A power system state monitoring method, system, device and storage medium

Abstract

The application discloses a kind of power system state monitoring method, system, equipment and storage medium, it is related to power state monitoring technical field, method includes: obtaining the historical monitoring data of different parameters of each power node generates early warning signal and carries out analysis, obtains the upper limit value and lower limit value of the parameter early warning threshold limit value corresponding to each power node different parameter respectively, determine the early warning range corresponding to each power node respectively;The interaction range between the early warning range of each power node is analyzed, and the interaction node list corresponding to each power node is generated;Real-time monitoring data of different parameters of each power node is obtained in real time, and early warning signal is generated by judging;The node early warning correlation of the application is clear, accurately distinguish multi-node fluctuation and single-node anomaly in real-time monitoring, avoid false alarm, can help operation and maintenance personnel to quickly locate fault source, improve monitoring accuracy and system stability.

Description

Technical Field

[0001] This invention relates to the field of power condition monitoring technology, and in particular to a power system condition monitoring method, system, device and storage medium. Background Technology

[0002] With the high proportion of new energy sources integrated into the power system, the volatility of operating parameters at various power nodes has increased significantly, posing a huge challenge to the safe and stable operation of the power system. Accurate monitoring and early warning of power node parameters have become crucial to ensuring the reliable operation of the power system. Therefore, improving the accuracy and reliability of monitoring systems and avoiding misjudgments and delays in fault handling have become urgent technical problems to be solved.

[0003] Currently, power systems typically employ various monitoring terminals to collect real-time data on multiple key parameters such as voltage, current, temperature, and vibration, issuing warning signals when these parameters exceed preset static thresholds. However, this traditional monitoring method has significant drawbacks. Because the warning threshold regions of different nodes in a power system may overlap, the system is prone to misjudgments during monitoring, issuing incorrect warning signals and reducing the reliability and accuracy of the monitoring system. Furthermore, existing systems only analyze parameter states independently for each node, ignoring the interactivity between warning regions between nodes. When power flow changes abruptly, multiple related nodes often enter the warning range simultaneously, leading to misjudgments of multi-point faults. This makes it difficult for maintenance personnel to quickly locate the true fault source, and may even cause them to overlook truly independent faults. In summary, traditional methods cannot clearly define the interrelationships between parameter warnings between nodes, cannot accurately identify multi-node coordinated fluctuations versus single-node independent anomalies, and are insufficient to effectively reduce false alarms. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: when multiple nodes simultaneously trigger early warnings within the interaction range, indicating systemic risks, and the early warning system issues multiple early warning signals, it makes it difficult for maintenance personnel to quickly locate the real source of the fault from a large amount of information, leading to delays in handling due to misjudgment.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a power system condition monitoring method, comprising:

[0008] Historical monitoring data of different parameters of each power node when generating early warning signals are obtained and analyzed to obtain the upper and lower limits of the parameter early warning thresholds for different parameters of each power node.

[0009] Based on the upper and lower limits of the parameter warning threshold, the warning range corresponding to each power node is determined.

[0010] Analyze the interaction range between the early warning ranges of each power node, and generate a list of interaction nodes corresponding to each power node;

[0011] Based on the parameter warning threshold and the list of interactive nodes, real-time monitoring data of different parameters of each power node are acquired and analyzed in real time, and warning signals are determined and generated.

[0012] As a preferred scheme for power system condition monitoring methods, wherein:

[0013] The step of acquiring and analyzing historical monitoring data when generating early warning signals for different parameters of each power node, and obtaining the upper and lower limits of the parameter early warning thresholds for different parameters of each power node, includes:

[0014] Randomly select one of the power nodes in the power system as the target node, and then randomly select one of the different parameters as the target parameter;

[0015] Historical monitoring data of target nodes when generating early warning signals multiple times is obtained, and the statistical characteristics of the historical monitoring data are analyzed.

[0016] Based on statistical characteristics and combined with preset adjustment factors, the upper and lower limits of the parameter warning threshold at the target node are determined.

[0017] As a preferred scheme for power system condition monitoring methods, wherein:

[0018] The step of acquiring and analyzing historical monitoring data when generating early warning signals for different parameters of each power node, and obtaining the upper and lower limits of the parameter early warning thresholds for different parameters of each power node, also includes:

[0019] Repeat the steps of selecting parameters, analyzing historical monitoring data, and determining parameter warning thresholds. Analyze the monitoring data when other remaining parameters of the target node generate warning signals one by one to obtain the upper and lower limits of the parameter warning thresholds corresponding to different parameters at the target node.

