Thermal power plant integrated energy system operation and maintenance regulation and control method based on BP neural network

Abstract

The invention relates to a thermal power plant integrated energy system operation and maintenance regulation and control method based on a BP neural network, and the method comprises the steps: collecting the real-time data and load demands of at least one device in a thermal power plant integrated energy system; according to the real-time data, generating at least one piece of prediction data of a target steam quantity, a target fuel quantity and a target electric load; comparing the at least one piece of prediction data with the real-time data; according to the fault information, the at least one piece of prediction data, the real-time data and the load demand, regulating and controlling the operation of the integrated energy system; and regulating and controlling the integrated energy system according to the load fluctuation condition, the real-time data and the at least one piece of prediction data so as to generate a regulation and control scheme of the integrated energy system. Therefore, the problems that the prior art does not consider the problems of power generation hour reduction, insufficient profitability and the like mainly faced by a power plant in combination with an integrated energy system, prediction is carried out only by using a neural network model, the prediction accuracy is insufficient, and design is not made from the angle of operation and maintenance of the power plant are solved.

Description

Technical Field

[0001] The present application relates to the technical field of integrated energy systems, and in particular to an operation and maintenance control method for an integrated energy system of a thermal power plant based on a BP neural network. Background Art

[0002] At present, efficient, energy-saving and environmentally friendly energy utilization technology is an important development trend at this stage, and the proportion of renewable energy power generation is increasing year by year. In the future, the annual utilization hours of thermal power units will drop sharply. If thermal power units continue to rely on simple power supply services, they will find it difficult to bear the high costs and will not be able to adapt to the actual needs of my country's energy transformation and energy conservation and emission reduction.

[0003] The integrated energy system of a thermal power plant is based on the source side of the thermal power plant. By coupling various new energy conversion technologies and energy storage technologies, it can realize multi-energy coupling supply, energy cascade utilization, hydrogen production and carbon capture in the region, etc. It can not only improve the profitability of the thermal power plant itself, but also reduce the energy costs of enterprises in the region, improve energy utilization efficiency, and reduce carbon dioxide emissions. As an integrated energy production, supply and marketing system, the integrated energy system of a thermal power plant involves multiple types of technologies, a wide variety of equipment, and a complex production process. The adjustment of some parameters takes a long time to take effect, making its production regulation and fault diagnosis very difficult. Therefore, it is of great significance to conduct research on the operation and maintenance and fault diagnosis and regulation of thermal power plants based on digital twins and BP neural network technology.

[0004] In the related technology, neural network technology can be used to screen the collected real-time data of thermal power plants, and then use the neural network model prediction module to make predictions, and then use the fault diagnosis module to diagnose faults on the predicted data.

[0005] However, the relevant technology has not considered combining with the integrated energy system to solve the problems that power plants are currently facing, such as the decline in generating hours and insufficient profitability. It only uses neural network models for prediction, which results in insufficient prediction accuracy. It only considers the prediction data and does not design from the perspective of power plant operation and maintenance, which urgently needs to be improved. Summary of the invention

[0006] The present application provides an operation and maintenance control method for an integrated energy system of a thermal power plant based on a BP neural network, in order to solve the problem that the relevant technology does not consider combining the integrated energy system to solve the problems mainly faced by power plants at this stage, such as the decline in the number of generating hours and insufficient profitability, and only uses a neural network model for prediction, resulting in insufficient prediction accuracy, and only considers obtaining prediction data without making a design from the perspective of the operation and maintenance of the power plant.

[0007] The first aspect of the present application provides an operation, maintenance and control method for an integrated energy system of a thermal power plant based on a BP neural network, comprising the following steps: collecting real-time data and load demand of at least one device in the integrated energy system of the thermal power plant; generating at least one prediction data of a target steam quantity, a target fuel quantity and a target electric load according to the real-time data; comparing the at least one prediction data with the real-time data to generate a comparison result, and determining fault information of the integrated energy system of the thermal power plant according to the comparison result; controlling the operation of the integrated energy system according to the fault information, the at least one prediction data, the real-time data and the load demand to generate an operation result; based on the operation result, determining the load fluctuation according to the load demand, and controlling the integrated energy system according to the load fluctuation, the real-time data and the at least one prediction data to generate a control plan for the integrated energy system.

[0008] Optionally, in one embodiment of the present application, generating at least one prediction data of a target steam quantity, a target fuel quantity and a target electric load according to the real-time data includes: optimizing the neural network loss function of the generator set by mechanism to generate a neural network after mechanism optimization, and using the neural network after mechanism optimization to predict operating parameters in the production process to generate first prediction data; processing the operating data of each equipment in the comprehensive energy system of the thermal power plant to obtain processed operating data, and based on the processed operating data, using a preset neural network to predict the operating parameters in the production process to generate second prediction data; determining the at least one prediction data according to the first prediction data and the second prediction data.

[0009] Optionally, in one embodiment of the present application, the calculation formula of the generator set neural network loss function is:

[0010] LOSS=αLOSS phys +αLOSS math ,

[0011] Among them, LOSS phys is the physical loss function, LOSS math Mathematically, it is the neural network loss function.

[0012] Optionally, in one embodiment of the present application, determining the fault information of the integrated energy system of the thermal power plant according to the comparison result includes: judging whether the real-time data meets a preset abnormal condition based on the comparison result and the value of the determination coefficient and the value of the mean square error; if the value of the determination coefficient and the value of the mean square error both exceed a preset threshold, then determining that the real-time data meets the preset abnormal condition, and determining that the integrated energy system of the thermal power plant has a fault, otherwise determining that the integrated energy system of the thermal power plant has not a fault.

[0013] Optionally, in one embodiment of the present application, the calculation formula of the value of the determination coefficient is:

[0014]

[0015] Among them, y real is the actual value of the data, y pred is the predicted value based on the model, is the average of the actual values;

[0016] The calculation formula of the mean square error value is:

[0017]

[0018] Among them, m is the total number of samples, y real is the actual value of the data, y pred is the predicted value based on the model.

