Multi-terminal cooperative power grid interaction control method and system

Abstract

The invention provides a multi-terminal collaborative power grid interaction control method and system, and the method comprises the steps: S1, querying terminals in a power grid system and built-in instruments of the terminals, obtaining power parameters based on the built-in instruments, and constructing a data structure body of each terminal; s2, determining relevancy among different terminals according to the data structure body of each terminal; s3, for any terminal, selecting the terminal of which the relevancy with the terminal reaches a preset relevancy threshold to obtain a terminal set; s4, analyzing the data structure body of any terminal, and determining a control demand and an alternative control instruction; and S5, taking the terminal with the control requirement as a target terminal, querying a terminal set corresponding to the target terminal, determining the other-end influence quantity of each alternative control instruction in the terminal set, and determining a final control instruction according to the other-end influence quantity.

Description

Technical Field

[0001] This invention proposes a multi-terminal collaborative power grid interactive control method and system, which relates to the field of power grid interactive control technology. Background Technology

[0002] Grid terminals refer to edge devices or system nodes in a power grid system that are perceptible, communicative, and controllable. These terminals are the basic units of grid interactive control, participating in data acquisition, status response, load regulation, energy optimization, and information exchange. With the increasing prevalence of smart devices, the control process of grid terminals is gradually becoming more remote and automated. However, the existing grid control process is still in the stage of independent control, performing independent analysis and control on each terminal. The power grid system itself is an integrated architecture, and changes in one terminal may affect other terminals. Therefore, how to introduce an integrated analysis process to improve the stability of the control process is the technical problem that this invention aims to solve. Summary of the Invention

[0003] In view of this, in order to fill the gaps and deficiencies in the existing technology, the present invention proposes a multi-terminal collaborative power grid interactive control method and system.

[0004] This invention proposes a multi-terminal collaborative power grid interaction control method and system, including the following:

[0005] This invention proposes a multi-terminal collaborative power grid interactive control method, characterized by the following:

[0006] Step S1: Query the terminals and their built-in meters in the power grid system, obtain power parameters based on the built-in meters, and construct the data structure for each terminal;

[0007] Step S2: Determine the correlation between different terminals based on the data structure of each terminal;

[0008] Step S3: For any terminal, select terminals whose relevance reaches a preset relevance threshold to obtain a terminal set;

[0009] Step S4: Analyze the data structure of any terminal to determine the control requirements and alternative control commands;

[0010] Step S5: Select the terminal with control requirements as the target terminal, query the terminal set corresponding to the target terminal, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0011] Further, step S1 includes the following:

[0012] Step S11: Establish a connection channel with the power grid system's registration database and query the terminal and its built-in instruments;

[0013] Step S12: Query the terminal model and installation location, and generate a terminal tag based on the model and installation location;

[0014] Step S13: Count the built-in instruments according to the preset order, determine the instrument labels, and create an array indexed by the instrument labels;

[0015] Step S14: Based on the time-inclusive power parameters obtained from the built-in meters using array statistics, obtain the data structure for each terminal.

[0016] Further, step S2 includes the following:

[0017] Step S21: Receive the analysis span uploaded by the administrator, and extract the data structure based on the analysis span; the analysis span is a time period;

[0018] Step S22: For any two terminals' data structures, compare the instrument labels in the data structures to determine the label intersection and label union;

[0019] Step S23: Determine the baseline coefficient based on the number of labels in the label intersection and the number of labels in the label union;

[0020] Step S24: For the instrument tags in the tag intersection, read the array corresponding to the instrument tags, compare the arrays, and determine the data relevance;

[0021] Step S25: Query the weight corresponding to the instrument tag, calculate the product with the benchmark coefficient based on the cumulative data relevance of the weight, and obtain the relevance between different terminals.

[0022] Furthermore, step S2 also includes the following:

[0023] Step S26: Read the instrument tags sequentially from the tag intersection;

[0024] Step S27: Read arrays from two data structures based on the instrument label;

[0025] Step S28: Extract the amplitude spectrum of the frequency domain array and calculate the Pearson correlation coefficient of the amplitude spectrum;

[0026] Step S29: Calculate the Pearson correlation coefficient of the frequency domain array as the data correlation.

