Frequency modulation system of thermal power unit based on multi-dimensional parameter monitoring

Abstract

The present application relates to the field of thermal power frequency modulation, and particularly relates to a kind of thermal power unit frequency modulation system based on multidimensional parameter monitoring, the electrical characteristics of the node in power grid and power grid frequency are collected, precursor performance analysis is carried out, a plurality of precursor performance nodes are determined, radiation association sub-electrical indexes and radiation association types reflecting the precursor performance nodes and a plurality of secondary associated nodes are determined, and nominal working condition interval is summarized, then radiation association group is constructed, subsequent detection is carried out on precursor node and secondary associated node according to radiation association group, and multidimensional verification is carried out, fine adjustment strategy is triggered in response to verification result, verification window is constructed for continuous verification, and stage adjustment strategy is matched.The present application predicts frequency modulation demand in advance through multi-node multidimensional joint analysis, and matches gradual stage adjustment, shortens the actual response time after frequency deviation, improves frequency modulation efficiency, and effectively avoids the impact of large pre-action on unit and power grid.

Description

Technical Field

[0001] This invention relates to the field of thermal power frequency regulation, and in particular to a thermal power unit frequency regulation system based on multi-dimensional parameter monitoring. Background Technology

[0002] Primary and secondary frequency regulation of thermal power units are the core means of maintaining grid frequency stability. When power disturbances occur in the grid, such as large-capacity power source disconnection, DC blocking, or sudden load changes, the grid frequency will deviate from the rated value. Thermal power units detect local speed or grid connection point frequency deviations through the speed control system and adjust the turbine inlet valve opening to change the output, thereby suppressing frequency changes. Therefore, the frequency regulation system of thermal power units is crucial and has always been a key direction for technological improvement and optimization.

[0003] For example, Chinese Patent Publication No. CN120999681A discloses a frequency regulation control method and system for thermal power units, relating to the field of frequency regulation control technology. The method includes: acquiring frequency regulation command signals from the power grid; decomposing the frequency regulation command signals to obtain multiple intrinsic mode function components and residual components; performing spectral analysis on each intrinsic mode function component, marking components whose main frequency exceeds the high-frequency disturbance frequency threshold as high-frequency disturbance components, and calculating the fluctuation amplitude and rate of change; generating thermal system control commands through a PID control algorithm to adjust the coal feed rate and main steam pressure; generating auxiliary compensation control commands for high-frequency disturbance components through a fuzzy control algorithm; converting the thermal system control commands and auxiliary compensation control commands into operating parameters for the thermal power unit control mechanism and issuing them for execution; triggering an optimization process when the control effect deviation continuously exceeds the deviation threshold, significantly improving the frequency regulation stability and equipment safety of the unit in an environment where a high proportion of new energy is connected to the power grid.

[0004] However, the existing technology still has the following problems: the frequency regulation of existing thermal power units relies on the response feedback of local frequency deviation, which has a lag and a single information dimension. It cannot use the spatiotemporal differences of the electrical characteristics of multiple nodes to capture the precursors of disturbances, and lacks quantitative analysis of the correlation between multiple nodes, making it difficult to achieve early and accurate predictive frequency regulation. Summary of the Invention

[0005] To address these issues, the present invention provides a frequency regulation system for thermal power units based on multi-dimensional parameter monitoring. This system overcomes the problems of existing thermal power unit frequency regulation systems that rely on local frequency deviation response feedback, which suffers from lag, limited information dimensions, inability to capture disturbance precursors by utilizing the spatiotemporal differences of multi-node electrical characteristics, lack of quantitative analysis of multi-node correlations, long frequency regulation response cycles, and slow response speeds.

[0006] To achieve the above objectives, the present invention provides a frequency regulation system for thermal power units based on multi-dimensional parameter monitoring, comprising: The parameter acquisition module is used to acquire the electrical characteristics of each selected node in the power grid and the power grid frequency in real time. The electrical characteristics include several types of sub-electrical indicators. The precursor screening module is used to analyze the precursor manifestation based on the electrical characteristics of each node in the precursor observation window, including screening the precursor mutation nodes based on the mutation time sequence of the electrical characteristics corresponding to each node, and determining the precursor manifestation node based on the manifestation form of the electrical characteristics corresponding to each precursor mutation node at the mutation time. The correlation analysis module is used to determine the secondary correlation nodes and radiation correlation types of each of the aforementioned precursor manifestation nodes within the precursor observation window, determine the corresponding radiation correlation sub-electrical indicators, and extract the nominal operating condition range based on the historical records of the radiation correlation sub-electrical indicators to construct a radiation correlation observation group. The verification module is used to monitor the real-time radiation correlation type between the corresponding precursor node and the secondary correlation node according to the radiation correlation observation group, extract the corresponding radiation correlation sub-electrical index, and verify the disturbance precursor according to the radiation correlation observation group. The frequency modulation control module responds to the verification result of the verification module to trigger the fine-tuning strategy, and constructs a verification window to continuously acquire the verification result in order to determine whether to continue to execute the stage frequency modulation strategy. The precursor observation window is a time window of a predetermined duration before the occurrence of power grid offset.

[0007] It should be noted that the precursor screening module screens the preceding mutation nodes based on the abrupt change timing of the electrical characteristics corresponding to each node, including: Identify the abrupt changes in the electrical characteristics of each node, and establish the correspondence between each node and the abrupt change time. Arrange the aforementioned mutation times in ascending order to obtain a time-series queue; Select a corresponding number of mutation moments at the head of the time sequence queue, and determine the node corresponding to each mutation moment as the preceding mutation node; Wherein, if any sub-electrical index of the electrical characteristic satisfies the mutation condition, it is determined that there is a mutation moment in the electrical characteristic. The mutation condition is that the sub-electrical index does not belong to the typical operating condition range and the instantaneous change rate is greater than the instantaneous change rate threshold.

