Method for evaluating and analyzing deep peak regulation capacity of coal-fired power generating unit

Abstract

The invention relates to the field of electric power, and discloses a method for evaluating and analyzing the deep peak regulation capability of a coal-fired power generating unit, which is used for introducing a nonlinear dynamic system theory into the field of evaluating the deep peak regulation capability of the coal-fired power generating unit. According to the method, the dynamic behavior of the system is described by using the phase space reconstruction technology, the stability boundary is quantitatively analyzed in combination with the Lyapunov index and the Hamiltonian system theory, and the evaluation model considering the multi-time scale coupling effect is established. The peak regulation limit is accurately recognized from the dynamic characteristic level in the unit, the problems that in an existing method, the mechanism is not clear, system analysis is isolated, and real-time performance is poor are solved, and guarantee is provided for improving the peak regulation capacity of the unit, guaranteeing safe and stable operation of a power grid and promoting renewable energy consumption.

Description

Technical Field

[0001] This invention relates to the field of power, and in particular to a method for evaluating and analyzing the deep peak-shaving capacity of coal-fired power generating units. Background Technology

[0002] With the transformation of my country's energy structure and the rapid growth of renewable energy installed capacity, the power grid's need for flexible resource regulation is becoming increasingly urgent. As the main power source in the current power system, the deep peak-shaving capacity of coal-fired power units directly affects the grid's ability to absorb renewable energy and the system's operational stability. Currently, the assessment of the deep peak-shaving capacity of coal-fired power units has the following main shortcomings: 1. Traditional methods are mostly based on steady-state empirical formulas or single parameter thresholds, which cannot accurately reflect the dynamic characteristics of the unit under deep peak-shaving conditions. This static evaluation method ignores the dynamic coupling relationship between boiler combustion, steam-water system, generator and power grid environment, resulting in a large deviation between the evaluation results and the actual operating conditions; 2. Although the existing monitoring system has collected a large amount of operational data, traditional analysis methods only use some key parameters and fail to make full use of the correlation information of multiphysics data; 3. Existing methods often analyze boiler systems, generator sets, and the power grid environment in isolation, lacking collaborative evaluation between systems. This analytical approach cannot accurately describe the overall performance under the coupled effects of multiple systems and makes it difficult to determine the globally optimal operating range.

[0003] Therefore, we propose a method for evaluating and analyzing the deep peak-shaving capacity of coal-fired power generating units to address the above-mentioned problems. Summary of the Invention

[0004] This invention provides a method for evaluating and analyzing the deep peak-shaving capacity of coal-fired power generating units, which introduces nonlinear dynamics system theory into the field of evaluating the deep peak-shaving capacity of coal-fired power generating units.

[0005] The first aspect of this invention provides a method for evaluating and analyzing the deep peak-shaving capacity of a coal-fired power generating unit. This method includes: real-time acquisition of boiler combustion parameters, generator monitoring parameters, and grid interface parameters via a sensor network; data alignment and filtering to generate a multi-physics operating database; processing the parameters in the multi-physics operating database to reconstruct the parameter sequences and generate a phase space evolution map; identifying stability boundaries based on the phase space evolution map and generating quantitative evaluation indicators for boiler combustion stability; establishing motion equations using the generator parameters in the multi-physics operating database, analyzing potential energy surface changes, and generating a dynamic stability boundary feature set for the generator; analyzing the quantitative evaluation indicators for boiler combustion stability and the dynamic stability boundary feature set for the generator to verify the system's stability and generate a final evaluation report.

[0006] Optionally, in the first implementation of the first aspect of the present invention, a stability parameter set is generated by collecting furnace pressure, combustion temperature, and flame intensity through a boiler combustion system; a dynamic parameter set is generated by collecting sequences through a steam-water system; a composition parameter set is generated by collecting sequences through a flue gas system; a state parameter set is generated by collecting sequences through a generator monitoring system; and a disturbance parameter set is generated by collecting sequences through a power grid interface. The stability parameter set, dynamic parameter set, composition parameter set, state parameter set, and disturbance parameter set are then fused to generate a multi-source heterogeneous operation database. The sequences in the multi-source heterogeneous operation database are processed to generate a multi-physics field operation data table. The rate of change of physical quantities in the multi-physics field operation data table is compared with a change threshold to identify and remove abnormal data points, generating an anomaly marker dataset. The anomaly marker dataset is then interpolated and repaired using statistical features to generate the multi-physics field operation database.

[0007] Optionally, in a second implementation of the first aspect of the present invention, a sequence is extracted from the multiphysics operating database and processed to generate a boiler combustion parameter sequence; using the boiler combustion parameter sequence, a delay time is calculated, the embedding dimension is determined, and a phase space reconstruction parameter set is generated; based on the phase space reconstruction parameter set, the boiler combustion parameter sequence is reconstructed to generate a phase space trajectory matrix; the evolution features of the trajectory are extracted from the phase space trajectory matrix to generate a boiler combustion dynamic feature set; based on the boiler combustion dynamic feature set, a three-dimensional projection map is constructed, trajectory distribution features are distinguished, and a phase space evolution map is generated.