[0027] Step S210: The calculation process for data relevance is as follows:

[0028]

[0029] In the formula, r represents the data correlation degree, X(k) is the value of the k-th frequency component in the amplitude spectrum of the first frequency domain array, and N is the total number of frequency components; X(i) is the value of the i-th frequency component in the amplitude spectrum of the first frequency domain array, Y(k0) is the value of the k-th frequency component in the amplitude spectrum of the second frequency domain array, and N is the total number of frequency components; Y(i) is the value of the i-th frequency component in the amplitude spectrum of the second frequency domain array, and r takes values ​​from -1 to 1; and All represent the mean of the corresponding frequency domain array.

[0030] Further, step S3 includes the following:

[0031] Step S31: For each terminal, query its relevance to other terminals. When the relevance reaches a preset relevance threshold, mark the corresponding terminal. Count all marked terminals to obtain a set of terminals, called the terminal set.

[0032] Further, step S4 includes the following:

[0033] Step S41: Extract the data structure of each terminal according to the preset backtracking duration to obtain the instantaneous structure;

[0034] Step S42: Query the standard structure corresponding to the terminal, compare the instantaneous structure with the standard structure, and locate the abnormal instrument and its degree of abnormality;

[0035] Step S43: Determine the control requirements based on the abnormal instruments and the degree of abnormality, and query the alternative control commands in the control command set corresponding to the terminal;

[0036] Step S44: Determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0037] Further, step S5 includes the following:

[0038] Step S51: Select the terminal with control requirements as the target terminal and query the terminal set corresponding to the target terminal;

[0039] Step S52: Query the data correlation between each terminal in the terminal set and the target terminal, query the impact of the alternative control commands on the target terminal, and determine the impact on other terminals based on the data correlation and the impact on the target terminal; where the impact is expressed as a percentage.

[0040] Step S53: Compare the absolute value of each other influence quantity with the corresponding threshold. When the absolute value of all other influence quantities is less than the corresponding threshold, calculate the sum of the absolute values ​​of all other influence quantities as the total influence quantity.

[0041] Step S54: Select the candidate control command with the smallest total impact as the final control command.

[0042] According to a second aspect of the present invention, a multi-terminal collaborative power grid interaction control system is proposed, characterized in that the multi-terminal collaborative power grid interaction control system includes the following:

[0043] The data structure creation module is used to query terminals and their built-in meters in the power grid system, obtain power parameters based on the built-in meters, and construct the data structure for each terminal.

[0044] The correlation analysis module is used to determine the correlation between different terminals based on the data structure of each terminal.

[0045] The terminal set creation module is used to select terminals whose relevance to any given terminal reaches a preset relevance threshold, thereby obtaining a terminal set.

[0046] The terminal analysis module is used to analyze the data structure of any terminal to determine control requirements and alternative control commands.

[0047] The influence quantity determination module is used to select the terminal with control requirements as the target terminal, query the terminal set corresponding to the target terminal, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0048] Furthermore, the data volume creation module includes the following:

[0049] The filing data query unit is used to establish a connection channel with the filing database of the power grid system and to query the terminal and the built-in instruments of the terminal.

[0050] The terminal tag generation unit is used to query the terminal model and installation location, and generate a terminal tag based on the model and installation location.

[0051] The array creation unit is used to count the built-in meters according to a preset order, determine the meter labels, and create an array indexed by the meter labels;

[0052] The data statistics unit is used to statistically analyze the time-inclusive power parameters obtained from the built-in meters based on array statistics, and to obtain the data structure for each terminal.

[0053] Furthermore, the correlation analysis module includes the following:

[0054] The first interception unit is used to receive the analysis span uploaded by the administrator and intercept the data structure according to the analysis span; the analysis span is a time period.

[0055] The intersection and union calculation unit is used to compare the instrument labels in the data structures of any two terminals to determine the intersection and union of the labels.