[0020] Repeat the steps of selecting target nodes and analyzing parameter thresholds, analyze the monitoring data of other remaining nodes when they generate early warning signals, and obtain the upper and lower limits of the parameter early warning thresholds for different parameters at each power node.

[0021] As a preferred scheme for power system condition monitoring methods, wherein:

[0022] The determination of the warning range for each power node based on the upper and lower limits of the parameter-based warning threshold includes:

[0023] The power nodes that have completed the parameter early warning threshold analysis are taken as the objects, and then sequentially selected as the target power nodes;

[0024] Draw a circle of the region with a preset radius, and draw parameter lines representing each parameter at equal intervals according to the number of parameters, with the origin of the region circle as the endpoint. Mark the nodes of the corresponding parameter unit values ​​on the parameter lines.

[0025] Based on the upper limit of the parameter warning threshold at the target power node, the corresponding data points are marked on the corresponding parameter lines, and the corresponding data points are connected in sequence to generate the outer boundary line of the target power node.

[0026] Based on the lower limit of the parameter warning threshold value of different parameters at the target power node, mark the corresponding data points on the corresponding parameter line, and connect the corresponding data points in sequence to generate the inner boundary line of the target power node;

[0027] The range between the outer boundary and the inner boundary of the target power node is defined as the early warning range of the target power node.

[0028] The same method was used to analyze the remaining nodes to determine the warning range corresponding to each power node.

[0029] As a preferred scheme for power system condition monitoring methods, wherein:

[0030] The analysis of the interaction range between the early warning ranges of each power node, and the generation of a list of interaction nodes corresponding to each power node, includes:

[0031] Select the warning range corresponding to the target node from the warning range of each power node, then select the comparison node from the remaining power nodes and obtain its warning range, draw the two in the same area, and determine the interaction range between the target node and the comparison node.

[0032] Calculate the warning distance of each parameter in the warning range of the target node and the interaction distance of each parameter in the interaction range. Based on the ratio of the interaction distance of each parameter to the warning distance, and combined with the preset threshold, calculate the interaction coefficient between the target node and the comparison node.

[0033] The same method is used to analyze the interaction range between the warning range of the target node and the warning range of other remaining nodes one by one, calculate the interaction coefficient between the target node and each other node, and calculate the absolute value of the difference between the mean, the maximum value and the minimum value. The corresponding power nodes with interaction coefficients greater than a specific interaction threshold are taken as the interaction nodes of the target node. The nodes are sorted from largest to smallest according to the interaction coefficient to obtain the list of interaction nodes corresponding to the target node and bind them to the target node.

[0034] The same method is used to analyze the interaction range between the warning ranges of the remaining nodes one by one, obtain the list of interaction nodes corresponding to each power node, and bind them to the corresponding power nodes.

[0035] As a preferred scheme for power system condition monitoring methods, wherein:

[0036] The process of acquiring and analyzing real-time monitoring data of different parameters of each power node based on parameter warning thresholds and an interactive node list, and determining and generating warning signals includes:

[0037] The system acquires real-time monitoring data of the target node's target parameters and compares it with the upper and lower limits of the corresponding parameter warning threshold. If the parameter is greater than the upper limit, a warning signal is generated; if it is less than the lower limit, no action is taken; if it is between the upper and lower limits, the parameter is preliminarily determined to be in a warning state.

[0038] If the warning status is initially determined to be valid, obtain the list of interactive nodes for that node, obtain the real-time monitoring data of the same parameter for each interactive node in the list, use the same comparison method to determine the warning signal for that parameter for each interactive node, and count the number of warning signals corresponding to the list of interactive nodes.

[0039] As a preferred scheme for power system condition monitoring methods, wherein:

[0040] The process of acquiring and analyzing real-time monitoring data of different parameters of each power node based on parameter warning thresholds and an interactive node list, and determining and generating warning signals, also includes:

[0041] Calculate the ratio of the number of early warning signals to the total number of interactive nodes, and compare it with a preset threshold. If the ratio is greater than the preset threshold, it is determined that the abnormality of the target node parameter is caused by the fluctuation due to normal interaction between nodes, and no early warning signal is generated. Otherwise, it is determined to be an independent abnormality, an abnormal signal is generated, and the corresponding power node and abnormal parameter are sent to the management personnel.