[0019] Optionally, in one embodiment of the present application, the integrated energy system is regulated according to the load fluctuation, the real-time data and the at least one predicted data to generate a regulation scheme for the integrated energy system, including: judging whether the output electric energy on the energy supply side of the integrated energy system is greater than or equal to the electric load on the user demand side; if the output electric energy on the energy supply side of the integrated energy system is greater than or equal to the electric load on the user demand side, and the output steam on the energy supply side is greater than or equal to the steam required for the cooling load and the heating load, then the first electric energy that meets the preset redundant condition is stored in the target energy storage device to obtain cooling energy and heating energy; if the output electric energy on the energy supply side of the integrated energy system is greater than or equal to the electric load on the user demand side, and the output steam on the energy supply side is less than the steam required for the cooling load and the heating load, then the first electric energy that meets the preset redundant condition is stored in the target energy storage device to obtain cooling energy and heating energy. The output electric energy of the energy supply side meeting the first preset partial conditions is supplied to the user side, and the output electric energy of the energy supply side meeting the second preset partial conditions is converted into the cold energy and the heat energy; if the output electric energy of the energy supply side in the comprehensive energy system is less than the electric load on the user demand side, and the output steam of the energy supply side is greater than or equal to the steam required for the cold load and the heat load, the output steam of the energy supply side is supplied to the target device to obtain the cold load and the heat load; if the output electric energy of the energy supply side in the comprehensive energy system is less than the electric load on the user demand side, and the output steam of the energy supply side is less than the steam required for the cold load and the heat load, the output steam of the energy supply side is supplied to the target device to obtain the cold load and the heat load; determine the control scheme of the comprehensive energy system according to the cold energy, heat energy, cold load and heat load.

[0020] The second aspect of the present application provides an operation and maintenance control device for an integrated energy system of a thermal power plant based on a BP neural network, comprising: a collection module, used to collect real-time data and load demand of at least one device in the integrated energy system of the thermal power plant; a prediction module, used to generate at least one prediction data of a target steam quantity, a target fuel quantity and a target electric load according to the real-time data; a detection module, used to compare the at least one prediction data with the real-time data to generate a comparison result, and determine the fault information of the integrated energy system of the thermal power plant according to the comparison result; an operation and maintenance module, used to control the operation of the integrated energy system according to the fault information, the at least one prediction data, the real-time data and the load demand to generate an operation result; a control module, used to determine the load fluctuation situation according to the load demand based on the operation result, and control the integrated energy system according to the load fluctuation situation, the real-time data and the at least one prediction data to generate a control plan for the integrated energy system.

[0021] Optionally, in one embodiment of the present application, the prediction module includes: a first prediction unit, used to optimize the neural network loss function of the generator set to generate a neural network after mechanism optimization, and use the neural network after mechanism optimization to predict operating parameters in the production process to generate first prediction data; a second prediction unit, used to process the operating data of each equipment in the comprehensive energy system of the thermal power plant to obtain processed operating data, and based on the processed operating data, use a preset neural network to predict the operating parameters in the production process to generate second prediction data; a determination unit, used to determine at least one prediction data based on the first prediction data and the second prediction data.

[0022] Optionally, in one embodiment of the present application, the calculation formula of the generator set neural network loss function is:

[0023] LOSS=αLOSS phys +αLOSS math ,

[0024] Among them, LOSS phys is the physical loss function, LOSS math Mathematically, it is the neural network loss function.

[0025] Optionally, in one embodiment of the present application, the detection module includes: a detection unit, used to detect whether the real-time data meets the preset abnormal condition based on the comparison result and according to the value of the determination coefficient and the value of the mean square error; a judgment unit, used to judge that the real-time data meets the preset abnormal condition and determines that the comprehensive energy system of the thermal power plant has a fault when the value of the determination coefficient and the value of the mean square error both exceed a preset threshold value; otherwise, it is determined that the comprehensive energy system of the thermal power plant has not a fault.

[0026] Optionally, in one embodiment of the present application, the calculation formula of the value of the determination coefficient is:

[0027]

[0028] Among them, y real is the actual value of the data, y pred is the predicted value based on the model, is the average of the actual values;

[0029] The calculation formula of the mean square error value is:

[0030]

[0031] Among them, m is the total number of samples, y real is the actual value of the data, y pred is the predicted value based on the model.

[0032] Optionally, in one embodiment of the present application, the control module includes: a judgment unit, used to judge whether the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side; a storage unit, used to store the first electric energy that meets the preset redundant conditions to the target energy storage device to obtain cold energy and heat energy when the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side, and the output steam of the energy supply side is greater than or equal to the steam required for the cold load and the heat load; a conversion unit, used to supply the output electric energy of the energy supply side that meets the first preset partial conditions to the user side, and to convert the output electric energy of the energy supply side that meets the second preset partial conditions to the user side when the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side, and the output steam of the energy supply side is less than the steam required for the cold load and the heat load. The output electric energy of the energy supply side under some conditions is converted into the cooling energy and the thermal energy; a first acquisition unit is used for supplying the output steam of the energy supply side to the target device to obtain the cooling load and the thermal load when the output electric energy of the energy supply side in the integrated energy system is less than the electric load on the user demand side, and the output steam of the energy supply side is greater than or equal to the steam required for the cooling load and the thermal load; a second acquisition unit is used for supplying the output steam of the energy supply side to the target device to obtain the cooling load and the thermal load when the output electric energy of the energy supply side in the integrated energy system is less than the electric load on the user demand side, and the output steam of the energy supply side is less than the steam required for the cooling load and the thermal load; a scheme determination unit is used for determining the control scheme of the integrated energy system according to the cooling energy, thermal energy, cooling load and thermal load.

[0033] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the operation, maintenance and control method of the comprehensive energy system of a thermal power plant based on a BP neural network as described in the above embodiment.

[0034] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned BP neural network-based operation and maintenance control method for the comprehensive energy system of a thermal power plant.

[0035] The fifth aspect of the present application provides a computer program product, which stores a computer program that, when executed by a processor, implements the above-mentioned BP neural network-based operation, maintenance and control method for the integrated energy system of a thermal power plant.