[0056] The baseline coefficient determination unit is used to determine the baseline coefficient based on the number of tags in the tag intersection and the number of tags in the tag union.

[0057] The array comparison unit is used to read the array corresponding to the instrument tags in the tag intersection, compare the arrays, and determine the data relevance.

[0058] The information accumulation unit is used to query the weight corresponding to the instrument tag, accumulate the relevance of the weighted data, calculate the product with the benchmark coefficient, and obtain the relevance between different terminals.

[0059] The terminal analysis module includes the following:

[0060] The second interception unit is used to intercept the data structure of each terminal according to the preset backtracking time to obtain the instantaneous structure;

[0061] The comparison and positioning unit is used to query the standard structure corresponding to the terminal, compare the instantaneous structure with the standard structure, and locate abnormal instruments and their degree of abnormality.

[0062] The alternative command query unit is used to determine control requirements based on abnormal instruments and the degree of abnormality, and to query alternative control commands from the control command set corresponding to the terminal.

[0063] The optimization unit is used to determine the external influence quantity of each candidate control command in the terminal set, and determine the final control command based on the external influence quantity.

[0064] The present invention has the following advantages:

[0065] This invention statistically analyzes the detection data of each terminal, determines the related terminals of each terminal, and when controlling any terminal, determines multiple control schemes. For each control scheme, the impact on related terminals is simultaneously determined, and then the control scheme is optimized to determine the optimal scheme. This process takes into account the synergistic effects, which greatly improves the stability of the control process. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating the overall steps of the present invention.

[0067] Figure 2 This is a first sub-flowchart of the present invention.

[0068] Figure 3 This is a second sub-flowchart of the present invention.

[0069] Figure 4 This is the third sub-flowchart of the present invention.

[0070] Figure 5 This is the fourth sub-flow flowchart of the present invention.

[0071] Figure 6 This is a block diagram showing the composition of the power grid interaction control system of the present invention. Detailed Implementation

[0072] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0073] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0074] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention; as used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise; furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0075] This invention proposes a multi-terminal collaborative power grid interaction control method and system, including the following:

[0076] like Figure 1 The present invention proposes a multi-terminal collaborative power grid interaction control method, characterized by the following:

[0077] Step S1: Query the terminals and their built-in meters in the power grid system, obtain power parameters based on the built-in meters, and construct the data structure for each terminal;

[0078] Step S2: Determine the correlation between different terminals based on the data structure of each terminal;

[0079] Step S3: For any terminal, select terminals whose relevance reaches a preset relevance threshold to obtain a terminal set;

[0080] Step S4: Analyze the data structure of any terminal to determine the control requirements and alternative control commands;

[0081] Step S5: Select the terminal with control requirements as the target terminal, query the terminal set corresponding to the target terminal, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0082] like Figure 2 As shown, step S1 further includes the following:

[0083] Step S11: Establish a connection channel with the power grid system's registration database and query the terminal and its built-in instruments;

[0084] Step S12: Query the terminal model and installation location, and generate a terminal tag based on the model and installation location;

[0085] Step S13: Count the built-in instruments according to the preset order, determine the instrument labels, and create an array indexed by the instrument labels;

[0086] Step S14: Based on the time-inclusive power parameters obtained from the built-in meters using array statistics, obtain the data structure for each terminal.

[0087] In one embodiment of the present invention, step S1 further includes:

[0088] Power equipment in a power grid system is called a terminal, including generators, substations, battery management systems, energy storage converters, distribution transformers, etc. These devices can continuously monitor and collect power parameters through sensors and data acquisition equipment. The data includes, but is not limited to, current, voltage, frequency, temperature, load, and power factor. To simplify the understanding process, the terminal can be limited to the power supply side terminal, corresponding to multiple generator sets. In the existing photovoltaic or wind power generation architecture, the number of generator sets is extremely large. The built-in instruments can be limited to ammeters to detect the output current. For any terminal that has been put into use, the location of its built-in instrument is also determined. A data structure is constructed based on the determined location, and then the power parameters obtained by the built-in instrument are statistically analyzed based on the data structure to obtain the data structure of each terminal.

