this embodiment, by adjusting the input of the DTW algorithm by introducing the number of nodes and the number of paths, it is possible to evaluate sequence similarity from multiple perspectives and improve the system's comprehensive analysis capabilities.

[0099] In one exemplary embodiment, such as Figure 6 The diagram illustrates another method for cross-regional power quality data aggregation and optimization control. In this embodiment, the method includes the following steps:

[0100] In step 601, various power parameters in the power systems of different power grid regions are collected in real time through the multi-protocol adaptation interface of the edge intelligent control terminal for data preprocessing to obtain power system collection data for each power grid region. In step 602, the power system collection data from each power grid region is fused, and regional labels for each power grid region are added to the fused data to obtain power data aggregation results. In step 603, reference information corresponding to different power quality indicators is obtained for power quality indicator comparison, and normal state information of power flow coherence indicators is obtained for power flow coherence indicator comparison. In step 604, if an anomaly is detected in the power quality indicator comparison results, and / or an anomaly is detected in the power flow coherence indicator comparison results, the power anomaly assessment result is determined to be an anomaly. In step 605, a control strategy is generated based on the power anomaly assessment result and sent to the power systems of each power grid region. In step 606, when an abnormal decline in the power flow coherence indicators of multiple power grid regions is detected, a target power grid region is determined from the multiple power grid regions. In step 607, abnormal source control processing is performed by analyzing the abnormal state of each power transmission path in the target power grid area.

[0101] It should be noted that the specific limitations of the above steps can be found in the above description of the specific limitations of a cross-regional power quality data aggregation and optimization control method, and will not be repeated here.

[0102] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0103] Based on the same inventive concept, this application also provides a cross-regional power quality data aggregation and optimization control device for implementing the cross-regional power quality data aggregation and optimization control method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more cross-regional power quality data aggregation and optimization control device embodiments provided below can be found in the limitations of the cross-regional power quality data aggregation and optimization control method described above, and will not be repeated here.

[0104] In one exemplary embodiment, such as Figure 7 As shown, an edge intelligent control terminal provides a cross-regional power quality data aggregation and optimization control device, comprising:

[0105] The power data acquisition module 701 is used to collect various power parameters in the power system of different power grid areas in real time through the multi-protocol adapter interface of the edge intelligent control terminal, perform data preprocessing, and obtain power system acquisition data for each power grid area.

[0106] The power data aggregation module 702 is used to fuse power system data collected from each of the power grid regions and add regional labels of each of the power grid regions to the fused data to obtain power data aggregation results; the regional labels are used to mark the data source.

[0107] The power anomaly assessment module 703 is used to perform power risk assessment based on the power quality indicators of each power grid region determined by the power data aggregation results and the power flow coherence indicators of each power grid region, and to obtain power anomaly assessment results; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions.

[0108] The control strategy generation module 704 is used to generate a control strategy based on the power anomaly assessment result and send the control strategy to the power system of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions.

[0109] In one embodiment, the power anomaly assessment module 703 is specifically used to obtain reference information corresponding to different power quality indicators for power quality indicator comparison, and to obtain normal state information of the power flow coherence indicators for power flow coherence indicator comparison; if an anomaly is detected in the power quality indicator comparison result, and / or an anomaly is detected in the power flow coherence indicator comparison result, the power anomaly assessment result is determined to be that there is a power anomaly.

[0110] In one embodiment, the device further includes:

[0111] The power risk prediction module is used to generate a power quality index change function based on the power quality index and historical index data of each power grid area; obtain the predicted power quality index of each power grid area based on the power quality index change function; and compare the predicted power quality index of each power grid area with the reference information to obtain the power risk prediction result.

[0112] In one embodiment, the device further includes:

[0113] The power flow coherence index acquisition module is used to determine the number of power transmission paths connecting any power grid region to other power grid regions based on the number of power transmission nodes in any power grid region; and to calculate the power flow coherence index of any power grid region based on the specified detection time period and the number of power transmission paths.

[0114] In one embodiment, the device further includes:

[0115] The anomaly source determination module is used to determine a target power grid region from the multiple power grid regions when an abnormal decline in the power flow coherence index is detected in multiple power grid regions; the target power grid region has the largest decline in the power flow coherence index compared with the normal state information; and performs anomaly source control processing by analyzing the abnormal state of each power transmission path in the target power grid region.

