Method and device for analyzing and evaluating operation state of power system

Abstract

The invention discloses an electric power system operation state analysis and evaluation method and device, and the method comprises the steps: carrying out the line node division of an electric power system, combining the load time division of a power supply region, and further discriminating the load change based on the load time; meanwhile, fluctuation nodes are identified by using the operation states of the line nodes and the power grid operation fluctuation degree is analyzed, so that the operation stability of the power system is evaluated. According to the method, limitation of traditional overall evaluation is broken through, fine transformation from macroscopic to microscopic and from static analysis to dynamic monitoring is achieved, the actual operation condition of the power system can be comprehensively and accurately reflected, and clear direction guidance is provided for formulating a targeted regulation and control strategy. In addition, the possibility that abnormal operation of the power system is covered can be effectively reduced, and potential risks are reduced to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operating status analysis, and in particular to a power system operating status analysis and evaluation method and device. Background Art

[0002] With socioeconomic development and changes in energy structures, power systems are becoming increasingly important in modern life. As a core component of the power system, they undertake the critical task of efficiently and stably transmitting electrical energy from power generation to load. Therefore, accurately assessing the operating status of power systems is of great significance.

[0003] Stability assessment is particularly important in evaluating the operating status of power systems. This is because the operation of power systems is not fixed but is affected by a variety of dynamic factors, the most important of which is the impact of load fluctuations. As user electricity demand changes over time, the grid load may fluctuate periodically or randomly. Such fluctuations may cause fluctuations in voltage and frequency, thereby affecting the stability of the power grid. When the power system is unstable, it may lead to a series of serious consequences. These consequences not only affect the normal operation of the power system, but may also have a wide-ranging and far-reaching impact on the social economy and user lives. Therefore, it is necessary to conduct power system stability assessments to identify potential problems in advance and take preventive measures to ensure that the power system maintains safe, reliable and efficient operation in the face of dynamic changes.

[0004] However, current power system operational stability assessments are often based on the volatility of overall operational indicators, failing to fully consider the differences in operational indicator volatility at different line nodes within the power system network when load changes. Due to the varying operational characteristics of each node, relying solely on the volatility assessment of overall operational indicators can only reflect whether the power system is experiencing operational instability, but it is difficult to accurately locate specific fluctuating nodes. This limitation makes it impossible to implement refined control measures for specific nodes, making it difficult to focus control strategies on key areas, affecting the overall control effect. It is also prone to the possibility that the entire power system may need to be strengthened to address potential risks, resulting in a waste of manpower, material, and financial resources.

[0005] In addition, the abnormal status of some key nodes may be masked by the overall assessment results, resulting in the failure to eliminate potential risks in a timely manner and increasing the probability of power system operation failures. Summary of the Invention

[0006] In order to address the deficiencies in the prior art, the present invention provides a method and device for analyzing and evaluating the operating status of an electric power system, which realizes a refined evaluation of the operating stability of the electric power system by utilizing the fluctuations of operating indicators of line nodes on the electric power system.

[0007] The present invention adopts the following technical solutions.

[0008] In a first aspect, the present invention provides a method for analyzing and evaluating the operating status of a power system, the method comprising:

[0009] Use the historical load records of the power transmission network supply area in the power system to divide the load period;

[0010] During the set observation period, load changes are identified based on the divided load periods;

[0011] When load changes are identified, the fluctuating nodes are screened out based on the monitoring values of the operating indicators of each line node;

[0012] Extracting nodes significantly related to the load change from the screened fluctuation nodes as effective fluctuation nodes, and analyzing the degree of power grid operation fluctuation of each effective fluctuation node;

[0013] The overall operational stability of the power system during the observation period is quantitatively evaluated based on the degree of grid operation fluctuation of each effective fluctuation node.

[0014] Optionally, the line nodes include: junction points, segmentation points and branch points in the power transmission network topology structure of the power system.

[0015] Optionally, the step of dividing the load time periods by using historical load records of the power transmission network power supply area in the power system includes:

[0016] Extract the load value, collection date and collection time from the historical load records, and arrange the historical load records in chronological order of collection date and collection time;

[0017] The load values of adjacent historical load records within each collection date are identified as being similar according to the arrangement order of the historical load records. The collection times of adjacent historical load records with similar load values constitute a load time interval, and the load values of adjacent historical load records constitute a load range. This is continued until the load value of the last historical load record is identified as being similar. Finally, a plurality of load time intervals formed within each collection date and a load range corresponding to each load time interval are obtained.

[0018] The load time intervals formed by each collection date are arranged in chronological order, and the load time intervals of each collection date that are ranked in the same position are clustered and analyzed according to the arrangement order, so as to determine the load time distribution of each power supply day.

[0019] Optionally, the step of performing cluster analysis on the load time intervals of each collection date that is ranked in the same order includes:

[0020] The load time interval that ranks at the same position on one of the collection dates is taken as the main sample, and the load time interval that ranks at the same position on other collection dates is taken as the reference sample. The similarity between the main sample and the other reference samples is calculated and compared with the set similarity threshold. Other samples with a similarity not lower than the similarity threshold are selected from them to form a load clustering group together with the main sample; based on the same process as above, each load clustering group is obtained when the load time interval that ranks at the same position on other collection dates is taken as the main sample; the load time interval corresponding to the load clustering group with the largest number of samples is selected from all load clusters as the load time period that ranks at the same position.

[0021] Optionally, the step of identifying load changes based on the divided load time periods includes:

[0022] Identify and intercept the period between adjacent load periods according to the divided load periods as a transition period;

[0023] A first set sampling frequency is used for load detection during each transition period, and a second set sampling frequency is used for load detection during a non-transition period; wherein the first set sampling frequency is greater than the second set sampling frequency;

[0024] The load value detected at the current moment is identified as similar to the load value monitored at the previous sampling moment. If the identification result is not similar, it is considered that a load change has occurred at the current moment, and the current moment is recorded as the load change time.