[0089] In one embodiment of the present invention, step S1 further includes:

[0090] The above describes the data statistics process: establishing a connection channel with the power grid system's registration database; querying terminals and their built-in meters; querying terminal models and installation locations; generating terminal tags based on model and location (in fact, each terminal may have its own unique tag, and reading this tag as the terminal tag is also feasible); then, within each terminal, counting the built-in meters according to a preset order and determining the meter tags, which are only related to the type of meter (e.g., an ammeter corresponds to one type of tag); creating an array indexed by the meter tags, the order of which can be set by the staff, for example, querying according to a preset direction; counting the built-in meters according to the preset order and simultaneously determining the meter tag for each meter; creating an array indexed by the meter tags; finally, based on the array, counting the time-inclusive power parameters obtained from the built-in meters to obtain the data structure for each terminal.

[0091] It should be noted that the final data structure is a collection of arrays. Since the data acquisition frequency of each built-in instrument is different, the number of elements in different arrays is different. Therefore, the data structure is not actually a matrix. If a more standardized form is desired, the array with the highest frequency needs to be used as the benchmark, and all arrays need to be time-domain registered. The time-domain registration method is to copy the existing data and then upsample it to make the number of elements in all arrays the same. At this point, the entire data structure becomes a matrix.

[0092] like Figure 3 As shown, step S2 further includes the following:

[0093] Step S21: Receive the analysis span uploaded by the administrator, and extract the data structure based on the analysis span; the analysis span is a time period;

[0094] Step S22: For any two terminals' data structures, compare the instrument labels in the data structures to determine the label intersection and label union;

[0095] Step S23: Determine the baseline coefficient based on the number of labels in the label intersection and the number of labels in the label union;

[0096] Step S24: For the instrument tags in the tag intersection, read the array corresponding to the instrument tags, compare the arrays, and determine the data relevance;

[0097] Step S25: Query the weight corresponding to the instrument tag, calculate the product with the benchmark coefficient based on the cumulative data relevance of the weight, and obtain the relevance between different terminals.

[0098] In one embodiment of the present invention, step S2 further includes the following:

[0099] The analysis span uploaded by the administrator is a time period, indicating the time period to be analyzed. The last time point of the analysis span is the current time, which changes continuously over time. Data structures are extracted based on the analysis span, and the correlation is calculated for any two data structures from different endpoints. Specifically, the calculation process consists of two steps:

[0100] First, for any two terminals' data structures, compare the instrument tags in the data structures to determine the tag intersection and tag union. This process indicates whether the two terminals have the same instrument tags. If so, how many are there and what percentage? The calculation method is to determine the tag intersection and tag union, query the number of tags in the tag intersection and the number of tags in the tag union, calculate the ratio of the number of tags in the intersection to the number of tags in the union, and obtain the baseline coefficient.

[0101] Then, the instrument labels in the label intersection are analyzed. The arrays corresponding to the instrument labels are read, the arrays are compared, and the data correlation between the arrays is calculated. Instruments corresponding to the same instrument label have the same frequency and the number of elements in the array is the same. The calculation process is not complicated. After the arrays corresponding to each instrument label are calculated, multiple correlations based on the arrays are obtained.

[0102] Finally, different instrument labels have weights, which are preset by the administrator. In the simplest case, all instrument labels have the same weight. The total data relevance is obtained by accumulating the data relevance based on the weights. This is equivalent to the relevance of the data in the intersection labels. Multiplying this by the benchmark coefficient, we get the relevance between terminals.

[0103] Furthermore, step S2 also includes the following:

[0104] Step S26: Read the instrument tags sequentially from the tag intersection;

[0105] Step S27: Read arrays from two data structures based on the instrument label;

[0106] Step S28: Extract the amplitude spectrum of the frequency domain array and calculate the Pearson correlation coefficient of the amplitude spectrum;

[0107] Step S29: Calculate the Pearson correlation coefficient of the frequency domain array as the data correlation.

