Unit operation monitoring management method and system based on electric power data

Abstract

The invention belongs to the technical field of generator set operation monitoring, and discloses a generator set operation monitoring management method and system based on electric power data, and the method comprises the steps: analyzing an association result between a frequency modulation index and a peak regulation index of a generator set based on the historical operation electric power data of the generator set, the frequency modulation adjustment compensation capability of the generator set is evaluated according to the association result; obtaining a performance monitoring index in the operation process of the generator set according to the frequency modulation adjustment compensation capability, and predicting the load capability of the generator set by using the performance monitoring index; and adjusting the operation management mode of the generator set based on the load capacity, obtaining an income value after the operation management mode is adjusted, verifying the adaptation degree of the operation management mode, and adjusting the operation management mode for the second time according to the adaptation degree. By analyzing the relation between the frequency modulation index and the peak regulation index, the regulation characteristics of the generator set can be known more accurately, and it is ensured that the generator set operates in the optimal working state.

Description

Technical Field

[0001] The present invention relates to the technical field of generator set operation monitoring, and in particular to a generator set operation monitoring management method and system based on power data. Background Art

[0002] A generator set is a device used to convert some form of energy into electrical energy. It is usually composed of multiple components and can be used for power supply in power plants or power grids. According to different energy sources, generator sets can be divided into various types such as thermal, hydro, wind, solar, etc. The operating performance of a generator set is closely related to its frequency regulation capability, load response capability, etc. Correct selection and operation of generator sets is the key to ensuring stable and efficient operation of the power grid.

[0003] The necessity of operation monitoring and management of generator sets during operation cannot be ignored. This is because generator sets are one of the core components in the power system, and their stable, economical and efficient operation is crucial to the reliability of power supply, optimization of energy consumption and control of environmental impact.

[0004] However, the existing technology cannot evaluate whether the unit can meet the grid load demand during the unit operation monitoring and management process, and thus cannot adjust the operation management mode according to the load capacity, making it impossible to ensure that the unit operates in the best working state.

[0005] Therefore, how to provide a unit operation monitoring and management method and system based on power data is a problem that needs to be solved urgently. Summary of the invention

[0006] The embodiment of the present invention provides a method and system for monitoring and managing unit operation based on power data, so as to solve the problem in the prior art that the operation management mode cannot be adjusted according to the load capacity.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not a general review, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for monitoring and managing unit operation based on power data is provided.

[0009] In one embodiment, a method for monitoring and managing unit operation based on power data includes:

[0010] Analyze the correlation between the frequency regulation index and the peak regulation index of the generator set based on the historical power data of the generator set, and evaluate the frequency regulation compensation capability of the generator set according to the correlation results;

[0011] Obtain performance monitoring indicators during the operation of the generator set based on the frequency regulation and compensation capability, and use the performance monitoring indicators to predict the load capacity of the generator set;

[0012] The operation management mode of the generator set is adjusted based on the load capacity, and the profit value after the operation management mode is adjusted is obtained, the adaptability of the operation management mode is verified, and the operation management mode is adjusted again according to the adaptability.

[0013] In one embodiment, analyzing the correlation result between the frequency regulation index and the peak regulation index of the generator set based on the historical power data of the generator set, and evaluating the frequency regulation compensation capability of the generator set according to the correlation result includes:

[0014] The frequency regulation response time and frequency deviation data of the generator set during historical operation are obtained as frequency regulation data, and the load tracking capability and maximum peak regulation capacity are collected as peak regulation data;

[0015] Use frequency regulation data and peak regulation data to analyze the frequency deviation regulation capability and load change response capability of the generator set, and define frequency regulation index and peak regulation index based on the frequency deviation regulation capability and load change response capability;

[0016] Analyze the distance vector between the frequency regulation index and the peak regulation index based on the regression analysis technology, and obtain the correlation result between the frequency regulation index and the peak regulation index of the generator set according to the distance vector;

[0017] The relationship between the frequency regulation response and peak load regulation capability of the generator set is analyzed according to the correlation results, and the frequency regulation and compensation capability of the generator set under different load conditions is evaluated based on the relationship results.