[0042] The same method is used to analyze the real-time monitoring data of different parameters of each power node, and generate early warning signals corresponding to different parameters of each power node.

[0043] Secondly, the present invention provides a power system condition monitoring system, comprising:

[0044] The historical data threshold analysis module is used to acquire and analyze historical monitoring data when different parameters of each power node generate early warning signals, and to obtain the upper and lower limits of the parameter early warning threshold values ​​corresponding to different parameters of each power node.

[0045] The warning range definition module is used to determine the warning range corresponding to each power node based on the upper and lower limits of the parameter warning threshold.

[0046] The interaction node list generation module is used to analyze the interaction range between the early warning ranges of various power nodes and generate an interaction node list corresponding to each power node.

[0047] The real-time data early warning judgment module is used to acquire and analyze real-time monitoring data of different parameters of each power node based on the parameter early warning threshold value and the interactive node list, and to judge and generate early warning signals.

[0048] Thirdly, the present invention provides a computer device, comprising:

[0049] Memory and processor;

[0050] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of a power system state monitoring method.

[0051] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of a power system state monitoring method.

[0052] The beneficial effects of this invention are as follows: This invention has significant advantages in actual power system monitoring scenarios. Traditional monitoring methods are prone to misjudgments due to overlapping warning areas of nodes, making it difficult for maintenance personnel to locate the fault source. This invention, by generating an interactive node list corresponding to each node, clarifies the interrelationship of parameter warnings between nodes, transforming the fuzzy relationship of overlapping warning areas between nodes into quantifiable interaction coefficients. During real-time monitoring, secondary verification using parameter warning thresholds and the interactive node list can accurately identify multi-node coordinated fluctuations and single-node independent anomalies. When multiple related nodes simultaneously enter the warning interval, the system can accurately determine whether it is a normal fluctuation or an independent fault, avoiding false alarms and allowing maintenance personnel to quickly locate the true fault source. This improves the accuracy and reliability of power system monitoring, ensuring the safe and stable operation of the power system. Attached Figure Description

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is an overall flowchart of a power system condition monitoring method provided by the present invention.

[0055] Figure 2 This is a schematic diagram of the structure of region circle C in a power system condition monitoring method provided by the present invention.

[0056] Figure 3 This is a schematic diagram of the early warning range F1, early warning range F2, and interaction range H1 of a power system status monitoring method provided by the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0058] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a power system condition monitoring method, including:

[0059] S1: Obtain and analyze historical monitoring data when different parameters of each power node generate early warning signals to obtain the upper and lower limits of the parameter early warning thresholds for different parameters of each power node;

[0060] S2: Based on the upper and lower limits of the parameter warning threshold, determine the warning range corresponding to each power node;

[0061] S3: Analyze the interaction range between the early warning ranges of each power node and generate a list of interaction nodes corresponding to each power node;

[0062] S4: Based on the parameter warning threshold and the list of interactive nodes, real-time monitoring data of different parameters of each power node are acquired and analyzed in real time, and warning signals are determined and generated.

[0063] It should be noted that by sequentially acquiring historical monitoring data to determine the parameter warning threshold value, clarifying the warning range based on the threshold value, analyzing the warning range to interactively generate an interactive node list, and combining the threshold value and the list to monitor and determine the warning signal in real time, the accuracy and reliability of power system monitoring can be effectively improved, faults can be accurately identified, misjudgments can be avoided, and maintenance personnel can be helped to quickly locate the fault source and ensure the safe and stable operation of the power system.

[0064] Example 2, refer to Figure 1 As one embodiment of the present invention, a power system state monitoring method is provided based on the previous embodiment, comprising:

[0065] In this embodiment, the historical monitoring data obtained and analyzed in step S1 when different parameters of each power node generate early warning signals, to obtain the upper and lower limits of the parameter early warning thresholds corresponding to different parameters of each power node, include:

[0066] One of the power nodes in the power system is randomly selected as the target node.

[0067] Randomly select one of the different parameters as the target parameter.

