Big data-based secondary reheating unit load operation optimization method and system

Abstract

The invention discloses an operation optimization method and system for a secondary reheating unit load based on big data, and relates to the technical field of operation optimization of thermal power generation equipment. Obtaining target electricity consumption collected in the target area in the current period, and inputting the target electricity consumption into the short-term electricity consumption prediction model to obtain predicted electricity consumption of the next period of the current period; determining a working scheme set according to the predicted electricity consumption, and calculating to obtain an equipment cost score for any set of working scheme in the working scheme set; working parameters of the secondary reheating unit are determined according to the target equipment cost score, so that the secondary reheating unit works according to the working parameters; by acquiring the target electricity consumption and planning the electric power resource by using the prediction model, the electricity consumption demand of the next period can be accurately estimated, various working schemes are formulated, the equipment cost is evaluated, and finally the power generation scheme with the optimal cost benefit is selected, so that the power consumption demand is met, the equipment wear is minimized, and the power generation efficiency and economy are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of thermal power generation equipment operation optimization, and specifically relates to a method and system for optimizing the operation of a secondary reheat unit load based on big data. Background Art

[0002] With the continuous adjustment of the global energy structure and the increasing environmental pressure, traditional coal-fired units can no longer meet the environmental protection needs of modern society. The secondary reheat unit significantly improves the thermal efficiency of the unit and effectively reduces pollutant emissions by dividing the heat of the fuel into two parts and recycling it in two different loops. The above technology is a technological innovation of traditional coal-fired units. By adopting secondary reheat technology, the unit can improve the overall efficiency under the same parameters while reducing the emissions of carbon dioxide, nitrogen oxides, etc.

[0003] Patent No.: CN114488798A, discloses a performance monitoring and operation optimization method for a secondary reheat unit based on data coordination, studies the dominant factors and action mechanisms that affect the characteristics of the main components of the thermal system, obtains the component characteristic curves within the full operating range, and establishes a high-precision mathematical model of the key components of the thermal system under all operating conditions; utilizes the measurement redundant information of the unit's thermal parameters and adopts a data coordination algorithm to reduce the measurement uncertainty of key data; provides accurate expected values ​​for component health status monitoring, and realizes a variety of performance monitoring functions by comparing monitoring values ​​with expected values; on the basis of a high-precision mathematical model of all operating conditions, obtains a system optimization model through overall integration, obtains the optimization potential of the unit through scenario assumption calculation, and adjusts parameters to achieve real-time operation optimization of the unit.

[0004] Although the above technology solves some problems, there are still some problems. For example, the above determination of the working plan for the secondary reheat unit does not fully take into account the assessment of equipment loss and future electricity consumption, resulting in abnormally high working costs and maintenance costs. Summary of the invention

[0005] The purpose of the present invention is to solve the problem that the determination of the working plan of the secondary reheat unit does not fully take into account the evaluation of equipment loss and future electricity consumption, resulting in abnormally increased working costs and maintenance costs, and to propose a secondary reheat unit load operation optimization method and system based on big data.

[0006] In a first aspect of the present invention, a method for optimizing the operation of a secondary reheat unit load based on big data is first proposed. The method comprises:

[0007] Obtaining target power consumption collected in the target area during the current cycle, and inputting the target power consumption into a short-term power consumption prediction model to obtain the predicted power consumption for the next cycle of the current cycle;

[0008] Determine a work plan set according to the predicted power consumption, and calculate an equipment cost score for any work plan in the work plan set;

[0009] The operating parameters of the secondary reheat unit are determined according to the target equipment cost score so that the secondary reheat unit operates according to the operating parameters; the target equipment cost score is the equipment cost score with serial number 1 in the work plan set.

[0010] Optionally, before inputting the power consumption into a short-term power consumption prediction model to obtain a predicted power consumption for the next cycle, the method further includes:

[0011] Acquire historical electricity consumption and historical meteorological data of the target area, align the historical electricity consumption and the historical meteorological data according to time series to obtain a training data set, and perform fuzzy clustering on the training data set to obtain a feature data set;

[0012] The feature data set is input into a NARX neural network model for training to obtain model parameters, and the weights and biases of each layer in the NARX neural network model are iteratively updated according to the model parameters to obtain a short-term electricity consumption prediction model;

[0013] Obtain the forecasted meteorological data of the target area in the next cycle of the current cycle, input the target power consumption and the forecasted meteorological data into a short-term power consumption forecast model, and obtain the forecasted power consumption of the next cycle.