[0018] In one embodiment, obtaining performance monitoring indicators during the operation of the generator set according to the frequency regulation compensation capability, and predicting the load capacity of the generator set using the performance monitoring indicators includes:

[0019] Analyze the maximum load change rate of the generator set in unit time and the upper and lower limits of the operable power based on the frequency regulation and compensation capability, and determine the load response and peak load performance monitoring indicators of the generator set during operation according to the maximum load change rate and the upper and lower limits of power;

[0020] Obtaining the behavior data and frequency measurement data during the operation of the generator set, and performing normalization processing on the behavior data and the frequency measurement data after data alignment respectively to obtain a frequency regulation load capacity data set;

[0021] A frequency regulation load capacity prediction model is constructed based on the frequency regulation load capacity data set, and the frequency regulation load capacity prediction model is used to analyze the load capacity of the generator sets under different working conditions in the future time period.

[0022] According to a second aspect of an embodiment of the present invention, a unit operation monitoring and management system based on power data is provided.

[0023] In one embodiment, the unit operation monitoring and management system based on power data includes:

[0024] A frequency regulation and compensation capability evaluation unit is used to analyze the correlation results between the frequency regulation index and the peak regulation index of the generator set based on the historical power operation data of the generator set, and evaluate the frequency regulation and compensation capability of the generator set according to the correlation results;

[0025] A generator set load capacity prediction unit is used to obtain performance monitoring indicators during the operation of the generator set according to the frequency regulation compensation capacity, and to predict the load capacity of the generator set using the performance monitoring indicators;

[0026] The unit operation management mode adjustment unit is used to adjust the operation management mode of the generator set based on the load capacity, obtain the profit value after the operation management mode is adjusted, verify the adaptability of the operation management mode, and adjust the operation management mode for the second time according to the adaptability.

[0027] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0028] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0029] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0030] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0031] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0032] 1. By analyzing the relationship between frequency regulation and peak regulation indicators, the present invention can more accurately understand the regulation characteristics of the generator set. At the same time, by obtaining the frequency regulation compensation capability and performance monitoring indicators of the unit, it can evaluate at any time whether the unit can meet the load demand of the power grid. Finally, the operation management mode is adjusted according to the prediction results of the load capacity to ensure that the unit operates in the best working state.

[0033] 2. The present invention objectively evaluates the frequency regulation and peak regulation capabilities of the unit by collecting and analyzing historical data, truly reflects the performance of the generator set under different working conditions, and can provide accurate basic data for subsequent analysis. By analyzing the frequency deviation regulation capability and load change response capability, the regulation capability of the unit can be evaluated from multiple angles, and regression analysis can be used to explore the correlation between frequency regulation and peak regulation indicators, thereby revealing the balance or mutual restraint relationship between the unit's frequency regulation capability and load response capability. By evaluating the unit's frequency regulation and compensation capability, it is ensured that the unit can maintain a good frequency regulation response capability under different load conditions, effectively ensuring the frequency stability of the power grid.

[0034] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0036] Figure 1 is a flow chart showing a method for monitoring and managing unit operation based on power data according to an exemplary embodiment;

[0037] Figure 2 is a principle block diagram of a unit operation monitoring and management system based on power data according to an exemplary embodiment;

[0038] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0039] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0040] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0041] As used herein, unless otherwise specified, the term "plurality" means two or more than two.

[0042] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0043] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0044] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0045] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0046] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0047] Figure 1 An embodiment of the unit operation monitoring and management method based on power data of the present invention is shown.

[0048] In this optional embodiment, the unit operation monitoring and management method based on power data includes:

[0049] Step S101, analyzing the correlation results between the frequency regulation index and the peak regulation index of the generator set based on the historical power data of the generator set, and evaluating the frequency regulation and compensation capability of the generator set according to the correlation results;

[0050] Step S102, obtaining performance monitoring indicators during the operation of the generator set according to the frequency regulation and compensation capability, and using the performance monitoring indicators to predict the load capacity of the generator set;

[0051] Step S103, adjusting the operation management mode of the generator set based on the load capacity, obtaining the benefit value after the operation management mode is adjusted, verifying the adaptability of the operation management mode, and re-adjusting the operation management mode according to the adaptability.