[0108] Step S210: The calculation process for data relevance is as follows:

[0109]

[0110] In the formula, r represents the data correlation degree, X(k) is the value of the k-th frequency component in the amplitude spectrum of the first frequency domain array, and N is the total number of frequency components; X(i) is the value of the i-th frequency component in the amplitude spectrum of the first frequency domain array, Y(k) is the value of the k-th frequency component in the amplitude spectrum of the second frequency domain array, and N is the total number of frequency components; Y(i) is the value of the i-th frequency component in the amplitude spectrum of the second frequency domain array, and r takes values ​​from -1 to 1; and All represent the mean of the corresponding frequency domain array.

[0111] In one embodiment of the present invention, step S2 further includes:

[0112] The instrument tags are read sequentially from the tag intersection. Based on the instrument tags, the corresponding arrays are read from the two data structures. This process has a default premise: only one of each type of instrument is set in a terminal. If there are multiple instruments, the process of reading the array involves combination problems. For example, if terminal A has two ammeters and terminal B has three ammeters, there are six possible combinations, which need to be compared. For the two selected arrays, the two arrays are transformed into frequency domain arrays to obtain frequency domain arrays. The Pearson correlation coefficient of the frequency domain arrays is calculated as the data correlation.

[0113] The Pearson correlation coefficient ranges from -1 to 1. The closer it is to 1, the more similar the two signals are in the frequency domain distribution, indicating a positive correlation. The closer it is to -1, the more similar the two signals are in the frequency domain distribution, indicating a negative correlation. If it is close to 0, it means that the two signals are not significantly linearly correlated in the spectral structure. In the above calculation process, the frequency domain transformation process, the amplitude spectrum extraction process, and the calculation process of the Pearson correlation coefficient are all conventional schemes, and will not be described in detail here.

[0114] Further, step S3 includes the following:

[0115] Step S31: For each terminal, query its relevance to other terminals. When the relevance reaches a preset relevance threshold, mark the corresponding terminal. Count all marked terminals to obtain a set of terminals, called the terminal set.

[0116] like Figure 4 As shown, step S4 further includes the following:

[0117] Step S41: Extract the data structure of each terminal according to the preset backtracking duration to obtain the instantaneous structure;

[0118] Step S42: Query the standard structure corresponding to the terminal, compare the instantaneous structure with the standard structure, and locate the abnormal instrument and its degree of abnormality;

[0119] Step S43: Determine the control requirements based on the abnormal instruments and the degree of abnormality, and query the alternative control commands in the control command set corresponding to the terminal;

[0120] Step S44: Determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0121] like Figure 4 As shown, in one embodiment of the present invention, step S4 further includes the following:

[0122] The above content describes the process of determining control requirements and alternative control commands. Based on a preset backtracking duration, the data structure of each terminal is truncated to obtain an instantaneous structure. This process is similar to step S201, but the backtracking duration is shorter, such as five minutes. This is equivalent to instantaneous data, but it differs from actual instantaneous data (measured in seconds). It is longer, and the identification results are more stable. The standard structure (preset standard state) corresponding to the terminal is queried. The structure of the same terminal is identical. The process of comparing the instantaneous structure and the standard structure is very simple: the difference between the instantaneous structure and the standard structure is calculated to obtain a difference structure. Abnormal data (with large absolute values) is queried in the difference structure, and then the instrument corresponding to the abnormal data is queried as the abnormal instrument. The mean of the abnormal data of the same instrument is calculated, and the degree of abnormality is determined based on the direct proportion of the mean.

[0123] Once the abnormal instruments and their degrees of abnormality are identified, it becomes clear where and to what extent the terminal is malfunctioning, thus determining the control requirements. The relationship between abnormal instruments, their degrees of abnormality, and control requirements can be pre-stored in a data table. After the data table is built using historical records, it can be directly read during practical applications. Of course, in existing technologies, once the location and degree of abnormality of the terminal are known, AI can be used for analysis to determine the control requirements.

[0124] The alternative control instructions corresponding to each control requirement are pre-set by the management personnel and stored in the emergency measures library. Each management party will build a corresponding emergency measures library, which will not be elaborated here.