[0014] Optionally, the equipment cost score calculated for any set of work plans in the work plan set includes:

[0015] Determine the working parameters of the secondary reheat unit according to the working plan, and perform simulation according to the working parameters to obtain simulation data; the simulation data includes: steam pressure and steam temperature;

[0016] Calculate the equipment cost score of each set of work plans in the work plan set according to the simulation data, and arrange the equipment cost scores in ascending order to obtain an equipment cost score table;

[0017] Equipment cost score calculation formula:

[0018]

[0019] Among them, F is the equipment cost score, T1 is the steam temperature, T0 is the preset standard steam temperature, P1 is the steam pressure, P0 is the preset standard steam pressure, t1 is the preset time period, and ω are constant proportional coefficients and are not zero.

[0020] Optionally, after the secondary reheat unit operates according to the operating parameters, the method further includes:

[0021] Real-time monitoring of the secondary reheat unit to obtain operating data, and calculating a real-time feedback score based on the operating data;

[0022] The constant proportionality coefficient of the equipment cost score calculation formula is updated according to the real-time feedback score.

[0023] Optionally, evaluating the real-time feedback score includes:

[0024] If the real-time feedback score is greater than the evaluation threshold, it is determined that the accuracy of the equipment cost score is missing, and the constant proportional coefficient in the equipment cost score calculation formula is updated;

[0025] If the absolute temperature difference is greater than the temperature threshold range, the equipment cost score calculation formula The coefficient is updated; the absolute temperature difference is calculated by the simulated steam temperature and the actual steam temperature;

[0026] If the absolute pressure difference is greater than the pressure threshold interval, the ω coefficient in the equipment cost score calculation formula is updated; the absolute pressure difference is calculated from the simulated steam pressure and the actual steam pressure.

[0027] In the second aspect of the implementation of the present invention, a secondary reheat unit load operation optimization system based on big data is proposed, including: a power consumption prediction module, a work plan determination module and a work parameter operation module:

[0028] The power consumption prediction module is used to obtain the target power consumption collected in the target area in the current cycle, and input the target power consumption into the short-term power consumption prediction model to obtain the predicted power consumption for the next cycle;

[0029] The working scheme determination module is used to determine a working scheme set according to the predicted power consumption, and calculate the equipment cost score for any set of working schemes in the working scheme set;

[0030] The working parameter operation module is used to determine the working parameters of the secondary reheat unit according to the target equipment cost score, so that the secondary reheat unit works according to the working parameters; the target equipment cost score is the equipment cost score with a serial number of one in the working scheme set.

[0031] Optionally, the power consumption prediction module includes: a training data acquisition module, a model update iteration module and a model prediction module:

[0032] The training data acquisition module is used to acquire historical electricity consumption and historical meteorological data of the target area, align the historical electricity consumption and the historical meteorological data according to time series to obtain a training data set, and perform fuzzy clustering on the training data set to obtain a feature data set;

[0033] The model update iteration module is used to input the feature data set into the NARX neural network model for training to obtain model parameters, and iteratively update the weights and biases of each layer in the NARX neural network model according to the model parameters to obtain a short-term power consumption prediction model;

[0034] The model prediction module is used to obtain the predicted meteorological data of the target area in the next cycle of the current cycle, input the target power consumption and the predicted meteorological data into the short-term power consumption prediction model, and obtain the predicted power consumption of the next cycle.

[0035] Optionally, the work plan determination module includes: a data simulation module and an equipment cost calculation module:

[0036] The data simulation module is used to determine the working parameters of the secondary reheat unit according to the working plan, and to perform simulation according to the working parameters to obtain simulation data; the simulation data includes: steam pressure and steam temperature;

[0037] The equipment cost calculation module is used to calculate the equipment cost score of each set of work plans in the work plan set according to the simulation data, and arrange the equipment cost scores in ascending order to obtain an equipment cost score table;

[0038] Equipment cost score calculation formula:

[0039]

[0040] Among them, F is the equipment cost score, T1 is the steam temperature, T0 is the preset standard steam temperature, P1 is the steam pressure, P0 is the preset standard steam pressure, t1 is the preset time period, and ω are constants and the proportional coefficients are not zero.