[0116] In one embodiment, the device further includes:

[0117] The coherence index control module is used to construct a time series vector of power coherence flow index for each power grid region using power flow coherence indices under different detection periods; determine the target power anomaly type based on the similarity between the time series vector and the anomaly sample sequence vector; and use the preset control strategy corresponding to the target power anomaly type as the power optimization control strategy.

[0118] Each module in the aforementioned cross-regional power quality data aggregation and optimization control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0119] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a cross-regional power quality data aggregation and optimization control method.

[0120] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0121] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0122] Through the multi-protocol adaptation interface of the edge intelligent control terminal, various power parameters in the power system of different power grid areas are collected in real time and data preprocessed to obtain power system data for each power grid area.

[0123] By fusing power system data collected from each of the aforementioned power grid regions and adding regional labels for each power grid region to the fused data, a power data aggregation result is obtained; the regional labels are used to mark the data source.

[0124] Based on the power quality indicators of each power grid region determined by the power data aggregation results, a power risk assessment is performed with the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions.

[0125] A control strategy is generated based on the power anomaly assessment results, and the control strategy is sent to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions.

[0126] In one embodiment, when the processor executes the computer program, it also implements the steps of the cross-regional power quality data aggregation and optimization control method in the other embodiments described above.

[0127] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0128] Through the multi-protocol adaptation interface of the edge intelligent control terminal, various power parameters in the power system of different power grid areas are collected in real time and data preprocessed to obtain power system data for each power grid area.

[0129] By fusing power system data collected from each of the aforementioned power grid regions and adding regional labels for each power grid region to the fused data, a power data aggregation result is obtained; the regional labels are used to mark the data source.

[0130] Based on the power quality indicators of each power grid region determined by the power data aggregation results, a power risk assessment is performed with the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions.

[0131] A control strategy is generated based on the power anomaly assessment results, and the control strategy is sent to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions.

[0132] In one embodiment, when the computer program is executed by a processor, it also implements the steps of the cross-regional power quality data aggregation and optimization control method in the other embodiments described above.

[0133] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0134] Through the multi-protocol adaptation interface of the edge intelligent control terminal, various power parameters in the power system of different power grid areas are collected in real time and data preprocessed to obtain power system data for each power grid area.

[0135] By fusing power system data collected from each of the aforementioned power grid regions and adding regional labels for each power grid region to the fused data, a power data aggregation result is obtained; the regional labels are used to mark the data source.

[0136] Based on the power quality indicators of each power grid region determined by the power data aggregation results, a power risk assessment is performed with the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions.

[0137] A control strategy is generated based on the power anomaly assessment results, and the control strategy is sent to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions.

[0138] In one embodiment, when the computer program is executed by a processor, it also implements the steps of the cross-regional power quality data aggregation and optimization control method in the other embodiments described above.

[0139] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0140] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for cross-regional power quality data aggregation and optimization control, characterized in that, The method, applied to an edge intelligent control terminal, includes: Through the multi-protocol adaptation interface of the edge intelligent control terminal, various power parameters in the power system of different power grid areas are collected in real time and data preprocessed to obtain power system data for each power grid area. By fusing power system data collected from each of the aforementioned power grid regions and adding regional labels for each power grid region to the fused data, a power data aggregation result is obtained; the regional labels are used to mark the data source. Based on the power quality indicators of each power grid region determined by the power data aggregation results, a power risk assessment is performed with the power flow coherence indicators of each power grid region to obtain a power anomaly assessment result; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions. A control strategy is generated based on the power anomaly assessment results, and the control strategy is sent to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions. The method further includes: constructing a time series vector of the power flow coherence index of each power grid region using power flow coherence indices under different detection periods; determining the target power anomaly type based on the similarity between the time series vector and the anomaly sample sequence vector; and using the preset control strategy corresponding to the target power anomaly type as the power optimization control strategy. The method further includes: comparing the time series vector with a template library, matching the similarity between the time series vector and the abnormal sample sequence vectors corresponding to various abnormal types stored in the template library according to a similarity algorithm, and taking the abnormal type corresponding to the highest similarity matching result as the target power anomaly type; The method further includes obtaining the similarity in the following manner: Assume the time series vector X is The abnormal sample sequence vector Y is The local distance D(i,j) between the i-th element of X and the j-th element of Y is: Where e is the natural constant, point x Let X be the number of power transmission nodes contained in the region corresponding to X, and line x Let X be the number of power transmission paths contained in the region, point y Let Y be the number of power transmission nodes contained in the region, line y Let Y be the number of power transmission paths contained in the region, vpoint be the average number of power transmission nodes contained in each region, and vline be the average number of power transmission paths contained in each region. Let be the value of the i-th element of X. Let be the value of the j-th element of Y; The obtained local distances are substituted into the dynamic time warping algorithm to obtain the similarity.