[0036] The embodiment of the present application can greatly improve the prediction accuracy of the digital twin model on the power generation side while ensuring the efficient operation of the model by modifying the neural network loss function on the power generation side of the thermal power plant in the digital twin model; quickly predict the corresponding results through the neural network, and feed back to the digital twin model control platform in real time to realize real-time monitoring and prediction of various data; combine the digital twin model evaluation index, monitor and compare the predicted data of the digital twin model with the various data of the comprehensive energy system of the thermal power plant in real time, report abnormal data, and realize the discovery and diagnosis of equipment failures in the comprehensive energy system; through the digital twin model, the system operation state is quickly predicted after the operation parameters are adjusted, and the data that is difficult to measure in the comprehensive energy system is efficiently predicted, so as to realize the rapid and accurate adjustment of the operation parameters of the comprehensive energy system of the thermal power plant; make judgments on the operation state of the equipment based on the matching relationship such as energy supply and demand, and realize the intelligent operation and maintenance of the comprehensive energy system of the thermal power plant. Thus, the relevant technology does not consider combining the comprehensive energy system to solve the problems of the decline in the number of power generation hours and insufficient profitability that the power plant is facing at this stage, and only uses the neural network model for prediction, which has insufficient prediction accuracy, and only considers the prediction data, and does not make a design from the perspective of the operation and maintenance of the power plant.

[0037] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0039] Figure 1 A flowchart of a method for operation and maintenance control of a comprehensive energy system of a thermal power plant based on a BP neural network according to an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the structure of a comprehensive energy system of a thermal power plant according to an embodiment of the present application;

[0041] Figure 3 A model diagram of a digital twin model of a comprehensive energy system of a thermal power plant according to an embodiment of the present application;

[0042] Figure 4 A schematic diagram of the structure of an operation and maintenance control device for an integrated energy system of a thermal power plant based on a BP neural network provided according to an embodiment of the present application;

[0043] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0045] The following describes the operation and maintenance control method of the integrated energy system of a thermal power plant based on a BP neural network according to an embodiment of the present application with reference to the accompanying drawings. In view of the fact that the related technologies mentioned in the above background technology have not considered combining with the integrated energy system to solve the problems that power plants are currently facing, such as the decline in generating hours and insufficient profitability, and only use neural network models for prediction, which results in insufficient prediction accuracy and only considers obtaining prediction data without making designs from the perspective of the operation and maintenance of the power plant, the present application provides an operation and maintenance control method of the integrated energy system of a thermal power plant based on a BP neural network. In this method, by correcting the mechanism of the neural network loss function on the power generation side of the thermal power plant in the digital twin model, while ensuring the efficient operation of the model, the prediction accuracy of the digital twin model on the power generation side is greatly improved; the neural network is used to quickly predict the The results are fed back to the digital twin model control platform in real time to achieve real-time monitoring and prediction of various data; combined with the digital twin model evaluation indicators, the predicted data of the digital twin model is monitored and compared with various data of the comprehensive energy system of the thermal power plant in real time, abnormal data is reported, and the failure of the equipment in the comprehensive energy system is discovered and diagnosed; the digital twin model is used to quickly predict the operating status of the system after the operating parameters are adjusted, and the data that is difficult to measure in the comprehensive energy system is efficiently predicted, so as to achieve rapid and accurate adjustment of the operating parameters of the comprehensive energy system of the thermal power plant; the operating status of the equipment is judged based on the matching relationship of energy supply and demand, so as to realize the intelligent operation and maintenance of the comprehensive energy system of the thermal power plant. In this way, the relevant technology does not consider combining the comprehensive energy system to solve the problems of the decline in the number of generating hours and insufficient profitability that the power plants are facing at this stage, and only uses the neural network model for prediction, which has insufficient prediction accuracy, and only considers the prediction data, and does not make designs from the perspective of the operation and maintenance of the power plant.

[0046] Specifically, Figure 1 A flow chart of a method for operation, maintenance and control of an integrated energy system of a thermal power plant based on a BP neural network is provided in an embodiment of the present application.

[0047] like Figure 1 As shown, the operation and maintenance control method of the comprehensive energy system of a thermal power plant based on the BP neural network includes the following steps:

[0048] In step S101, real-time data and load demand of at least one device in the integrated energy system of a thermal power plant are collected.

[0049] It is understandable that if Figure 2 As shown, at least one device in the comprehensive energy system of a thermal power plant includes:

[0050] 1: Thermal power generation unit, including steam turbine unit, generator and coal-fired boiler, is used to realize the whole process from fuel to electricity generation. Among them, the coal-fired boiler is used to burn coal powder and other gas fuels to generate high-temperature flue gas to heat water to form high-temperature steam; the steam turbine unit realizes the process of converting steam thermal energy into mechanical energy; the generator converts the mechanical energy generated by the steam turbine into electrical energy. Generate the first electrical energy.

[0051] 2: Garbage gasification furnace, used to convert domestic garbage into gaseous fuel for boiler combustion.

[0052] 3: Biomass gasifier, used to convert biomass into gaseous fuel for boiler combustion.

[0053] 4: Coal mill, used to grind coal into coal powder for combustion in boiler.

[0054] 5: Electric-driven compression refrigeration equipment is a refrigeration equipment driven by electric energy, used to provide cooling load and generate the first cooling energy.

[0055] 6: Electric-driven compression heat pump equipment is a heat pump equipment driven by electric energy, used to provide heat load and generate first heat energy.

[0056] 7: Absorption refrigeration unit equipment is a refrigeration equipment driven by high temperature steam, used to provide cooling load and generate secondary cold energy.

[0057] 8: Heat exchanger heating equipment cools down the steam and exchanges heat to provide heat load for users. Generates secondary heat energy.

[0058] 9: Energy storage equipment, including electricity storage, heat storage, and cold storage equipment, is used to store excess capacity during the troughs of load demand and provide storage capacity to meet the load during peak periods.

[0059] 10: New energy power generation equipment, used to convert new energy such as wind and light into secondary electrical energy.

[0060] In the actual implementation process, the embodiment of the present application can collect real-time data and load demand of 1 to 10 devices in the integrated energy system and feed it back to Figure 3 The digital twin prediction module, fault detection module and integrated operation and maintenance module in the system can realize real-time monitoring and prediction of various data.

[0061] In step S102 , prediction data of at least one of a target steam quantity, a target fuel quantity, and a target electric load is generated according to real-time data.

[0062] It can be understood that the prediction data in the embodiments of the present application include: the amount of steam required under certain cold and hot load conditions, the amount of fuel required when the output electric energy on the energy supply side is less than the electric load on the user demand side, the supplementary electric load, the electric-driven compression refrigeration equipment and the electric-driven compression heat pump equipment to supplement the electric load of the thermal power unit when the cold and hot loads are insufficient, etc.