[0041] Optionally, the system further includes: a feedback score calculation module and an evaluation update coefficient module

[0042] The feedback score calculation module is used to monitor the secondary reheat unit in real time to obtain operating data, and calculate the real-time feedback score according to the operating data;

[0043] The evaluation update coefficient module is used to update the constant proportional coefficient of the equipment cost score calculation formula according to the real-time feedback score.

[0044] Optionally, the evaluation update coefficient module includes: a coefficient judgment module, a first execution update module and a second execution update module:

[0045] The coefficient judgment module is used to determine that the accuracy of the equipment cost score is missing if the real-time feedback score is greater than the evaluation threshold, and to update the constant proportional coefficient in the equipment cost score calculation formula;

[0046] The first execution update module is used to update the equipment cost score calculation formula if the temperature absolute difference is greater than the temperature threshold interval. The coefficient is updated; the absolute temperature difference is calculated by the simulated steam temperature and the actual steam temperature;

[0047] Beneficial effects of the present invention:

[0048] The present invention proposes an operation optimization method for the load of a secondary reheat unit based on big data, which obtains the target power consumption collected in the target area in the current cycle, inputs the target power consumption into the short-term power consumption prediction model to obtain the predicted power consumption for the next cycle of the current cycle; determines a work plan set according to the predicted power consumption, and calculates the equipment cost score for any set of work plans in the work plan set; determines the working parameters of the secondary reheat unit according to the target equipment cost score, so that the secondary reheat unit works according to the working parameters; obtains the target power consumption and plans power resources using the prediction model, can accurately estimate the power demand for the next cycle, and then formulates a variety of work plans and evaluates the equipment cost, and finally selects the most cost-effective power generation plan to ensure that while meeting the power demand, equipment wear is minimized, thereby improving power generation efficiency and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] Figure 1 A flow chart of an operation optimization method of a secondary reheat unit load based on big data is provided for an embodiment of the present invention;

[0051] Figure 2 A framework diagram of an operation optimization system for a secondary reheat unit load based on big data is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the present invention, the description of "first", "second", etc. is only used for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0053] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0054] The embodiment of the present invention provides a method for optimizing the operation of a secondary reheat unit load based on big data. Figure 1 , Figure 1 A flow chart of a method for optimizing the operation of a secondary reheat unit load based on big data provided in an embodiment of the present invention. The method comprises the following steps:

[0055] S101, obtaining target power consumption collected in a target area in a current cycle, and inputting the target power consumption into a short-term power consumption prediction model to obtain predicted power consumption for the next cycle of the current cycle;

[0056] S102, determining a work plan set according to the predicted power consumption, and calculating an equipment cost score for any work plan in the work plan set;

[0057] S103, determining the operating parameters of the secondary reheat unit according to the target equipment cost score, so that the secondary reheat unit operates according to the operating parameters;

[0058] The target equipment cost score is the equipment cost score with sequence number 1 in the work plan set.

[0059] The operation optimization method of the secondary reheat unit load based on big data provided by the embodiment of the present invention can accurately estimate the power demand for the next cycle by obtaining the target power consumption and using the prediction model to plan the power resources, and then formulate a variety of work plans and evaluate the equipment cost, and finally select the most cost-effective power generation plan to ensure that while meeting the power demand, equipment wear is minimized and the power generation efficiency and economy are improved.

[0060] In one implementation, the load is the power consumption, and the working parameter is the coal transportation volume per unit time; the target power consumption collected in the target area in the current cycle is obtained, and the power consumption of the next cycle is predicted using the short-term power consumption prediction model, which can accurately plan power resources, and then determine multiple work plans based on the prediction results, and calculate the equipment cost score of each plan, so as to select the most cost-effective plan, that is, to generate electricity with minimal equipment wear while ensuring that the power consumption of the next cycle is met. The above steps not only improve the efficiency of power management, but also ensure economic benefits.

