Intelligent power plant anomaly analysis system and method based on power internet of things

Abstract

The application discloses a smart power plant abnormality analysis system and method based on a power internet of things, relates to the technical field of power plants, and comprises four modules: a micro dynamic monitoring module monitors whether the coal powder combustion dynamics of each monitoring point is unbalanced by configuring micro hardware in each combustion chamber of a target power plant; a control strategy analysis module analyzes the control strategy of the corresponding combustion chamber when detecting that the combustion dynamics is unbalanced; a creep damage evaluation value analysis module sets multiple evaluation time points when the combustion chamber is in operation, and quantitatively analyzes the creep damage evaluation values of each component; and a hidden damage risk evaluation module accurately judges the hidden damage risk grade of each combustion chamber component according to the evaluation values of each time point. The system relies on the power internet of things technology, realizes comprehensive monitoring and risk evaluation of the combustion state of the combustion chamber of the power plant and equipment damage, and provides strong support for smart power plant abnormality analysis and safe operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power plants, in particular to a smart power plant abnormality analysis system and method based on a power Internet of Things. BACKGROUND

[0002] With the development of the power industry, traditional power plants face many challenges, such as improving production efficiency, reducing costs, ensuring safe production, and achieving energy saving and emission reduction. The concept of a smart power plant has emerged, which aims to use advanced information technology such as the Internet of Things, big data, artificial intelligence, etc. to comprehensively perceive, optimize decision-making and intelligently control the production process of the power plant, so as to improve the overall performance and competitiveness of the power plant. Therefore, a smart power plant abnormality analysis system and method based on a power Internet of Things is needed.

[0003] Prior art such as the invention application patent published as CN117553315A discloses an intelligent monitoring system for boiler combustion. The system detects coal powder and fuel parameters during the micro-oil ignition combustion process through a combustion medium detection module. It monitors the operating parameters of the primary combustion chamber, secondary combustion chamber, fuel oil sprayer, and ignition device, including inlet air volume, coal powder concentration, oil injection time, and ignition action, through an operating process detection module. The operating analysis module determines the compliance level of the inlet air volume and coal powder concentration of the primary combustion chamber according to the air volume standard, calculates the ideal inlet air volume and coal powder concentration of the secondary combustion chamber, and analyzes the ideal ignition time and ideal oil injection time. The operating control module adjusts the actions of the air supply device, fuel oil sprayer, and ignition device based on the analysis results to achieve control. The display management module displays the analysis results and control process. The invention realizes the monitoring and control of the micro-oil ignition coal powder combustion system through the intelligent monitoring system, improves the combustion efficiency and safety, and reduces energy waste and environmental pollution.

[0004] For the above-mentioned solution, the present application finds that the above-mentioned technology at least has the following technical problems: 1. The existing technology lacks micro and multi-dimensional monitoring means, making it difficult to accurately obtain key indicators such as coal powder combustion, and making it difficult to adapt to micro hardware, resulting in limited precision of monitoring data and inability to timely detect subtle combustion abnormalities in the combustion chamber. At the same time, accurately determining whether the combustion dynamics is unbalanced may lead to inaccurate judgment of the combustion condition and missed optimal adjustment opportunities. When the combustion dynamics is unbalanced, there is no detailed control strategy analysis for different imbalance levels. This may lead to over-adjustment or insufficient adjustment, affecting combustion efficiency and pollutant emission control.

[0005] 2、The prior art lacks systematic and diversified coping strategies, and when facing different degrees of combustion dynamic imbalance, it cannot provide a comprehensive and effective solution, and may only take some conventional and general measures, and cannot achieve personalized optimization control. At the same time, multiple damage evaluation time points are not set, and the creep damage of each component in the combustion chamber cannot be evaluated in real time or periodically, and potential damage to the components may not be discovered in time, increasing the risk of equipment failure.

[0006] 3、The prior art is difficult to accurately judge the risk level of the hidden damage of each component, which is not conducive to formulating maintenance plans and taking preventive measures in advance, and may lead to sudden equipment failure, affecting the normal operation of the power plant. At the same time, the prior art may be relatively independent of each monitoring and control link, without forming an integrated system based on the power Internet of Things, and cannot realize data sharing and collaborative analysis, reducing the efficiency and accuracy of power plant anomaly analysis. It is difficult to use advanced technologies such as Internet of Things and data analysis to realize intelligent anomaly analysis and decision support, and a large amount of data processing and judgment need to be performed manually, which is not only inefficient, but also prone to human errors. SUMMARY

[0007] In view of the above technical deficiencies, the purpose of the present application is to provide a smart power plant anomaly analysis system and method based on the power Internet of Things.

[0008] To solve the above technical problems, the present application adopts the following technical solutions: the present application provides a smart power plant anomaly analysis system based on the power Internet of Things in the first aspect, comprising: a micro dynamic monitoring module: for configuring micro hardware in each combustion chamber of the target power plant, so as to evaluate whether the corresponding coal combustion dynamics in each combustion chamber of the target power plant is unbalanced at each monitoring point.

[0009] A control strategy analysis module is used to analyze the control strategy corresponding to the combustion chamber in the target power plant at the monitoring point when the corresponding coal combustion dynamics in the combustion chamber of the target power plant at the monitoring point is unbalanced.

[0010] A creep damage evaluation value analysis module is used to set several damage evaluation time points when each combustion chamber of the target power plant is running, and then analyze the creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point.

[0011] A hidden damage risk assessment module is used to judge the risk level of the hidden damage of each component in each combustion chamber of the target power plant at each damage evaluation time point according to the creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point.

[0012] The application provides a smart power plant abnormality analysis method based on a power Internet of Things in a second aspect, comprising: step one, micro-dynamic monitoring: configuring micro-hardware in each combustion chamber of the target power plant, so as to evaluate whether the corresponding coal powder combustion dynamics in each combustion chamber of the target power plant at each monitoring point is unbalanced.