[0068] Obtain historical monitoring data of the target node when generating early warning signals multiple times, and mark it as D. n , where n represents the different monitoring data numbers when the target node generates an early warning signal. Acquire monitoring data D. n Standard deviations U and D n The maximum value D max and minimum value D min ; set the minimum value D min The absolute value of the difference between the target parameter and the standard deviation U is used as the lower limit X of the parameter warning threshold at the target node. A11 , the maximum value D max The sum of the products of the standard deviation U and the preset multiplier β is used as the upper limit of the parameter warning threshold at the target node; expressed as:

[0069] X A11 =|D min -U∣

[0070] X B11 =D max +β×U

[0071] Among them, X B11 This represents the upper limit of the parameter warning threshold at the target node. The specific value of the preset multiple β is determined by relevant personnel according to actual needs, and the range is 3≥β≥1.

[0072] Repeat the steps of selecting parameters and analyzing monitoring data described above, and analyze the monitoring data when generating early warning signals for the remaining parameters one by one, thereby obtaining the upper limit value X of the parameter early warning threshold value corresponding to different parameters at the target node. A1i and lower limit value X B1i , where i represents different parameters, i=1,2,...,a1, where a1 is the total number of parameters, a1 is a positive integer and a1≥1.

[0073] By repeating the steps of selecting the target node and analyzing the parameter threshold values, and analyzing the monitoring data of the remaining nodes when they generate early warning signals, the upper limit value X of the parameter early warning threshold value corresponding to different parameters at each power node can be obtained. Aji and lower limit value X Bji , where j represents different power nodes in the power system, and j is also used as the node number corresponding to each power node, j=1, 2, ..., a2, where a2 is the total number of power nodes, a2 is a positive integer and a2≥2.

[0074] It should be noted that, based on the maximum, minimum and standard deviation of the statistical characteristics of historical early warning data, the upper and lower limits of the personalized early warning threshold are calculated for each parameter of each node. This breaks through the limitations of the traditional fixed threshold. The threshold is dynamically generated by the node's own historical data, adapting to the parameter characteristics of different nodes, so that the threshold neither misses potential anomalies nor is overly sensitive to cause misjudgment.

[0075] In this embodiment, the determination of the warning range corresponding to each power node based on the upper and lower limits of the parameter warning threshold in step S2 above includes:

[0076] Taking the power nodes that completed the parameter warning threshold analysis in step S1 as the objects, they are sequentially taken as target power nodes.

[0077] To visually represent the warning range of each parameter, a circle C is drawn with the preset value R as its radius, as shown below. Figure 2 As shown, the warning threshold values ​​of each parameter of the power node are displayed in a graphical form. Specifically, parameter lines are drawn at equal intervals with the origin as the endpoint of the area circle C, and each parameter line is used to represent a parameter.

[0078] Based on the total number of parameters a2, draw a2 parameter lines of length R at equal intervals within the drawing area circle C, with the origin of the area circle C as the endpoint. Mark multiple nodes at the same distance on each parameter line. Different nodes on different parameter lines correspond to the unit value of the parameter. The specific value of the preset value R is determined by relevant personnel according to actual needs. In this embodiment, R=10cm is preferred.

[0079] Based on the upper limit value X of the parameter warning threshold corresponding to different parameters at the power node analysis point. A1i The values, on different parameter lines, represent the upper limits X corresponding to different parameters at the power nodes being analyzed. A1i Data point D Ai Label each data point D Ai The connections are made sequentially to generate the outer boundary L of the target power node. A .

[0080] Based on different parameter lines, the lower limit value X corresponding to different parameters at the power node analysis points is determined. B1i The values, on different parameter lines, correspond to the lower limit values ​​X corresponding to different parameters at the power node in the analysis. B1i Data point D Bi Label each data point D Bi The inner boundary line L of the target power node is generated by sequentially connecting the nodes. B .

[0081] The outer boundary L of the target power node A and inner boundary line L B The range between these ranges is used as the early warning range F1 for the target power node.

[0082] The same method is used to analyze the remaining nodes one by one, thereby obtaining the warning range F corresponding to each power node. j .

[0083] It should be noted that the dynamic threshold values ​​of all parameters of each node, namely the upper and lower limits, are mapped to a multi-dimensional space to form a visualized warning range. This range is defined by inner and outer boundaries, representing the normal fluctuation boundaries of the node's parameters. The complex threshold values ​​of multiple parameters are presented graphically, making the system status clear at a glance and easy to understand and analyze. This multi-dimensional warning range provides basic data and a visualization model for subsequent node interaction analysis, facilitating the comparison of warning status between different nodes.