[0061] In one implementation, after determining the work plan, the working parameters of the secondary reheat unit are finely adjusted according to the equipment cost score to ensure that it operates in the optimal state. This step enhances the scientificity and accuracy of decision-making through precise data analysis and model prediction, thereby improving the operating efficiency and reliability of the secondary reheat unit; the current cycle is based on a week (seven days), and the next cycle is the next cycle with the current cycle as the benchmark cycle.

[0062] In one implementation, the working plan is the amount of coal fed when the secondary reheat unit is in operation, that is, the amount of coal transported per unit time; the secondary reheat unit is responsible for power supply in the target area. When the power consumption is greater than the power generation, the secondary reheat unit needs to increase the power generation, so the amount of coal transported per unit time needs to be increased. The increase in the amount of coal transport will cause abnormal wear of the equipment of the secondary reheat unit, for example: expansion and contraction caused by abnormal temperature of the equipment, and a sharp increase in steam pressure and gas flow in a short period of time, causing the fan blades of the steam turbine to be impacted.

[0063] In one embodiment, before step S101, the following steps are included:

[0064] Obtain the historical electricity consumption and historical meteorological data of the target area, align the historical electricity consumption and historical meteorological data according to the time series to obtain a training data set, and perform fuzzy clustering on the training data set to obtain a feature data set;

[0065] The feature data set is input into the NARX neural network model for training to obtain model parameters, and the weights and biases of each layer in the NARX neural network model are iteratively updated according to the model parameters to obtain a short-term electricity consumption prediction model;

[0066] Obtain the forecasted meteorological data for the next cycle of the target area in the current cycle, input the target power consumption and the forecasted meteorological data into the short-term power consumption forecast model, and obtain the forecasted power consumption for the next cycle.

[0067] In one implementation, the NARX neural network model has a powerful time series modeling capability. Using the NARX neural network model as a short-term electricity consumption forecasting model can significantly improve the accuracy and reliability of short-term electricity consumption forecasting. By using the time series alignment and fuzzy clustering processing of historical electricity consumption and historical meteorological data, deeper feature information can be mined, and the correlation between weather and electricity consumption can be enhanced, so that the model can more accurately predict the electricity consumption of the target area when facing the forecast meteorological data of the new cycle; after completing the feature identification and extraction of electricity consumption and meteorological data, the features are used as the input of the model for training to obtain a short-term load forecasting model.

[0068] In one implementation, similar electricity consumption curves are divided into the same category by fuzzy clustering of historical electricity consumption. The electricity consumption curve is a curve with time as the horizontal axis and electricity consumption as the vertical axis, which is used to reflect the change of electricity consumption in the time series. Meteorological data is also mapped in the time series, which can enhance the correlation between meteorological data and electricity consumption data. The advantage of fuzzy clustering is that it can handle the uncertainty in the data, making the classification results more flexible and accurate. The first feature extraction is performed on the load curve to identify important feature points in the load curve, such as peak values, valley values, etc., to provide a basis for subsequent feature identification. Based on the first feature extraction, the statistical frequency distribution method is used to further screen and optimize the features. By statistically analyzing the frequency of occurrence of various load features, the most representative features are selected as overall feature markers to improve the accuracy and generalization ability of the subsequent prediction model.

[0069] In one embodiment, step S102 includes:

[0070] Determine the working parameters of the secondary reheat unit according to the working plan, and perform simulation according to the working parameters to obtain simulation data; the simulation data includes: steam pressure and steam temperature;

[0071] Calculate the equipment cost score of each work plan in the work plan set according to the simulation data, and arrange the equipment cost scores in ascending order to obtain an equipment cost score table;

[0072] Equipment cost score calculation formula:

[0073]

[0074] Among them, F is the equipment cost score, T1 is the steam temperature, T0 is the preset standard steam temperature, P1 is the steam pressure, P0 is the preset standard steam pressure, t1 is the preset time period, and ω are constant proportional coefficients and are not zero.

