Dynamic test method for thermal efficiency of boiler in thermal power plant

Abstract

The invention discloses a thermal power plant boiler thermal efficiency dynamic test method, and relates to the technical field of thermal power plant boiler state monitoring. The method comprises the following steps: in a target boiler system to be measured, setting input and output energy boundaries required by thermal efficiency calculation, and arranging a monitoring unit in a target boiler area; setting a continuous operation condition sequence including load increasing, load decreasing and set range fluctuation, and generating a composite load curve; executing a data recording mechanism; according to the technical key points, efficiency under different loads is converted to standard working conditions by introducing a quadratic polynomial normalization function based on load percentage, transverse comparison of cross-load working conditions is achieved, in addition, an efficient area, a low-efficiency area and a transition area are recognized by revealing the internal rule that boiler efficiency changes along with loads, and the efficiency of the boiler is improved. The problems that a traditional test result is isolated and optimization operation cannot be guided are solved, and linkage of energy efficiency diagnosis and operation optimization is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power plant boiler state monitoring, in particular to a dynamic test method for thermal efficiency of a power plant boiler. BACKGROUND

[0002] Power plant boiler state monitoring refers to a technical means of obtaining key physical quantities and chemical parameters in real time during the operation of a boiler body and auxiliary systems through the arrangement of various sensors and data acquisition devices, so as to reflect the current operation state of the equipment. In the prior art, flow, pressure and temperature measuring instruments are usually installed at the inlet of the boiler economizer and the outlet of the superheater, for monitoring the working parameters of the feed water and the main steam. Fuel metering devices are configured in the burner area, and the calorific value of the fuel into the furnace is obtained in combination with the coal quality database. Flue gas analyzers are arranged at the inlet and outlet of the air preheater, for continuously measuring the concentrations of O2, CO and other components in the flue gas, and evaluating the heat loss of the exhaust gas in cooperation with the flue gas temperature measuring point. Electric power consumption monitoring units are installed on the auxiliary equipment such as the pulverizing system and the induced draft fan, for collecting the power consumption data of the plant. Some systems are also equipped with online monitoring devices or regular manual sampling analysis of fly ash carbon content, for evaluating the combustion efficiency. In addition, the boiler furnace pressure, water wall temperature, reheater outlet temperature and other safety-related parameters are also included in the scope of routine monitoring. The state monitoring data are mainly used for operation monitoring, alarm triggering and periodic performance evaluation. Some power plants realize trend display and simple statistical analysis through a data platform, to support the operation personnel to judge and adjust the operation state of the boiler.

[0003] In the current practice of power plant boiler performance monitoring, traditional thermal efficiency test methods are generally relied on. These methods usually require the unit to be stably operated at a certain fixed load for a long time, so as to obtain the energy efficiency data under the steady state. For example, in the periodic performance test of a certain 600 MW unit, in order to measure the boiler efficiency at 75% load, the unit must be forced to maintain at 450 MW without any peak shaving operation. This practice not only occupies valuable power generation time, but also seriously deviates from the actual operation scenario. The existing power grid is configured to frequently peak shave, and the unit is often in a continuous variable load state, which causes the measured steady state efficiency to be unable to truly reflect the actual energy consumption level of the boiler during load increase and decrease. In addition, the existing technology mostly uses the positive balance method to calculate the efficiency, which highly depends on the accurate measurement of the main steam flow. However, during the variable state process, due to the sharp fluctuations of pressure and temperature, the differential pressure flowmeter is prone to measurement lag and drift, causing serious imbalance between the input and output energy. At the same time, although the parameter acquisition system is connected to the DCS, it lacks synchronous alignment processing of multi-source data such as fuel, flue gas and fly ash, and the phenomenon of O2 concentration and fuel quantity record out of synchronization often occurs, resulting in distorted calculation results of the negative balance method. More prominent is that even if the efficiency value of several discrete load points is obtained, there is no effective normalization method for horizontal comparison, and a continuous efficiency trend map cannot be formed, so that the operation personnel cannot judge which is more economical under 80% load and 60% load, and cannot identify the dynamic lag characteristic of efficiency response to load change, so that the test result is relatively isolated and cannot support real-time and actual optimization decision. SUMMARY