2. The method according to claim 1, characterized in that, The power quality indicators for each power grid region determined based on the power data aggregation results are used to conduct a power risk assessment with the power flow coherence indicators for each power grid region to obtain a power anomaly assessment result, including: The reference information corresponding to each of the different power quality indicators is obtained for comparison of power quality indicators, and the normal state information of the power flow coherence indicators is obtained for comparison of power flow coherence indicators. If an anomaly is detected in the power quality index comparison results, and / or an anomaly is detected in the power flow coherence index comparison results, the power anomaly assessment result is determined to indicate the presence of a power anomaly.

3. The method according to claim 2, characterized in that, The method further includes: Based on the power quality indicators and historical data of each power grid region, a power quality indicator change function is generated. Based on the power quality index change function, the predicted power quality index for each of the power grid regions is obtained. The predicted power quality indicators for each power grid region are compared with the reference information to obtain the power risk prediction results.

4. The method according to claim 1, characterized in that, The method further includes: Based on the number of power transmission nodes in any power grid region, determine the number of power transmission paths connecting the any power grid region to other power grid regions; Based on the specified time period and the number of power transmission paths, the power flow coherence index of any power grid region is calculated.

5. The method according to claim 2, characterized in that, The method further includes: When an abnormal decrease in the power flow coherence index is detected in multiple power grid regions, a target power grid region is determined from the multiple power grid regions; the target power grid region has the largest decrease in the power flow coherence index compared to the normal state information. By analyzing the abnormal states of each power transmission path in the target power grid area, abnormal source control processing is performed.

6. A cross-regional power quality data aggregation and optimization control device, characterized in that, The device, applied to an edge intelligent control terminal, includes: The power data acquisition module is used to collect various power parameters in the power system of different power grid areas in real time through the multi-protocol adapter interface of the edge intelligent control terminal, perform data preprocessing, and obtain the power system acquisition data of each power grid area. The power data aggregation module is used to fuse power system data collected from each of the power grid regions and add regional labels of each of the power grid regions to the fused data to obtain power data aggregation results; the regional labels are used to mark the data source. The power anomaly assessment module is used to assess power risk based on the power quality indicators of each power grid region determined by the power data aggregation results and the power flow coherence indicators of each power grid region, and to obtain the power anomaly assessment results; the power flow coherence indicators are dynamically updated by the edge intelligent control terminal based on real-time communication between power grid regions, and are used to characterize the power transmission coherence coefficient between the corresponding power grid region and other power grid regions; A control strategy generation module is used to generate a control strategy based on the power anomaly assessment results and send the control strategy to the power systems of each of the power grid regions; the control strategy is a strategy for differentiated power optimization control for different power grid regions. The coherence index control module is used to construct a time series vector of the power flow coherence index of each power grid region using power flow coherence indices under different detection periods; determine the target power anomaly type based on the similarity between the time series vector and the anomaly sample sequence vector; and use the preset control strategy corresponding to the target power anomaly type as the power optimization control strategy. The coherence index control module is also used to compare the time series vector with the template library, and match the similarity between the time series vector and the abnormal sample sequence vector corresponding to various abnormal types stored in the template library according to the similarity algorithm, so as to take the abnormal type corresponding to the highest similarity matching result as the target power abnormal type. The coherence index control module is also used to obtain the similarity in the following way: assuming the time series vector X is... The abnormal sample sequence vector Y is The local distance D(i,j) between the i-th element of X and the j-th element of Y is: Where e is the natural constant, point x Let X be the number of power transmission nodes contained in the region corresponding to X, and line x Let X be the number of power transmission paths contained in the region, point y Let Y be the number of power transmission nodes contained in the region, line y Let Y be the number of power transmission paths contained in the region, vpoint be the average number of power transmission nodes contained in each region, and vline be the average number of power transmission paths contained in each region. Let be the value of the i-th element of X. Let be the element value of the j-th element of Y; substitute each of the obtained local distances into the dynamic time warping algorithm to obtain the similarity.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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