[0025] Optionally, the proximity identification includes:

[0026] If the absolute difference or relative difference between the load values at two adjacent sampling moments meets the set maximum allowable difference, the load values at the two adjacent sampling moments are similar; otherwise, they are dissimilar.

[0027] Optionally, the step of screening out the fluctuating nodes according to the operation indicator monitoring values of each line node includes:

[0028] Based on the monitoring values of various operating indicators of each line node at the current load change moment and the operating indicator values at the moment before the load change, the fluctuation degree of each operating indicator of each line node at the current load change moment is calculated respectively;

[0029] The calculated fluctuation of each operating indicator is compared with a preset allowable fluctuation threshold, and the line node to which the fluctuation of the operating indicator exceeding the allowable fluctuation threshold belongs is marked as a fluctuation node.

[0030] Optionally, the calculation formula for the volatility of the operating indicator is as follows:

[0031]

[0032] Where, It represents the fluctuation of the kth operating index of line node i at the current load change time t, and They represent the monitoring values of the kth operating indicator at the current load change time t and the time t-1 before the load change, respectively.

[0033] Optionally, the operating indicators include: voltage amplitude, current amplitude, active power, reactive power and / or frequency.

[0034] Optionally, extracting nodes significantly related to the load variation from the filtered fluctuation nodes as valid fluctuation nodes comprises the following steps:

[0035] The fluctuation nodes selected from each load change during the observation period are classified and counted to obtain the frequency of each fluctuation node, and the frequency ratio of each fluctuation node is calculated respectively;

[0036] Fluctuation nodes whose occurrence frequency is greater than the configured effective occurrence frequency are considered to have a preliminary significant correlation with load changes;

[0037] The correlation analysis method is used to further verify the correlation strength between the fluctuation nodes and load changes for the fluctuation nodes with preliminary significant correlation;

[0038] The verified fluctuation nodes are confirmed to have a significant correlation with the load fluctuation.

[0039] Optionally, the method of further verifying the strength of the correlation between the fluctuation node and the load change by using correlation analysis on the fluctuation node with preliminary significant correlation includes the following steps:

[0040] Extract the operating indicator values of the fluctuation nodes with preliminary significant correlation at each load change time from the load change data recorded during the observation period, and ensure that the timestamps of the data are aligned with the load change events;

[0041] Extract the load value change of each load change event;

[0042] Normalize the fluctuating node operation index values and load change data that have preliminary significant correlations, substitute them into the correlation analysis formula to calculate the correlation strength, and compare it with the set threshold of the correlation strength;

[0043] If the correlation strength between the operating index value of the fluctuation node with preliminary significant correlation and the load change data exceeds the set threshold, it is considered that there is a significant correlation between the two.

[0044] Optionally, the step of analyzing the power grid operation fluctuation degree of each effective fluctuation node includes:

[0045] Using the fluctuation of each operating indicator corresponding to the load change time of each effective fluctuation node exceeding the allowable fluctuation threshold during the observation period, the fluctuation change curve of each corresponding operating indicator is constructed respectively;

[0046] The upper and lower limit range differences of the operation fluctuation degree are extracted from the fluctuation change curves of each operation index of the effective fluctuation node, and the maximum upper and lower limit range difference in each operation index fluctuation change curve is taken as the power grid operation fluctuation degree of the effective fluctuation node.

[0047] Optionally, the quantitative evaluation of the overall operational stability of the power system within the observation period based on the degree of grid operation fluctuation of each effective fluctuation node includes the following steps:

[0048] Count the number n of effective fluctuation nodes in each line node during the observation period, and set different weight influence values for each effective fluctuation node based on its different position;

[0049] The weighted influence value of each effective fluctuation node is combined with the degree of grid operation fluctuation to quantify the overall stability of the power system operation through the following formula:

[0050]

[0051] Where R represents the overall operational stability of the power system during the observation period, e is a mathematical constant, and W i and S i They represent the weighted influence value of the i-th effective fluctuation node and the degree of fluctuation in power grid operation respectively;

[0052] Based on the stability quantification results and the set compliance threshold, it is evaluated whether the power system operation stability meets the standards.

[0053] Optionally, the quantitative evaluation of the overall operational stability of the power system within the observation period based on the degree of grid operation fluctuation of each effective fluctuation node further includes the following steps:

[0054] When the power system operation stability is assessed to be not up to standard, the output shows the location of each effective fluctuation node in the transmission network.

[0055] In a second aspect, the present invention provides a device for analyzing and evaluating the operating status of a power system, which performs the steps of any one of the methods described in the first aspect of the present invention, and the device includes:

[0056] A division module for dividing load periods using historical load records of the power transmission network supply area in the power system;

[0057] The identification module is used to identify load changes based on the divided load time periods within the set observation period;

[0058] The screening module is used to screen out the fluctuating nodes according to the monitoring values of the operating indicators of each line node when load fluctuations are identified;

[0059] An extraction module is used to extract nodes that are significantly related to the load change from the screened fluctuation nodes as effective fluctuation nodes, and analyze the power grid operation fluctuation degree of each effective fluctuation node;

[0060] The quantitative evaluation module is used to quantitatively evaluate the overall operational stability of the power system during the observation period based on the degree of grid operation fluctuation of each effective fluctuation node.

[0061] In a third aspect, the present invention provides a terminal including a processor and a storage medium;

[0062] The storage medium is used to store instructions;

[0063] The processor is configured to operate according to the instructions to execute the steps of any one of the methods described in the first aspect of the present invention.

[0064] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect of the present invention.