[0052] In this optional embodiment, when analyzing the correlation results between the frequency regulation index and the peak shaving index of the generator set based on the historical operating power data of the generator set, and evaluating the frequency regulation and compensation capability of the generator set according to the correlation results, the frequency regulation response time and frequency deviation data during the historical operation of the generator set can be obtained as frequency regulation data, and the load tracking capability and the maximum peak shaving capacity can be collected as peak shaving data; the frequency deviation regulation capability and the load change response capability of the generator set are analyzed using the frequency regulation data and the peak shaving data, and the frequency regulation index and the peak shaving index are defined based on the frequency deviation regulation capability and the load change response capability; the distance vector between the frequency regulation index and the peak shaving index is analyzed based on the regression analysis technology, and the correlation results between the frequency regulation index and the peak shaving index of the generator set are obtained according to the distance vector; the relationship between the frequency regulation response and the peak shaving capability of the generator set is analyzed according to the correlation results, and the frequency regulation and compensation capability of the generator set under different load conditions is evaluated based on the relationship results.

[0053] It should be explained that when defining frequency regulation indicators and peak regulation indicators, the frequency regulation response time (i.e. the speed at which the unit responds to frequency fluctuations) and frequency deviation data (i.e. the actual deviation of the grid frequency from the set value) can be extracted from the historical operating data of the unit. The data collection method is usually to obtain this data in real time through the monitoring system, data acquisition and monitoring system of the power dispatching center.

[0054] The specific frequency regulation response time usually refers to the time difference after the frequency changes, when the generator set starts to respond and adjust the frequency, while the frequency deviation data refers to the difference between the actual frequency of the power grid and the set frequency. The load tracking capability refers to the unit's regulation capability under different load conditions, that is, the unit's ability to adjust output according to load changes. The maximum peak-shaving capacity refers to the maximum power output capacity that the generator set can provide during peak load periods.

[0055] Organize the collected data into a structured format to ensure the accuracy and completeness of the data, clean the data, remove invalid values ​​and abnormal values, generate frequency regulation data (frequency regulation response time, frequency deviation) and peak regulation data (load tracking capability, maximum peak regulation capacity), and use frequency regulation data (such as frequency regulation response time and frequency deviation) to evaluate the frequency deviation regulation capability of the generator set:

[0056] Frequency deviation adjustment capability: evaluate how quickly the unit can respond and to what extent it can correct the frequency deviation when the grid frequency fluctuates, analyze the relationship between the frequency regulation response time and the frequency deviation, and determine whether the unit's frequency regulation response is timely and whether the deviation can be adjusted to the set value in time;

[0057] Use peak load data (such as load following capability and maximum peak load capacity) to assess the load response capability of the generator set:

[0058] Load change response capability: refers to the ability of a generator set to adjust its output in time to adapt to load changes when the load fluctuates. Combined with the load tracking capability and the maximum peak-shaving capacity, the adaptability of the generator set to load changes is evaluated, and its capability is measured by the accuracy of load regulation.

[0059] At the same time, in the process of defining the frequency modulation index and the peak modulation index, the frequency modulation index is defined based on the frequency deviation adjustment capability. The response time can be set: the frequency modulation response speed per unit time; the frequency correction capability: the amplitude of the frequency deviation correction (such as the time and adjustment power required to adjust the deviation from +0.5Hz to +0.1Hz).

[0060] The peak load index can be defined based on the load change response capability and is set as follows:

[0061] Load tracking accuracy: the ability of the unit to track load changes, that is, the matching degree between the changes in the unit's power generation and the changes in load demand during load changes; peak-shaving response speed: the time it takes for the unit to increase from a low-load state to a high-load state; maximum peak-shaving capacity: the maximum power output that the unit can provide in a short period of time. Output results: frequency regulation indicators (such as response time, frequency correction capability) and peak-shaving indicators (such as load tracking accuracy, peak-shaving response speed, and maximum peak-shaving capacity).

[0062] In this optional embodiment, when analyzing the distance vector between the frequency regulation index and the peak regulation index based on the regression analysis technology, and obtaining the correlation result between the frequency regulation index and the peak regulation index of the generator set according to the distance vector, the frequency regulation index and the peak regulation index can be subjected to static analysis respectively to obtain the frequency regulation correlation knowledge, frequency correlation knowledge and peak regulation correlation knowledge, and use the frequency regulation correlation knowledge as the centroid and the peak regulation correlation knowledge as the feature to judge the frequency regulation distance vector between the frequency regulation index and the peak regulation index; normalizing the frequency regulation distance vector based on the regression analysis technology to obtain a frequency regulation similarity index list, and use the frequency correlation knowledge as the centroid and the peak regulation correlation knowledge as the feature to judge the frequency distance vector between the frequency regulation index and the peak regulation index; normalizing the frequency distance vector based on the regression analysis technology to obtain a frequency similarity index list, and use the frequency similarity index list and the frequency regulation similarity index list to form an correlation pair; analyze the similarity between the frequency regulation correlation knowledge, the frequency correlation knowledge and the peak regulation correlation knowledge according to the correlation pair, and analyze the correlation result corresponding to the frequency regulation index and the peak regulation index of the generator set based on the similarity result.