[0084] In this embodiment, step S3 above analyzes the interaction range between the warning ranges of each power node and generates a list of interaction nodes corresponding to each power node, including:

[0085] The warning range F corresponding to each power node j Obtain the warning range F1 corresponding to the target node, select one of the remaining power nodes as a comparison node, and determine the warning range F1 of each power node. jObtain the warning range F2 corresponding to the comparison node, and plot the warning range F1 of the target node and the warning range F2 of the comparison node within the same area circle C. Mark the overlapping part between the warning range F1 of the target node and the warning range F2 of the comparison node as the interaction range H1 (V1, V2, ..., V2) between the target node and the comparison node. a1 ),like Figure 3 As shown, the overlapping portion of the warning ranges F1 and F2 constitutes the interaction range H1, and the intersection of their numerical intervals is used to calculate the interaction distance K. i and warning distance L i ;where V a1 This is the intersection of the numerical intervals corresponding to the upper and lower limits of each parameter in the warning range corresponding to the target node and the numerical intervals corresponding to the upper and lower limits of each parameter in the comparison node.

[0086] The absolute value of the difference between the upper and lower limits of each parameter in the target node's early warning range F1 is denoted as the early warning distance L. i Simultaneously, the intersection V of all parameters within the interaction range H1 is obtained from the region circle C. a1 The corresponding numerical span is denoted as the interaction distance and is represented as:

[0087] K i =∣V maxi -V mini |

[0088] Among them, K i V represents the interaction distance of the i-th parameter. maxi and V mini These are the upper and lower limits of each parameter within the interaction range H1, respectively, and the interaction distance K of each parameter is... i Its corresponding warning distance L i The ratios between them are respectively related to each preset threshold θ i The sum of the products between the target node and the comparison node is used as the interaction coefficient X1 between the target node and the comparison node, with a preset threshold θ. i The specific value will be determined by the relevant personnel based on actual needs.

[0089] By employing the aforementioned method of analyzing interaction range and calculating interaction coefficients, the interaction range between the target node's warning range and the warning ranges of other remaining nodes is analyzed one by one, thereby obtaining the interaction coefficient X between the target node and each other node. e , where e represents the different power nodes remaining after removing the target node from the various power nodes in the power system, e=1,2,...,a2-1.

[0090] Calculate the interaction coefficient X e mean X pand the interaction coefficient X e The absolute value J of the difference between the maximum and minimum values ​​is the interaction coefficient X. e Power nodes with interaction coefficients greater than the interaction threshold are designated as interaction nodes for the target node. These interaction nodes are sorted in descending order of their interaction coefficient values ​​to obtain a list of interaction nodes corresponding to the target node, which is then bound to the target node. The interaction threshold is expressed as X. p +J.

[0091] Using the same method described above, the interaction ranges between the warning ranges of the remaining nodes are analyzed one by one to obtain a list of interaction nodes corresponding to each power node, and then these nodes are bound to the corresponding power nodes.

[0092] It should be noted that the interaction distance K between nodes in the early warning range is calculated. i and warning distance L i Using the interaction distance K i Distance L from the warning i The ratio is used to calculate the interaction coefficient X between nodes. e Nodes with interaction coefficients higher than the threshold are selected as interaction nodes, and a list of interaction nodes corresponding to the nodes is generated. The interrelationship of parameter warnings between nodes is clarified, and the fuzzy relationship of the intersection of warning areas between nodes is transformed into a quantifiable interaction coefficient, providing a basis for secondary verification of subsequent real-time monitoring, so that the system can accurately identify multi-node coordinated fluctuations and single-node independent anomalies.

[0093] In this embodiment, step S4 above, based on the parameter warning threshold and the list of interactive nodes, involves acquiring and analyzing real-time monitoring data of different parameters of each power node, determining and generating a warning signal, including:

[0094] Obtain real-time monitoring data M1 of the target parameters at the target node. When the real-time monitoring data M1 is greater than X... B11 When the real-time monitoring data M1 is less than the lower limit value X, an early warning signal is generated for the corresponding parameter of the power node. A11 If the real-time monitoring data M1 satisfies X, no action is taken. B11 ≥M1≥X A11 If so, it is preliminarily determined that the parameter of that node is in a warning state.