[0065] The beneficial effect of the present invention is that, compared with the prior art,

[0066] (1) This invention divides the power supply area into load periods, identifies load changes based on the load periods, and uses the operating status of line nodes to identify fluctuating nodes and analyze the degree of grid operation fluctuation, thereby evaluating the operational stability of the power system. This method breaks through the limitations of traditional overall evaluation and achieves a refined transformation from macro to micro, from static analysis to dynamic monitoring. This not only comprehensively and accurately reflects the actual operating status of the power system, but also provides clear direction for formulating targeted control strategies.

[0067] (2) When the present invention identifies load changes and screens out fluctuation nodes, it adds correlation analysis between fluctuation nodes and load changes, and uses the grid operation fluctuation degree of effective fluctuation nodes to evaluate grid operation stability only when there is a significant correlation between the two. It can focus on key fluctuation nodes and ensure that the evaluation results only reflect the actual impact of load changes on the grid operation status, thereby improving the scientificity and reliability of the evaluation results, reducing in-depth analysis of irrelevant nodes, improving overall evaluation efficiency, and reducing calculation and analysis costs.

[0068] (3) The present invention implements a real-time load monitoring strategy with differentiated time periods and frequencies, which not only improves monitoring efficiency but also effectively balances the relationship between data acquisition accuracy and computing resource consumption. High-frequency detection focuses on critical time periods (transition periods) to ensure accurate capture of dynamic changes; while low-frequency detection covers non-critical time periods, meeting conventional monitoring needs while reducing the generation of redundant data. The present invention can effectively reduce the possibility of power system operation anomalies being concealed and minimize the probability of missing potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is a flow chart of a method for analyzing and evaluating the operating status of a power system according to an embodiment of the present invention;

[0070] Figure 2 1 is a schematic diagram of a transition period in a load period divided in an embodiment of the present invention;

[0071] Figure 3 2 is a block diagram of the structural principle of the power system operation status analysis and evaluation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0072] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. The embodiments described in the present invention are only part of the embodiments of the present invention, not all of the embodiments. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making any creative efforts are all within the scope of protection of the present invention.

[0073] Example 1:

[0074] Reference Figure 1 The embodiment of the present invention provides a method for analyzing and evaluating the operating status of a power system, which specifically includes the following steps:

[0075] Step 1: Use the historical load records of the power transmission network supply area in the power system to divide the load period;

[0076] Step 2: Within the set observation period, identify load changes based on the divided load periods;

[0077] Step 3: When load fluctuations are detected, the fluctuating nodes are selected based on the monitoring values of the operating indicators of each line node;

[0078] Step 4: extracting nodes significantly related to the load change from the screened fluctuation nodes as effective fluctuation nodes, and analyzing the power grid operation fluctuation degree of each effective fluctuation node;

[0079] Step 5: Quantitatively evaluate the overall operational stability of the power system during the observation period based on the degree of grid operation fluctuation of each effective fluctuation node.

[0080] Specifically, in this embodiment, the power system is divided into line nodes, and an operation monitoring terminal is installed at each line node. The division of the power system into line nodes is described in the following process:

[0081] Constructing a grid topology model based on the power system layout diagram: First, an accurate and detailed power system layout diagram is required. This information typically includes the location of substations and their electrical parameters (such as voltage level), information about transmission lines (starting and ending substations, length, conductor type, etc.), and the connections between power plants and substations. This data can be obtained from power company design documents, geographic information systems (GIS), or through SCADA systems. Based on the obtained layout diagram, a mathematical topology model is constructed to represent the entire power grid. After the grid topology model is constructed, the junction points, segmentation points, and branch points are identified and marked in the constructed grid topology model as line nodes of the power system.

[0082] It is important to know that the selection of junction points, segmentation points, and branch points as line nodes can be explained from three aspects: location, sensitivity of electrical characteristics, and information aggregation and conduction functions.

[0083] The first aspect is explained by the location of junctions, segmentation points, and branching points. Junctions are typically the connection points between different power supply areas or different voltage levels. These points are important gateways for power transmission. Load changes are directly transmitted to adjacent areas or networks through junctions, and their operating status has a significant impact on the entire power system.

[0084] Section points are used to divide transmission lines into multiple independent sections, each of which may have different load characteristics. When load fluctuations occur, the section points can reflect the impact of load changes within the section on the overall network.

[0085] The branch point is the key node connecting the trunk line and the branch line, responsible for distributing electricity to different load centers or user groups. Load fluctuations often first appear at the branch point and then spread to the trunk line through the branch point.

[0086] The second aspect is explained from the sensitivity of electrical characteristics. The voltage levels at junctions and branch points are greatly affected by load changes, and voltage fluctuations or deviations are prone to occur. Segmentation points and branch points are key nodes for power flow. Load fluctuations will lead to power flow redistribution, thereby causing changes in power flow.

[0087] The short-circuit capacity of junctions and branch points is relatively high, and load fluctuations may cause local short-circuit risks, further affecting the stability of the nodes.

[0088] Third, from the perspective of information aggregation and transmission, junctions, segmentation points, and branching points are the core locations for information aggregation in power systems. When load fluctuates, these nodes can quickly capture and reflect the impact of local load changes on the overall network.

[0089] These nodes are not only information convergence points but also key paths for information transmission. When load fluctuations spread from one area to another, these nodes act as bridges, clearly demonstrating the fluctuation's propagation process and impact range.

[0090] As an embodiment of the present invention, the division of load periods in step 1 is specifically implemented as follows:

[0091] Step 1.1: Extract the load value, collection date and collection time from the historical load records, and arrange the historical load records in chronological order of collection date and collection time;

[0092] Step 1.2: Identify the load values of adjacent historical load records on each collection date according to the order of the historical load records. If the load values of adjacent historical load records are similar, the collection time of the adjacent historical load records is formed into a load time interval, and the load values of the adjacent historical load records are formed into a load range. This is continued until the load value of the last historical load record is identified. Finally, several load time intervals formed on each collection date and the load range corresponding to each load time interval are obtained.