[0008] It should be noted that the precursor screening module determines the precursor manifestation nodes based on the manifestation of the electrical characteristics corresponding to each precursor mutation node at the mutation time, including: The electrical characteristics of the precursor mutation node within several precursor observation windows are determined, and sub-electrical indicators that do not belong to the corresponding typical operating condition range are extracted to construct the time domain curves of each sub-electrical indicator. Determine the mean curve fitting similarity of the time-domain curves corresponding to each of the aforementioned sub-electrical indicators; Determine the average deviation rate of each of the aforementioned sub-electrical indicators from the corresponding typical operating condition range; Precursor mutation nodes that meet the electrical characteristic performance conditions are identified as precursor performance nodes. Among them, the electrical characteristic performance conditions include that the average similarity of the curve fitting corresponding to the sub-electrical index is greater than the preset curve fitting threshold, and the average deviation ratio is greater than the deviation ratio threshold.

[0009] It should be noted that the correlation parsing module determines the secondary correlation nodes and radial correlation types of each precursor manifestation node within the precursor observation window, and determines the corresponding radial correlation sub-electrical indicators, including: The correlation characteristics between precursor nodes and their corresponding sub-electrical indices within several precursor observation windows were analyzed, including the difference, the time difference when the abrupt change condition was met, and the linear correlation coefficient. Based on the association characteristics, sub-electrical indicators that meet the association conditions are determined, and the radial association type corresponding to the remaining nodes is determined based on the satisfied association conditions. The remaining nodes that have radial association types are identified as secondary association nodes; Sub-electrical indicators that meet the correlation conditions are identified as radially correlated sub-electrical indicators; The association conditions include spatiotemporal association conditions, amplitude association conditions, and linear association conditions. The radiation association types include spatiotemporal association, numerical association, and linear association. Each association condition corresponds one-to-one with the corresponding radiation association type.

[0010] It should be noted that the association parsing module stores association conditions, wherein the spatiotemporal association condition is that the time difference distribution range of the time difference is within a preset time difference distribution constraint range; The amplitude correlation condition is that the difference distribution range of the sub-electrical index is within a preset numerical distribution constraint range; The linear correlation condition is that there exists a sub-electrical index whose linear correlation coefficient is greater than a predetermined linear correlation threshold.

[0011] It should be noted that the correlation analysis module extracts the nominal operating condition range based on the historical records of the radiation-related electrical indicators to construct the radiation-related observation group, including: Record the radiation correlation type between each of the aforementioned precursor nodes and several associated secondary nodes, as well as the correlation characteristics of the corresponding radiation correlation sub-electrical indicators within several precursor observation windows in the historical cycle. Determine the normal distribution of the recorded associated features and determine the confidence interval, using the confidence interval as the nominal operating condition interval; The radiation correlation observation group includes the radiation correlation type, radiation correlation sub-electrical indicators, and corresponding nominal operating condition ranges between a single precursor manifestation node and several secondary correlation nodes.

[0012] It should be noted that the verification module verifies the precursors of disturbances based on the radiation-related observation group, including: Based on the radiation correlation observation group, query the radiation correlation types of the precursor nodes and secondary correlation nodes to be monitored, and query the radiation correlation sub-electrical indicators and corresponding nominal operating condition ranges to be monitored. The correlation characteristics are determined based on the current electrical indices of the radiation correlators. If the verification conditions are met, then the verification shows that there are no precursors to disturbances. If the verification conditions are not met, then the presence of a disturbance precursor is verified. The verification conditions include that the real-time radiation association types of each of the secondary associated nodes and the precursor manifestation nodes are consistent with the queried radiation association type, and the distribution range of the associated features corresponding to the radiation association sub-electrical indicators belongs to the queried nominal operating condition range.

[0013] It should be noted that the frequency modulation control module responds to the verification module's verification of the presence of disturbance precursors and triggers a fine-tuning strategy, wherein the fine-tuning strategy includes the fine-tuning control object and control parameters in the thermal power unit.

[0014] It should be noted that the frequency modulation control module constructs a verification window to continuously acquire verification results in order to determine whether to continue executing the phased frequency modulation strategy, including: Obtain the verification results of the verification module in consecutive verification windows; If the verification result shows that there are no signs of disturbance, then stop frequency modulation and restore the initial operating conditions; If the verification result indicates the presence of a disturbance precursor, the phased frequency regulation strategy will continue to be implemented until a grid offset occurs. The duration of the verification window must be shorter than the duration of the precursor observation window.

[0015] It should be noted that the frequency modulation strategy executed by the frequency modulation control module during the execution phase includes: Different adjustment strategies are set according to the timing sequence of the verification windows, so that the corresponding adjustment strategy is executed when the verification window ends; The verification window corresponds one-to-one with the adjustment strategy, and each adjustment strategy includes the control object and control parameters in the thermal power unit.

[0016] Compared with existing technologies, this invention collects the electrical characteristics of nodes in the power grid and the grid frequency, analyzes precursor behaviors, identifies several precursor behavior nodes, reflects the radial correlation sub-electrical indicators and radial correlation types between the precursor behavior nodes and several secondary related nodes, summarizes the nominal operating condition range, and then constructs a radial correlation group. Subsequently, based on the radial correlation group, targeted detection of precursor nodes and secondary related nodes is carried out, and multi-dimensional verification is performed. In response to the verification results, a fine-tuning strategy is triggered, and a verification window is constructed for continuous verification, and a phased adjustment strategy is matched. This invention, through multi-node, multi-dimensional joint analysis, predicts frequency regulation demand in advance and matches gradual, phased adjustments, shortening the actual response time after frequency deviation. While improving frequency regulation efficiency, it effectively avoids the impact of large-scale pre-action on units and the power grid.