[0095] If the initial determination of the warning status is established, the list of interactive nodes for that node is further obtained. Real-time monitoring data of the same parameter for each interactive node in the list is acquired, and in the same way as the real-time monitoring data M1 of the target parameter at the target node is analyzed, it is compared and analyzed with the lower and upper limits of the corresponding parameter warning threshold to determine the generation of warning signals at the target parameter of each interactive node, and thus the number of warning signals corresponding to the list of interactive nodes is obtained.

[0096] When the ratio between the number of warning signals and the total number of interactive nodes in the target node's interactive node list is greater than the threshold Y1, it is determined that the target parameter of the target node is in an abnormal state. The same parameter of multiple interactive nodes in the corresponding interactive node list exceeds the threshold Y1 at the same time. Therefore, it is considered that the abnormal state of the target parameter of the target node is due to normal fluctuations caused by interaction, rather than an independent fault of a single node. Therefore, no action is taken and no warning signal is generated for the target node, thus avoiding false alarms caused by normal correlation between nodes. Conversely, the abnormal state of the target parameter of the target node is determined to be an independent abnormality. An abnormal signal corresponding to the target parameter of the target node is generated, and the corresponding power node and the corresponding abnormal parameter are sent to the power system management personnel. The specific value of the threshold Y1 is greater than or equal to three-fifths.

[0097] Using the same method, real-time monitoring data of different parameters of each power node can be analyzed to generate early warning signals corresponding to different parameters of each power node.

[0098] It should be noted that by acquiring real-time monitoring data of node parameters and comparing it with the corresponding parameter warning threshold, a preliminary warning status is determined. If the preliminary warning status is established, the corresponding parameter status of the interactive nodes is further analyzed. If interactive nodes simultaneously exhibit warning status and the proportion exceeds the threshold Y1, it is determined to be normal interaction between nodes, and no action is taken. Otherwise, it is determined to be an independent node anomaly, and an anomaly signal is generated to notify management personnel. Real-time and accurate judgment of node anomaly status significantly improves the accuracy of power system parameter warnings and effectively avoids false alarms caused by normal correlation between nodes. By using the previously calculated parameter warning threshold and interactive node list to monitor real-time data of the power system, and accurately identifying which parameter of which node is abnormal when an anomaly is detected, while considering the interaction between nodes to avoid false alarms, it can accurately distinguish between single node anomalies and normal fluctuations in node interaction, greatly improving the accuracy of power system status monitoring and effectively solving the problem of misjudgment caused by overlapping node parameter warning areas.

[0099] Example 3: The above is an illustrative scheme of a power system condition monitoring method according to this embodiment. It should be noted that the technical solution of a power system condition monitoring system and the technical solution of the power system condition monitoring method described above belong to the same concept. Details not described in detail in the technical solution of the power system condition monitoring system in this embodiment can be found in the description of the technical solution of the power system condition monitoring method described above.

[0100] This embodiment also provides a power system condition monitoring system, including:

[0101] The historical data threshold analysis module is used to acquire and analyze historical monitoring data when different parameters of each power node generate early warning signals, and to obtain the upper and lower limits of the parameter early warning threshold values ​​corresponding to different parameters of each power node.

[0102] The warning range definition module is used to determine the warning range corresponding to each power node based on the upper and lower limits of the parameter warning threshold.

[0103] The interaction node list generation module is used to analyze the interaction range between the early warning ranges of various power nodes and generate an interaction node list corresponding to each power node.

[0104] The real-time data early warning judgment module is used to acquire and analyze real-time monitoring data of different parameters of each power node based on the parameter early warning threshold value and the interactive node list, and to judge and generate early warning signals.

[0105] This embodiment also provides an electronic device applicable to a power system condition monitoring method, including:

[0106] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a power system status monitoring method as described in the above embodiments.

[0107] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a power system status monitoring method as proposed in the above embodiments.