[0075] In one implementation, in the simulation of the power generation mode of the secondary reheat unit, by constructing an accurate mathematical model and inputting relevant parameters, the operating state of the unit under different working schemes is simulated, and the equipment cost score is calculated. The operation process is efficient and accurate, and the equipment cost score table arranged in ascending order of cost can be quickly generated and the serial number can be assigned in the equipment cost score table, from small to large. For example, the serial number with the smallest equipment cost score is 1, and it is arranged in this order, providing strong data support for optimizing the power generation mode; in the simulation of the steam system or thermal system, the steam pressure is a key simulation parameter. By setting the initial conditions, boundary conditions and physical properties of the system, the change of steam pressure can be simulated. For example, in the dynamic simulation analysis of the steam accumulator, the change of steam pressure can be predicted by simulation. In the simulation model of some equipment, the equipment temperature sets the heat exchange process of the equipment such as heat conduction and heat convection, as well as the initial temperature of the equipment, and the change of the equipment temperature can be simulated; a single simulation obtains more than one value (steam pressure and steam temperature), so the difference in simulation values ​​can be reduced by taking the average value.

[0076] In one implementation, the coal transportation speed is determined according to the predicted power consumption, that is, the power generation must be completed within the specified time while ensuring the minimum equipment loss. Equipment loss is inevitable during the operation of the secondary reheat unit. Therefore, by reducing its wear, the service life of the equipment can be maximized and the cost can be further reduced; the equipment cost scores are arranged in ascending order to obtain an equipment cost score table, that is, the equipment cost scores are arranged in numerical order from small to large, and the smallest one is the working parameter that satisfies the minimum equipment loss.

[0077] In one embodiment, step S102 further includes:

[0078] Real-time monitoring of the secondary reheat unit to obtain operating data, and real-time feedback scores are calculated based on the operating data;

[0079] The constant proportional coefficient of the equipment cost score calculation formula is updated according to the real-time feedback score.

[0080] In one implementation, the real-time feedback score calculation formula is:

[0081]

[0082] Among them, α, β and γ are all constant proportional coefficients and are not zero, T2 is the device temperature, T maxis the highest temperature the device can withstand, T3 is the preset standard temperature of the device, t0 is the preset time period, t is the duration of the noise in the t0 time period, N i is the noise intensity of the i-th device, n is the total number of times the device noise occurs, Z j is the equipment amplitude of the jth equipment, m is the total number of times the equipment amplitude occurs, P3 is the preset standard steam pressure in the gas pipeline, P2 is the steam pressure in the steam pipeline, T3 is the preset standard temperature in the steam pipeline, T2 is the steam temperature in the steam pipeline, L0 is the preset steam flow in the steam pipeline, and L is the steam flow of the steam pipeline.

[0083] In one implementation, the steam pipeline and steam turbine in the secondary reheat unit are monitored in real time according to the working parameters, which can effectively obtain the status of the equipment. The status of the equipment can be quantified through the real-time feedback score calculation formula, thereby realizing accurate monitoring and rapid response to the unit's operating status; the real-time feedback score is evaluated, and the constant proportional coefficient of the equipment cost score calculation formula is updated, which can ensure that the equipment cost evaluation can be more accurately performed when the task is determined next time, so as to reduce wear and tear on the equipment.

[0084] In one implementation, the real-time feedback score calculation formula includes monitoring of the equipment part and monitoring of the steam pipeline. These two aspects can more accurately reflect the effects of operation according to the working parameters. By comprehensively monitoring the vibration, noise, and temperature parameters of the equipment (steam turbine), as well as key indicators such as steam pressure, steam temperature, and steam flow of the steam pipeline, the operating status and performance of the equipment can be reflected in real time. The monitoring data not only helps to accurately evaluate the degree of equipment loss, but also optimizes the equipment's working parameters and equipment cost score calculation formula, thereby improving operating efficiency and stability, while reducing energy consumption and operating costs.

[0085] In one embodiment, evaluating the real-time feedback score includes:

[0086] If the real-time feedback score is greater than the evaluation threshold, the accuracy of the equipment cost score is determined to be missing, and the constant proportional coefficient in the equipment cost score calculation formula is updated;

[0087] If the absolute temperature difference is greater than the temperature threshold range, the equipment cost score calculation formula The coefficient is updated; the absolute temperature difference is calculated from the simulated steam temperature and the actual steam temperature;

[0088] If the absolute pressure difference is greater than the pressure threshold interval, the ω coefficient in the equipment cost score calculation formula is updated; the absolute pressure difference is calculated from the simulated steam pressure and the actual steam pressure.