[0063] In this optional embodiment, the similarity calculation formula is:

[0064] ;

[0065] In the formula, S represents the similarity between frequency modulation related knowledge m, frequency related knowledge d and peak modulation related knowledge n, A mThe feature vector representing the FM associated knowledge, A represents the set of FM feature vectors, B n represents the feature vector of peak-shaving related knowledge, B represents the peak-shaving feature vector set, C d represents the feature vector of frequency association knowledge, C represents the set of frequency feature vectors, and FPR(m,n,d) represents the association pair of frequency modulation association knowledge m, frequency association knowledge d and peak modulation association knowledge n.

[0066] In this optional embodiment, when analyzing the relationship between the frequency regulation response and the peak-shaving capability of the generator set according to the correlation results, and evaluating the frequency regulation and compensation capability of the generator set under different load conditions based on the relationship results, a linear regression model can be established based on the correlation results corresponding to the frequency regulation index and the peak-shaving index of the generator set to determine the impact value of the frequency regulation response on the peak-shaving capability, and the relationship results between the frequency regulation response and the peak-shaving capability are obtained according to the impact judgment results; the factors affecting the frequency regulation and peak-shaving capability are analyzed according to the relationship results, the load data is set using the factor analysis results, and a load forecasting model is established based on the deep confidence network; an evaluation index including the absorption capacity and the compensation capacity is constructed, and the evaluation index is combined with the load forecasting model to construct a frequency regulation and compensation capability evaluation model to evaluate the frequency regulation and compensation capability of the generator set under different load conditions.

[0067] In this optional embodiment, the calculation formula of the impact value is:

[0068] ;

[0069] Where PC represents the impact of frequency modulation response on peak regulation capability, α and β represent regression coefficients, and FRC represents the frequency modulation capability index.

[0070] In this optional embodiment, when obtaining the performance monitoring indicators during the operation of the generator set based on the frequency regulation compensation capability, and using the performance monitoring indicators to predict the load capacity of the generator set, the maximum load change rate of the generator set per unit time and the upper and lower limits of the operable power can be analyzed based on the frequency regulation compensation capability, and the load response and peak-shaving performance monitoring indicators of the generator set during operation are determined based on the maximum load change rate and the upper and lower limits of power; the behavior data and frequency measurement data during the operation of the generator set are obtained, and normalization processing is performed on the behavior data and the frequency measurement data after data alignment to obtain a frequency regulation load capacity data set; a frequency regulation load capacity prediction model is constructed based on the frequency regulation load capacity data set, and the frequency regulation load capacity prediction model is used to analyze the load capacity of the generator set under different working conditions in the future time period.

[0071] In this optional embodiment, when constructing a frequency regulation load capacity prediction model based on the frequency regulation load capacity data set, and using the frequency regulation load capacity prediction model to analyze the load capacity of the generator set under different working conditions in the future time period, the target usage state quantity can be obtained based on the historical operating power data of the generator set, and the preset data prediction model can be trained using the target usage state quantity to obtain the initial weight of the data prediction model; the target real-time usage state quantity is obtained based on the frequency regulation load capacity data set, and the next usage state quantity is predicted using the target real-time usage state quantity and the data prediction model; the initial weight of the data prediction model is updated based on the next usage state quantity and the target real-time usage state quantity, and a trained frequency regulation load capacity prediction model is obtained based on the update result; the frequency regulation load capacity prediction model is used to analyze the load capacity of the generator set under short-term, long-term and extreme working conditions in the future time period.

[0072] It needs to be explained that in the process of using the frequency regulation load capacity prediction model to analyze the load capacity of the generator set under short-term, long-term and extreme conditions in the future time period, the frequency regulation load capacity prediction model is used to predict the load capacity of the generator set under short-term (5 minutes, 15 minutes, 1 hour), long-term (1 week, 1 month) and extreme conditions (frequency mutation, sudden load increase).