[0004] To achieve the above object, the present application is realized by the following technical solutions: The method for dynamic test of thermal efficiency of a boiler in a thermal power plant comprises the following steps: In the target boiler system to be tested, the input and output energy boundaries required for thermal efficiency calculation are set, and monitoring units are arranged in the target boiler region; A continuous operation condition sequence including load increase, load decrease and fluctuation in a specified range is set, and a composite load curve is generated; A data recording mechanism is executed, during the execution of the continuous operation condition sequence, the time series parameters collected by the monitoring units are called, and after time stamp alignment, the running parameter matrix with equal time intervals is stored; The sliding time window length T_w and the time step Δt are defined; For any central time t_c, N sampling points in the interval [t_c−T_w+Δt, t_c] are taken, the arithmetic mean x_avg(t_c) of each parameter in the running parameter matrix is calculated, and the net efficiency η b (t_c) of the boiler is calculated based on the counterbalance method; The load normalization function F_corr(P) is established, and the normalized efficiency η b (t_c) is generated based on F_corr(P) and η norm (t_c); All η norm (t_c) corresponding to t_c are arranged in time sequence, the η norm (t) curve is drawn, the intervals are divided in combination with the load curve, the average efficiency η _avg, the standard deviation σ_η and the change rate Δη / Δt of each section are counted, so as to obtain the dynamic map of thermal efficiency; and the energy efficiency characteristics of each section are identified according to the dynamic map of thermal efficiency.

[0005] Further, the body of the target boiler is delimited as the boundary, the input energy includes the low calorific value of fuel and the sensible heat of air entering the furnace, and the output energy includes the heat absorption of working medium, the heat carried away by flue gas, the loss of unburned loss, the heat loss and the physical heat loss of ash.

[0006] Further, the basis for generating the composite load curve is that the initial load is M% of the rated load, gradually increases to (M+20)% at a rate of q% of the rated load per minute, remains for a set period of time, then decreases to (M-10)%, and then increases to (M+15)%; wherein, the value range of M is a positive integer greater than 10, and the value of q is greater than 0.

[0007] Further, the time series parameters include: fuel instantaneous mass flow m_fuel(t), fuel low heat value Q_net(t), main steam flow D_ms(t), main steam enthalpy value h_ms(t), feedwater enthalpy value h_fw(t), flue gas temperature T_ey(t), flue gas oxygen content O2(t), weighted average of carbon content of fly ash and slag sampling C_ash(t), and power consumption of the milling system and the fan E_{mill&fan}(t).

[0008] Further, the net efficiency of the boiler is calculated based on the counterbalance method b (t_c) : η b (t_c) = 100% - (q2 + q3 + q4 + q5 + q6); wherein each heat loss term is defined as: q2 is flue gas heat loss, q3 is combustible gas incomplete combustion heat loss, q4 is solid incomplete combustion heat loss, q5 is heat loss, and q6 is ash and slag physical heat loss.

[0009] Further, the calculation basis of the flue gas heat loss q2 is: q2 = ((T_ey - T_ref) x c_pg x (1 + a_ey x V_air)) / Q_net x 100%; wherein, T_ref represents the reference environment temperature, c_pg represents the specific heat capacity of flue gas, a_ey represents the excess air coefficient at the flue gas, V_air represents the theoretical air amount of fuel, Q_net represents the received fuel low heat value, and the calculation basis of the excess air coefficient a_ey at the flue gas is: a_ey = 21 / (21 - O2); wherein, O2 represents the oxygen content of flue gas in dry flue gas; the calculation basis of the combustible gas incomplete combustion heat loss q3 is: q3 = (C0 x Q_Co x V_gas) / Q_net x 100%; wherein, C0 represents the volume concentration of carbon monoxide in flue gas, Q_Co represents the combustion heat of CO, and V_gas represents the actual flue gas volume.

[0010] Further, the calculation basis of the solid incomplete combustion heat loss q4 is: q4 = (C_ash × Q_unburn × m_ash) / (m_fuel × Q_net) × 100%; where C_ash represents the weighted average carbon content of fly ash and slag samples, Q_unburn represents the calorific value of unburned carbon, m_ash represents the total mass flow rate of ash and slag, and m_fuel represents the mass flow rate of fuel; the heat loss q5 is calculated based on: q5 = a × (P / P_e) -b Where a1 and b1 are both empirical coefficients; P and P_e are the current load and rated load, respectively; the calculation basis for the physical heat loss q6 of ash is: q6=(m_slag×c_slag×(T_slag-T_ref)) / (m_fuel×Q_net)×100%; where m_slag represents the bottom ash mass flow rate, c_slag represents the specific heat capacity of ash, and T_slag represents the bottom ash discharge temperature.