[0089] In one implementation, the equipment cost score calculation formula The coefficient corresponds to the steam temperature. The ω coefficient in the equipment cost score calculation formula corresponds to the steam pressure. When the real-time feedback score is greater than the evaluation threshold, it means that the evaluation of the equipment cost at this time is inaccurate. According to the actual situation, the coefficient of the steam temperature needs to be updated and corrected to make the equipment cost of the next simulation more accurate. Update and correct, for example: when the coefficient ω is 0.4, and the real-time feedback score at this time is greater than the evaluation threshold, the absolute difference between the simulated steam pressure and the actual steam pressure is not within the pressure threshold interval, then a correction update is required. When the absolute pressure difference is greater than the upper limit of the pressure threshold interval, the coefficient ω is increased from 0.4 to 0.45. The specific numerical changes need to be determined by the staff according to the actual situation; absolute pressure difference = |simulated steam pressure-actual steam pressure|, and the absolute temperature difference is calculated by |simulated steam temperature-actual steam temperature|.

[0090] Based on the same inventive concept, the embodiment of the present invention also provides an operation optimization system for the secondary reheat unit load based on big data. Figure 2 , Figure 2 A schematic diagram of the structure of the operation optimization system of the secondary reheat unit load based on big data provided by an embodiment of the present invention, comprising: a power consumption prediction module, a work plan determination module and a work parameter operation module:

[0091] The power consumption prediction module is used to obtain the target power consumption collected in the target area in the current cycle, and input the target power consumption into the short-term power consumption prediction model to obtain the predicted power consumption for the next cycle;

[0092] A working plan determination module is used to determine a working plan set according to the predicted power consumption, and calculate the equipment cost score for any set of working plans in the working plan set;

[0093] The working parameter operation module is used to determine the working parameters of the secondary reheat unit according to the target equipment cost score so that the secondary reheat unit works according to the working parameters; the target equipment cost score is the equipment cost score with a serial number of one in the working plan set.

[0094] The operation optimization system for the secondary reheat unit load based on big data provided by the embodiment of the present invention can accurately estimate the electricity demand for the next cycle by obtaining the target electricity consumption and planning the power resources using the prediction model, and then formulate a variety of work plans and evaluate the equipment cost, and finally select the most cost-effective power generation plan to ensure that while meeting the electricity demand, equipment wear is minimized and the power generation efficiency and economy are improved.

[0095] In one embodiment, the power consumption prediction module includes: a training data acquisition module, a model update iteration module and a model prediction module:

[0096] A training data acquisition module is used to obtain historical electricity consumption and historical meteorological data of the target area, align the historical electricity consumption and historical meteorological data according to time series to obtain a training data set, and perform fuzzy clustering on the training data set to obtain a feature data set;

[0097] The model update iteration module is used to input the feature data set into the NARX neural network model for training to obtain model parameters, and iteratively update the weights and biases of each layer in the NARX neural network model according to the model parameters to obtain a short-term electricity consumption prediction model;

[0098] The model prediction module is used to obtain the predicted meteorological data of the target area in the next cycle of the current cycle, input the target power consumption and the predicted meteorological data into the short-term power consumption prediction model, and obtain the predicted power consumption in the next cycle.

[0099] In one embodiment, the work plan determination module includes: a data simulation module and an equipment cost calculation module:

[0100] The data simulation module is used to determine the working parameters of the secondary reheat unit according to the working plan, and to perform simulation according to the working parameters to obtain simulation data; the simulation data includes: steam pressure and steam temperature;

[0101] An equipment cost calculation module is used to calculate the equipment cost score of each work plan in the work plan set according to the simulation data, and arrange the equipment cost scores in ascending order to obtain an equipment cost score table;

[0102] Equipment cost score calculation formula:

[0103]

[0104] Among them, F is the equipment cost score, T1 is the steam temperature, T0 is the preset standard steam temperature, P1 is the steam pressure, P0 is the preset standard steam pressure, t1 is the preset time period, and ω are constant proportional coefficients and are not zero.