[0073] In this optional embodiment, when adjusting the operation management mode of the generator set based on the load capacity, obtaining the profit value of the adjusted operation management mode, verifying the adaptability of the operation management mode, and re-adjusting the operation management mode according to the adaptability, a load capacity prediction model (such as short-term, long-term and extreme operating condition prediction models) can be used to analyze the load capacity of the generator set in different time periods. The load capacity includes: the maximum adjustable load of the unit, the unit adjustment rate and the load tracking accuracy; according to the results of the load capacity analysis, different operation management modes of the generator set are defined, for example: standard mode: the generator set operates under normal load to adapt to conventional load fluctuations; peak load mode: the generator set operates during peak load periods to meet the increase in load demand, and usually needs to increase output; frequency regulation mode: the generator set adjusts the frequency when the grid frequency fluctuates to ensure grid stability; emergency mode: to deal with extreme load conditions, quickly respond to load surges or frequency changes; adjust the operation mode of the unit according to the predicted load capacity and actual load demand. For example: if it is predicted that the load will increase rapidly during a certain period of time, the unit will be adjusted to enter the peak load mode to quickly increase the load capacity; if the grid frequency fluctuates, the unit will be adjusted to enter the frequency regulation mode to ensure the stability of the grid frequency; for periods of time when the load demand changes less, the unit can be maintained in the standard mode to optimize operating costs and efficiency.

[0074] After adjusting the operation and management mode, it is necessary to evaluate the benefits brought by the adjustment, including power generation benefits: whether the adjusted operation mode can improve the power generation efficiency of the unit or reduce energy waste. For example, when entering the peak-shaving mode, the unit may provide higher power output, but at the same time it may also require higher fuel consumption. The benefit value should consider the balance between output power and fuel cost; cost savings: for example, when the load is low, maintaining the unit at low load operation (standard mode) can reduce fuel consumption and thus reduce costs; system stability improvement: whether the adjustment of the operation and management mode has improved the stability of the power grid and reduced the system risks caused by frequency fluctuations or load changes, thereby avoiding the indirect costs caused by system failures.

[0075] By comprehensively considering these factors, the benefit value is calculated, which can be estimated by the following formula: benefit value = power generation benefit + cost savings + system stability improvement; according to the adjusted benefit value, the effect of different operation modes is evaluated. If the adjusted benefit value is higher than the original operation mode, it means that the adjustment is effective. Otherwise, the mode adjustment strategy needs to be further optimized. The adaptability refers to whether the adjusted operation management mode matches the current demand of the power grid and the load capacity of the generator set. The goal of the adaptability assessment is to confirm whether the current operation mode can maintain system stability and improve economic benefits under specific load and frequency conditions.

[0076] Load adaptability: that is, whether the operating mode of the generator set can effectively cope with the load changes of the power grid. If the load is too high and the unit fails to adjust the operating mode in time (for example, fails to switch to peak-shaving mode in time), the adaptability is low, otherwise it is high.

[0077] Frequency adaptability: If the grid frequency fluctuates greatly, whether the unit can adjust to the frequency regulation mode in time as needed to ensure frequency stability.

[0078] Economic adaptability: whether the adjusted operating mode can achieve the lowest operating cost and ensure the maximum power generation efficiency.

[0079] The suitability is usually verified through model evaluation, simulation and historical data comparison to ensure that the new operating mode meets the system requirements.

[0080] If the adaptability assessment finds that some operation management modes cannot fully adapt to actual needs (such as excessive load fluctuations, untimely frequency adjustment, etc.), secondary adjustments are required. The secondary adjustments can be made in the following ways:

[0081] Optimize mode switching timing: adjust the switching timing of the operating mode according to the load forecast and frequency fluctuation forecast to ensure earlier or later switching to the mode that adapts to the current load; strengthen dynamic adjustment capabilities: strengthen the unit's real-time response capability to load changes and improve the flexibility of peak load regulation and frequency regulation.

[0082] Through multiple adaptability assessments and secondary adjustments, the operation and management model is continuously optimized to ultimately achieve the optimal load capacity matching and economic benefits. After each adjustment, the profit value needs to be recalculated and the adaptability verification is performed to ensure that the adjusted model can effectively improve the efficiency and stability of the power grid operation.

[0083] Figure 2 An embodiment of the unit operation monitoring and management system based on power data of the present invention is shown.