[0093] The process of identifying the similar load values corresponding to adjacent historical load records mentioned above is as follows:

[0094] First, a similarity judgment standard is set based on load characteristics, namely the maximum allowable difference between the load values of two adjacent historical load records. The maximum allowable difference can be an absolute difference or a relative difference. For example, the maximum allowable difference in absolute terms can be 50 kW, and the maximum allowable difference in relative terms can be 5% of the load value. In other words, the difference between the load values of two adjacent load records must not exceed 5% of the larger load value.

[0095] The load difference between adjacent historical load records within each collection date is calculated according to the arrangement order of the historical load records and compared with the set maximum allowable difference. If the load difference between adjacent historical load records meets the maximum allowable difference, the load values of the two load records are considered to be similar.

[0096] It's important to note that, in this context, load refers to the amount of electrical energy demanded by power system users at a specific moment, i.e., instantaneous load or power consumption at that moment. When identifying similar load values between adjacent load records, we're actually comparing instantaneous load values at two consecutive points in time, rather than evaluating load changes over a single time period.

[0097] For instantaneous load values, each acquisition time point corresponds to a single load value. Therefore, when determining the similarity of adjacent load records, the load values at these two time points are directly compared. If the difference between the two exceeds the preset threshold, it indicates that a significant load jump occurred between these adjacent time points. In this case, due to the large load difference, these load records will not be identified as similar, thus avoiding incorrectly categorizing data with significant fluctuations into the same load range.

[0098] In short, by directly comparing the instantaneous load values at adjacent time points and deciding whether to consider them similar based on a preset difference threshold, we can effectively identify the time points containing load jumps, ensure that only load data with small fluctuations are merged into the same load time interval, and thus accurately reflect the true change characteristics of the load pattern.

[0099] The above-mentioned identification of similar load values in adjacent historical load records can effectively identify time periods with relatively stable load changes. This method can capture stable intervals with small load fluctuations.

[0100] Step 1.3: Arrange the load time intervals formed on each collection date in chronological order, and perform cluster analysis on the load time intervals of each collection date that is ranked in the same order, so as to determine the load time distribution of each power supply day.

[0101] Specifically, when clustering analysis is performed on load time intervals that are ranked at the same position, each performance range of the load time interval that is ranked at the same position on each collection date is used as a main sample, and the performance ranges on other collection dates are used as reference samples. Thus, similarity calculations are performed on the main sample and other samples to obtain the similarity between each main sample and other samples, and the similarity is compared with the set similarity threshold. Other samples that meet the similarity threshold are selected from the main sample to form a load cluster group, and then the number of load cluster groups formed is counted. At the same time, the number of samples in each load cluster group is obtained, and the load time interval corresponding to the load cluster group with the largest number of samples is selected as the load time period that is ranked at the same position.

[0102] In the above operation example, it is assumed that there are five load time intervals formed by collection dates and they are arranged in chronological order as shown in Table 1.

[0103] Table 1

[0104]

[0105]

[0106] From Table 1, the load time intervals that rank first on different collection dates are 6:00-8:30, 6:00-8:00, 6:00-8:00, 6:00-8:30, and 6:00-8:30. First, any one of them is selected as the main sample, and the main sample and other reference samples are similarly calculated. Specifically, the similarity (Jaccard similarity coefficient) can be calculated by dividing the intersection by the union. The similarity threshold is set, illustratively, 80%. In this way, load time intervals similar to the main sample are screened out to form a load cluster group. Then, the number of samples in the load cluster group is compared to determine the load time period that ranks first.

[0107] It's important to understand that in the above scheme, the load time intervals generated within each collection date are first arranged in chronological order. Next, cluster analysis is performed on the load time intervals that appear in the same position based on their order within each date. Specifically, this process involves clustering the load time interval that appears first across all collection dates, then performing the same process on the load time interval that appears second, and so on.

[0108] Given the regularity of daily electricity consumption patterns among power system users, the number of load time intervals between different collection dates is generally highly similar. Therefore, in most cases, the number of load time intervals within each collection date is consistent. However, for those rare cases where there are inconsistencies (for example, some dates contain 5 load time intervals, while other dates contain 6), only the first five load time intervals with the same ranking will be compared. For the load time intervals exceeding the limit, due to the lack of a basis for comparison with the corresponding time intervals on other dates, these additional time intervals will be treated as a separate load period.

[0109] This processing method ensures that cluster analysis can be performed effectively and the load period distribution of each power supply day can be accurately identified even when there are slight differences in the number of load time intervals.

[0110] Then, the second load time intervals on different collection dates are extracted and similar operations are performed until the last load time interval is reached, thereby obtaining the load period distribution in the order of one day.

[0111] In one example, the load periods according to the time sequence of a day are distributed as follows: 6:00-8:00, 8:30-11:30, 12:00-13:30, 14:00-17:00, 18:00-20:00, 21:00-24:00, and 24:00-6:00.

[0112] The present invention performs time period analysis based on historical load data, reduces interference from human experience, and makes load period division more scientific and reliable.

[0113] Reference Figure 2 In this embodiment, step 2 performs load change identification based on the divided load time periods, specifically including the following process:

[0114] Step 2.1: Based on the divided load periods, further identify and intercept the transition periods between adjacent load periods.

[0115] Applying the above embodiment, the transition periods between adjacent load periods are 8:00-8:30, 11:30-12:00, 13:30-14:00, 17:00-18:00, and 20:00-21:00.

[0116] Step 2.2: For each transition period, a first set sampling frequency is used to perform load detection, and for each non-transition period, a second set sampling frequency is used to perform load detection; wherein the first set sampling frequency is greater than the second set sampling frequency;

[0117] Preferably, in the actual operation process of the current power supply area, this embodiment adopts a high sampling frequency (first set sampling frequency) for the transition period between adjacent load periods to perform real-time load detection to capture the dynamic characteristics of rapid load changes, and adopts a lower sampling frequency (second set sampling frequency) for load detection in the non-transition period to reduce the burden of data collection and processing while meeting conventional monitoring needs.