[0011] Furthermore, the load normalization function F_corr(P) is established based on: F_corr(P) = a² × (P / P_e) 2 +b2×(P / P_e)+c2; where a2, b2, and c2 are regression coefficients; the normalization efficiency is calculated based on: η norm (t_c)=η b (t_c)×F_corr(P(t_c)); where η norm (t_c) represents the normalized efficiency.

[0012] Furthermore, the energy efficiency characteristics of each segment are identified as follows: when the minimum value of the absolute difference between the average efficiency η_avg of any segment and other segments exceeds a set threshold, and the standard deviation σ_η exceeds 0.2%, it is determined to be an inefficient segment; when the average efficiency η_avg of any segment is the highest and the standard deviation σ_η is the lowest, it is determined to be an efficient segment; when the rate of change Δη / Δt changes continuously for more than 5 minutes, it is determined to be a transitional segment.

[0013] This invention provides a method for dynamic testing of the thermal efficiency of boilers in thermal power plants, which has the following beneficial effects: This scheme sets up a continuous operating sequence including load increases, decreases, and fluctuating operating conditions to realistically simulate the power grid peak shaving process, realizing the dynamic capture of the boiler's thermal efficiency under non-steady-state operating conditions; at the same time, it avoids the limitation of traditional steady-state testing requiring long-term maintenance of fixed load, solves the technical problem that traditional methods cannot reflect the actual variable operating condition energy efficiency level, and realizes rapid evaluation of the boiler's full-load operating characteristics. The scheme synchronously collects multiple source parameters such as fuel, steam, flue gas and ash, constructs a complete operation parameter matrix, ensures the completeness and synchronization of input data required for heat balance calculation, overcomes the calculation deviation caused by asynchronous or missing parameters in traditional testing, solves the problem of energy item matching error in dynamic process, and realizes high-fidelity reconstruction at the data level. In one aspect, the scheme averages the dynamic parameters by sliding time window, and calculates the heat loss based on the counterbalance method, effectively suppresses the noise interference caused by instantaneous fluctuation, and improves the stability of efficiency calculation; on the other hand, the counterbalance method avoids the measurement lag and distortion problem of main steam flow caused by pressure fluctuation during variable load process, solves the defect of error amplification of the positive balance method under dynamic conditions, and realizes high-precision and anti-interference efficiency solution. The scheme converts the efficiency under different loads to the standard working condition by introducing a quadratic polynomial normalization function based on load percentage, realizes horizontal comparison across load conditions, and in addition, by revealing the internal law of boiler efficiency change with load, identifies the high-efficiency area, low-efficiency area and transition area, solves the problem of isolated traditional test results that cannot guide optimization operation, and realizes the linkage of energy efficiency diagnosis and operation optimization. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 It is a step flowchart of the boiler thermal efficiency dynamic test method of the power plant in the application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0016] Please refer to Figure 1 The embodiment provides a boiler thermal efficiency dynamic test method of a power plant, and the specific steps are as follows: S1, determine the test boundary conditions and the arrangement of measuring points: In the target boiler system to be measured, set the input and output energy boundaries required for heat efficiency calculation, and arrange monitoring units in the target boiler area; Wherein, by demarcating the body of the target boiler as the boundary of the thermal system, the input energy includes the low-grade heat of fuel and the sensible heat of air entering the furnace, and the output energy includes the heat absorbed by the working medium, the heat taken away by flue gas, the unburned loss, the heat loss and the physical heat loss of ash and slag; the low-grade heat of fuel refers to the heat released by the water vapor in the combustion products when it remains gaseous after complete combustion of unit mass of fuel, which does not include the latent heat of condensation of water vapor and is the standard benchmark for boiler thermal calculation; the sensible heat of air entering the furnace refers to the heat energy carried by the air entering the furnace due to its higher temperature than the environmental reference temperature; the heat absorbed by the working medium represents the difference between the enthalpy of main steam and the enthalpy of feedwater; the monitoring unit includes: a feedwater flow measuring device arranged at the inlet of the economizer of the target boiler, a main steam flow and parameter measuring device arranged at the outlet of the superheater; a flue gas composition analyzer and a flue gas temperature sensor arranged at the inlet and outlet of the air preheater respectively; a fuel flow metering sensor arranged in the burner area and connected to the real-time database of low-grade heat of fuel; an electric power consumption monitoring sensor arranged in the pulverizing system and the induced draft fan area; the signals of the measuring points arranged or set in the monitoring unit can be connected to the distributed control system DCS, and the sampling frequency is not less than 1Hz; it should be noted that the arrangement of each measuring point in the monitoring unit needs to cover all energy items required for heat balance calculation, the fuel input energy is determined by the fuel mass flow and the low-grade heat; the effective utilization of energy is determined by the difference between the enthalpy of main steam and the enthalpy of feedwater; each loss depends on parameters such as flue gas temperature, oxygen content and carbon content of fly ash; therefore, selecting high-frequency data acquisition is an important basis for subsequent dynamic modeling.