[0108] The storage medium proposed in this embodiment belongs to the same inventive concept as the power system status monitoring method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for monitoring the condition of a power system, characterized in that, include: Historical monitoring data of different parameters of each power node when generating early warning signals are obtained and analyzed to obtain the upper and lower limits of the parameter early warning thresholds for different parameters of each power node. Based on the upper and lower limits of the parameter warning threshold, the warning range of different parameters corresponding to each node is determined. Analyze the interaction range between the early warning ranges of each power node, and generate a list of interaction nodes corresponding to each power node; The analysis of the interaction range between the early warning ranges of each power node, and the generation of a list of interaction nodes corresponding to each power node, includes: Select the warning range corresponding to the target node from the warning range of each power node, then select the comparison node from the remaining power nodes and obtain its warning range, draw the two in the same area, and determine the interaction range between the target node and the comparison node. Calculate the warning distance of each parameter within the warning range of the target node and the interaction distance of each parameter within the interaction range. Based on the ratio of the interaction distance to the warning distance of each parameter, and combined with the preset weight of each parameter, calculate the interaction coefficient between the target node and the comparison node. The same method is used to analyze the interaction range between the warning range of the target node and the warning range of other remaining nodes one by one, calculate the interaction coefficient between the target node and each other node, and take the corresponding power node with the interaction coefficient greater than a certain interaction threshold as the interaction node of the target node. Sort the interaction nodes in descending order of interaction coefficient to obtain the list of interaction nodes corresponding to the target node and bind them to the target node. The same method is used to analyze the interaction range between the warning ranges of other remaining nodes one by one, obtain the list of interaction nodes corresponding to each power node, and bind them to the corresponding power nodes. Based on the parameter warning threshold and the list of interactive nodes, real-time monitoring data of different parameters of each power node are acquired and analyzed in real time to determine and generate warning signals. The process of acquiring and analyzing real-time monitoring data of different parameters of each power node based on parameter warning thresholds and an interactive node list, and determining and generating warning signals includes: The system acquires real-time monitoring data of the target node's target parameters and compares it with the upper and lower limits of the corresponding parameter warning threshold. If the parameter is greater than the upper limit, a warning signal is generated; if it is less than the lower limit, no action is taken; if it is between the upper and lower limits, the parameter is preliminarily determined to be in a warning state. If the warning status is initially determined to be valid, obtain the list of interactive nodes for that node, obtain the real-time monitoring data of the same parameter for each interactive node in the list, use the same comparison method to determine the warning signal for that parameter for each interactive node, and count the number of warning signals corresponding to the list of interactive nodes.

2. The power system condition monitoring method as described in claim 1, characterized in that, The step of acquiring and analyzing historical monitoring data when generating early warning signals for different parameters of each power node, and obtaining the upper and lower limits of the parameter early warning thresholds for different parameters of each power node, includes: Randomly select one of the power nodes in the power system as the target node, and then randomly select one of the different parameters as the target parameter; Historical monitoring data of target nodes when generating early warning signals multiple times is obtained, and the statistical characteristics of the historical monitoring data are analyzed. Based on statistical characteristics and combined with preset adjustment factors, the upper and lower limits of the parameter warning threshold at the target node are determined.

3. The power system condition monitoring method as described in claim 2, characterized in that, The step of acquiring and analyzing historical monitoring data when generating early warning signals for different parameters of each power node, and obtaining the upper and lower limits of the parameter early warning thresholds for different parameters of each power node, also includes: Repeat the steps of selecting parameters, analyzing historical monitoring data, and determining parameter warning thresholds. Analyze the monitoring data when other remaining parameters of the target node generate warning signals one by one to obtain the upper and lower limits of the parameter warning thresholds corresponding to different parameters at the target node. Repeat the steps of selecting target nodes and analyzing parameter thresholds, analyze the monitoring data of other remaining nodes when they generate early warning signals, and obtain the upper and lower limits of the parameter early warning thresholds for different parameters at each power node.