[0118] The high sampling frequency (first set sampling frequency) refers to the frequency of frequently collecting load data in a relatively short time interval, usually in seconds or minutes, such as collecting data every 5 seconds, 10 seconds, or 30 seconds. The low sampling frequency (second set sampling frequency) refers to the frequency of collecting load data in a relatively long time interval, usually in minutes or hours, such as collecting data every 10 minutes or 30 minutes.

[0119] It should be emphasized that the transition period is usually a critical period for the load to change from one stable state to another, which may be accompanied by a rapid increase or decrease in load; therefore, implementing high-frequency detection during these periods will help to promptly detect potential abnormal conditions or grid operation risks.

[0120] In the current operational environment of the power supply region, implementing a time-segmented, frequency-differentiated real-time load monitoring strategy not only improves monitoring efficiency but also effectively balances data collection accuracy with computing resource consumption. High-frequency monitoring focuses on critical periods (transition periods) to ensure accurate capture of dynamic changes, while low-frequency monitoring covers non-critical periods, meeting routine monitoring needs while reducing the generation of redundant data.

[0121] Step 2.3: The load value detected at the current moment is similar to the load value monitored at the previous sampling moment. If the identification result is not similar, it is considered that a load change has occurred at the current moment, and the current moment is recorded as the load change time.

[0122] The aforementioned similarity identification is similar to the similarity identification process for corresponding load values in adjacent historical load records. That is, if the absolute or relative difference between the load values at two adjacent sampling moments meets the set maximum allowable difference, the load values at the two adjacent sampling moments are similar; otherwise, they are dissimilar.

[0123] As an embodiment of the present invention, in step 3, the fluctuating nodes are screened out according to the operation index monitoring values of each line node. The specific process is as follows:

[0124] When load changes are identified, the operation monitoring terminals deployed at each line node of the power system are used to monitor the power system operation indicators. The operation indicator value of each line node in the power system at the moment of load change is extracted and compared with the operation indicator value at the moment before the load change. The degree of change in the operation indicator between the two moments is quantified and the operation indicator fluctuation is calculated.

[0125] The aforementioned operating indicators can be selected based on the specific application scenario. Common ones include voltage amplitude, current amplitude, active power, reactive power, and frequency. Fluctuations in different indicators may reflect different aspects of the grid's operating status. For example, voltage fluctuations reflect voltage stability, while power fluctuations reflect load changes.

[0126] The operating index fluctuation degree mentioned above can be obtained by calculating the difference between the operating index value at the time of load change and the operating index value at the time before the load change, taking the absolute value and dividing it by the operating index value at the time before the load change. The specific calculation formula is as follows:

[0127]

[0128] Where, It represents the fluctuation of the kth operating index of line node i at the current load change time t, and They represent the monitoring values of the kth operating indicator at the current load change time t and the time t-1 before the load change, respectively.

[0129] The calculated operating indicator fluctuation is compared with the pre-set allowable fluctuation threshold. If the fluctuation of an operating indicator of a line node at the moment of load change exceeds the allowable fluctuation threshold, the node is marked as a fluctuating node.

[0130] It's important to note that the setting of the permissible fluctuation threshold must be considered in conjunction with the actual operational requirements of the power system and the characteristics of the equipment. For example, voltage indicators typically refer to the permissible voltage deviation range specified in power system standards (such as IEEE or State Grid standards); frequency indicators are based on relevant regulations for grid frequency stability.

[0131] Identifying fluctuating nodes helps locate potential problem areas in grid operation. These nodes may experience voltage instability, power imbalance, or other abnormal conditions due to load fluctuations, requiring further analysis and regulatory measures to ensure safe and stable grid operation.

[0132] As an embodiment of the present invention, in step 4, nodes significantly related to the load change are extracted from the filtered fluctuation nodes as effective fluctuation nodes. The specific operation is as follows:

[0133] Step 4.1: Classify and count the same fluctuation nodes selected from each load change during the observation period, obtain the frequency of occurrence of each fluctuation node, and calculate the frequency ratio of each fluctuation node respectively.

[0134] It should be noted that the length of the observation period may affect the accuracy of the results. If the period is too short, sufficient load change events may not be captured; if the period is too long, it may lead to data redundancy or mask short-term fluctuation characteristics. Based on the frequency and characteristics of load changes, technicians in this field can select an appropriate observation period (such as one month) according to the specific situation to ensure that the observation period contains a sufficient number of load change events to improve the accuracy of statistical analysis.

[0135] Step 4.2: Compare the frequency ratio of each fluctuation node with the configured effective frequency ratio. If the frequency ratio of a fluctuation node reaches the effective frequency ratio, it is determined that there is a preliminary significant correlation between the fluctuation node and the load change.

[0136] Step 4.3: Use correlation analysis methods (such as Pearson correlation coefficient, mutual information, etc.) to further verify the correlation strength between the fluctuation nodes and load changes for the fluctuation nodes with preliminary significant correlation.

[0137] Step 4.4: Confirm that the verified fluctuation nodes are significantly associated with load changes.

[0138] In this embodiment, the correlation evaluation between the fluctuation node and the load change is based on statistical analysis of the actual performance of the fluctuation node in the load change. It is a data-driven approach that can objectively reflect the relationship between the fluctuation node and the load change.

[0139] In the improved implementation of the above operation, the correlation analysis method is used to further verify the correlation strength between the fluctuation nodes and load changes on the fluctuation nodes with preliminary significant correlation. See the following process:

[0140] Step a: Extract the operating index values of the fluctuation nodes with preliminary significant correlation at each load change moment from the load change data recorded during the observation period, and ensure that the timestamp of the data is aligned with the load change event.

[0141] Step b: Extract the load value change of each load change event. If the time lag effect needs to be considered, a time delay parameter can be introduced.