[0063] In the actual execution process, the embodiment of the present application can generate medium prediction data of target steam volume, target fuel volume and target electric load according to real-time data. The embodiment of the present application can design an efficient and accurate digital twin model based on the comprehensive energy system of the thermal power plant, realize the efficient operation and maintenance, accurate prediction and fault warning of the comprehensive energy system of the thermal power plant, which can not only reduce the operation and maintenance cost and difficulty of the thermal power plant, and reduce the operating cost of the thermal power plant, but also improve the production and operation stability of the thermal power plant, ensure the stability of regional energy consumption, and have important significance for improving the economy and environmental protection of existing thermal power plants.

[0064] Optionally, in one embodiment of the present application, at least one prediction data of a target steam quantity, a target fuel quantity and a target electric load is generated based on real-time data, including: optimizing the neural network loss function of the generator set by mechanism to generate a neural network after mechanism optimization, and using the neural network after mechanism optimization to predict operating parameters in the production process to generate first prediction data; processing the operating data of each equipment in the comprehensive energy system of the thermal power plant to obtain processed operating data, and based on the processed operating data, using a preset neural network to predict operating parameters in the production process to generate second prediction data; determining at least one prediction data based on the first prediction data and the second prediction data.

[0065] It can be understood that the embodiments of the present application can realize the processing of real-time data of thermal power units and the real-time prediction of the required amount of fuel under load fluctuation conditions.

[0066] Specifically, if Figure 3 As shown, the embodiment of the present application can process the real-time data of the thermal power unit equipment collected by the data acquisition module, use the neural network optimized by the mechanism to predict the relevant operating parameters in the production process, and supply the predicted data to the fault detection module for comparison with the actual operating parameters. Receive the real-time demand of the fluctuating load of the integrated energy system, and use the neural network optimized by the mechanism to predict the relevant data, fuel quantity, etc. in the production process. Receive the load data sent by the integrated operation and maintenance module, use the neural network optimized by the mechanism to predict the relevant data, fuel quantity, etc. produced under the instruction load, and generate the first prediction data, which is supplied to the integrated operation and maintenance module for use.

[0067] Furthermore, the embodiment of the present application adopts a mechanism-optimized loss function to improve prediction accuracy. The generator set neural network loss function is mechanism-optimized, wherein, in one embodiment of the present application, the calculation formula of the generator set neural network loss function is:

[0068] LOSS=αLOSS phys +αLOSS math ,

[0069] Among them, LOSS phys is the physical loss function, LOSS math Mathematically, it is the neural network loss function, and α+β=1 is the weight of the two loss functions.

[0070] Utilizing the constraints of physical conservation in the mechanism model, the sum of the squared logarithmic errors of the deviations of the predicted values ​​from equilibrium is defined as the loss function at the physical model level. The weights of the mathematical loss and the physical loss are pre-adjusted for hyperparameters: the cross-validation method is used to obtain the optimal weights of the mathematical loss and the physical loss through training and validation on different training sets and validation sets.

[0071] Furthermore, if Figure 3 As shown, the embodiment of the present application can process the operation data of each device of the integrated energy system collected by the data collection module, and use the neural network to predict the relevant operation parameters in the production process. Receive the real-time demand of the fluctuating load of the integrated energy system, and use the neural network to predict the relevant data of each device in the production process, the amount of fuel, etc. Receive the load data sent by the integrated operation and maintenance module, use the neural network optimized by the mechanism to predict the relevant data and fuel amount of production under the instruction load, and generate the second prediction data. The second prediction data is provided to the integrated operation and maintenance module for use, ensuring that at least one prediction data is determined based on the first prediction data and the second prediction data.

[0072] The embodiment of the present application can greatly improve the prediction accuracy of the digital twin model on the power generation side while ensuring efficient operation of the model by correcting the neural network loss function on the power generation side of the thermal power plant in the digital twin model, and ensure that the digital twin model on the power generation side is synchronized with the normal working state. The digital twin model receives various data of the comprehensive energy system of the thermal power plant in real time, quickly predicts the corresponding results through the neural network, and feeds back to the digital twin model control platform in real time, thereby realizing real-time monitoring and prediction of various data.

[0073] In step S103, at least one item of predicted data is compared with the real-time data to generate a comparison result, and fault information of the integrated energy system of the thermal power plant is determined according to the comparison result.

[0074] As a possible way to achieve this, Figure 3As shown, the embodiment of the present application can receive relevant operating parameters, fuel quantity, etc. related to the power generation process in the data acquisition module and the thermal power unit prediction module, compare at least one prediction data in the thermal power unit prediction module with the real-time data in the data acquisition module, determine whether a fault occurs in the comprehensive energy system of the thermal power plant based on the comparison result, and prompt the fault in the event of a fault.

[0075] The embodiment of the present application can combine the digital twin model evaluation indicators to conduct real-time monitoring and comparison of the predicted data of the digital twin model with various data of the comprehensive energy system of the thermal power plant, report abnormal data, and realize the discovery and diagnosis of equipment failures in the comprehensive energy system.

[0076] Optionally, in one embodiment of the present application, the fault information of the integrated energy system of the thermal power plant is determined according to the comparison result, including: based on the comparison result, judging whether the real-time data meets the preset abnormal condition according to the value of the determination coefficient and the value of the mean square error; if the value of the determination coefficient and the value of the mean square error both exceed the preset threshold value, then it is determined that the real-time data meets the preset abnormal condition, and it is determined that the integrated energy system of the thermal power plant has a fault, otherwise it is determined that the integrated energy system of the thermal power plant has not a fault.

[0077] It can be understood that the preset abnormal condition in the embodiment of the present application may be a real-time data abnormality.

[0078] In the actual implementation process, Figure 3 As shown, the embodiment of the present application can calculate the determination coefficient R 2 The value of and the mean square error (MSE) are used to determine whether the real-time data is abnormal. When both the value of the determination coefficient and the value of the mean square error exceed the set threshold, the real-time data is determined to be abnormal, and the abnormality is immediately fed back to the integrated operation and maintenance module, and it is determined that a fault has occurred in the comprehensive energy system of the thermal power plant, prompting a fault. Otherwise, it is determined that there is no fault in the comprehensive energy system of the thermal power plant.

[0079] The embodiments of the present application can further report abnormal data to achieve the discovery and diagnosis of equipment failures in the integrated energy system.