[0125] For any candidate control command, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0126] like Figure 5 As shown, step S5 further includes the following:

[0127] Step S51: Select the terminal with control requirements as the target terminal and query the terminal set corresponding to the target terminal;

[0128] Step S52: Query the data correlation between each terminal in the terminal set and the target terminal, query the impact of the alternative control commands on the target terminal, and determine the impact on other terminals based on the data correlation and the impact on the target terminal; where the impact is expressed as a percentage.

[0129] Step S53: Compare the absolute value of each other influence quantity with the corresponding threshold. When the absolute value of all other influence quantities is less than the corresponding threshold, calculate the sum of the absolute values ​​of all other influence quantities as the total influence quantity.

[0130] Step S54: Select the candidate control command with the smallest total impact as the final control command.

[0131] like Figure 5 As shown, in one embodiment of the present invention, step S5 includes the following:

[0132] The above content describes the analysis process of the impact on the other end. The terminal with control requirements is taken as the target terminal. The terminal set corresponding to the target terminal is queried. The data correlation between each terminal in the terminal set and the target terminal is queried. The impact of the alternative control instructions on the target terminal is queried. Furthermore, the impact on the other end is determined based on the data correlation and the impact on the target terminal. The simplest way is to directly calculate the product. Since the correlation of the technical solution of this invention is based on the correlation coefficient of Pearson, which takes a value of -1 to 1, including the case of negative correlation, the impact on the other end also includes the case of negative impact.

[0133] For each terminal in the terminal set, the influence quantity of each other terminal is compared with the corresponding threshold. When the absolute value of all other terminal influence quantities is less than the corresponding threshold, the candidate control instruction is considered to meet the requirements (will not cause any excessive impact). Then, the sum of the absolute values ​​of all other terminal influence quantities is calculated as the total influence quantity. The candidate control instruction with the smallest total influence quantity is selected as the final control instruction.

[0134] like Figure 6 As shown, according to a second aspect of the present invention, the present invention proposes a multi-terminal collaborative power grid interaction control system, characterized in that the multi-terminal collaborative power grid interaction control system includes the following:

[0135] The data structure creation module is used to query terminals and their built-in meters in the power grid system, obtain power parameters based on the built-in meters, and construct the data structure for each terminal.

[0136] The correlation analysis module is used to determine the correlation between different terminals based on the data structure of each terminal.

[0137] The terminal set creation module is used to select terminals whose relevance to any given terminal reaches a preset relevance threshold, thereby obtaining a terminal set.

[0138] The terminal analysis module is used to analyze the data structure of any terminal to determine control requirements and alternative control commands.

[0139] The influence quantity determination module is used to select the terminal with control requirements as the target terminal, query the terminal set corresponding to the target terminal, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

[0140] Furthermore, the data volume creation module includes the following:

[0141] The filing data query unit is used to establish a connection channel with the filing database of the power grid system and to query the terminal and the built-in instruments of the terminal.

[0142] The terminal tag generation unit is used to query the terminal model and installation location, and generate a terminal tag based on the model and installation location.

[0143] The array creation unit is used to count the built-in meters according to a preset order, determine the meter labels, and create an array indexed by the meter labels;

[0144] The data statistics unit is used to statistically analyze the time-inclusive power parameters obtained from the built-in meters based on array statistics, and to obtain the data structure for each terminal.

[0145] Furthermore, the correlation analysis module includes the following:

[0146] The first interception unit is used to receive the analysis span uploaded by the administrator and intercept the data structure according to the analysis span; the analysis span is a time period.

[0147] The intersection and union calculation unit is used to compare the instrument labels in the data structures of any two terminals to determine the intersection and union of the labels.

[0148] The baseline coefficient determination unit is used to determine the baseline coefficient based on the number of tags in the tag intersection and the number of tags in the tag union.

[0149] The array comparison unit is used to read the array corresponding to the instrument tags in the tag intersection, compare the arrays, and determine the data relevance.