[0142] It's important to note that the time delay parameter, when analyzing the relationship between fluctuation nodes and load changes, accounts for the potential lag in the impact of certain factors on load. In other words, a change in a fluctuation node may not immediately lead to a change in load, but may only manifest itself after a period of time. This period is known as the "time delay."

[0143] By introducing a time delay parameter, the dynamic relationship between two variables can be captured more accurately, especially when the relationship does not occur instantaneously.

[0144] Step c: Normalize the operating indicators and load change data of the fluctuation nodes that have a preliminary significant correlation, determine the correlation analysis formula, such as the Pearson correlation coefficient and mutual information, and substitute the operating indicators and load change data of the fluctuation nodes that have a preliminary significant correlation into the correlation analysis formula to obtain the correlation strength, and compare it with the set threshold value of the correlation strength;

[0145] Step d: If the correlation strength between the operating indicators of the fluctuation node with preliminary significant correlation and the load change exceeds the set threshold, it is considered that there is a significant correlation between the two.

[0146] It should be noted that the aforementioned operation indicators and load variation data of the fluctuation nodes with preliminary significant correlation are substituted into the correlation analysis formula. The operation indicators substituted into the correlation analysis formula refer to operation indicators that exceed the allowable fluctuation threshold.

[0147] The present invention adds correlation analysis between fluctuation nodes and load fluctuations when identifying load fluctuations and screening out fluctuation nodes, and uses the power grid operation fluctuation degree of the fluctuation nodes to evaluate the power grid operation stability only when there is a significant correlation between the two. It can focus on key fluctuation nodes and ensure that the evaluation results only reflect the actual impact of load changes on the power grid operation status, thereby improving the scientific nature and reliability of the evaluation results, reducing in-depth analysis of irrelevant nodes, improving overall evaluation efficiency, and reducing calculation and analysis costs.

[0148] As an embodiment of the present invention, in step 4, the power grid operation fluctuation degree of the effective fluctuation node is analyzed, and the specific operation is as follows:

[0149] Step 4.5: Using the fluctuation of each operating indicator corresponding to each load change time of the effective fluctuation node exceeding the allowable fluctuation threshold during the observation period, construct the fluctuation change curve of each corresponding operating indicator;

[0150] Step 4.6: Extract the upper and lower limit range differences of the operating fluctuation degree (i.e., the upper limit value minus the lower limit value) from each operating indicator fluctuation change curve of the effective fluctuation node, and take the largest upper and lower limit range difference in each operating indicator fluctuation change curve as the power grid operation fluctuation degree of the effective fluctuation node.

[0151] It should be noted that the aforementioned operating indicator fluctuation curve is constructed using the operating indicator fluctuation corresponding to each load change at an effective fluctuation node during the observation period that exceeds the allowable fluctuation threshold. The upper and lower limits of the operating indicator fluctuation mentioned above refer to the maximum and minimum fluctuations of the operating indicator; the grid operation fluctuation degree of the effective fluctuation node refers to the range formed by the upper and lower limits of the operating indicator fluctuation, which is an interval.

[0152] As an embodiment of the present invention, the stability assessment in step 5 is a dynamic assessment for each observation period. The overall operational stability of the power system is comprehensively assessed based on the degree of grid operation fluctuation of the effective fluctuation nodes, and the specific implementation is as follows:

[0153] Step 5.1: Count the number n of valid fluctuation nodes in each line node during the observation period, and set different weight influence values for each valid fluctuation node based on its different position;

[0154] In this embodiment, the introduction of weighted influence values considers the importance of the location of effective fluctuation nodes (such as hub substations, important load centers, etc.), assigns different weighted influences to different nodes, and improves the rationality of the evaluation results. Specifically, the weighted influence is formulated as:

[0155] W i=f(voltage level, short-circuit capacity, geographical location)

[0156] Among them, W i and represent the weighted influence value of the i-th valid fluctuation node, i represents the valid fluctuation node number; f is a comprehensive evaluation function, which can be designed using existing methods according to actual needs. It is not the focus of the invention research and will not be repeated here.

[0157] Step 5.2: Quantify the overall stability of the power system operation by combining the weighted influence value of each effective fluctuation node with the degree of grid operation fluctuation using the following formula:

[0158]

[0159] Where R represents the overall operational stability of the power system during the observation period, e is a mathematical constant, and W i and S i They represent the weighted influence value of the i-th effective fluctuation node and the degree of fluctuation in power grid operation respectively.

[0160] Since the power system operation stability is a positive parameter, which means that the higher the stability, the more stable the grid operation, and the grid operation fluctuation degree of the effective fluctuation node is a negative parameter, which means that the greater the fluctuation degree, the worse the grid operation stability, it is necessary to use a reduction function conversion when using the grid operation fluctuation degree of the effective fluctuation node to quantify the power system operation stability. -x This function transformation is because it has the characteristics of smooth transition and can better reflect the relationship between the degree of fluctuation and stability, and e -x The value range is between 0 and 1, which is convenient for standardization and comparison.

[0161] Step 5.3: Based on the stability quantification results and the set compliance threshold, evaluate whether the power system operation stability meets the standards.

[0162] In the above-mentioned evaluation of whether the power system operation stability meets the standards based on the quantitative results, it is necessary to determine a threshold value for the power system operation stability. A reasonable threshold range can be set based on the power system operation experience and relevant standards (such as IEEE or State Grid standards).

[0163] Further preferably, the comprehensive evaluation of the overall operational stability of the power system also includes:

[0164] Step 5.4: When the power system operation stability is assessed to be not up to standard, the output shows the location of the effective fluctuation node in the power system.

[0165] The display of the effective fluctuation nodes mentioned above can use the power system topology map to intuitively mark the location of the effective fluctuation nodes on the map, and provide detailed information (such as node number, weight influence, power grid fluctuation degree, etc.) to facilitate dispatchers to quickly locate problem areas.