[0013] Step two, analysis of control strategy: when the corresponding coal powder combustion dynamics in the combustion chamber of the target power plant at the monitoring point is unbalanced, the corresponding control strategy in the combustion chamber of the target power plant at the monitoring point is analyzed.

[0014] Step three, analysis of creep damage evaluation value: when each combustion chamber of the target power plant is running, a plurality of damage evaluation time points are set, and then the creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point is analyzed.

[0015] Step four, evaluation of hidden damage risk: according to the creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point, the risk level of the hidden damage of each component in each combustion chamber of the target power plant at each damage evaluation time point is judged.

[0016] The beneficial effects of the application are as follows: 1. In the embodiment of the application, the micro-hardware is configured in each combustion chamber of the target power plant through the micro-dynamic monitoring module, which can accurately perceive the subtle changes of coal powder combustion. According to the number of abnormal working conditions, the hardware parameters are intelligently adjusted, so that the monitoring of coal powder combustion dynamics is more targeted and more accurate. Coal powder fluidization indicators and flame free radical indicators are obtained from different angles, and a scientific evaluation model is used to calculate the coal powder combustion dynamic evaluation value, which provides strong support for accurately judging whether the combustion is unbalanced, thereby effectively improving the combustion efficiency and ensuring the stable progress of the combustion process. When the coal powder combustion dynamics is unbalanced, the control strategy analysis module can develop and implement a fine control strategy according to the specific degree of imbalance. Whether it is a slight imbalance to fine-tune the secondary air ratio, ammonia injection position, or a moderate or severe imbalance to make a large adjustment of each parameter and change the injection mode, these measures can quickly and effectively correct the combustion imbalance state, not only improve the energy utilization rate, but also reduce the emission of pollutants caused by insufficient combustion, achieve the goal of energy saving and emission reduction, and at the same time, ensure the stable operation of the combustion equipment.

[0017] 2、The embodiment of the present application, the creep damage evaluation value analysis module and the hidden damage risk assessment module are combined, the creep damage of each component in each combustion chamber of the target power plant is comprehensively and deeply monitored and evaluated. Through the ultrasonic signal obtained by the high-temperature-resistant piezoelectric composite material sensor, combined with the operating parameters collected by the DCS system, the creep damage evaluation value is obtained by substituting into the calculation formula, and then compared with the set interval to determine the hidden damage risk grade. This way can find the potential damage risk of the equipment in advance, so that the maintenance personnel can reasonably arrange the maintenance plan according to different risk levels, avoid the downtime loss caused by equipment failure, prolong the service life of the equipment, reduce the operation and maintenance cost of the equipment, and improve the reliability and safety of the equipment operation. Realize the intelligent management of power plant equipment and combustion process. The data interaction and cooperative work between the modules enable the system to process a large amount of monitoring data in real time and make accurate analysis and judgment quickly. At the same time, according to the analysis result, the control strategy is automatically adjusted or the early warning information is sent, which provides a scientific decision basis for the operation and management of the power plant, improves the intelligent level and management efficiency of the power plant, and helps to realize the goal of smart power plant.

[0018] 3、The embodiment of the present application, in terms of hardware configuration, dynamically adjusts the micro hardware parameters according to the number of abnormal working conditions, which not only ensures the accuracy and effectiveness of the monitoring, but also avoids the waste of resources caused by excessive configuration. For the combustion chamber with less abnormal working conditions, hardware devices with lower performance parameters are used, while for the combustion chamber with more abnormal working conditions, the performance parameters of the hardware devices are improved. This reasonable use of resources reduces the construction and operation cost of the system and improves the performance-price ratio of the system. In terms of pollution control, by optimizing the combustion control strategy, such as ammonia injection optimization and soot suppression, the emission of pollutants such as nitrogen oxides and soot can be significantly reduced. This not only makes the power plant meet the strict environmental protection regulations and reduces the negative impact on the environment, but also improves the social image of the power plant and lays a foundation for the sustainable development of the power plant. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0020] Figure 1 It is a schematic diagram of the system module of the present application.

[0021] Figure 2 It is a flow chart of the method implementation steps of the present application. DETAILED DESCRIPTION

[0022] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0023] The embodiment of the present application comprises Figure 1 As shown, the power internet of things-based smart power plant anomaly analysis system comprises a micro dynamic monitoring module, a control strategy analysis module, a creep damage evaluation value analysis module and a hidden damage risk evaluation module.

[0024] The control strategy analysis module is connected with the micro dynamic monitoring module and the creep damage evaluation value analysis module, and the creep damage evaluation value analysis module is connected with the hidden damage risk evaluation module.

[0025] The micro dynamic monitoring module is used for configuring micro hardware in each combustion chamber of the target power plant, so as to evaluate whether the corresponding coal powder combustion dynamics in each combustion chamber of the target power plant is unbalanced at each monitoring point.

[0026] In a specific embodiment, the micro hardware is configured in each combustion chamber of the target power plant, and the specific configuration process is as follows: A1, the number of abnormal working conditions corresponding to each combustion chamber of the target power plant is obtained, and the number of abnormal working conditions corresponding to each combustion chamber of the target power plant is compared with the set threshold value.

[0027] It should be noted that the abnormal working conditions include flame extinguishing, flickering, deflagration, backfire, carbon smoke exceeding the standard and burner blockage, etc. The number of abnormal working conditions is counted according to the time stamp by screening the records triggering the alarm from the power plant SIS monitoring information system.