[0017] The advantage of this invention lies in its ability to screen precursor nodes from preceding mutation nodes. Precursor mutation nodes are those with earlier anomaly labels in the time sequence, thus possessing greater observational value. Furthermore, the screening process comprehensively considers the manifestation of electrical characteristics corresponding to each precursor mutation node. Specifically, it utilizes the mean similarity of curve fitting to assess the homogeneity of corresponding sub-electrical indicators within multiple precursor observation windows. Simultaneously, it uses the mean deviation ratio to screen sub-electrical indicators with more significant shift effects. This reduces noise data interference and identifies the precursor nodes with the most significant indication of disturbing precursors. Among these precursor nodes, there are sub-electrical indicators with significant observational value, thereby providing support for the subsequent construction of a radiological correlation observation group.

[0018] The advantage lies in that this invention identifies the secondary associated nodes and radial association types corresponding to the precursor nodes, and simultaneously determines the corresponding radial association sub-electrical indices. In reality, grid disturbances do not act in isolation on a single node, but propagate along the grid topology and cause associated responses in a series of nodes, which are then reflected in electrical characteristics. Furthermore, different sub-electrical indices exhibit different forms and response degrees to the same disturbance, resulting in multiple association scenarios. Therefore, this invention analyzes the association types between secondary associated nodes and radial associated nodes, determines the corresponding radial association sub-electrical indices, and constructs a radial association observation group. This facilitates subsequent verification of disturbance precursors using the radial association observation group. During the verification process, multiple dimensions can be analyzed collaboratively. First, for different secondary associated nodes, only radial association sub-electrical indices with strong data representation are verified, saving computational resources. The actual radial association type of each secondary associated node is used as one of the verification dimensions. Furthermore, it is necessary to further analyze the relationship between the distribution range of the association characteristics determined based on the radial association sub-electrical indices and the characterization interval. Thus, multiple dimensions of association collaborative verification are performed on multiple secondary associated nodes within the radial association observation group, leading to more accurate and efficient prediction of disturbance precursors.

[0019] The beneficial effect is that, when verifying the presence of disturbance precursors, this invention first triggers a fine-tuning strategy for a tentative pre-response, avoiding premature and significant adjustments that could cause unnecessary impact on the power grid before the disturbance has materialized. Simultaneously, a verification window is constructed to continuously acquire verification results. At the end of each verification window, a determination is made based on correlation stability to determine whether to continue executing the phased frequency regulation strategy. At the end of each verification window, a corresponding adjustment strategy is matched, forming a gradual adjustment. This hierarchical flexible control mechanism moves the unit's response starting point forward to the disturbance propagation stage, completing pre-loading before the arrival of the formal frequency regulation signal, thereby effectively shortening the response time after the actual frequency shift. Attached Figure Description

[0020] Figure 1 This is a simplified structural diagram of a thermal power unit frequency regulation system based on multi-dimensional parameter monitoring, as described in an embodiment of the invention. Figure 2 A logic block diagram for determining the precursor manifestation node in an embodiment of the invention; Figure 3 A logic block diagram for determining the radiation association type in an embodiment of the invention; Figure 4 This is a logic block diagram for verifying the presence of disturbance precursors in an embodiment of the invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] Please see Figure 1 The diagram shown is a simplified structural diagram of a thermal power unit frequency regulation system based on multi-dimensional parameter monitoring according to an embodiment of the invention. The thermal power unit frequency regulation system based on multi-dimensional parameter monitoring according to this embodiment of the invention includes: The parameter acquisition module is used to acquire the electrical characteristics of each selected node in the power grid and the power grid frequency in real time. The electrical characteristics include several types of sub-electrical indicators. The precursor screening module is used to analyze the precursor manifestation based on the electrical characteristics of each node in the precursor observation window, including screening the precursor mutation nodes based on the mutation time sequence of the electrical characteristics corresponding to each node, and determining the precursor manifestation node based on the manifestation form of the electrical characteristics corresponding to each precursor mutation node at the mutation time. The correlation analysis module is used to determine the secondary correlation nodes and radiation correlation types of each of the aforementioned precursor manifestation nodes within the precursor observation window, determine the corresponding radiation correlation sub-electrical indicators, and extract the nominal operating condition range based on the historical records of the radiation correlation sub-electrical indicators to construct a radiation correlation observation group. The verification module is used to monitor the real-time radiation correlation type between the corresponding precursor node and the secondary correlation node according to the radiation correlation observation group, extract the corresponding radiation correlation sub-electrical index, and verify the disturbance precursor according to the radiation correlation observation group. The frequency modulation control module responds to the verification result of the verification module to trigger the fine-tuning strategy, and constructs a verification window to continuously acquire the verification result in order to determine whether to continue to execute the stage frequency modulation strategy. The precursor observation window is a time window of a predetermined duration before the occurrence of power grid offset.

[0024] In practical implementation, a node in the power grid refers to a measurable electrical interface in the power grid topology that has a clear function of power transmission, distribution, or access. Specifically, it can be the outgoing end of a line, the voltage measurement end of a bus, or the grid connection end of a power source / load. For example, the outgoing ends of lines on both sides of an inter-regional tie line or DC transmission line, the high-voltage side outgoing end after the step-up transformer of a major power plant or a new energy collection station, etc. The above are just examples and will not be elaborated further.

[0025] In specific implementation, the sub-electrical indicators of electrical characteristics include current amplitude, voltage amplitude, active power, reactive power, voltage phase angle, and fundamental frequency. There are no restrictions on the acquisition method of the sub-electrical indicators. The acquired sub-electrical indicators are timestamped to ensure time sequence alignment. Those skilled in the art can set up corresponding detection equipment at corresponding nodes to acquire each sub-electrical indicator. The acquisition methods of each sub-electrical indicator are all existing technologies and will not be described in detail here.