[0080] It should be noted that the preset abnormal conditions can be set by those skilled in the art according to actual conditions and are not specifically limited here.

[0081] In one embodiment of the present application, the calculation formula of the determination coefficient is:

[0082]

[0083] Among them, y real is the actual value of the data, y pred is the predicted value based on the model, is the average of the actual values;

[0084] The calculation formula for the mean square error value is:

[0085]

[0086] Among them, m is the total number of samples, y real is the actual value of the data, y pred is the predicted value based on the model.

[0087] For other equipment in the integrated energy system, it is also necessary to determine whether the physical parameters of the links between the equipment are consistent.

[0088] In step S104, the operation of the integrated energy system is regulated according to the fault information, at least one prediction data, the real-time data and the load demand to generate an operation result.

[0089] Specifically, if Figure 3 As shown, the embodiment of the present application can receive real-time data and load demand in the data acquisition module, at least one prediction data in the digital twin prediction module, and fault information transmitted by the fault detection module, analyze the received data through a neural network, determine the status of each device in the integrated energy system under the current load and provide feedback, and regulate the operation of the integrated energy system to generate an operation result. The received relevant data is visualized and displayed in real time. The staff can monitor the operation of the integrated energy system in real time according to the prediction results, adjust the energy supply in time, and eliminate system failures.

[0090] The embodiment of the present application can use the digital twin model to quickly predict the system operating status after the operating parameters are adjusted, and to efficiently predict the data that is difficult to measure in the comprehensive energy system, so as to achieve rapid and accurate adjustment of the operating parameters of the comprehensive energy system of the thermal power plant based on this. The equipment operating status is judged based on the matching relationship such as energy supply and demand, and the intelligent operation and maintenance of the comprehensive energy system of the thermal power plant is realized.

[0091] In step S105, based on the operation result, the load fluctuation is determined according to the load demand, and the integrated energy system is regulated according to the load fluctuation, the real-time data and at least one predicted data to generate a regulation plan for the integrated energy system.

[0092] In the actual implementation process, Figure 3 As shown, the embodiment of the present application can determine the load fluctuation based on the load demand based on the operation results, and regulate the integrated energy system based on the load fluctuation in the data acquisition module, real-time data, and predicted data such as the fuel quantity predicted by the digital twin prediction module to generate a regulation plan for the integrated energy system.

[0093] Optionally, in one embodiment of the present application, the integrated energy system is regulated according to load fluctuations, real-time data and at least one predicted data to generate a regulation scheme for the integrated energy system, including: judging whether the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user's demand side; if the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user's demand side, and the output steam of the energy supply side is greater than or equal to the steam required for the cooling load and the heating load, then storing the first electric energy that meets the preset excess condition in the target energy storage device to obtain cooling energy and heating energy; if the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user's demand side, and the output steam of the energy supply side is less than the steam required for the cooling load and the heating load, The output electric energy from the energy supply side that meets the first preset part of the conditions will be supplied to the user side, and the output electric energy from the energy supply side that meets the second preset part of the conditions will be converted into cold energy and heat energy; if the output electric energy from the energy supply side in the integrated energy system is less than the electric load on the user's demand side, and the output steam from the energy supply side is greater than or equal to the steam required for the cold load and the heat load, the output steam from the energy supply side will be supplied to the target equipment to obtain the cold load and the heat load; if the output electric energy from the energy supply side in the integrated energy system is less than the electric load on the user's demand side, and the output steam from the energy supply side is less than the steam required for the cold load and the heat load, the output steam from the energy supply side will be supplied to the target equipment to obtain the cold load and the heat load; determine the control plan of the integrated energy system based on cold energy, heat energy, cold load and heat load.

[0094] It is understandable that the operation mode of this application under the operation and maintenance control state is:

[0095] (a) If Figure 3 As shown, the data acquisition module receives real-time data from various equipment in the comprehensive energy system of the thermal power plant and passes the data to the digital twin prediction module and the comprehensive control module.

[0096] Among them, the data passed to the digital twin prediction module includes: cooling load, heating load, and electrical load.

[0097] The data transmitted to the comprehensive control module include: electric load, first electric energy, second electric energy, and output steam volume.

[0098] (ii) The digital twin prediction module processes the received data, obtains the prediction results, and passes them to the comprehensive control module.

[0099] Among them, the data predicted by the digital twin prediction module include: the amount of steam required under certain conditions of cold and heat loads, the amount of fuel required when the output electricity on the energy supply side is less than the electric load on the user demand side, the supplementary electric load, the electric-driven compression refrigeration equipment and the electric-driven compression heat pump equipment to supplement the electric load of thermal power units when the cold and heat loads are insufficient, etc.

[0100] (3) The comprehensive control module provides a control plan for the integrated energy system based on the data in the data acquisition module and the digital twin prediction module.

[0101] Among them, the first electric energy and the second electric energy generated in the thermal power units and the new energy power generation equipment are collectively referred to as the output electric energy on the energy supply side; the first cold energy and the second cold energy generated by the electric-driven compression refrigeration equipment and the absorption refrigeration unit equipment are collectively referred to as the output cold energy; the first heat energy and the second heat energy generated by the electric-driven compression heat pump equipment and the heat exchanger thermal equipment are collectively referred to as the output heat energy.

[0102] The control logic of the comprehensive control module is as follows:

[0103] like Figure 2 As shown, when the output electric energy on the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side, and the output steam on the energy supply side is greater than or equal to the steam required by the cold and hot loads, the excess first electric energy is stored in the power storage device. If the power storage device is fully charged, the electric energy is converted into cold energy and heat energy by using the electric-driven compression refrigeration device and the electric-driven compression heat pump device, and stored in the corresponding cold storage and heat storage devices. At the same time, the output steam is converted into cold energy and heat energy by using the absorption refrigeration unit equipment and the heat exchanger heating equipment, and stored in the corresponding cold storage and heat storage devices.

[0104] When the power output on the energy supply side of the integrated energy system is greater than or equal to the user demand side power load, but the steam output on the energy supply side is less than the steam required for the cold and heat loads, part of the power output on the energy supply side will be supplied to the user side to meet the user side power load, and the rest of the power will be used to drive the electric drive compression refrigeration equipment and the electric drive compression heat pump equipment to convert the power into cold energy and heat energy; the steam output on the energy supply side will be used for the absorption refrigeration unit equipment and the heat exchanger heating equipment to convert it into cold energy and heat energy to meet the user's cold and heat loads. If the output cold energy and heat energy are higher than the cold and heat loads on the user side, the excess cold and heat energy will be stored in the corresponding cold storage and heat storage equipment.