[0028] A2, if the number of abnormal working conditions corresponding to each combustion chamber of the target power plant is within the first threshold value, the microwave resonance imaging equipment frequency range is selected to be 2-5GHz, the power is 1-5W, the scanning range covers 60%-80% of the cross section of the conveying pipeline, the spatial resolution is 5-10mm, the CCD camera selects the frame rate to be 25-50 frames / second, the pixel is 1024x768-2048x1536, the photosensitivity is 800-1600, the TDLAS sensor selects the wavelength range to be 1.3-1.4μm, the CH free radical can be selected in the wavelength range of 3.2-3.4μm, the accuracy is ±5%, the measurement range is 0-100ppm, the ultraviolet imager selects the wavelength range to be 200-400nm, the resolution is 320x240-640x480, and the dynamic range is 10-10000lux.

[0029] A3, if the number of abnormal conditions corresponding to each combustion chamber of the target power plant is between the first threshold value and the second threshold value, the microwave resonance imaging device selects a frequency range of 5-10 GHz, a power of 5-10 W, a scanning range covering 80%-90% of the cross-section of the conveying pipeline, a spatial resolution of 2-5 mm, a super-high-speed CCD camera with a frame rate of 100-200 frames / s, a pixel of 2048x1536-4096x3072, a sensitivity of 1600-3200, a humidity sensor with a measurement range of 0-20% RH and an accuracy of ±1% RH, a TDLAS sensor with a wavelength range of 1.3-1.4 μm, an adjacent sensor spacing of 5-10 cm, an accuracy of ±3%, a measurement range of 0-50 ppm, and an ultraviolet imager with a resolution of 640x480-1024x768 and a dynamic range of 5-50000 lux.

[0030] A4, if the number of abnormal conditions corresponding to each combustion chamber of the target power plant is greater than the second threshold value, the microwave resonance imaging device selects a frequency range of 10-20 GHz, a power of 10-20 W, a scanning range covering the cross-section of the conveying pipeline, a spatial resolution of 1-2 mm, a super-high-speed CCD camera with a frame rate of 500-1000 frames / s, a pixel of 4096x3072-8192x6144, a sensitivity of 3200-6400, a data analysis system with a capacity of processing 10-100 GB of data per second, a parallel computing architecture, a data storage capacity of 1-10 TB, a TDLAS sensor with a wavelength range of 1.5-1.6 μm, an adjacent sensor spacing of 2-5 cm, an accuracy of ±1%, a measurement range of 0-20 ppm, a response time of less than 10 ms, and an ultraviolet imager with a resolution of 1024x768-2048x1536 and a dynamic range of 1-100000 lux.

[0031] In a specific embodiment, the micro-dynamic monitoring module further comprises a coal combustion dynamic evaluation value analysis unit: the coal combustion dynamic evaluation value analysis unit is used to obtain the corresponding coal flow uniformity evaluation value and flame free radical evaluation value in each combustion chamber of each monitoring point target power plant, and is respectively denoted as and , wherein n represents the corresponding number of each monitoring point, , w is any integer greater than 2, m represents the corresponding number of each combustion chamber, , r is any integer greater than 2, and then the coal combustion dynamic evaluation value in each combustion chamber of each monitoring point target power plant is analyzed.

[0032] In a specific embodiment, the analysis obtains the corresponding coal combustion dynamic evaluation value in each combustion chamber of each monitoring point target power plant, and the specific analysis process is as follows: the corresponding coal flow uniform evaluation value and flame free radical evaluation value in each combustion chamber of each monitoring point target power plant are substituted into the calculation formula: In a specific embodiment, the analysis obtains the corresponding coal combustion dynamic evaluation value in each combustion chamber of each monitoring point target power plant, and the specific analysis process is as follows: the corresponding coal flow uniform evaluation value and flame free radical evaluation value in each combustion chamber of each monitoring point target power plant are substituted into the calculation formula: In a specific embodiment, the analysis obtains the corresponding coal combustion dynamic evaluation value in each combustion chamber of each monitoring point target power plant, and the specific analysis process is as follows: the corresponding coal flow uniform evaluation value and flame free radical evaluation value in each combustion chamber of each monitoring point target power plant are substituted into the calculation formula: and respectively, are the corresponding standard coal flow uniform evaluation value and standard flame free radical evaluation value in the combustion chamber, represents the temperature in each combustion chamber of each monitoring point target power plant, represents the temperature mean value in each combustion chamber of each monitoring point target power plant.

[0033] It should be noted that the formula considers the deviation of the coal flow uniform evaluation value and the deviation of the flame free radical evaluation value, and through the cubic root operation, it can comprehensively reflect the deviation of the coal flow uniformity and the flame free radical state in the combustion chamber relative to the standard value, and more comprehensively reflect the dynamic change of the combustion state.

[0034] The denominator of the formula uses the difference between the current temperature and the historical period temperature mean value for exponential operation, and considers the influence of temperature on the combustion state evaluation. Temperature is an important factor affecting the combustion process, and such setting can adjust the evaluation value according to the temperature condition, so that the evaluation is more in line with the actual combustion condition, and the accuracy and reliability of the evaluation result are enhanced. The coal combustion dynamic evaluation value calculated by the formula can provide a quantitative index for the operation monitoring of each combustion chamber of the target power plant. It is convenient to evaluate whether the coal combustion dynamic is unbalanced, and to provide data support for optimizing combustion adjustment, improving combustion efficiency, and reducing pollutant emissions.

[0035] In a specific embodiment, the corresponding coal flow uniform evaluation value and flame free radical evaluation value in each combustion chamber of each monitoring point target power plant are obtained, and the specific obtaining process is as follows: B1, the corresponding coal flow uniform index and flame free radical index in each combustion chamber of each monitoring point target power plant are obtained, the coal flow uniform index includes coal concentration uniformity index, coal particle average speed, and coal particle size distribution range, and the flame free radical index includes carbon smoke concentration, carbon smoke average particle size, and the corresponding combustion concentration of each free radical.