[0166] The beneficial effect of the present invention is that, compared with the prior art,

[0167] (1) This invention divides the power supply area into load periods, identifies load changes based on the load periods, and uses the operating status of line nodes to identify fluctuating nodes and analyze the degree of grid operation fluctuation, thereby evaluating the operational stability of the power system. This method breaks through the limitations of traditional overall evaluation and achieves a refined transformation from macro to micro, from static analysis to dynamic monitoring. This not only comprehensively and accurately reflects the actual operating status of the power system, but also provides clear direction for formulating targeted control strategies.

[0168] (2) When the present invention identifies load changes and screens out fluctuation nodes, it adds correlation analysis between fluctuation nodes and load changes, and uses the grid operation fluctuation degree of effective fluctuation nodes to evaluate grid operation stability only when there is a significant correlation between the two. It can focus on key fluctuation nodes and ensure that the evaluation results only reflect the actual impact of load changes on the grid operation status, thereby improving the scientificity and reliability of the evaluation results, reducing in-depth analysis of irrelevant nodes, improving overall evaluation efficiency, and reducing calculation and analysis costs.

[0169] (3) The present invention implements a real-time load monitoring strategy with differentiated time periods and frequencies, which not only improves monitoring efficiency but also effectively balances the relationship between data acquisition accuracy and computing resource consumption. High-frequency detection focuses on critical time periods (transition periods) to ensure accurate capture of dynamic changes; while low-frequency detection covers non-critical time periods, meeting conventional monitoring needs while reducing the generation of redundant data. The present invention can effectively reduce the possibility of power system operation anomalies being concealed and minimize the probability of missing potential risks.

[0170] Example 2:

[0171] like Figure 3 As shown, the present invention provides a device for analyzing and evaluating the operating status of a power system, which is used to implement the steps of the method in the above embodiment 1, and specifically includes:

[0172] A division module for dividing load periods using historical load records of the power transmission network supply area in the power system;

[0173] The identification module is used to identify load changes based on the divided load time periods within the set observation period;

[0174] The screening module is used to screen out the fluctuating nodes according to the monitoring values of the operating indicators of each line node when load fluctuations are identified;

[0175] An extraction module is used to extract nodes that are significantly related to the load change from the screened fluctuation nodes as effective fluctuation nodes, and analyze the power grid operation fluctuation degree of each effective fluctuation node;

[0176] The quantitative evaluation module is used to quantitatively evaluate the overall operational stability of the power system during the observation period based on the degree of grid operation fluctuation of each effective fluctuation node.

[0177] The power system operation status analysis and evaluation device provided in the embodiment of the present invention and the power system operation status analysis and evaluation method provided in Example 1 are based on the same technical concept and can produce the beneficial effects as described in Example 1. For the contents not described in detail in this embodiment, please refer to Example 1.

[0178] Example 3:

[0179] An embodiment of the present invention provides a terminal including a processor and a storage medium;

[0180] The storage medium is used to store instructions;

[0181] The processor is configured to operate according to the instruction to execute the steps of the method according to any one of the first embodiments.

[0182] Example 4:

[0183] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method described in any one of the first embodiments are implemented.

[0184] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0185] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0186] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0187] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing and evaluating the operating status of a power system, characterized in that: Methods include: Use the historical load records of the power transmission network supply area in the power system to divide the load period; During the set observation period, load changes are identified based on the divided load periods; When load changes are identified, the fluctuating nodes are screened out based on the monitoring values of the operating indicators of each line node; Extracting nodes significantly related to the load change from the screened fluctuation nodes as effective fluctuation nodes, and analyzing the degree of power grid operation fluctuation of each effective fluctuation node; The overall operational stability of the power system during the observation period is quantitatively evaluated based on the degree of grid operation fluctuation of each effective fluctuation node.

2. The method for analyzing and evaluating the operating status of a power system according to claim 1, wherein: The line nodes include: junction points, segmentation points and branch points in the topological structure of the power transmission network of the power system.

3. The method for analyzing and evaluating the operating status of a power system according to claim 1, wherein: The step of dividing the load period by using the historical load records of the power transmission network power supply area in the power system includes: Extract the load value, collection date and collection time from the historical load records, and arrange the historical load records in chronological order of collection date and collection time; According to the arrangement order of the historical load records, the load values of adjacent historical load records within each collection date are similarly identified, the collection times of adjacent historical load records with similar load values are combined into a load time interval, and the load values of adjacent historical load records are combined into a load range, until the load value of the last historical load record is similarly identified, and finally a plurality of load time intervals formed within each collection date and the load range corresponding to each load time interval are obtained; The load time intervals formed by each collection date are arranged in chronological order, and the load time intervals of each collection date that are ranked in the same position are clustered and analyzed according to the arrangement order, so as to determine the load time distribution of each power supply day.

4. The method for analyzing and evaluating the operating status of a power system according to claim 3, wherein: The steps of clustering the load time intervals of each collection date that are ranked in the same order include: The load time interval that ranks at the same position on one of the collection dates is taken as the main sample, and the load time interval that ranks at the same position on other collection dates is taken as the reference sample. The similarity between the main sample and the other reference samples is calculated and compared with the set similarity threshold. Other samples with a similarity not lower than the similarity threshold are selected from them to form a load clustering group together with the main sample; based on the same process as above, each load clustering group is obtained when the load time interval that ranks at the same position on other collection dates is taken as the main sample; the load time interval corresponding to the load clustering group with the largest number of samples is selected from all load clusters as the load time period that ranks at the same position.