[0105] When the power output on the energy supply side of the integrated energy system is less than the power load on the user's demand side, but the steam output on the energy supply side is greater than or equal to the steam required for the cold and hot loads, the steam output on the energy supply side is provided to the absorption refrigeration unit equipment and the heat exchanger heating equipment to provide cold and hot loads. The power output on the energy supply side is all supplied to the user side, and the power storage device provides power to the outside. If the remaining power in the power storage device cannot cover the power load on the user side, the power load on the user's demand side, the power output of the power storage device, and the power output data on the energy supply side are transmitted to the digital twin prediction module, and the digital twin prediction module is used to predict the load and fuel required for the thermal power unit, and the load of the thermal power unit is increased accordingly.

[0106] When the output power of the energy supply side in the integrated energy system is less than the electric load on the user demand side, but the output steam of the energy supply side is less than the steam required for the cold and hot loads, all the output steam of the energy supply side is provided to the absorption refrigeration unit equipment and the heat exchanger heating equipment to provide cold and hot loads, and the output power of the energy supply side is all supplied to the user side, and the power storage, heat storage, and cold storage equipment provide energy to the user side. If the energy provided by the power storage, cold storage, and heat storage equipment cannot meet the user side electric load and cold and hot load, the user demand side electric load, the output power of the power storage equipment, and the output power data of the energy supply side, as well as the cold and hot load on the user demand side, the output cold and heat of the energy storage equipment, and the output cold and hot energy are transmitted to the digital twin prediction module. The digital twin prediction module is used to predict the load and fuel amount of the thermal power unit to meet the user side electric load, and the corresponding output steam amount is calculated at the same time. If the predicted output steam amount can meet the user side cold and hot load, the load of the thermal power unit is increased according to the prediction results. If the predicted output steam volume cannot meet the cold and heat loads on the user side, the insufficient cold and heat loads will be transmitted to the digital twin prediction module, and the power consumption and the corresponding load of the thermal power unit required to achieve the required cold and heat loads using the electric-driven compression refrigeration equipment and the electric-driven compression heat pump equipment will be calculated.

[0107] Furthermore, if Figure 3 As shown, an embodiment of the present application may include a data acquisition module, a thermal power unit prediction unit, an integrated energy system equipment prediction unit, a digital twin prediction module, a fault detection module and an integrated operation and maintenance module.

[0108] The operation mode of this application in the real-time monitoring and fault detection state is as follows:

[0109] (1) The data acquisition module receives real-time data from various devices in the integrated energy system of the thermal power plant and transmits the data to the digital twin prediction module, fault monitoring module and integrated operation and maintenance module;

[0110] Among them, the real-time data received and transmitted to the digital twin prediction module include: cooling load, heating load, exhaust load, electrical load, cold exhaust volume, hot exhaust volume, waste incineration volume, and biomass gasification volume.

[0111] The real-time data received and transmitted to the fault detection module include: pulverized coal flow, steam pressure and temperature of thermal power units, fuel flow, heat pump inlet and outlet temperatures and flow, absorption refrigeration unit inlet and outlet temperatures and flow, cooling load, heating load, extraction load, electrical load, hot extraction volume, cold extraction volume, coal consumption, main steam flow, three-stage extraction parameters, reheat heater outlet parameters, and reheat extraction parameters.

[0112] The real-time data received and transmitted to the integrated operation and maintenance module include: temperature and pressure of the garbage gasifier, garbage incineration volume, feed flow rate, temperature and pressure of the biomass gasifier, feed flow rate, biomass gasification volume, pulverizer temperature, coal powder flow rate, steam pressure and temperature of the thermal power unit, fuel flow rate, heat pump inlet and outlet temperature and flow rate, absorption refrigeration unit inlet and outlet temperature and flow rate, energy storage status, cooling load, heating load, exhaust load, electrical load, hot exhaust volume, cold exhaust volume, new energy power generation, coal consumption, main steam flow rate, three-stage exhaust parameters, regenerative heater outlet parameters, and regenerative exhaust parameters.

[0113] (ii) The thermal power unit prediction unit and the integrated energy system equipment prediction unit (i.e., the digital twin prediction module) process the received data, obtain the prediction results, and pass them to the fault detection module and the integrated operation and maintenance module respectively;

[0114] Among them, the predicted data include: coal consumption, main steam flow, three-stage extraction parameters, regenerative heater outlet parameters, and regenerative extraction parameters.

[0115] (III) The prediction results of the prediction unit of the thermal power unit are passed to the fault detection module, and the presence of faults is determined by comparing the data. The results are passed to the comprehensive operation and maintenance module.

[0116] (IV) The integrated operation and maintenance module receives various data and statuses and displays them in the integrated operation and maintenance visualization module according to different partitions.

[0117] According to the operation and maintenance control method of the comprehensive energy system of a thermal power plant based on a BP neural network proposed in the embodiment of the present application, by correcting the mechanism of the neural network loss function on the power generation side of the thermal power plant in the digital twin model, the prediction accuracy of the digital twin model on the power generation side is greatly improved while ensuring the efficient operation of the model; the corresponding results are quickly predicted by the neural network and fed back to the digital twin model control platform in real time to realize real-time monitoring and prediction of various data; combined with the digital twin model evaluation indicators, the predicted data of the digital twin model and the various data of the comprehensive energy system of the thermal power plant are monitored and compared in real time, and abnormal data is reported to realize the discovery and diagnosis of equipment faults in the comprehensive energy system; the digital twin model is used to quickly predict the operating status of the system after the operating parameters are adjusted, and the data that is difficult to measure in the comprehensive energy system is efficiently predicted, so as to realize the rapid and accurate adjustment of the operating parameters of the comprehensive energy system of the thermal power plant; the equipment operating status is judged based on matching relationships such as energy supply and demand, so as to realize the intelligent operation and maintenance of the comprehensive energy system of the thermal power plant. As a result, the problem of related technologies not considering combining with integrated energy systems to solve the main problems faced by power plants at this stage, such as decreased generating hours and insufficient profitability, was solved. Only neural network models were used for prediction, resulting in insufficient prediction accuracy and only considering the prediction data without making designs from the perspective of power plant operation and maintenance was solved.