[0026] In practice, nodes that are spatially close to thermal power units should be prioritized in the power grid. The number of nodes should not be too large. Ten nodes were selected in the implementation to avoid introducing noise due to too many nodes and to reduce the complexity of data processing while ensuring the reliability of precursor capture.

[0027] In practice, after a power disturbance occurs in the power grid, electromechanical waves propagate from the disturbance source to the entire grid. The time difference between different nodes sensing the anomaly is typically tens to hundreds of milliseconds. However, the complete evolution of this precursor process—from the first node sensing the anomaly to a significant frequency shift, and then to an observable change in the correlation—requires a slightly longer time window to fully capture it. A precursor observation window of 1 to 5 seconds is sufficient to cover the entire process from the initial appearance of the disturbance to the critical point of frequency shift. In practice, the duration of the precursor observation window is set to 3 seconds.

[0028] It is understandable that determining a frequency deviation in the power grid means that the absolute value of the difference between the measured frequency and the rated frequency exceeds the preset frequency dead zone threshold. The rated frequency of the power grid is usually 50Hz, and the frequency dead zone threshold for primary frequency regulation of thermal power units is usually set to 0.033Hz.

[0029] In practice, both the fine-tuning strategy and the staged frequency modulation strategy are applied to the primary frequency modulation process.

[0030] It should be noted that the multiple functional modules involved in this application are only a logical division based on the functions implemented according to the present invention, and are not a strict limitation on the physical structure; in practical applications, the above functional modules can be implemented by one or more integrated circuits, a processor executing program code in memory, or a combination of the above devices.

[0031] To elaborate, please refer to Figure 2 As shown, it is a logic block diagram for determining precursor nodes according to an embodiment of the invention. The precursor screening module filters precursor mutation nodes based on the abrupt change timing of the electrical characteristics corresponding to each node, including: Identify the abrupt changes in the electrical characteristics of each node, and establish the correspondence between each node and the abrupt change time. Arrange the aforementioned mutation times in ascending order to obtain a time-series queue; Select a corresponding number of mutation moments at the head of the time sequence queue, and determine the node corresponding to each mutation moment as the preceding mutation node; Wherein, if any sub-electrical index of the electrical characteristic satisfies the mutation condition, it is determined that there is a mutation moment in the electrical characteristic. The mutation condition is that the sub-electrical index does not belong to the typical operating condition range and the instantaneous change rate is greater than the instantaneous change rate threshold.

[0032] In practice, the typical operating condition range and the instantaneous rate of change threshold are determined based on the electrical characteristics of each node recorded in several non-precursor observation windows within the historical period. When determining the typical operating condition range, the closed interval formed by the maximum and minimum values ​​of the individual sub-electrical indicators of a single node within the historical period is statistically analyzed to reflect the range that the sub-electrical indicators of each node should be in under normal operating conditions. The closed interval is used as the typical operating condition range of the corresponding sub-electrical indicators of a single node.

[0033] Understandably, each sub-electrical indicator of each node has a corresponding typical operating condition range.

[0034] When determining the instantaneous rate of change threshold, the instantaneous rate of change of a single sub-electrical index of a single node within the non-precursor observation window of the historical cycle is statistically analyzed, and the 95th percentile is determined as the instantaneous rate of change threshold, reflecting the high value of the instantaneous rate of change under normal operating conditions.

[0035] In practice, the number of nodes selected at the head of the time-series queue is determined based on the length of the time-series queue and is set to 25% of the time-series queue length, so that nodes at the head of the queue that exhibit mutation behavior earlier can be selected.

[0036] It is understandable that, as time goes on, the mutation moments that occur later in the time sequence are considered larger values. Therefore, an ascending order is used to find nodes that exhibit mutations earlier in the time sequence.

[0037] For further details, please refer to the following article. Figure 2 As shown, it is a logic block diagram for determining precursor manifestation nodes according to an embodiment of the invention. The precursor screening module determines the precursor manifestation nodes based on the manifestation of the electrical characteristics corresponding to each precursor mutation node at the mutation time, including: The electrical characteristics of the precursor mutation node within several precursor observation windows are determined, and sub-electrical indicators that do not belong to the corresponding typical operating condition range are extracted to construct the time domain curves of each sub-electrical indicator. Determine the mean curve fitting similarity of the time-domain curves corresponding to each of the aforementioned sub-electrical indicators; Determine the average deviation rate of each of the aforementioned sub-electrical indicators from the corresponding typical operating condition range; Precursor mutation nodes that meet the electrical characteristic performance conditions are identified as precursor performance nodes. Among them, the electrical characteristic performance conditions include that the average similarity of the curve fitting corresponding to the sub-electrical index is greater than the preset curve fitting threshold, and the average deviation ratio is greater than the deviation ratio threshold.

[0038] It is understandable that, since there are multiple precursor observation windows, time-domain curves within multiple precursor windows can be obtained for a single sub-electrical index, and then the curve fitting similarity between the time-domain curves can be calculated separately, and the mean value of the curve fitting similarity can be calculated. During implementation, the separation ratio of sub-electrical indicators within each precursor observation window is determined, and then the average separation ratio is calculated.

[0039] In practice, the time-domain curves of each sub-electrical indicator are curves showing how the sub-electrical indicator changes over time.

[0040] In practice, there are no restrictions on the method for determining the similarity between time-domain curves. For example, the cosine similarity method can be used to vectorize the two time-domain curves and solve for the cosine similarity, which can then be used as the similarity.

[0041] Understandably, when calculating the deviation ratio of a sub-electrical indicator from the typical operating condition range, if the maximum value of the sub-electrical indicator is greater than the upper limit of the typical operating condition range or / and the minimum value of the sub-electrical indicator is less than the lower limit of the typical operating condition range, it is considered to have deviated from the typical operating condition range. The difference between the maximum value of the sub-electrical indicator and the upper limit of the typical operating condition range is taken as the upper limit deviation amount or / and the difference between the minimum value of the sub-electrical indicator and the lower limit of the typical operating condition range is taken as the lower limit deviation amount. Calculate the ratio of the sum of the upper and lower limit separation amounts to the interval length to obtain the separation ratio.