[0105] In one embodiment, the system further includes: a feedback score calculation module and an evaluation update coefficient module

[0106] A feedback score calculation module is used to monitor the secondary reheat unit in real time to obtain operating data, and calculate the real-time feedback score based on the operating data;

[0107] The evaluation update coefficient module is used to update the constant proportional coefficient of the equipment cost score calculation formula according to the real-time feedback score.

[0108] In one embodiment, the evaluation update coefficient module includes: a coefficient determination module, a first execution update module and a second execution update module:

[0109] A coefficient judgment module is used to determine that the accuracy of the equipment cost score is missing if the real-time feedback score is greater than the evaluation threshold, and to update the constant proportional coefficient in the equipment cost score calculation formula;

[0110] The first execution update module is used to update the equipment cost score calculation formula if the absolute temperature difference is greater than the temperature threshold interval. The coefficient is updated; the absolute temperature difference is calculated from the simulated steam temperature and the actual steam temperature;

[0111] The second execution update module is used to update the ω coefficient in the equipment cost score calculation formula if the absolute pressure difference is greater than the pressure threshold interval; the absolute pressure difference is calculated by the simulated steam pressure and the actual steam pressure.

[0112] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. The operation optimization method of the secondary reheat unit load based on big data is characterized in that: The method comprises: Obtaining target power consumption collected in the target area during the current cycle, and inputting the target power consumption into a short-term power consumption prediction model to obtain the predicted power consumption for the next cycle of the current cycle; Determine a work plan set according to the predicted power consumption, and calculate an equipment cost score for any work plan in the work plan set; The operating parameters of the secondary reheat unit are determined according to the target equipment cost score so that the secondary reheat unit operates according to the operating parameters; the target equipment cost score is the equipment cost score with serial number 1 in the work plan set.

2. The operation optimization method of the secondary reheat unit load based on big data according to claim 1 is characterized in that: Before inputting the power consumption into a short-term power consumption prediction model to obtain the predicted power consumption for the next cycle, the method further includes: Acquire historical electricity consumption and historical meteorological data of the target area, align the historical electricity consumption and the historical meteorological data according to time series to obtain a training data set, and perform fuzzy clustering on the training data set to obtain a feature data set; The feature data set is input into a NARX neural network model for training to obtain model parameters, and the weights and biases of each layer in the NARX neural network model are iteratively updated according to the model parameters to obtain a short-term electricity consumption prediction model; Obtain the forecasted meteorological data of the target area in the next cycle of the current cycle, input the target power consumption and the forecasted meteorological data into a short-term power consumption forecast model, and obtain the forecasted power consumption of the next cycle.

3. The operation optimization method of secondary reheat unit load based on big data according to claim 1 is characterized in that: The equipment cost score calculated for any set of work plans in the work plan set includes: Determine the working parameters of the secondary reheat unit according to the working plan, and perform simulation according to the working parameters to obtain simulation data; the simulation data includes: steam pressure and steam temperature; Calculate the equipment cost score of each set of work plans in the work plan set according to the simulation data, and arrange the equipment cost scores in ascending order to obtain an equipment cost score table; Equipment cost score calculation formula: Among them, F is the equipment cost score, T1 is the steam temperature, T0 is the preset standard steam temperature, P1 is the steam pressure, P0 is the preset standard steam pressure, t1 is the preset time period, and ω are constants and the proportional coefficients are not zero.

4. The operation optimization method of the secondary reheat unit load based on big data according to claim 3 is characterized in that: After the secondary reheat unit operates according to the operating parameters, the method further includes: Real-time monitoring of the secondary reheat unit to obtain operating data, and calculating a real-time feedback score based on the operating data; The constant proportionality coefficient of the equipment cost score calculation formula is updated according to the real-time feedback score.