[0036] It should be noted that the coal powder concentration uniformity index: use microwave resonance imaging equipment. By scanning the image of the coal powder in the conveying pipeline, analyzing the distribution of coal powder at different positions, and converting the image data into the concentration of coal powder at each position through the calibration relationship between the image gray value or other characteristic parameters and the concentration of coal powder. Calculate the average value (arithmetic mean) of the concentration at each position; calculate the standard deviation (reflecting the degree of data dispersion) of the concentration data; adopt the coal powder concentration uniformity index formula = 1-(standard deviation ÷ average concentration), get the coal powder concentration uniformity index. The average speed of coal powder particles: with the help of CCD camera or ultra-high-speed CCD camera. By shooting the image of coal powder particle movement, using image analysis technology such as particle image velocimetry (PIV), tracking the position of coal powder particles at different times, using the formula particle movement speed = displacement ÷ time interval, the particle movement speed is obtained, and then the arithmetic average is calculated to obtain the average speed of particle movement. The range of coal powder particle size distribution: combine microwave resonance imaging equipment and TDLAS sensor. Microwave resonance imaging equipment obtains coal powder particle distribution information, TDLAS sensor measures particle size information through wavelength laser and coal powder particle interaction, and comprehensive particle size distribution range is obtained. Based on Mie scattering theory or other light scattering models, the corresponding relationship between signal intensity and particle size is established, and the number distribution of particles of different sizes is calculated. Select the minimum particle size and the maximum particle size whose cumulative particle number reaches a certain threshold, and define it as the particle size distribution range.

[0037] It should also be noted that the soot concentration: use ultraviolet imager. Use it to image the flame in a specific wavelength range, extract pixel-level light attenuation information through image analysis algorithm according to the absorption and scattering characteristics of soot to ultraviolet light, and combine the standard curve to calculate the soot concentration using the formula soot concentration = (image light attenuation value - background light attenuation value) ÷ curve slope. Soot average particle size: combine TDLAS sensor and data analysis system. TDLAS sensor measures optical signals related to soot, and data analysis system processes and analyzes sensor data to calculate the average particle size of soot. Use the formula average particle size = Σ (particle size x particle size probability) to weight and sum all particle sizes and their probabilities to obtain the average particle size of soot. Each radical corresponding to the combustion concentration: rely on TDLAS sensor. Based on laser absorption spectroscopy technology, according to the absorption degree of radicals to specific wavelength laser, combined with instrument accuracy, calculate the combustion concentration of each radical, TDLAS sensor emits laser of corresponding radical absorption wavelength, measures incident light intensity and exit light intensity. Concentration calculation: according to the Lambert-Beer law exit light intensity = incident light intensity x e^(-absorption coefficient x radical concentration x optical path), the formula is transformed to radical concentration = -ln(exit light intensity ÷ incident light intensity) ÷ (absorption coefficient x optical path), combined with instrument accuracy (such as ±5%), to obtain the radical combustion concentration.

[0038] B2, taking the coal powder concentration uniformity index, the coal powder particle average speed, and the coal powder particle size distribution range in each combustion chamber of each monitoring point target power plant as input information, performing normalization processing, and inputting into the coal powder fluidization uniformity evaluation value analysis model, performing calculation and analysis of the coal powder fluidization uniformity evaluation value analysis model, and finally outputting the coal powder fluidization uniformity evaluation value in each combustion chamber of each monitoring point target power plant .

[0039] It should be noted that the analysis process of the coal powder fluidization uniformity evaluation value in each combustion chamber of each monitoring point target power plant is as follows: the coal powder concentration uniformity index, the coal powder particle average speed, and the coal powder particle size distribution range in each combustion chamber of each monitoring point target power plant are respectively denoted as 、 and , substituted into the analysis formula , and the coal powder fluidization uniformity evaluation value in each combustion chamber of each monitoring point target power plant is obtained .

[0040] B3, taking the soot concentration, the soot average particle size, and the combustion concentration of each free radical in each combustion chamber of each monitoring point target power plant as input information, performing normalization processing, inputting into the flame free radical evaluation value analysis model, performing calculation and analysis of the flame free radical evaluation value analysis model, and finally outputting the flame free radical evaluation value in each combustion chamber of each monitoring point target power plant .

[0041] It should be noted that the flame free radical evaluation value in each combustion chamber of each monitoring point target power plant is obtained according to the analysis process of the coal powder fluidization uniformity evaluation value in each combustion chamber of each monitoring point target power plant.

[0042] In a specific embodiment, the specific evaluation process of whether the coal powder combustion dynamics in each combustion chamber of each monitoring point target power plant is unbalanced is as follows: comparing the coal powder combustion dynamics evaluation value in each combustion chamber of each monitoring point target power plant with the set coal powder combustion dynamics evaluation value in the standard combustion chamber, if the coal powder combustion dynamics evaluation value in each combustion chamber of each monitoring point target power plant is less than the set coal powder combustion dynamics evaluation value in the standard combustion chamber, it is evaluated that the coal powder combustion dynamics in each combustion chamber of each monitoring point target power plant is unbalanced, and if the coal powder combustion dynamics evaluation value in each combustion chamber of each monitoring point target power plant is greater than or equal to the set coal powder combustion dynamics evaluation value in the standard combustion chamber, it is evaluated that the coal powder combustion dynamics in each combustion chamber of each monitoring point target power plant is unbalanced.

[0043] The control strategy analysis module is configured to analyze the control strategy of the monitoring point target power plant corresponding to the combustion chamber when the coal combustion dynamic of the monitoring point target power plant corresponding to the combustion chamber is unbalanced.