[0084] In this optional embodiment, the unit operation monitoring and management system based on power data includes:

[0085] The frequency regulation and compensation capability evaluation unit 201 is used to analyze the correlation result between the frequency regulation index and the peak regulation index of the generator set based on the historical operating power data of the generator set, and evaluate the frequency regulation and compensation capability of the generator set according to the correlation result;

[0086] The generator set load capacity prediction unit 202 is used to obtain the performance monitoring index during the operation of the generator set according to the frequency regulation compensation capacity, and predict the load capacity of the generator set using the performance monitoring index;

[0087] The unit operation management mode adjustment unit 203 is used to adjust the operation management mode of the generator set based on the load capacity, obtain the profit value after the operation management mode is adjusted, verify the adaptability of the operation management mode, and adjust the operation management mode for the second time according to the adaptability.

[0088] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0089] Those skilled in the art will understand that Figure 3The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0090] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0091] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0092] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0093] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A unit operation monitoring and management method based on power data, characterized in that: include: Analyze the correlation between the frequency regulation index and the peak regulation index of the generator set based on the historical power data of the generator set, and evaluate the frequency regulation compensation capability of the generator set according to the correlation results; Obtain performance monitoring indicators during the operation of the generator set based on the frequency regulation and compensation capability, and use the performance monitoring indicators to predict the load capacity of the generator set; The operation management mode of the generator set is adjusted based on the load capacity, and the profit value after the operation management mode is adjusted is obtained, the adaptability of the operation management mode is verified, and the operation management mode is adjusted again according to the adaptability.

2. The unit operation monitoring and management method based on power data according to claim 1 is characterized in that: The analysis of the correlation results between the frequency regulation index and the peak regulation index of the generator set based on the historical power data of the generator set, and the evaluation of the frequency regulation and compensation capability of the generator set according to the correlation results includes: The frequency regulation response time and frequency deviation data of the generator set during historical operation are obtained as frequency regulation data, and the load tracking capability and maximum peak regulation capacity are collected as peak regulation data; Use frequency regulation data and peak regulation data to analyze the frequency deviation regulation capability and load change response capability of the generator set, and define frequency regulation index and peak regulation index based on the frequency deviation regulation capability and load change response capability; Analyze the distance vector between the frequency regulation index and the peak regulation index based on the regression analysis technology, and obtain the correlation result between the frequency regulation index and the peak regulation index of the generator set according to the distance vector; The relationship between the frequency regulation response and peak load regulation capability of the generator set is analyzed according to the correlation results, and the frequency regulation and compensation capability of the generator set under different load conditions is evaluated based on the relationship results.

3. The unit operation monitoring and management method based on power data according to claim 2 is characterized in that: The distance vector between the frequency regulation index and the peak regulation index is analyzed based on the regression analysis technology, and the correlation result between the frequency regulation index and the peak regulation index of the generator set is obtained according to the distance vector, including: Perform static analysis on the frequency modulation index and the peak modulation index to obtain frequency modulation related knowledge, frequency related knowledge and peak modulation related knowledge respectively, and use the frequency modulation related knowledge as the centroid and the peak modulation related knowledge as the feature to determine the frequency modulation distance vector between the frequency modulation index and the peak modulation index. Based on the regression analysis technology, the frequency modulation distance vector is normalized to obtain a frequency modulation similarity index list, and the frequency distance vector between the frequency modulation index and the peak modulation index is determined by taking the frequency association knowledge as the centroid and the peak modulation association knowledge as the feature. Based on the regression analysis technology, the frequency distance vector is normalized to obtain a frequency similarity index list, and the frequency similarity index list and the frequency modulation similarity index list are used to form an association pair; The similarities among frequency regulation associated knowledge, frequency associated knowledge and peak regulation associated knowledge are analyzed according to the associated pairs, and the associated results corresponding to the frequency regulation index and the peak regulation index of the generator set are analyzed based on the similarity results.

4. The unit operation monitoring and management method based on power data according to claim 3 is characterized in that: The similarity calculation formula is: ； In the formula, S represents the similarity between frequency modulation related knowledge m, frequency related knowledge d and peak modulation related knowledge n, A m The feature vector representing the FM associated knowledge, A represents the set of FM feature vectors, B n represents the feature vector of peak-shaving related knowledge, B represents the peak-shaving feature vector set, C d represents the feature vector of frequency association knowledge, C represents the set of frequency feature vectors, and FPR(m,n,d) represents the association pair of frequency modulation association knowledge m, frequency association knowledge d and peak modulation association knowledge n.