5. The operation optimization method of the secondary reheat unit load based on big data according to claim 4 is characterized in that: Updating the constant proportionality coefficient of the equipment cost score calculation formula according to the real-time feedback score includes: If the real-time feedback score is greater than the evaluation threshold, it is determined that the accuracy of the equipment cost score is missing, and the constant proportional coefficient in the equipment cost score calculation formula is updated; If the absolute temperature difference is greater than the temperature threshold range, the equipment cost score calculation formula The coefficient is updated; the absolute temperature difference is calculated by the simulated steam temperature and the actual steam temperature; If the absolute pressure difference is greater than the pressure threshold interval, the ω coefficient in the equipment cost score calculation formula is updated; the absolute pressure difference is calculated from the simulated steam pressure and the actual steam pressure.

6. The operation optimization system of the secondary reheat unit load based on big data is characterized by: The system includes: a power consumption prediction module, a work plan determination module and a work parameter operation module: The power consumption prediction module is used to obtain the target power consumption collected in the target area in the current cycle, and input the target power consumption into the short-term power consumption prediction model to obtain the predicted power consumption for the next cycle; The working scheme determination module is used to determine a working scheme set according to the predicted power consumption, and calculate the equipment cost score for any set of working schemes in the working scheme set; The working parameter operation module is used to determine the working parameters of the secondary reheat unit according to the target equipment cost score, so that the secondary reheat unit works according to the working parameters; the target equipment cost score is the equipment cost score with a serial number of one in the working scheme set.

7. The operation optimization system of the secondary reheat unit load based on big data according to claim 6 is characterized in that: The power consumption prediction module includes: a training data acquisition module, a model update iteration module and a model prediction module: The training data acquisition module is used to acquire historical electricity consumption and historical meteorological data of the target area, align the historical electricity consumption and the historical meteorological data according to time series to obtain a training data set, and perform fuzzy clustering on the training data set to obtain a feature data set; The model update iteration module is used to input the feature data set into the NARX neural network model for training to obtain model parameters, and iteratively update the weights and biases of each layer in the NARX neural network model according to the model parameters to obtain a short-term power consumption prediction model; The model prediction module is used to obtain the predicted meteorological data of the target area in the next cycle of the current cycle, input the target power consumption and the predicted meteorological data into the short-term power consumption prediction model, and obtain the predicted power consumption of the next cycle.

8. The operation optimization system of the secondary reheat unit load based on big data according to claim 6 is characterized in that: The work plan determination module includes: a data simulation module and an equipment cost calculation module: The data simulation module is used to determine the working parameters of the secondary reheat unit according to the working plan, and to perform simulation according to the working parameters to obtain simulation data; the simulation data includes: steam pressure and steam temperature; The equipment cost calculation module is used to calculate the equipment cost score of each set of work plans in the work plan set according to the simulation data, and arrange the equipment cost scores in ascending order to obtain an equipment cost score table; Equipment cost score calculation formula: Among them, F is the equipment cost score, T1 is the steam temperature, T0 is the preset standard steam temperature, P1 is the steam pressure, P0 is the preset standard steam pressure, t1 is the preset time period, and ω are constants and the proportional coefficients are not zero.

9. The operation optimization system of the secondary reheat unit load based on big data according to claim 8 is characterized in that: The system also includes: a feedback score calculation module and an evaluation update coefficient module The feedback score calculation module is used to monitor the secondary reheat unit in real time to obtain operation data, and calculate the real-time feedback score according to the operation data; The evaluation update coefficient module is used to update the constant proportional coefficient of the equipment cost score calculation formula according to the real-time feedback score.

10. The operation optimization system of the secondary reheat unit load based on big data according to claim 9, characterized in that: The evaluation update coefficient module includes: a coefficient judgment module, a first execution update module and a second execution update module: The coefficient judgment module is used to determine that the accuracy of the equipment cost score is missing if the real-time feedback score is greater than the evaluation threshold, and to update the constant proportional coefficient in the equipment cost score calculation formula; The first execution update module is used to update the equipment cost score calculation formula if the temperature absolute difference is greater than the temperature threshold interval. The coefficient is updated; the absolute temperature difference is calculated by the simulated steam temperature and the actual steam temperature; The second execution update module is used to update the ω coefficient in the equipment cost score calculation formula if the absolute pressure difference is greater than the pressure threshold interval; the absolute pressure difference is calculated by the simulated steam pressure and the actual steam pressure.

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