[0044] In a specific embodiment, the control strategy of the monitoring point target power plant corresponding to the combustion chamber is analyzed, and the specific analysis process is as follows: C1, if the coal combustion dynamic of the monitoring point target power plant corresponding to the combustion chamber is unbalanced, and the coal combustion dynamic evaluation value of the monitoring point target power plant corresponding to the combustion chamber is greater than the coal combustion dynamic evaluation value of the standard combustion chamber by 10% or less, which is recorded as a slight imbalance, then in the staged combustion, the proportion of secondary air is increased, the proportion of secondary air in the total air volume is increased by 3%-5%, the distribution of secondary air at different heights or regions is adjusted, the air volume of upper secondary air is increased by 5%-8%, and the air volume of lower secondary air is reduced by 3%-5%, in the ammonia injection optimization, the ammonia injection amount is increased by 2%-5%, the position of ammonia injection is adjusted, the ammonia injection port is appropriately moved to the combustion center area by 5-10 cm, in the soot suppression, the soot generation is suppressed by increasing the steam injection amount, the steam injection amount is increased by 5%-10%, and the addition amount of combustion improver is increased by 3%-5%.

[0045] C2, if the coal combustion dynamic of the monitoring point target power plant corresponding to the combustion chamber is unbalanced, and the coal combustion dynamic evaluation value of the monitoring point target power plant corresponding to the combustion chamber is greater than the coal combustion dynamic evaluation value of the standard combustion chamber by 10% to 20%, which is recorded as a moderate imbalance, then in the staged combustion, the proportion of secondary air in the total air volume is increased by 8%-12%, the distribution of secondary air at different heights or regions is adjusted, the air volume of upper secondary air is increased by 15%-20%, and the air volume of lower secondary air is reduced by 10%-15%, in the ammonia injection optimization, the ammonia injection amount is increased by 8%-12%, the ammonia injection mode is changed from continuous injection to pulse injection, the frequency of pulse injection is set to 1-2 times per second, and the duration of each injection is 0.5-1 second, in the soot suppression, the steam injection amount is increased by 15%-20%, and the addition amount of combustion improver is increased by 10%-15%.

[0046] C3, if the corresponding coal combustion dynamic imbalance in the combustion chamber of the monitoring point target power plant, and the corresponding coal combustion dynamic evaluation value in the combustion chamber of the monitoring point target power plant is greater than 20% of the set standard coal combustion dynamic evaluation value, it is recorded as serious imbalance, then in the staged combustion, the proportion of secondary air is increased by 15%-20%, the lower secondary air is closed, the air volume of the upper secondary air is increased by 30%-40%, in the ammonia injection optimization, the ammonia injection amount is increased by 20%-30%, in the combustion chamber, the ammonia injection port is set in the upper, middle and lower three regions of the combustion chamber, the ammonia injection is carried out in the three regions of the combustion chamber at the same time, in the soot suppression, the steam injection amount is increased by 30%-40%, the high-efficiency soot suppressant is added, and the addition amount is 0.5%-1% of the mass of the coal powder.

[0047] The creep damage evaluation value analysis module is used to set a plurality of damage evaluation time points when each combustion chamber of the target power plant is running, and then analyze the corresponding creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point.

[0048] In a specific embodiment, the analysis of the corresponding creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point is as follows: the base frequency ultrasonic signal and the second harmonic signal of each component in each combustion chamber of the target power plant at each damage evaluation time point are obtained through the high-temperature piezoelectric composite material sensor, and then the base frequency amplitude , second harmonic amplitude and base frequency wavelength of each component in each combustion chamber of the target power plant at each damage evaluation time point are recorded. , stress and running time of each component in each combustion chamber of the target power plant at each damage evaluation time point are collected from the thermal power equipment DCS system, wherein m represents the corresponding number of each combustion chamber, r is any integer greater than 2, k represents the corresponding number of each damage evaluation time point, q is any integer greater than 2, h represents the corresponding number of each component, y is any integer greater than 2, and the calculation formula is: wherein a, b, c and d are fitting coefficients, is a natural constant.

[0049] It should be noted that the multi-dimensional data fusion analysis: using high-temperature piezoelectric composite sensor to obtain the fundamental frequency ultrasonic signal and the second harmonic signal, and record the fundamental frequency amplitude, the second harmonic amplitude and the fundamental frequency wavelength. The ultrasonic signal is sensitive to the internal microstructure changes of the component. Different creep damage degrees will cause the characteristics of ultrasonic signal to change. Through these signal parameters, the damage related information such as lattice distortion and dislocation in the internal material of the component can be reflected from the micro level. The temperature, stress and running time are collected from the DCS system of the thermal power equipment. The temperature and stress are the key external factors that cause the component to creep, and the running time accumulates the creep damage effect. The multi-dimensional data fusion comprehensively considers the internal and external factors that affect the component creep damage, and the evaluation is more accurate. Through a specific calculation formula, the creep damage evaluation value is obtained, and the complex multi-source data is converted into a quantitative index. This enables the power plant staff to intuitively understand the creep damage degree of each combustion chamber component at different damage evaluation time points, facilitating the timely discovery of severely damaged components and providing clear basis for maintenance, replacement and other decisions. The fitting coefficients in the formula can be adjusted and calibrated according to the material properties of different combustion chamber components, actual operating conditions and other factors. It can better adapt to different types of components and different operating environments, making the evaluation model more practical and improving the universality and accuracy of the evaluation scheme.

[0050] The hidden damage risk assessment module is used to determine the risk level of hidden damage of each component in each combustion chamber of the target power plant at each damage evaluation time point according to the creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point.

[0051] In a specific embodiment, the determination of the risk level of hidden damage of each component in each combustion chamber of the target power plant at each damage evaluation time point is as follows: compare the creep damage evaluation value of each component in each combustion chamber of the target power plant at each damage evaluation time point with the creep damage evaluation value interval corresponding to each hidden damage risk level set, and if the creep damage evaluation value of a component in a combustion chamber of the target power plant at a damage evaluation time point is located in the creep damage evaluation value interval corresponding to a hidden damage risk level set, the hidden damage risk level set is recorded as the hidden damage risk level of the component in the combustion chamber of the target power plant at the damage evaluation time point.