[0017] S2, set a dynamic test operation condition sequence: The set at least includes a continuous operation condition sequence of load increase, load decrease and fluctuation in a given range; The starting load is M% of the rated load, which is gradually increased to (M+20)% at a change rate of q% rated load per minute, then decreased to (M-10)% after maintaining for a set time, and then increased to (M+15)%, to generate a composite load curve; Wherein, the given range fluctuation refers to a small fluctuation, and the fluctuation range is within the given range; the value range of M is a positive integer greater than 10, and in the embodiment, the value is 70 to meet the actual demand; the value range of q is a number greater than 0, and in the embodiment, q can be 1.5; therefore, the initial load is 70% of the rated load, gradually increases to 90% at a change rate of 1.5% of the rated load per minute, and then decreases to 60% after maintaining for a set time, and then increases to 85% to generate a composite load curve; it should be noted that the duration of the whole process should be no less than 90 minutes, and major operation interventions such as soot blowing, starting and stopping of the coal mill, etc. are prohibited to ensure that the fuel type, the volatile matter of the coal, and the moisture fluctuation range are controlled within ±5%.

[0018] By adopting the above technical scheme, the continuous operation sequence including the load increase, load decrease and fluctuation working conditions is set, the power grid peak regulation process is simulated, the dynamic capture of the thermal efficiency of the boiler under the non-steady state operation condition is realized, the limitation of the traditional steady state test which needs to maintain the fixed load for a long time is avoided, the technical problem that the actual working condition energy efficiency level cannot be reflected by the traditional method is solved, and the rapid evaluation of the full load operation characteristics of the boiler is realized.

[0019] S3, synchronously collecting a multi-source dynamic operation parameter sequence: A data recording mechanism is executed, and during the execution of the continuous operation working condition sequence, the time sequence parameters collected by the monitoring unit are called, and after time stamp alignment, the operation parameter matrix with equal time intervals is stored; Wherein, the time sequence parameters at least include: fuel instantaneous mass flow m_fuel(t), unit: kg / s; fuel low heat value Q_net(t), unit: kJ / kg; main steam flow D_ms(t), unit: kg / s; main steam enthalpy h_ms(t) obtained by looking up the steam table according to pressure P_ms(t) and temperature T_ms(t), unit: kJ / kg; feed water enthalpy h_fw(t), unit: kJ / kg; flue gas temperature T_ey(t), unit: ℃; flue gas oxygen content O2(t), unit: %; weighted average of carbon content of fly ash and slag sampling C_ash(t), unit: %; power consumption of the milling system and the induced draft fan E_{mill&fan}(t), unit: kW; it should be noted that the above parameters constitute the input set of the thermal efficiency calculation; wherein, h_ms(t) and h_fw(t) are obtained by the international standard IAPWS-IF97 steam property formula or the table lookup method to ensure the thermodynamic accuracy; the time interval is usually set to 1s in the embodiment.