[0150] The information accumulation unit is used to query the weight corresponding to the instrument tag, accumulate the relevance of the weighted data, calculate the product with the benchmark coefficient, and obtain the relevance between different terminals.

[0151] The terminal analysis module includes the following:

[0152] The second interception unit is used to intercept the data structure of each terminal according to the preset backtracking time to obtain the instantaneous structure;

[0153] The comparison and positioning unit is used to query the standard structure corresponding to the terminal, compare the instantaneous structure with the standard structure, and locate abnormal instruments and their degree of abnormality.

[0154] The alternative command query unit is used to determine control requirements based on abnormal instruments and the degree of abnormality, and to query alternative control commands from the control command set corresponding to the terminal.

[0155] The optimization unit is used to determine the external influence quantity of each candidate control command in the terminal set, and determine the final control command based on the external influence quantity.

[0156] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A multi-terminal collaborative power grid interactive control method, characterized in that, Includes the following: Step S1: Query the terminals and their built-in meters in the power grid system, obtain power parameters based on the built-in meters, and construct the data structure for each terminal; Step S2: Determine the correlation between different terminals based on the data structure of each terminal; Step S3: For any terminal, select terminals whose relevance reaches a preset relevance threshold to obtain a terminal set; Step S4: Analyze the data structure of any terminal to determine the control requirements and alternative control commands; Step S5: Select the terminal with control requirements as the target terminal, query the terminal set corresponding to the target terminal, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

2. The multi-terminal collaborative power grid interaction control method according to claim 1, characterized in that, Step S1 includes the following: Step S11: Establish a connection channel with the power grid system's registration database and query the terminal and its built-in instruments; Step S12: Query the terminal model and installation location, and generate a terminal tag based on the model and installation location; Step S13: Count the built-in instruments according to the preset order, determine the instrument labels, and create an array indexed by the instrument labels; Step S14: Based on the time-inclusive power parameters obtained from the built-in meters using array statistics, obtain the data structure for each terminal.

3. The multi-terminal collaborative power grid interaction control method according to claim 1, characterized in that, Step S2 includes the following: Step S21: Receive the analysis span uploaded by the administrator, and extract the data structure based on the analysis span; the analysis span is a time period; Step S22: For any two terminals' data structures, compare the instrument labels in the data structures to determine the label intersection and label union; Step S23: Determine the baseline coefficient based on the number of labels in the label intersection and the number of labels in the label union; Step S24: For the instrument tags in the tag intersection, read the array corresponding to the instrument tags, compare the arrays, and determine the data relevance; Step S25: Query the weight corresponding to the instrument tag, calculate the product with the benchmark coefficient based on the cumulative data relevance of the weight, and obtain the relevance between different terminals.

4. The multi-terminal collaborative power grid interaction control method according to claim 3, characterized in that, Step S2 also includes the following: Step S26: Read the instrument tags sequentially from the tag intersection; Step S27: Read arrays from two data structures based on the instrument label; Step S28: Extract the amplitude spectrum of the frequency domain array and calculate the Pearson correlation coefficient of the amplitude spectrum; Step S29: Calculate the Pearson correlation coefficient of the frequency domain array as the data correlation. Step S210: The calculation process for data relevance is as follows: Where r is the data correlation, X(k) is the value of the k-th frequency component in the amplitude spectrum of the first frequency domain array, and N is the total number of frequency components; X(i) is the value of the i-th frequency component in the amplitude spectrum of the first frequency domain array, Y(k) is the value of the k-th frequency component in the amplitude spectrum of the second frequency domain array, and N is the total number of frequency components; Y(i) is the value of the i-th frequency component in the amplitude spectrum of the second frequency domain array, and r takes values ​​from -1 to 1; and All represent the mean of the corresponding frequency domain array.

5. The multi-terminal collaborative power grid interaction control method according to claim 1, characterized in that, Step S3 includes the following: Step S31: For each terminal, query its relevance to other terminals. When the relevance reaches a preset relevance threshold, mark the corresponding terminal. Count all marked terminals to obtain a set of terminals, called the terminal set.