[0042] The curve fitting threshold is a set value. Since each sub-electrical indicator is in a relatively stable stage under normal operating conditions, the sub-electrical indicators corresponding to each node in several non-precursor observation windows of historical periods are statistically analyzed in advance. For the same sub-electrical indicator, the time-domain curves in each time period are determined, the curve fitting degree between the time-domain curves is calculated, and the mean curve fitting degree is calculated. This reflects that under stable operating conditions, the time-domain curve is close to the typical value of the curve fitting degree. Considering that there is a certain instability under abnormal operating conditions, the mean curve fitting degree can be appropriately reduced as the curve fitting threshold. In specific implementation, 0.85 times the mean curve fitting degree is set as the curve fitting threshold. Of course, the scaling ratio can be adjusted by those skilled in the art.

[0043] The departure ratio threshold is a set value. The departure ratio of each sub-electrical indicator in several precursor observation windows of the statistical historical period is selected as the departure ratio threshold from the statistical data. This is to facilitate the screening of nodes with obvious abnormal phenomena. In practice, the 60th percentile of the statistical departure ratio is used as the departure ratio threshold.

[0044] This invention screens precursor nodes from early mutation nodes. Precursor mutation nodes are those with earlier anomaly labels in the time sequence, thus possessing better observational value. Based on this, the screening process comprehensively considers the manifestation of electrical features corresponding to each precursor mutation node. Specifically, the mean similarity of curve fitting is used to consider the homogeneity of corresponding sub-electrical indicators of electrical features within multiple precursor observation windows. At the same time, the mean deviation ratio is used to screen sub-electrical indicators with more significant deviation effects. This reduces noise data interference and identifies the precursor nodes with the most significant indication of disturbing precursors. Among these precursor nodes, there are sub-electrical indicators with significant observational value, thus providing support for the subsequent construction of radiological correlation observation groups.

[0045] To elaborate, please refer to Figure 3 As shown, it is a logic block diagram for determining the radiation association type according to an embodiment of the invention. The association parsing module determines the secondary association nodes and radiation association types of each of the precursor manifestation nodes within the precursor observation window, and determines the corresponding radiation association sub-electrical indicators, including: The correlation characteristics between precursor nodes and their corresponding sub-electrical indices within several precursor observation windows were analyzed, including the difference, the time difference when the abrupt change condition was met, and the linear correlation coefficient. Based on the association characteristics, sub-electrical indicators that meet the association conditions are determined, and the radial association type corresponding to the remaining nodes is determined based on the satisfied association conditions. The remaining nodes that have radial association types are identified as secondary association nodes; Sub-electrical indicators that meet the correlation conditions are identified as radially correlated sub-electrical indicators; The association conditions include spatiotemporal association conditions, amplitude association conditions, and linear association conditions. The radiation association types include spatiotemporal association, numerical association, and linear association. Each association condition corresponds one-to-one with the corresponding radiation association type.

[0046] It is understandable that if the correlation characteristics of sub-electrical indicators meet the spatiotemporal correlation conditions, they are identified as spatiotemporal correlations; if the correlation characteristics of sub-electrical indicators meet the amplitude correlation conditions, they are identified as numerical correlations; and if the correlation characteristics of sub-electrical indicators meet the linear correlation conditions, they are identified as linear correlations.

[0047] It is understandable that the process of calculating associated features includes: Extract the time when the sub-electrical characteristics of the precursor node satisfy the abrupt change condition, determine the time when the sub-electrical characteristics of the remaining nodes satisfy the abrupt change condition, and solve for the time difference; Extract the sub-electrical features of the precursor nodes, extract the sub-electrical features of the remaining nodes, solve for the difference between the sub-electrical features at the corresponding time, and obtain the difference value at the corresponding time. The time series of sub-electrical characteristics of precursor nodes at each time step is extracted, and the time series of sub-electrical characteristics of the remaining nodes at each time step is extracted. The Pearson correlation coefficient is calculated based on the two time series and is used as the linear correlation coefficient. The calculation of the Pearson correlation coefficient of the time series is an existing technique and will not be elaborated here.

[0048] It is understandable that a single secondary associated node may have multiple associations with precursor manifestation nodes, and may have multiple radiological associated electrical indices.

[0049] To elaborate, the association parsing module stores association conditions, wherein the spatiotemporal association condition is that the time difference distribution range of the time difference is within a preset time difference distribution constraint range; The amplitude correlation condition is that the difference distribution range of the sub-electrical index is within a preset numerical distribution constraint range; The linear correlation condition is that there exists a sub-electrical index whose linear correlation coefficient is greater than a predetermined linear correlation threshold.

[0050] In practice, the time difference distribution range is a closed interval formed by the maximum and minimum values ​​of the time difference; The range of the difference distribution is a closed interval formed by the maximum and minimum values ​​of the difference.

[0051] In practice, the purpose of setting time difference distribution range constraints is to limit the distribution range of each time difference in multiple windows or multiple events to be sufficiently concentrated. In practice, the sudden change times of each node in several precursor observation windows within the historical period can be statistically analyzed. The sudden change times are arranged in ascending order to obtain a time series queue. The time difference between adjacent sudden change times is calculated based on the time series queue. The 20th percentile and 80th percentile of each time difference are statistically analyzed to form a closed interval. The closed interval is used as the distribution range constraint. This closed interval reflects the typical time difference distribution area where relevant nodes generate perceptible electrical responses in sequence when abnormal disturbances in the power grid propagate between nodes.