[0020] By adopting the technical scheme, high-frequency synchronous collection of multi-source parameters such as fuel, steam, flue gas and ash is performed, a complete operation parameter matrix is constructed, and the completeness and synchronism of input data required for heat balance calculation are ensured; calculation deviation caused by asynchronous or missing parameter collection in traditional testing is overcome, the problem of energy item matching error in a dynamic process is solved, high-fidelity reconstruction at a data level is realized, and the effect is established on the basis of dynamic working condition execution, thereby providing high-quality data support for sliding window calculation, and forming a closed loop with the effect given in the foregoing.

[0021] S4, a heat efficiency calculation model of a sliding time window is constructed: a sliding time window length T_w and a time step Δt are defined; for any center time t_c, N sampling points in the interval [t_c-T_w+Δt, t_c] are taken, and the arithmetic average x_avg(t_c) of each parameter in the operation parameter matrix is calculated; the net efficiency η of the boiler is calculated based on the counterbalance method b (t_c): η b (t_c)=100%-(q2+q3+q3+q5+q6); wherein each heat loss term is defined as: q2 is flue gas heat loss, q3 is combustible gas incomplete combustion heat loss, q4 is solid incomplete combustion heat loss, q5 is heat loss, and q6 is ash physical heat loss; The calculation basis of flue gas heat loss q2 is: q2=((T_ey-T_ref)×c_pg×(1+α_ey×V_air)) / Q_net×100%; wherein T_ref represents a reference environment temperature, which is usually 25℃; c_pg represents a specific heat capacity of flue gas, which can be 1.38 kJ / (kg·℃); α_ey represents an excess air coefficient at the flue gas; V_air represents a theoretical air amount of fuel, which is Nm³ / kg and is calculated by fuel element analysis; Q_net represents a fuel received base low heat value, which is kJ / kg; the calculation basis of the excess air coefficient α_ey at the flue gas is: α_ey=21 / (21-O2); wherein O2 represents a flue gas oxygen content in dry flue gas; The calculation basis of combustible gas incomplete combustion heat loss q3 is: q3=(Co×Q_Co×V_gas) / Q_net×100%; wherein Co represents a carbon monoxide volume concentration in flue gas, which is %; Q_Co represents a CO combustion heat, which can be 12700 kJ / Nm³ in the embodiment; V_gas represents an actual flue gas amount, which is Nm³ / kg and is calculated from α_ey×V_air, and will not be described here; The calculation basis of the solid incomplete combustion heat loss q4 is: q4=(C_ash×Q_unburn×m_ash) / (m_fuel×Q_net)×100%; wherein, C_ash represents the weighted average of the carbon content of fly ash and slag sampling; Q_unburn represents the heat of release of unburned carbon, which can be selected as 32564 kJ / kg in the embodiment; m_ash represents the total mass flow of ash, in kg / s; and m_fuel represents the fuel mass flow, in kg / s. The calculation basis of the heat dissipation loss q5 is: q5=a×(P / P_e) -b ; wherein, a1 and b1 both represent empirical coefficients, which are provided by the manufacturer or measured and regressed, and the value range is greater than 0; P and P_e are the current load and the rated load respectively, both in MW; The calculation basis of the ash slag physical heat loss q6 is: q6=(m_slag×c_slag×(T_slag-T_ref)) / (m_fuel×Q_net)×100%; wherein, m_slag represents the bottom slag mass flow, in kg / s; c_slag represents the specific heat capacity of ash slag, which can be selected as 0.96 kJ / (kg·℃) in the embodiment; and T_slag represents the bottom slag discharge temperature, in ℃. It should be noted that the sliding window method weakens random noise through time averaging and retains trend characteristics; and the counterbalance method avoids the lag and distortion of the main steam flow measurement in the dynamic process, and improves the calculation stability.

[0022] By adopting the above technical solutions, on the one hand, the dynamic parameters are processed by the sliding time window, and the heat loss is calculated based on the counterbalance method, which effectively suppresses the noise interference caused by instantaneous fluctuations and improves the stability of efficiency calculation; on the other hand, the counterbalance method avoids the measurement lag and distortion of the main steam flow caused by pressure fluctuations in the variable load process, solves the defect of error amplification of the positive balance method under dynamic conditions, and realizes high-precision and anti-interference efficiency solution.