6. The multi-terminal collaborative power grid interaction control method according to claim 1, characterized in that, Step S4 includes the following: Step S41: Extract the data structure of each terminal according to the preset backtracking duration to obtain the instantaneous structure; Step S42: Query the standard structure corresponding to the terminal, compare the instantaneous structure with the standard structure, and locate the abnormal instrument and its degree of abnormality; Step S43: Determine the control requirements based on the abnormal instruments and the degree of abnormality, and query the alternative control commands in the control command set corresponding to the terminal; Step S44: Determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

7. The multi-terminal collaborative power grid interaction control method according to claim 1, characterized in that, Step S5 includes the following: Step S51: Select the terminal with control requirements as the target terminal and query the terminal set corresponding to the target terminal; Step S52: Query the data correlation between each terminal in the terminal set and the target terminal, query the impact of the alternative control commands on the target terminal, and determine the impact on other terminals based on the data correlation and the impact on the target terminal; where the impact is expressed as a percentage. Step S53: Compare the absolute value of each other influence quantity with the corresponding threshold. When the absolute value of all other influence quantities is less than the corresponding threshold, calculate the sum of the absolute values ​​of all other influence quantities as the total influence quantity. Step S54: Select the candidate control command with the smallest total impact as the final control command.

8. A multi-terminal collaborative power grid interactive control system, characterized in that, The aforementioned multi-terminal collaborative power grid interaction control system includes the following: The data structure creation module is used to query terminals and their built-in meters in the power grid system, obtain power parameters based on the built-in meters, and construct the data structure for each terminal. The correlation analysis module is used to determine the correlation between different terminals based on the data structure of each terminal. The terminal set creation module is used to select terminals whose relevance to any given terminal reaches a preset relevance threshold, thereby obtaining a terminal set. The terminal analysis module is used to analyze the data structure of any terminal to determine control requirements and alternative control commands. The influence quantity determination module is used to select the terminal with control requirements as the target terminal, query the terminal set corresponding to the target terminal, determine the other-end influence quantity of each candidate control command in the terminal set, and determine the final control command based on the other-end influence quantity.

9. A multi-terminal collaborative power grid interaction control system according to claim 8, characterized in that, The data volume creation module includes the following: The filing data query unit is used to establish a connection channel with the filing database of the power grid system and to query the terminal and the built-in instruments of the terminal. The terminal tag generation unit is used to query the terminal model and installation location, and generate a terminal tag based on the model and installation location. The array creation unit is used to count the built-in meters according to a preset order, determine the meter labels, and create an array indexed by the meter labels; The data statistics unit is used to statistically analyze the time-inclusive power parameters obtained from the built-in meters based on array statistics, and to obtain the data structure for each terminal.

10. A multi-terminal collaborative power grid interaction control system according to claim 8, characterized in that, The correlation analysis module includes the following: The first interception unit is used to receive the analysis span uploaded by the administrator and intercept the data structure according to the analysis span; the analysis span is a time period. The intersection and union calculation unit is used to compare the instrument labels in the data structures of any two terminals to determine the intersection and union of the labels. The baseline coefficient determination unit is used to determine the baseline coefficient based on the number of tags in the tag intersection and the number of tags in the tag union. The array comparison unit is used to read the array corresponding to the instrument tags in the tag intersection, compare the arrays, and determine the data relevance. The information accumulation unit is used to query the weight corresponding to the instrument tag, accumulate the relevance of the weighted data, calculate the product with the benchmark coefficient, and obtain the relevance between different terminals. The terminal analysis module includes the following: The second interception unit is used to intercept the data structure of each terminal according to the preset backtracking time to obtain the instantaneous structure; The comparison and positioning unit is used to query the standard structure corresponding to the terminal, compare the instantaneous structure with the standard structure, and locate abnormal instruments and their degree of abnormality. The alternative command query unit is used to determine control requirements based on abnormal instruments and the degree of abnormality, and to query alternative control commands from the control command set corresponding to the terminal. The optimization unit is used to determine the external influence quantity of each candidate control command in the terminal set, and determine the final control command based on the external influence quantity.

Images