[0052] In practice, the numerical distribution constraint range is also used to limit the fluctuation range of each difference in multiple windows or multiple events to keep it stable, thereby ensuring that the amplitude correlation between the two nodes is repeatable. First, a sequence of difference samples for the same sub-electrical index between nodes within several precursor observation windows in the historical period is statistically analyzed. The mean value corresponding to the difference samples is statistically analyzed to reflect the typical distribution value of the difference. Then, based on this mean value, 1.35 times the mean value is set as the upper limit of the numerical distribution constraint range, and 0.65 times the mean value is set as the lower limit of the numerical distribution constraint range.

[0053] In practice, the linear correlation threshold is a set value. In engineering practice, the closer the Pearson correlation coefficient is to 1, the stronger the correlation. Usually, a value greater than 0.5 indicates a certain correlation. In practice, it can be selected within the range of [0.5, 0.8] as the linear correlation threshold to characterize the linear correlation between sub-electrical indicators.

[0054] To elaborate, the correlation analysis module extracts the nominal operating condition range based on the historical records of the radiation-related electrical indicators to construct a radiation-related observation group, including: Record the radiation correlation type between each of the aforementioned precursor nodes and several associated secondary nodes, as well as the correlation characteristics of the corresponding radiation correlation sub-electrical indicators within several precursor observation windows in the historical cycle. Determine the normal distribution of the recorded associated features and determine the confidence interval, using the confidence interval as the nominal operating condition interval; The radiation correlation observation group includes the radiation correlation type, radiation correlation sub-electrical indicators, and corresponding nominal operating condition ranges between a single precursor manifestation node and several secondary correlation nodes.

[0055] In practice, the confidence interval is the 95% confidence interval of the normal distribution.

[0056] The radiation correlation type, radiation correlation sub-electrical indicators, and corresponding nominal operating condition ranges between a single precursor node and several secondary related nodes are stored in a single dataset to form a radiation correlation observation group.

[0057] In practice, the radiation-related observation group can be updated regularly to ensure reliability.

[0058] This invention identifies the secondary associated nodes and radial association types corresponding to the precursor nodes, and simultaneously determines the corresponding radial association sub-electrical indices. In reality, power grid disturbances do not act in isolation on a single node, but propagate along the power grid topology and cause associated responses in a series of nodes, which are then reflected in electrical characteristics. Furthermore, different sub-electrical indices exhibit different manifestations and response degrees to the same disturbance, resulting in multiple association scenarios. Therefore, this invention analyzes the association types between secondary associated nodes and radial associated nodes, determines the corresponding radial association sub-electrical indices, and constructs a radial association observation group. This facilitates subsequent verification of disturbance precursors using the radial association observation group. During the verification process, multiple dimensions can be analyzed collaboratively. First, for different secondary associated nodes, only radial association sub-electrical indices with strong data representation are verified to save computational resources. The actual radial association type of each secondary associated node is used as one of the verification dimensions. Furthermore, it is necessary to further analyze the relationship between the distribution range of the association characteristics determined based on the radial association sub-electrical indices and the characterization interval. Thus, multiple dimensions of association collaborative verification are performed on multiple secondary associated nodes within the radial association observation group, resulting in more accurate and efficient prediction of disturbance precursors.

[0059] To elaborate, please refer to Figure 4 The diagram shown is a logical block diagram of an embodiment of the invention for verifying the existence of precursory disturbances. The verification module verifies precursory disturbances based on the radiation correlation observation group, including: Based on the radiation correlation observation group, query the radiation correlation types of the precursor nodes and secondary correlation nodes to be monitored, and query the radiation correlation sub-electrical indicators and corresponding nominal operating condition ranges to be monitored. The correlation characteristics are determined based on the current electrical indices of the radiation correlators. If the verification conditions are met, then the verification shows that there are no precursors to disturbances. If the verification conditions are not met, then the presence of a disturbance precursor is verified. The verification conditions include that the real-time radiation association types of each of the secondary associated nodes and the precursor manifestation nodes are consistent with the queried radiation association type, and the distribution range of the associated features corresponding to the radiation association sub-electrical indicators belongs to the queried nominal operating condition range.

[0060] Understandably, since there are multiple correlation features, it is necessary to extract and observe them according to the corresponding radial correlation type. For example, for spatiotemporal correlation, extract the time difference when the mutation condition is met; for numerical correlation, extract the difference; and for linear correlation, extract the linear correlation coefficient.

[0061] Understandably, once the electrical indices of the radiocorrelations are determined, analysis can be performed to determine the corresponding real-time radiocorrelation type, as well as the correlation characteristics and their distribution range.

[0062] In more detail, the frequency modulation control module, in response to the verification module's detection of a disturbance precursor, triggers a fine-tuning strategy. This fine-tuning strategy includes the fine-tuning control object and control parameters within the thermal power unit. The fine-tuning control object preferentially selects relevant facilities in the thermal power unit's regenerative system. Optional, including: The fine-tuning control object is the condensate pump outlet valve in the regenerative system, and the control parameter is the valve opening. Reducing the valve opening will reduce the condensate by 5%.

[0063] Adjust the opening of the condensate pump outlet valve to instantly reduce the condensate flow rate, and utilize the heat storage of the low-pressure heater regeneration system to quickly and slightly increase the unit output without changing the boiler combustion.

[0064] Condensate pumps are core auxiliary equipment in the thermal power unit's thermal system and are a fixed component of the unit. Their main function is to extract condensate from the condenser hot well, heat it through the low-pressure heater system, and then send it to the deaerator to complete the steam-water cycle.

[0065] In the thermodynamic cycle of a thermal power unit, the exhaust steam from the turbine condenses into water in the condenser. The condensate pump powers this condensate to overcome the resistance of the low-pressure heater system before it enters the deaerator. The water in the deaerator is then pressurized by the feedwater pump, heated by the high-pressure heater, and enters the boiler, completing the full thermodynamic cycle.