[0023] S5, a load normalized efficiency correction factor is introduced: A load normalization function F_corr(P) is established for eliminating the influence of load variation on efficiency horizontal comparison, and the basis is: F_corr(P)=a2×(P / P_e) 2 +b2×(P / P_e)+c2; wherein, a2, b2 and c2 are regression coefficients, which are fitted by historical stable test data, and the value range is greater than 0; The calculation basis of the normalized efficiency is: η norm (t_c)=η b (t_c)×F_corr(P(t_c)); wherein, η norm(t_c) represents the normalized efficiency; it should be noted that the boiler efficiency-load curve is parabolic, and the normalization function converts the efficiency under different loads to the standard working condition, facilitating horizontal comparison and optimization decision.

[0024] S6, generating a dynamic efficiency trend map and identifying a characteristic interval: η norm (t_c) are arranged in chronological order, and η norm (t) curve is drawn, intervals are divided in combination with the load curve, and the average efficiency η _avg, standard deviation σ_η, and change rate Δη / Δt of each section are counted to obtain a dynamic map of thermal efficiency; It should be noted that the dynamic map of thermal efficiency shows the dynamic response characteristics of efficiency, identifies the low efficiency interval, and provides a basis for combustion optimization; Δη is the difference between the adjacent two normalized efficiency calculation times t_c; According to the dynamic map of thermal efficiency, the energy efficiency characteristics of each section are identified: When the absolute value of the difference between the average efficiency η _avg of any section and other sections is the minimum value, and the standard deviation σ_η is more than 0.2%, it is determined as a low efficiency zone, typically, the low load section has poor combustion organization leading to efficiency drop; when the average efficiency η _avg of any section is the highest and the standard deviation σ_η is the smallest, it is determined as a high efficiency zone, indicating that the interval is stable and has optimal energy efficiency; when the change rate Δη / Δt continuously changes for more than 5 minutes, it is determined as a transition zone, indicating that the system has thermal inertia delay; by identifying and dividing the high efficiency zone, low efficiency zone, and transition zone, operation optimization and control strategy adjustment are guided, and how to optimize and adjust here is operated according to actual settings, which will not be described here; other cases are not considered in the scope of the embodiment.

[0025] By adopting the above technical scheme, a quadratic polynomial normalization function based on load percentage is introduced to convert the efficiency under different loads to the standard working condition, realizing horizontal comparison across load working conditions. In addition, by revealing the internal law of boiler efficiency change with load, high efficiency zone, low efficiency zone, and transition zone are identified, solving the problem of isolated traditional test results that cannot guide optimization operation, realizing the linkage of energy efficiency diagnosis and operation optimization. This effect is finally formed into a dynamic map that can guide combustion adjustment by integrating the first three achievements, embodying the integrity of the present application from data acquisition to decision support.

[0026] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art can be aware that units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether the functions are performed by hardware or software depends on the specific application and design constraints of the technical solutions.

[0027] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, and can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0028] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A dynamic test method for the thermal efficiency of boilers in thermal power plants, characterized in that, The steps of this method are as follows: In the target boiler system under test, the input and output energy boundaries required for thermal efficiency calculation are set, and monitoring units are deployed in the target boiler area. Set a continuous operating condition sequence including load increase, load decrease, and fluctuations within a predetermined range, and generate a composite load curve; The data recording mechanism is implemented. During the execution of the continuous operating condition sequence, the time series parameters collected by the monitoring unit are called, and after being aligned with the timestamp, they are stored as an operating parameter matrix with equal time intervals. Define the sliding time window length T_w and the time step Δt; For any center time t_c, take N sampling points within the interval [t_c−T_w+Δt, t_c], calculate the arithmetic mean x_avg(t_c) of each parameter in the operating parameter matrix, and calculate the boiler net efficiency η based on the inverse balance method. b (t_c); Establish a load normalization function F_corr(P), based on F_corr(P) and η b (t_c) generates the normalized efficiency η norm (t_c); All η corresponding to t_c norm (t_c) Arranged in chronological order, plot η norm (t) curve, combined with load curve to divide intervals, statistically analyze the mean efficiency η_avg, standard deviation σ_η and rate of change Δη / Δt of each interval to obtain a dynamic thermal efficiency spectrum; based on the dynamic thermal efficiency spectrum, identify the energy efficiency characteristics of each interval.

2. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 1, characterized in that: The target boiler body is defined as the boundary. The input energy includes: the lower heating value of the fuel and the sensible heat of the air entering the furnace. The output energy includes: the heat absorbed by the working fluid, the heat carried away by the flue gas, the unburned loss, the heat dissipation loss, and the physical heat loss of ash and slag.

3. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 1, characterized in that: The basis for generating the composite load curve is as follows: the initial load is M% of the rated load, and it is gradually increased to (M+20)% at a rate of change of q% of the rated load per minute. After maintaining this rate for a set period, it is reduced to (M-10)% and then increased back to (M+15)%. Among these, the value of M is a positive integer greater than 10, and the value of q is greater than 0.

4. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 1, characterized in that: The time series parameters include: instantaneous fuel mass flow rate m_fuel(t), fuel lower heating value Q_net(t), main steam flow rate D_ms(t), main steam enthalpy h_ms(t), feedwater enthalpy h_fw(t), flue gas temperature T_ey(t), flue gas oxygen content O2(t), weighted average carbon content of fly ash and slag samples Cash(t), and power consumption of the pulverizing system and forced and induced draft fans E_{mill&fan}(t).

5. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 1, characterized in that: Boiler net efficiency η calculated using the inverse balance method b (t_c): η b (t_c)=100%-(q2+q3+q4+q5+q6); where each heat loss term is defined as: q2 is the heat loss from flue gas exhaust, q3 is the heat loss from incomplete combustion of combustible gases, q4 is the heat loss from incomplete combustion of solids, q5 is the heat dissipation loss, and q6 is the physical heat loss from ash and slag.

6. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 5, characterized in that: The calculation basis for flue gas heat loss q2 is: q2=(( T_ey-T_ref)×c_pg×(1+α_ey×V_air)) / Q_net×100%; where T_ref represents the reference ambient temperature, c_pg represents the specific heat capacity of flue gas, α_ey represents the excess air coefficient at the flue gas outlet, V_air represents the theoretical air volume of fuel, and Q_net represents the lower heating value of fuel received on a baseline. The calculation basis for the excess air coefficient α_ey at the flue gas outlet is: α_ey=21 / (21-O2); where O2 represents the oxygen content of flue gas in dry flue gas. The calculation basis for the heat loss q3 due to incomplete combustion of combustible gas is: q3=(Co×Q_Co×V_gas) / Q_net×100%; where Co represents the volume concentration of carbon monoxide in flue gas, Q_Co represents the heat of combustion of CO, and V_gas represents the actual flue gas volume.

7. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 5, characterized in that: The calculation basis for the heat loss q4 due to incomplete combustion of solids is: q4 = (C_ash × Q_unburn × m_ash) / (m_fuel × Q_net) × 100%; where C_ash represents the weighted average carbon content of fly ash and slag samples, Q_unburn represents the calorific value of unburned carbon, m_ash represents the total mass flow rate of ash and slag, and m_fuel represents the mass flow rate of fuel; the calculation basis for the heat loss q5 is: q5 = a × (P / P_e) -b Where a1 and b1 are both empirical coefficients; P and P_e are the current load and rated load, respectively; the calculation basis for the physical heat loss q6 of ash is: q6=(m_slag×c_slag×(T_slag-T_ref)) / (m_fuel×Q_net)×100%; where m_slag represents the bottom ash mass flow rate, c_slag represents the specific heat capacity of ash, and T_slag represents the bottom ash discharge temperature.

8. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 1, characterized in that: The load normalization function F_corr(P) is established based on the following formula: F_corr(P) = a² × (P / P_e) 2 +b2×(P / P_e)+c2; where a2, b2, and c2 are regression coefficients; the normalization efficiency is calculated based on: η norm (t_c)=η b (t_c)×F_corr(P(t_c)); where η norm (t_c) represents the normalized efficiency.

9. The dynamic test method for thermal efficiency of a thermal power plant boiler according to claim 1, characterized in that: The energy efficiency characteristics of each segment are identified as follows: if the minimum value of the absolute difference between the average efficiency η_avg of any segment and other segments exceeds a set threshold, and the standard deviation σ_η exceeds 0.2%, then it is determined to be an inefficient segment; if the average efficiency η_avg of any segment is the highest and the standard deviation σ_η is the lowest, then it is determined to be an efficient segment; if the rate of change Δη / Δt changes continuously for more than 5 minutes, then it is determined to be a transitional segment.

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