[0052] It should be noted that the risk level of hidden damage includes: level I, level II, level III and level IV.

[0053] It also needs to be explained that when the component is in a safe state (level I), the operation and maintenance mainly monitor and maintain the normal state. Keep daily ultrasonic detection and DCS data record, and implement weekly historical data archiving at the same time, to lay a solid data foundation for trend analysis. At the same time, carry out surface cleaning operation according to the established plan, carefully check the integrity of the corrosion protection coating, update the component health record in time and mark "normal" state, and ensure that the equipment maintains stable operation trend. When the component is in low risk warning (level II), the operation and maintenance strategy turns to strengthen monitoring and pre-maintenance preparation. The detection frequency is encrypted to once an hour, and the change rate of beta value and D value is focused on. At the same time, vibration and acoustic emission monitoring is used to assist in verifying damage trend. In this stage, spare parts such as sealing gasket and vulnerable sensors are reserved in advance, and preliminary maintenance plan is developed, and potential replacement component list is sorted out, to gain the initiative for subsequent maintenance. When the component reaches medium risk warning (level III), multiple measures are needed to reduce the risk of damage. On the one hand, the device load is reduced to 70% of the rated power, and the start-stop times are strictly controlled to reduce thermal fatigue; on the other hand, the micro cracks are explored by means of metallographic microscope during shutdown window period, and the material degradation range is evaluated by hardness gradient test. In addition, a special repair team is formed to clearly divide the work, ensure that the maintenance tools and special welding consumables are available at any time within 48 hours, and build a complete emergency response system. When the component encounters high risk emergency state (level IV), the operation and maintenance must immediately start the emergency disposal process. Quickly trigger the shutdown program, cut off the fuel supply and force cooling, isolate the fault component and set warning signs. The emergency repair link gives priority to removing the damaged components to carry out fracture SEM analysis to locate the failure root cause, simultaneously replacing key components such as turbine blades and pipe bends, and restoring the structure by using rapid welding process. After that, the same batch and same condition components are screened comprehensively, the monitoring model parameters are corrected, and the pre-warning threshold is optimized, to realize the upgrading of systematic risk control.

[0054] The embodiment of the present application comprises Figure 2 As shown in the figure, the abnormal analysis method of the smart power plant based on the power Internet of Things comprises the following steps: step one, micro-dynamic monitoring: configure micro-hardware in each combustion chamber of the target power plant, so as to evaluate whether the corresponding coal powder combustion dynamics in each combustion chamber of the target power plant is unbalanced at each monitoring point.

[0055] Step two, analysis of control strategy: when the corresponding coal powder combustion dynamics in the combustion chamber of the target power plant at the monitoring point is unbalanced, the control strategy corresponding to the combustion chamber in the target power plant at the monitoring point is analyzed.

[0056] Step three, analysis of creep damage evaluation value: when each combustion chamber of the target power plant is running, a plurality of damage evaluation time points are set, and then the creep damage evaluation value of each component in each combustion chamber of the target power plant is analyzed at each damage evaluation time point.

[0057] Step four, the assessment of the risk of hidden damage: according to the creep damage assessment values of each component in each combustion chamber of the target power plant at each damage assessment time point, the risk level of the hidden damage of each component in each combustion chamber of the target power plant at each damage assessment time point is judged.

[0058] The above is only an example and a description of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, as long as they do not deviate from the concept of the present application or exceed the scope defined in the specification, which shall belong to the protection scope of the present application.

Claims

1. A smart power plant anomaly analysis system based on the power Internet of Things, characterized in that, include: Microscopic dynamic monitoring module: used to configure microscopic hardware in each combustion chamber of the target power plant, thereby assessing whether the pulverized coal combustion dynamics in each combustion chamber of the target power plant are unbalanced at each monitoring point; The micro-dynamic monitoring module also includes a coal powder combustion dynamic evaluation value analysis unit: The pulverized coal combustion dynamic evaluation value analysis unit is used to obtain the pulverized coal fluidization uniformity evaluation value and flame free radical evaluation value corresponding to each combustion chamber of the target power plant at each monitoring point, and denoted as follows: and Where n represents the number corresponding to each monitoring point, w is any integer greater than 2, and m represents the number corresponding to each combustion chamber. r is any integer greater than 2, and then the dynamic evaluation value of pulverized coal combustion in each combustion chamber of the target power plant at each monitoring point is obtained through analysis; The analysis yielded dynamic evaluation values ​​of pulverized coal combustion in each combustion chamber of the target power plant at each monitoring point. The specific analysis process is as follows: Substitute the coal pulverization homogenization assessment value and flame free radical assessment value corresponding to each combustion chamber of the target power plant at each monitoring point into the calculation formula: In this process, dynamic evaluation values ​​of pulverized coal combustion corresponding to each combustion chamber of the target power plant at each monitoring point were obtained. ,in, and These are the standard pulverized coal fluidization homogenization evaluation value and the standard flame free radical evaluation value corresponding to the set combustion chamber. This indicates the temperature corresponding to each combustion chamber in the target power plant at each monitoring point. This represents the average temperature in each combustion chamber of the target power plant over a historical period. Control strategy analysis module: When the dynamic imbalance of pulverized coal combustion in a combustion chamber of a target power plant at a certain monitoring point is found, the control strategy corresponding to that combustion chamber of the target power plant at that monitoring point is analyzed. Creep damage assessment value analysis module: used to set several damage assessment time points during the operation of each combustion chamber of the target power plant, and then analyze the creep damage assessment value of each component in each combustion chamber of the target power plant at each damage assessment time point. Latent damage risk assessment module: It is used to determine the risk level of latent damage corresponding to each component in each combustion chamber of the target power plant at each damage assessment time point based on the creep damage assessment value of each component in each combustion chamber of the target power plant at each damage assessment time point.