5. The method for analyzing and evaluating the operation status of a power system according to claim 1, wherein: The step of identifying load changes based on the divided load time periods includes: Identify and intercept the period between adjacent load periods according to the divided load periods as a transition period; A first set sampling frequency is used for load detection during each transition period, and a second set sampling frequency is used for load detection during a non-transition period; wherein the first set sampling frequency is greater than the second set sampling frequency; The load value detected at the current moment is identified as similar to the load value monitored at the previous sampling moment. If the identification result is not similar, it is considered that a load change has occurred at the current moment, and the current moment is recorded as the load change time.

6. The power system operating status analysis and evaluation method according to claim 3 or 5, characterized in that: The proximity identification includes: If the absolute difference or relative difference between the load values at two adjacent sampling moments meets the set maximum allowable difference, the load values at the two adjacent sampling moments are similar; otherwise, they are dissimilar.

7. The method for analyzing and evaluating the operation status of a power system according to claim 1, wherein: The step of screening out the fluctuating nodes according to the operation index monitoring values of each line node includes: Based on the monitoring values of various operating indicators of each line node at the current load change moment and the operating indicator values at the moment before the load change, the fluctuation degree of each operating indicator of each line node at the current load change moment is calculated respectively; The calculated fluctuation of each operating indicator is compared with a preset allowable fluctuation threshold, and the line node to which the fluctuation of the operating indicator exceeding the allowable fluctuation threshold belongs is marked as a fluctuation node.

8. The method for analyzing and evaluating the operating status of a power system according to claim 7, wherein: The calculation formula of the volatility of the operating index is as follows: Where, It represents the fluctuation of the kth operating index of line node i at the current load change time t, and They represent the monitoring values of the kth operating indicator at the current load change time t and the time t-1 before the load change, respectively.

9. The method for analyzing and evaluating the operation status of a power system according to claim 7 or 8, characterized in that: The operating indicators include: voltage amplitude, current amplitude, active power, reactive power and / or frequency.

10. The method for analyzing and evaluating the operation status of a power system according to claim 1, wherein: The step of extracting nodes significantly related to the load variation from the filtered fluctuation nodes as effective fluctuation nodes comprises the following steps: The fluctuation nodes selected from each load change during the observation period are classified and counted to obtain the frequency of each fluctuation node, and the frequency ratio of each fluctuation node is calculated respectively; The fluctuation nodes whose occurrence frequency ratio is greater than the configured effective occurrence frequency ratio are considered to have a preliminary significant correlation with the load change; The correlation analysis method is used to further verify the correlation strength between the fluctuation nodes and load changes for the fluctuation nodes with preliminary significant correlation; The verified fluctuation nodes are confirmed to have a significant correlation with the load fluctuation.

11. The method for analyzing and evaluating the operation status of a power system according to claim 10, wherein: The method of using correlation analysis to further verify the correlation strength between the fluctuation nodes and the load changes for the fluctuation nodes with preliminary significant correlation includes the following steps: Extract the operating indicator values of the fluctuation nodes with preliminary significant correlation at each load change time from the load change data recorded during the observation period, and ensure that the timestamps of the data are aligned with the load change events; Extract the load value change of each load change event; Normalize the fluctuating node operation index values and load change data that have preliminary significant correlations, substitute them into the correlation analysis formula to calculate the correlation strength, and compare it with the set threshold of the correlation strength; If the correlation strength between the operating index value of the fluctuation node with preliminary significant correlation and the load change data exceeds the set threshold, it is considered that there is a significant correlation between the two.

12. The method for analyzing and evaluating the operation status of a power system according to claim 1, wherein: The step of analyzing the power grid operation fluctuation degree of each effective fluctuation node includes: Using the fluctuation of each operating indicator corresponding to the load change time of each effective fluctuation node exceeding the allowable fluctuation threshold during the observation period, the fluctuation change curve of each corresponding operating indicator is constructed respectively; The upper and lower limit range differences of the operation fluctuation degree are extracted from the fluctuation change curves of each operation index of the effective fluctuation node, and the maximum upper and lower limit range difference in each operation index fluctuation change curve is taken as the power grid operation fluctuation degree of the effective fluctuation node.

13. The method for analyzing and evaluating the operation status of a power system according to claim 1 or 12, characterized in that: The quantitative evaluation of the overall operational stability of the power system within the observation period based on the degree of grid operation fluctuation of each effective fluctuation node includes the following steps: Count the number n of effective fluctuation nodes in each line node during the observation period, and set different weight influence values for each effective fluctuation node based on its different position; The weighted influence value of each effective fluctuation node is combined with the degree of grid operation fluctuation to quantify the overall stability of the power system operation through the following formula: Where R represents the overall operational stability of the power system during the observation period, e is a mathematical constant, and W i and S i They represent the weighted influence value of the i-th effective fluctuation node and the degree of fluctuation in power grid operation respectively; Based on the stability quantification results and the set compliance threshold, the power system operation stability is evaluated to see whether it meets the standards.

14. The method for analyzing and evaluating the operation status of a power system according to claim 13, wherein: The quantitative evaluation of the overall operational stability of the power system within the observation period based on the degree of grid operation fluctuation of each effective fluctuation node further includes the following steps: When the power system operation stability is assessed to be not up to standard, the output shows the location of each effective fluctuation node in the transmission network.

15. A device for analyzing and evaluating the operation status of a power system, which runs the method for analyzing and evaluating the operation status of a power system according to any one of claims 1 to 14, characterized in that: The device includes: A division module for dividing load periods using historical load records of the power transmission network supply area in the power system; The identification module is used to identify load changes based on the divided load time periods within the set observation period; The screening module is used to screen out the fluctuating nodes according to the monitoring values of the operating indicators of each line node when load fluctuations are identified; An extraction module is used to extract nodes that are significantly related to the load change from the screened fluctuation nodes as effective fluctuation nodes, and analyze the power grid operation fluctuation degree of each effective fluctuation node; The quantitative evaluation module is used to quantitatively evaluate the overall operational stability of the power system during the observation period based on the degree of grid operation fluctuation of each effective fluctuation node.

16. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 14.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 14 are implemented.

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