4. The power system condition monitoring method as described in claim 3, characterized in that, The determination of the warning range for different parameters at each node based on the upper and lower limits of the parameter warning threshold includes: The power nodes that have completed the parameter early warning threshold analysis are taken as the objects, and then sequentially selected as the target power nodes; Draw a circle of the region with a preset radius, and draw parameter lines representing each parameter at equal intervals according to the number of parameters, with the origin of the region circle as the endpoint. Mark the nodes of the corresponding parameter unit values ​​on the parameter lines. Based on the upper limit of the parameter warning threshold at the target power node, the corresponding data points are marked on the corresponding parameter lines, and the corresponding data points are connected in sequence to generate the outer boundary line of the target power node. Based on the lower limit of the parameter warning threshold value of different parameters at the target power node, mark the corresponding data points on the corresponding parameter line, and connect the corresponding data points in sequence to generate the inner boundary line of the target power node; The range between the outer boundary and the inner boundary of the target power node is defined as the early warning range of the target power node. The process involves drawing a circle and parameter lines for the target power node, marking the node, marking data points based on the upper and lower limits of the parameter warning threshold, connecting them to generate the outer and inner boundary lines, and then determining the warning range. The remaining nodes are then analyzed to determine the warning range for each node corresponding to different parameters.

5. A power system condition monitoring method as described in claim 4, characterized in that, The process of acquiring and analyzing real-time monitoring data of different parameters of each power node based on parameter warning thresholds and an interactive node list, and determining and generating warning signals includes: Calculate the ratio of the number of early warning signals to the total number of interactive nodes, and compare it with a preset threshold. If the ratio is greater than the preset threshold, it is determined that the abnormal target parameter of the target node is a fluctuation caused by normal interaction between nodes, and no early warning signal is generated. Otherwise, it is determined to be an independent abnormality, an abnormal signal is generated, and the corresponding power node and abnormal parameter are sent to the management personnel. The process involves acquiring real-time monitoring data of target parameters at each power node, comparing it with the parameter warning threshold, determining the warning status, counting the number of warning signals from interactive nodes, calculating the ratio, and comparing it with a preset threshold to determine whether to generate a warning signal. This process analyzes the real-time monitoring data of different parameters at each power node and generates warning signals corresponding to different parameters at each power node.

6. A power system condition monitoring system, employing the method described in any one of claims 1 to 5, characterized in that, include: The historical data threshold analysis module is used to acquire and analyze historical monitoring data when different parameters of each power node generate early warning signals, and to obtain the upper and lower limits of the parameter early warning threshold values ​​corresponding to different parameters of each power node. The warning range definition module is used to determine the warning range of different parameters at each node based on the upper and lower limits of the parameter warning threshold. The interaction node list generation module is used to analyze the interaction range between the early warning ranges of various power nodes and generate an interaction node list corresponding to each power node. The analysis of the interaction range between the early warning ranges of each power node, and the generation of a list of interaction nodes corresponding to each power node, includes: Select the warning range corresponding to the target node from the warning range of each power node, then select the comparison node from the remaining power nodes and obtain its warning range, draw the two in the same area, and determine the interaction range between the target node and the comparison node. Calculate the warning distance of each parameter within the warning range of the target node and the interaction distance of each parameter within the interaction range. Based on the ratio of the interaction distance to the warning distance of each parameter, and combined with the preset weight of each parameter, calculate the interaction coefficient between the target node and the comparison node. The same method is used to analyze the interaction range between the warning range of the target node and the warning range of other remaining nodes one by one, calculate the interaction coefficient between the target node and each other node, and take the corresponding power node with the interaction coefficient greater than a certain interaction threshold as the interaction node of the target node. Sort the interaction nodes in descending order of interaction coefficient to obtain the list of interaction nodes corresponding to the target node and bind them to the target node. The same method is used to analyze the interaction range between the warning ranges of other remaining nodes one by one, obtain the list of interaction nodes corresponding to each power node, and bind them to the corresponding power nodes. The real-time data early warning judgment module is used to acquire and analyze real-time monitoring data of different parameters of each power node based on the parameter early warning threshold value and the interactive node list, and to judge and generate early warning signals. The process of acquiring and analyzing real-time monitoring data of different parameters of each power node based on parameter warning thresholds and an interactive node list, and determining and generating warning signals includes: The system acquires real-time monitoring data of the target node's target parameters and compares it with the upper and lower limits of the corresponding parameter warning threshold. If the parameter is greater than the upper limit, a warning signal is generated; if it is less than the lower limit, no action is taken; if it is between the upper and lower limits, the parameter is preliminarily determined to be in a warning state. If the warning status is initially determined to be valid, obtain the list of interactive nodes for that node, obtain the real-time monitoring data of the same parameter for each interactive node in the list, use the same comparison method to determine the warning signal for that parameter for each interactive node, and count the number of warning signals corresponding to the list of interactive nodes.

7. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 5.

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