2. The smart power plant anomaly analysis system based on the power Internet of Things as described in claim 1, characterized in that, The specific configuration process for configuring micro-hardware in each combustion chamber of the target power plant is as follows: A1. Obtain the number of abnormal operating conditions corresponding to each combustion chamber of the target power plant, and compare the number of abnormal operating conditions corresponding to each combustion chamber of the target power plant with the set thresholds. A2. If the number of abnormal operating conditions corresponding to each combustion chamber of the target power plant is within the first threshold, the microwave resonance imaging equipment should be selected with a frequency range of 2-5GHz, a power of 1-5W, a scanning range covering 60%-80% of the cross-section of the conveying pipeline, a spatial resolution of 5-10mm, a CCD camera with a frame rate of 25-50 frames / second, a pixel count of 1024×768-2048×1536, a sensitivity of 800-1600, a TDLAS sensor with a wavelength range of 1.3-1.4μm, a CH radical sensor with a wavelength range of 3.2-3.4μm, an accuracy of ±5%, a measurement range of 0-100ppm, and an ultraviolet imager with a wavelength range of 200-400nm, a resolution of 320×240-640×480, and a dynamic range of 10-10000lux. A3. If the number of abnormal operating conditions corresponding to each combustion chamber of the target power plant is between the first threshold and the second threshold, then the frequency range of the microwave resonance imaging equipment should be selected as 5-10GHz, the power as 5-10W, the scanning range should cover 80%-90% of the cross-section of the conveying pipeline, and the spatial resolution should reach 2-5mm. The ultra-high-speed CCD camera should have a frame rate of 100-200 frames / second, a pixel count of 2048×1536-4096×3072, and a sensitivity of 1600-3200. The humidity sensor should have a measurement range of 0-20%RH and an accuracy of ±1%RH. The TDLAS sensor should have a wavelength range of 1.3-1.4μm, an adjacent sensor spacing of 5-10cm, an accuracy improved to ±3%, and a measurement range of 0-50ppm. The ultraviolet imager should have a resolution improved to 640×480-1024×768 and a dynamic range of 5-50000lux. A4. If the number of abnormal operating conditions corresponding to each combustion chamber of the target power plant exceeds the second threshold, the microwave resonance imaging equipment should be selected with a frequency range of 10-20GHz, a power of 10-20W, a scanning range that completely covers the cross-section of the conveying pipeline, and a spatial resolution of 1-2mm. The ultra-high-speed CCD camera should have a frame rate of 500-1000 frames / second, a pixel count of 4096×3072-8192×6144, and a sensitivity of 3200-6400. The data analysis system should be capable of processing 10-100GB of data per second, adopt a parallel computing architecture, and have a data storage capacity of 1-10TB. The TDLAS sensor should have a wavelength range of 1.5-1.6μm, a spacing between adjacent sensors of 2-5cm, an accuracy of ±1%, a measurement range of 0-20ppm, and a response time of less than 10ms. The ultraviolet imager should have a resolution of 1024×768-2048×1536 and a dynamic range of 1-100000lux.

3. The smart power plant anomaly analysis system based on the power Internet of Things as described in claim 1, characterized in that, The specific process for obtaining the pulverized coal fluidization uniformity assessment value and flame free radical assessment value corresponding to each combustion chamber of the target power plant at each monitoring point is as follows: B1. Obtain the pulverized coal fluidization index and flame free radical index corresponding to each combustion chamber of the target power plant at each monitoring point. The pulverized coal fluidization index includes the pulverized coal concentration uniformity index, the average velocity of pulverized coal particles, and the pulverized coal particle size distribution range. The flame free radical index includes the soot concentration, the average soot particle size, and the combustion concentration corresponding to each free radical. B2. The uniformity index of pulverized coal concentration, average velocity of pulverized coal particles, and particle size distribution range of pulverized coal in each combustion chamber of the target power plant at each monitoring point are used as input information and normalized. Simultaneously, these are entered into the pulverized coal fluidization uniformity evaluation value analysis model. After calculation and analysis by the pulverized coal fluidization uniformity evaluation value analysis model, the final output is the pulverized coal fluidization uniformity evaluation value for each combustion chamber of the target power plant at each monitoring point. ; B3. The input information includes the soot concentration, average soot particle size, and combustion concentration of each free radical in each combustion chamber of the target power plant at each monitoring point. This information is then normalized and entered into the flame free radical assessment value analysis model. After calculation and analysis by the model, the flame free radical assessment values ​​for each combustion chamber of the target power plant at each monitoring point are finally output. .

4. The smart power plant anomaly analysis system based on the power Internet of Things as described in claim 3, characterized in that, The specific assessment process for evaluating whether the pulverized coal combustion dynamics in each combustion chamber of the target power plant are unbalanced at each monitoring point is as follows: The dynamic evaluation value of pulverized coal combustion in each combustion chamber of the target power plant at each monitoring point is compared with the dynamic evaluation value of pulverized coal combustion in the set standard combustion chamber. If the dynamic evaluation value of pulverized coal combustion in a certain combustion chamber of the target power plant at a certain monitoring point is less than the dynamic evaluation value of pulverized coal combustion in the set standard combustion chamber, then the dynamic evaluation value of pulverized coal combustion in that combustion chamber of the target power plant at that monitoring point is not unbalanced. If the dynamic evaluation value of pulverized coal combustion in a certain combustion chamber of the target power plant at a certain monitoring point is greater than or equal to the dynamic evaluation value of pulverized coal combustion in the set standard combustion chamber, then the dynamic evaluation value of pulverized coal combustion in that combustion chamber of the target power plant at that monitoring point is unbalanced.