[0118] Next, the operation, maintenance and control device of the comprehensive energy system of a thermal power plant based on a BP neural network proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0119] Figure 4 It is a structural schematic diagram of an operation and maintenance control device for a comprehensive energy system of a thermal power plant based on a BP neural network according to an embodiment of the present application.

[0120] like Figure 4 As shown, the thermal power plant integrated energy system operation, maintenance and control device 10 based on BP neural network includes: a collection module 100, a prediction module 200, a detection module 300, an operation and maintenance module 400 and a control module 500.

[0121] Specifically, the collection module 100 is used to collect real-time data and load demand of at least one device in the comprehensive energy system of the thermal power plant.

[0122] The prediction module 200 is used to generate prediction data of at least one of a target steam quantity, a target fuel quantity and a target electric load according to real-time data.

[0123] The detection module 300 is used to compare at least one predicted data with the real-time data to generate a comparison result, and determine the fault information of the comprehensive energy system of the thermal power plant according to the comparison result.

[0124] The operation and maintenance module 400 is used to regulate the operation of the integrated energy system according to the fault information, at least one prediction data, the real-time data and the load demand to generate the operation result.

[0125] The control module 500 is used to determine the load fluctuation situation according to the load demand based on the operation result, and to control the integrated energy system according to the load fluctuation situation, real-time data and at least one predicted data to generate a control plan for the integrated energy system.

[0126] Optionally, in one embodiment of the present application, the prediction module 200 includes: a first prediction unit, a second prediction unit and a determination unit.

[0127] Among them, the first prediction unit is used to optimize the neural network loss function of the generator set to generate a neural network after the mechanism is optimized, and use the neural network after the mechanism is optimized to predict the operating parameters in the production process to generate the first prediction data.

[0128] The second prediction unit is used to process the operating data of each device in the comprehensive energy system of the thermal power plant to obtain the processed operating data, and based on the processed operating data, use a preset neural network to predict the operating parameters in the production process to generate second prediction data.

[0129] A determination unit is used to determine at least one item of prediction data according to the first prediction data and the second prediction data.

[0130] Optionally, in one embodiment of the present application, the calculation formula of the generator set neural network loss function is:

[0131] LOSS=αLOSS phys +βLOSS math ,

[0132] Among them, LOSS phys is the physical loss function, LOSS math Mathematically, it is the neural network loss function.

[0133] Optionally, in one embodiment of the present application, the fault detection module 300 includes: a detection unit and a determination unit.

[0134] Among them, the detection unit is used to detect whether the real-time data meets the preset abnormal condition based on the comparison result, according to the value of the determination coefficient and the value of the mean square error.

[0135] The determination unit is used to determine that the real-time data meets the preset abnormal condition and determines that the comprehensive energy system of the thermal power plant has a fault when the value of the determination coefficient and the value of the mean square error both exceed the preset threshold value, otherwise it is determined that the comprehensive energy system of the thermal power plant has not a fault.

[0136] Optionally, in one embodiment of the present application, the calculation formula of the value of the determination coefficient is:

[0137]

[0138] Among them, y real is the actual value of the data, y pred is the predicted value based on the model, is the average of the actual values;

[0139] The calculation formula for the mean square error value is:

[0140]

[0141] Among them, m is the total number of samples, y real is the actual value of the data, y pred is the predicted value based on the model.

[0142] Optionally, in one embodiment of the present application, the control module 500 includes:

[0143] A judgment unit, used to judge whether the output electric energy on the energy supply side of the comprehensive energy system is greater than or equal to the electric load on the user demand side;

[0144] A storage unit, for storing first electric energy that meets a preset excess condition in a target energy storage device to obtain cold energy and heat energy when the electric energy outputted by the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side, and the steam outputted by the energy supply side is greater than or equal to the steam required by the cold load and the heat load;

[0145] A conversion unit, for supplying the output electric energy of the energy supply side that meets the first preset partial conditions to the user side, and converting the output electric energy of the energy supply side that meets the second preset partial conditions into cold energy and heat energy when the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load of the user demand side, and the output steam of the energy supply side is less than the steam required for the cold load and the heat load;

[0146] The first acquisition unit is used to supply the steam output from the energy supply side to the target device to obtain the cooling load and the heating load when the electric energy output from the energy supply side in the integrated energy system is less than the electric load on the user demand side and the steam output from the energy supply side is greater than or equal to the steam required for the cooling load and the heating load;

[0147] The second acquisition unit is used to supply the steam output from the energy supply side to the target device to obtain the cooling load and the heating load when the electric energy output from the energy supply side in the integrated energy system is less than the electric load on the user demand side and the steam output from the energy supply side is less than the steam required for the cooling load and the heating load;

[0148] The scheme determination unit is used to determine the control scheme of the comprehensive energy system according to the cooling energy, heating energy, cooling load and heating load.

[0149] It should be noted that the aforementioned explanation of the embodiment of the operation and maintenance control method of the comprehensive energy system of a thermal power plant based on the BP neural network is also applicable to the operation and maintenance control device of the comprehensive energy system of a thermal power plant based on the BP neural network of this embodiment, and will not be repeated here.

[0150] According to the BP neural network-based operation and maintenance control device for the integrated energy system of a thermal power plant proposed in the embodiment of the present application, by making a mechanism correction to the neural network loss function on the power generation side of the thermal power plant in the digital twin model, the prediction accuracy of the digital twin model on the power generation side is greatly improved while ensuring the efficient operation of the model; the corresponding results are quickly predicted by the neural network and fed back to the digital twin model control platform in real time to realize real-time monitoring and prediction of various data; combined with the digital twin model evaluation indicators, the predicted data of the digital twin model and various data of the integrated energy system of the thermal power plant are monitored and compared in real time, and abnormal data is reported to realize the discovery and diagnosis of equipment failures in the integrated energy system; the digital twin model is used to quickly predict the operating status of the system after the operating parameters are adjusted, and the data that is difficult to measure in the integrated energy system is efficiently predicted, so as to realize the rapid and accurate adjustment of the operating parameters of the integrated energy system of the thermal power plant; the equipment operating status is judged based on matching relationships such as energy supply and demand, so as to realize the intelligent operation and maintenance of the integrated energy system of the thermal power plant. As a result, the problem of related technologies not considering combining with integrated energy systems to solve the main problems faced by power plants at this stage, such as decreased generating hours and insufficient profitability, was solved. Only neural network models were used for prediction, resulting in insufficient prediction accuracy and only considering the prediction data without making designs from the perspective of power plant operation and maintenance was solved.