[0066] Of course, other methods can also be used. For example, the fine-tuning control object can be set as the coal feeder, and the control parameter can be the speed. Since the boiler combustion thermal inertia is large, the speed of the coal feeder can be fine-tuned to start the combustion side response in advance and prepare fuel for possible subsequent continuous frequency regulation.

[0067] To elaborate, the frequency modulation control module constructs a verification window to continuously acquire verification results in order to determine whether to continue executing the phased frequency modulation strategy, including: Obtain the verification results of the verification module in consecutive verification windows; If the verification result shows that there are no signs of disturbance, then stop frequency modulation and restore the initial operating conditions; If the verification result indicates the presence of a disturbance precursor, the phased frequency regulation strategy will continue to be implemented until a grid offset occurs. The duration of the verification window must be shorter than the duration of the precursor observation window.

[0068] In practice, when the grid frequency deviation actually occurs, that is, when the frequency deviation of the unit's grid connection point exceeds the preset dead zone, the thermal power unit can directly respond using conventional primary frequency regulation methods based on the current operating conditions. These methods include, but are not limited to, controlling the opening of the turbine high-pressure valve according to the speed unequal rate curve and calling upon boiler heat storage for power support. These are existing technologies and will not be elaborated further.

[0069] To elaborate, the frequency modulation strategy executed by the frequency modulation control module during the execution phase includes: Different adjustment strategies are set according to the timing sequence of the verification windows, so that the corresponding adjustment strategy is executed when the verification window ends; The verification window corresponds one-to-one with the adjustment strategy, and each adjustment strategy includes the control object and control parameters in the thermal power unit.

[0070] During implementation, three verification windows were set up. The length of the verification window was set based on the length of the precursor observation window, and was set to 0.1 times the length of the precursor observation window. After the first verification window is completed, the corresponding adjustment strategy is as follows: taking the condensate pump outlet valve as the controlled object and the valve opening as the control parameter, the fine-tuning strategy is executed again. This fine-tuning is a trial-and-error adjustment based on the first fine-tuning, and the adjustment range of condensate flow rate is still limited to a small range to ensure the reversibility of the unit's operating conditions and to prevent irreversible impacts on the main system due to small-scale trials.

[0071] After the second verification window is completed, the corresponding adjustment strategy is as follows: The high-pressure valve of the steam turbine is the controlled object, and the valve opening is the control parameter. Based on the current valve opening, the valve opening is appropriately increased to slowly and steadily increase the output power of the thermal power unit. For example, the valve opening is increased by 5%, that is, if the opening before adjustment is 40%, then the opening after adjustment will be increased to 45%.

[0072] After the third verification window is completed, the corresponding adjustment strategy is as follows: The turbine high-pressure valves and the coal feeder are the controlled objects, with the control parameters being valve opening and coal feeder speed, respectively. The valve opening is increased again based on the adjustment in the second verification window, and the coal feeder speed is increased synchronously. The valve opening increases by 5%, and the speed increases by 5% of the original speed. Through the synchronous incremental adjustment of the high-pressure valves and the coal feeder, the unit's output preparation range is further increased, and the boiler-side combustion response is initiated in advance to prepare the fuel side for possible full-load operation.

[0073] After passing verification through three consecutive verification windows, the confidence level for the occurrence of frequency deviation in the power grid has reached a high level. Therefore, after the completion of the third window, the generating units, through the aforementioned gradual increase in output adjustment, are now in a high output readiness state. This allows them to quickly superimpose the conventional primary frequency regulation response when an actual frequency deviation occurs in the power grid, effectively shortening the equivalent frequency regulation delay and providing timely active power support to the power grid.

[0074] By adopting small-amplitude, gradual adjustments, the system operating conditions can be reversed at any time, and there is no additional disturbance to the power grid.

[0075] When verifying the presence of disturbance precursors, this invention first triggers a fine-tuning strategy for a tentative pre-response, avoiding premature and significant adjustments that could cause unnecessary impact on the power grid before the disturbance has materialized. Simultaneously, a verification window is constructed to continuously acquire verification results. At the end of each verification window, a determination is made based on the correlation stability to determine whether to continue executing the phased frequency regulation strategy. At the end of each verification window, a corresponding adjustment strategy is matched, forming a gradual adjustment. This hierarchical flexible control mechanism moves the unit's response starting point forward to the disturbance propagation stage, completing pre-loading before the arrival of the formal frequency regulation signal, thereby effectively shortening the response time after the actual frequency shift.

[0076] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A frequency regulation system for thermal power units based on multi-dimensional parameter monitoring, characterized in that, include: The parameter acquisition module is used to acquire the electrical characteristics of each selected node in the power grid and the power grid frequency in real time. The electrical characteristics include several types of sub-electrical indicators. The precursor screening module is used to analyze the precursor manifestation based on the electrical characteristics of each node in the precursor observation window, including screening the precursor mutation nodes based on the mutation time sequence of the electrical characteristics corresponding to each node, and determining the precursor manifestation node based on the manifestation form of the electrical characteristics corresponding to each precursor mutation node at the mutation time. The correlation analysis module is used to determine the secondary correlation nodes and radiation correlation types of each of the aforementioned precursor manifestation nodes within the precursor observation window, determine the corresponding radiation correlation sub-electrical indicators, and extract the nominal operating condition range based on the historical records of the radiation correlation sub-electrical indicators to construct a radiation correlation observation group. The verification module is used to monitor the real-time radiation correlation type between the corresponding precursor node and the secondary correlation node according to the radiation correlation observation group, extract the corresponding radiation correlation sub-electrical index, and verify the disturbance precursor according to the radiation correlation observation group. The frequency modulation control module responds to the verification result of the verification module to trigger the fine-tuning strategy, and constructs a verification window to continuously acquire the verification result in order to determine whether to continue to execute the stage frequency modulation strategy. The precursor observation window is a time window of a predetermined duration before the occurrence of power grid offset.

2. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 1, characterized in that, The precursor screening module filters out preceding mutation nodes based on the mutation timing of the electrical characteristics corresponding to each node, including: Identify the abrupt changes in the electrical characteristics of each node, and establish the correspondence between each node and the abrupt change time. Arrange the aforementioned mutation times in ascending order to obtain a time-series queue; Select a corresponding number of mutation moments at the head of the time sequence queue, and determine the node corresponding to each mutation moment as the preceding mutation node; Wherein, if any sub-electrical index of the electrical characteristic satisfies the mutation condition, it is determined that there is a mutation moment in the electrical characteristic. The mutation condition is that the sub-electrical index does not belong to the typical operating condition range and the instantaneous change rate is greater than the instantaneous change rate threshold.

3. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 2, characterized in that, The precursor screening module determines the precursor manifestation nodes based on the electrical characteristic manifestation of each precursor mutation node at the mutation time, including: The electrical characteristics of the precursor mutation node within several precursor observation windows are determined, and sub-electrical indicators that do not belong to the corresponding typical operating condition range are extracted to construct the time domain curves of each sub-electrical indicator. Determine the mean curve fitting similarity of the time-domain curves corresponding to each of the aforementioned sub-electrical indicators; Determine the average deviation rate of each of the aforementioned sub-electrical indicators from the corresponding typical operating condition range; Precursor mutation nodes that meet the electrical characteristic performance conditions are identified as precursor performance nodes. Among them, the electrical characteristic performance conditions include that the average similarity of the curve fitting corresponding to the sub-electrical index is greater than the preset curve fitting threshold, and the average deviation ratio is greater than the deviation ratio threshold.

4. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 2, characterized in that, The correlation parsing module determines the secondary correlation nodes and radial correlation types of each precursor manifestation node within the precursor observation window, and determines the corresponding radial correlation sub-electrical indicators, including: The correlation characteristics between precursor nodes and their corresponding sub-electrical indices within several precursor observation windows were analyzed, including the difference, the time difference when the abrupt change condition was met, and the linear correlation coefficient. Based on the association characteristics, sub-electrical indicators that meet the association conditions are determined, and the radial association type corresponding to the remaining nodes is determined based on the satisfied association conditions. The remaining nodes that have radial association types are identified as secondary association nodes; Sub-electrical indicators that meet the correlation conditions are identified as radially correlated sub-electrical indicators; The association conditions include spatiotemporal association conditions, amplitude association conditions, and linear association conditions. The radiation association types include spatiotemporal association, numerical association, and linear association. Each association condition corresponds one-to-one with the corresponding radiation association type.

5. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 4, characterized in that, The association parsing module stores association conditions, wherein the spatiotemporal association condition is that the time difference distribution range of the time difference is within a preset time difference distribution constraint range; The amplitude correlation condition is that the difference distribution range of the sub-electrical index is within a preset numerical distribution constraint range; The linear correlation condition is that there exists a sub-electrical index whose linear correlation coefficient is greater than a predetermined linear correlation threshold.

6. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 4, characterized in that, The correlation analysis module extracts the nominal operating condition range based on the historical records of the radiation-related sub-electrical indicators to construct a radiation-related observation group, including: Record the radiation correlation type between each of the aforementioned precursor nodes and several associated secondary nodes, as well as the correlation characteristics of the corresponding radiation correlation sub-electrical indicators within several precursor observation windows in the historical cycle. Determine the normal distribution of the recorded associated features and determine the confidence interval, using the confidence interval as the nominal operating condition interval; The radiation correlation observation group includes the radiation correlation type, radiation correlation sub-electrical indicators, and corresponding nominal operating condition ranges between a single precursor manifestation node and several secondary correlation nodes.

7. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 6, characterized in that, The verification module verifies the precursors of the disturbance based on the radiation-related observation group, including: Based on the radiation correlation observation group, query the radiation correlation types of the precursor nodes and secondary correlation nodes to be monitored, and query the radiation correlation sub-electrical indicators and corresponding nominal operating condition ranges to be monitored. The correlation characteristics are determined based on the current electrical indices of the radiation correlators. If the verification conditions are met, then the verification shows that there are no precursors to disturbances. If the verification conditions are not met, then the presence of a disturbance precursor is verified. The verification conditions include that the real-time radiation association types of each of the secondary associated nodes and the precursor manifestation nodes are consistent with the queried radiation association type, and the distribution range of the associated features corresponding to the radiation association sub-electrical indicators belongs to the queried nominal operating condition range.

8. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 1, characterized in that, The frequency modulation control module responds to the verification module's verification of the presence of disturbance precursors and triggers a fine-tuning strategy, wherein the fine-tuning strategy includes the fine-tuning control object and control parameters in the thermal power unit.

9. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 1, characterized in that, The frequency modulation control module constructs a verification window to continuously acquire verification results in order to determine whether to continue executing the phased frequency modulation strategy, including: Obtain the verification results of the verification module in consecutive verification windows; If the verification result shows that there are no signs of disturbance, then stop frequency modulation and restore the initial operating conditions; If the verification result indicates the presence of a disturbance precursor, the phased frequency regulation strategy will continue to be implemented until a grid offset occurs. The duration of the verification window must be shorter than the duration of the precursor observation window.

10. The frequency regulation system for thermal power units based on multi-dimensional parameter monitoring according to claim 1, characterized in that, The frequency modulation control module executes the following frequency modulation strategies during the execution phase: Different adjustment strategies are set according to the timing sequence of the verification windows, so that the corresponding adjustment strategy is executed when the verification window ends; The verification window corresponds one-to-one with the adjustment strategy, and each adjustment strategy includes the control object and control parameters in the thermal power unit.

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