5. The smart power plant anomaly analysis system based on the power Internet of Things as described in claim 4, characterized in that, The control strategy corresponding to the combustion chamber of the target power plant at the monitoring point is analyzed, and the specific analysis process is as follows: C1. If the dynamic imbalance of pulverized coal combustion in a certain combustion chamber of a target power plant at a certain monitoring point is found, and the dynamic evaluation value of pulverized coal combustion in that combustion chamber of the target power plant at that monitoring point is greater than the dynamic evaluation value of pulverized coal combustion in the set standard combustion chamber by less than 10%, it is considered a slight imbalance. In staged combustion, the proportion of secondary air is increased by 3%-5% to increase the proportion of secondary air to the total air volume. The distribution of secondary air at different heights or in different areas is adjusted by increasing the air volume of upper secondary air by 5%-8% and decreasing the air volume of lower secondary air by 3%-5%. In ammonia injection optimization, the ammonia injection volume is increased by 2%-5%. The position of ammonia injection is adjusted by moving the ammonia injection nozzle 5-10cm towards the center of combustion. In soot suppression, soot generation is suppressed by increasing the steam injection volume by 5%-10%. The amount of combustion aid added is increased by 3%-5%. C2. If the dynamic imbalance of pulverized coal combustion in a certain combustion chamber of a target power plant at a certain monitoring point is found, and the dynamic evaluation value of pulverized coal combustion in that combustion chamber is 10% to 20% greater than the dynamic evaluation value of pulverized coal combustion in the set standard combustion chamber, it is considered a moderate imbalance. In staged combustion, the proportion of secondary air to total air volume is increased by 8%-12%, the distribution of secondary air at different heights or areas is adjusted, the air volume of upper secondary air is increased by 15%-20%, and the air volume of lower secondary air is reduced by 10%-15%. In ammonia injection optimization, the ammonia injection volume is increased by 8%-12%, the ammonia injection mode is changed from continuous injection to pulse injection, the frequency of pulse injection is set to 1-2 times per second, and the duration of each injection is 0.5-1 second. In soot suppression, the steam injection volume is increased by 15%-20%, and the amount of combustion aid added is increased by 10%-15%. C3. If the dynamic imbalance of pulverized coal combustion in a certain combustion chamber of a target power plant at a certain monitoring point is found, and the dynamic evaluation value of pulverized coal combustion in that combustion chamber of the target power plant at that monitoring point is more than 20% greater than the dynamic evaluation value of pulverized coal combustion in the set standard combustion chamber, it is considered a serious imbalance. In staged combustion, the proportion of secondary air is increased by 15%-20% of the total air volume, the lower secondary air is closed, and the air volume of the upper secondary air is increased by 30%-40%. In ammonia injection optimization, the ammonia injection volume is increased by 20%-30%, and ammonia injection ports are set in the upper, middle, and lower areas of the combustion chamber, and ammonia injection is carried out simultaneously in the three areas of the combustion chamber. In soot suppression, the steam injection volume is increased by 30%-40%, and a high-efficiency soot inhibitor is added at a rate of 0.5%-1% of the pulverized coal mass.

6. The smart power plant anomaly analysis system based on the power Internet of Things as described in claim 1, characterized in that, The specific analysis process for analyzing the creep damage assessment values ​​of each component in each combustion chamber of the target power plant at each damage assessment time point is as follows: The fundamental frequency ultrasonic signal and second harmonic signal of each component in each combustion chamber of the target power plant at each damage assessment time point were acquired using a high-temperature resistant piezoelectric composite material sensor. The fundamental frequency amplitude of each component in each combustion chamber of the target power plant at each damage assessment time point was then recorded. Second harmonic amplitude and fundamental wavelength Simultaneously, the temperatures of each component in each combustion chamber of the target power plant at each damage assessment time point are collected from the DCS system of the thermal power equipment. ,stress and runtime Where m represents the number corresponding to each combustion chamber, r is any integer greater than 2, and k represents the number corresponding to each damage assessment time point. q is any integer greater than 2, and h represents the number corresponding to each component. Let y be any integer greater than 2. Substitute it into the calculation formula: In this study, creep damage assessment values ​​for each component in each combustion chamber of the target power plant at each damage assessment time point were obtained, where a, b, c, and d are fitting coefficients. It is a natural constant.

7. The smart power plant anomaly analysis system based on the power Internet of Things as described in claim 6, characterized in that, The process for determining the risk level of latent damage to each component in each combustion chamber of the target power plant at each damage assessment time point is as follows: The creep damage assessment values ​​of each component in each combustion chamber of the target power plant at each damage assessment time point are compared with the creep damage assessment value ranges corresponding to each set latent damage risk level. If the creep damage assessment value of a component in a combustion chamber of the target power plant at a certain damage assessment time point is within the creep damage assessment value range corresponding to a set latent damage risk level, then the set latent damage risk level is recorded as the latent damage risk level of that component in that combustion chamber of the target power plant at that damage assessment time point.

8. A method for anomaly analysis of a smart power plant based on the Internet of Things (IoT) and implementing the anomaly analysis system for a smart power plant based on the Internet of Things as described in any one of claims 1-7, characterized in that, include: Step 1: Monitoring of micro-dynamics: Micro-hardware is installed in each combustion chamber of the target power plant to assess whether the pulverized coal combustion dynamics in each combustion chamber of the target power plant are unbalanced at each monitoring point; Step 2: Analysis of Control Strategies: When the dynamic imbalance of pulverized coal combustion in a certain combustion chamber of a target power plant at a certain monitoring point is found, the control strategy corresponding to that combustion chamber of the target power plant at that monitoring point is analyzed. Step 3: Analysis of creep damage assessment values: During the operation of each combustion chamber of the target power plant, several damage assessment time points are set, and then the creep damage assessment values ​​of each component in each combustion chamber of the target power plant are analyzed at each damage assessment time point. Step 4: Assessment of latent damage risk: Based on the creep damage assessment values ​​of each component in each combustion chamber of the target power plant at each damage assessment time point, the risk level of latent damage corresponding to each component in each combustion chamber of the target power plant at each damage assessment time point is determined.

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