[0151] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0152] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0153] When the processor 502 executes the program, the operation, maintenance and control method of the comprehensive energy system of a thermal power plant based on the BP neural network provided in the above embodiment is implemented.

[0154] Furthermore, the electronic device further comprises:

[0155] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0156] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0157] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0158] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0159] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0160] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0161] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned operation, maintenance and control method of the comprehensive energy system of a thermal power plant based on a BP neural network is implemented.

[0162] The embodiment of the present application also provides a computer program product, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned BP neural network-based operation, maintenance and control method of the comprehensive energy system of a thermal power plant is implemented.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0164] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0165] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0166] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0167] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0168] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0169] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0170] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for operation and maintenance control of a comprehensive energy system of a thermal power plant based on a BP neural network, characterized in that: The following steps are involved: Collect real-time data and load demand of at least one device in the comprehensive energy system of a thermal power plant; generating at least one prediction data of a target steam quantity, a target fuel quantity and a target electric load according to the real-time data; Comparing the at least one predicted data with the real-time data to generate a comparison result, and determining fault information of the integrated energy system of the thermal power plant according to the comparison result; Regulating the operation of the integrated energy system according to the fault information, the at least one item of prediction data, the real-time data and the load demand to generate an operation result; Based on the operation result, the load fluctuation is determined according to the load demand, and the integrated energy system is regulated according to the load fluctuation, the real-time data and the at least one predicted data to generate a regulation plan for the integrated energy system.

2. The method according to claim 1, characterized in that The generating, according to the real-time data, at least one prediction data of a target steam quantity, a target fuel quantity and a target electric load comprises: Mechanism-optimizing the generator set neural network loss function to generate a mechanism-optimized neural network, and using the mechanism-optimized neural network to predict operating parameters in the production process to generate first prediction data; Processing the operating data of each device of the comprehensive energy system of the thermal power plant to obtain processed operating data, and based on the processed operating data, using a preset neural network to predict operating parameters in the production process to generate second prediction data; The at least one item of prediction data is determined according to the first prediction data and the second prediction data.

3. The method according to claim 2, characterized in that The calculation formula of the generator set neural network loss function is: LOSS=αLOSS phys +βLOSS math , Among them, LOSS phys is the physical loss function, LOSS math Mathematically, it is the neural network loss function.

4. The method according to claim 1, characterized in that: Determining the fault information of the comprehensive energy system of the thermal power plant according to the comparison result includes: Based on the comparison result, detecting whether the real-time data meets a preset abnormal condition according to a value of a determination coefficient and a value of a mean square error; If the value of the determination coefficient and the value of the mean square error both exceed the preset threshold value, it is determined that the real-time data meets the preset abnormal condition and it is determined that a fault has occurred in the comprehensive energy system of the thermal power plant; otherwise, it is determined that no fault has occurred in the comprehensive energy system of the thermal power plant.

5. The method according to claim 4, characterized in that The calculation formula for the value of the determination coefficient is: Among them, y real is the actual value of the data, y pred is the predicted value based on the model, is the average of the actual values; The calculation formula of the mean square error value is: Among them, m is the total number of samples, y real is the actual value of the data, y pred is the predicted value based on the model.

6. The method according to claim 1, characterized in that The regulating the integrated energy system according to the load fluctuation, the real-time data and the at least one predicted data to generate a regulation scheme for the integrated energy system includes: Determine whether the output electric energy on the energy supply side of the integrated energy system is greater than or equal to the electric load on the user demand side; If the electric energy outputted by the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side, and the steam outputted by the energy supply side is greater than or equal to the steam required by the cooling load and the heating load, the first electric energy satisfying the preset excess condition is stored in the target energy storage device to obtain cooling energy and heating energy; If the output electric energy of the energy supply side in the integrated energy system is greater than or equal to the electric load on the user demand side, and the output steam of the energy supply side is less than the steam required by the cooling load and the heating load, the output electric energy of the energy supply side that meets the first preset partial conditions is supplied to the user side, and the output electric energy of the energy supply side that meets the second preset partial conditions is converted into the cooling energy and the heating energy; If the electric energy output from the energy supply side in the integrated energy system is less than the electric load on the user demand side, and the steam output from the energy supply side is greater than or equal to the steam required by the cooling load and the heating load, the steam output from the energy supply side is supplied to the target device to obtain the cooling load and the heating load; If the electric energy outputted by the energy supply side in the integrated energy system is less than the electric load on the user demand side, and the steam outputted by the energy supply side is less than the steam quantity required by the cooling load and the heating load, the steam outputted by the energy supply side is supplied to the target device to obtain the cooling load and the heating load; A control scheme for the integrated energy system is determined based on the cold energy, heat energy, cold load and heat load.

7. A thermal power plant integrated energy system operation and maintenance control device based on BP neural network, characterized in that: include: A collection module, used to collect real-time data and load demand of at least one device in the comprehensive energy system of a thermal power plant; A prediction module, configured to generate prediction data of at least one of a target steam quantity, a target fuel quantity and a target electric load according to the real-time data; A detection module, used for comparing the at least one predicted data with the real-time data to generate a comparison result, and determining the fault information of the integrated energy system of the thermal power plant according to the comparison result; An operation and maintenance module, used for regulating the operation of the integrated energy system according to the fault information, the at least one prediction data, the real-time data and the load demand to generate an operation result; A control module is used to determine the load fluctuation situation based on the operation result and the load demand, and to control the integrated energy system according to the load fluctuation situation, the real-time data and the at least one predicted data to generate a control plan for the integrated energy system.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the operation, maintenance and control method of the comprehensive energy system of a thermal power plant based on a BP neural network as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the operation, maintenance and control method of a comprehensive energy system of a thermal power plant based on a BP neural network as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the operation, maintenance and control method of the comprehensive energy system of a thermal power plant based on a BP neural network as described in any one of claims 1 to 5.

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