5. The unit operation monitoring and management method based on power data according to claim 4 is characterized in that: Analyzing the relationship between the frequency regulation response and the peak regulation capability of the generator set according to the correlation result, and evaluating the frequency regulation and compensation capability of the generator set under different load conditions based on the relationship result includes: Based on the correlation results between the frequency regulation index and the peak regulation index of the generator set, a linear regression model is established to determine the impact of the frequency regulation response on the peak regulation capacity, and the relationship between the frequency regulation response and the peak regulation capacity is obtained according to the impact judgment results; Analyze the factors that affect frequency regulation and peak regulation capabilities based on the relationship results, use the factor analysis results to set load data, and establish a load forecasting model based on a deep belief network; Construct evaluation indicators that include absorption capacity and compensation capacity, and combine the evaluation indicators with the load forecasting model to construct a frequency regulation and compensation capacity evaluation model to evaluate the frequency regulation and compensation capacity of the generating set under different load conditions.

6. The unit operation monitoring and management method based on power data according to claim 5 is characterized in that: The calculation formula of the impact value is: ； Where PC represents the impact of frequency modulation response on peak regulation capability, α and β represent regression coefficients, and FRC represents the frequency modulation capability index.

7. The unit operation monitoring and management method based on power data according to claim 1 is characterized in that: The method of obtaining the performance monitoring index during the operation of the generator set according to the frequency regulation and compensation capability, and predicting the load capacity of the generator set by using the performance monitoring index includes: Analyze the maximum load change rate of the generator set in unit time and the upper and lower limits of the operable power based on the frequency regulation and compensation capability, and determine the load response and peak load performance monitoring indicators of the generator set during operation according to the maximum load change rate and the upper and lower limits of power; Obtaining the behavior data and frequency measurement data during the operation of the generator set, and performing normalization processing on the behavior data and the frequency measurement data after data alignment respectively to obtain a frequency regulation load capacity data set; A frequency regulation load capacity prediction model is constructed based on the frequency regulation load capacity data set, and the frequency regulation load capacity prediction model is used to analyze the load capacity of the generator sets under different working conditions in the future time period.

8. The unit operation monitoring and management method based on power data according to claim 7 is characterized in that: The method of constructing a frequency regulation load capacity prediction model according to the frequency regulation load capacity data set and using the frequency regulation load capacity prediction model to analyze the load capacity of the generator set under different working conditions in the future time period includes: The target usage state quantity is obtained based on the historical power data of the generator set, and the preset data prediction model is trained using the target usage state quantity to obtain the initial weight of the data prediction model; Based on the frequency regulation load capacity data set, the target real-time usage state quantity is obtained, and the next usage state quantity is predicted using the target real-time usage state quantity and the data prediction model; According to the next usage state quantity and the target real-time usage state quantity, the initial weight of the data prediction model is updated, and a trained frequency regulation load capacity prediction model is obtained based on the update result; The frequency regulation load capacity prediction model is used to analyze the load capacity of the generator set under short-term, long-term and extreme conditions in the future time period.

9. The unit operation monitoring and management method based on power data according to claim 8 is characterized in that: The short-term operating conditions include the operating conditions of the generator set in the next 5 minutes, 15 minutes and 1 hour, the long-term operating conditions include the operating conditions of the generator set in the next 1 week and 1 month, and the extreme operating conditions include the operating conditions when the generator set has a sudden frequency change or a sudden load increase.

10. A unit operation monitoring and management system based on power data, characterized in that: include: A frequency regulation and compensation capability evaluation unit is used to analyze the correlation results between the frequency regulation index and the peak regulation index of the generator set based on the historical power operation data of the generator set, and evaluate the frequency regulation and compensation capability of the generator set according to the correlation results; A generator set load capacity prediction unit is used to obtain performance monitoring indicators during the operation of the generator set according to the frequency regulation compensation capacity, and to predict the load capacity of the generator set using the performance monitoring indicators; The unit operation management mode adjustment unit is used to adjust the operation management mode of the generator set based on the load capacity, obtain the profit value after the operation management mode is adjusted, verify the adaptability of the operation management mode, and adjust the operation management mode for the second time according to the adaptability.

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