[0008] Optionally, in a third implementation of the first aspect of the present invention, features are extracted from the phase space evolution map to calculate the divergence rate and generate a divergence rate sequence of trajectory point pairs; based on the divergence rate sequence of trajectory point pairs, a logarithmic growth curve is fitted to generate a calculated value of the maximum Lyapunov exponent; features are extracted from the phase space evolution map to calculate the correlation function value and generate a relationship curve; based on the relationship curve, the curve slope is calculated to generate an estimated value of the correlation dimension; the calculated value of the maximum Lyapunov exponent and the relationship curve are comprehensively analyzed to construct a combustion stability criterion and generate a quantitative evaluation index for boiler combustion stability.

[0009] Optionally, in the fourth implementation of the first aspect of the present invention, sequences are extracted from the multiphysics operating database to construct state vectors and generate a generator electromechanical transient state parameter set; based on the generator electromechanical transient state parameter set, an extended rotor motion equation is established to generate a generator nonlinear dynamic equation set; using the generator nonlinear dynamic equation set, a Hamiltonian energy function expression is generated based on the total energy function; based on the Hamiltonian energy function expression, the distribution of extreme points is calculated, the variation law is identified, and a potential energy surface feature parameter set is generated; the variation trend of the potential energy surface feature parameter set is analyzed, boundary conditions are determined, and a generator dynamic stability boundary feature set is generated.

[0010] Optionally, in the fifth implementation of the first aspect of the present invention, time-scale features are extracted from the quantitative evaluation index of boiler combustion stability and from the dynamic stability boundary feature set of generator to generate a time-scale separation parameter set; based on the time-scale separation parameter set, a dynamic correlation model is established to generate a dynamic interaction equation set; using the dynamic interaction equation set, a composite Lyapunov function is constructed to generate a coupled system stability verification function; based on the coupled system stability verification function, energy dissipation characteristics and potential energy surface changes are analyzed to generate a coupled system stability verification report; combining the quantitative evaluation index of boiler combustion stability, the dynamic stability boundary feature set of generator, and the coupled system stability verification report, a deep peak-shaving capability feature parameter set is constructed to generate a final evaluation report of deep peak-shaving capability.

[0011] Optionally, in the sixth implementation of the first aspect of the present invention, a verification mechanism is established based on the final evaluation report of the deep peak shaving capability, the compliance rate is compared, and an evaluation result verification report is generated; using the evaluation result verification report and monitoring data, a dynamic boundary correction model is constructed, and an adaptive boundary correction parameter set is generated; based on the adaptive boundary correction parameter set, the final evaluation report of the deep peak shaving capability is updated, and a deep peak shaving capability evaluation report is generated; using the deep peak shaving capability evaluation report, a preventive control strategy library is established, and a deep peak shaving preventive control strategy set is generated; based on the deep peak shaving preventive control strategy set and status monitoring, an online system is constructed, and a deep peak shaving operation decision support report is generated.

[0012] The mechanism of this invention is as follows: By constructing a comprehensive analysis framework based on phase space reconstruction and stability theory, the peak-shaving boundary is accurately identified from the perspective of the unit's inherent dynamic characteristics. The slow-changing process of the boiler combustion system is coupled with the fast-changing dynamics of the generator unit through multi-timescale analysis. Lyapunov stability theory and Hamiltonian system energy function are used to reveal the stability evolution law of the system under deep peak-shaving conditions, establishing a mechanism-driven evaluation system that does not rely on machine learning models.

[0013] Beneficial effects: The deep coupling of boiler combustion stability assessment and generator dynamic stability analysis overcomes the limitations of traditional single-system assessments. Through collaborative analysis of fast and slow time scales, dynamic matching of the combustion and electromechanical processes is achieved, enabling the system to maintain optimal operating conditions under deep peak-shaving conditions. The synergistic effect of multi-physics data collected by sensor networks and nonlinear dynamic models constructs a complete link from data acquisition to decision support. Real-time monitoring data provides input for the model, and the model analysis results guide adjustments to operating strategies, forming a virtuous cycle of continuous optimization. This invention not only provides real-time evaluation results but also achieves continuous improvement of the evaluation system through adaptive boundary correction and strategy library updates. This synergy between short-term accurate evaluation and long-term incremental optimization ensures that the system maintains optimal performance at different operational stages. Furthermore, this invention achieves accurate evaluation and optimization of the deep peak-shaving capability of coal-fired power generating units through a multi-system collaborative mechanism. This method organically integrates the boiler combustion system, the generating unit, and the power grid environment into a complete closed-loop evaluation system. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of an embodiment of a method for evaluating and analyzing the deep peak-shaving capacity of a coal-fired power generating unit according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an embodiment of a deep peak-shaving capacity assessment and analysis device for coal-fired power generating units according to an embodiment of the present invention; Detailed Implementation

[0015] This invention provides a method, apparatus, device, and storage medium for evaluating the deep peak-shaving capacity of coal-fired power generating units, which introduces nonlinear dynamics system theory into the field of deep peak-shaving capacity evaluation of coal-fired power generating units. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for evaluating and analyzing the deep peak-shaving capacity of a coal-fired power generating unit according to the present invention includes: 101. By installing a sensor network in the boiler combustion system, steam-water system, and flue gas system, boiler combustion stability parameters, thermodynamic cycle parameters, and flue gas composition parameters are collected in real time. At the same time, rotor dynamic parameters and stator electrical parameters are collected through the generator monitoring system, and system frequency fluctuation parameters and tie-line power parameters are collected through the power grid interface. These multi-source heterogeneous operating data are time-scaled and outliers are removed to generate a standardized multi-physics operating database.

[0017] It is understood that the executing entity of this invention can be a deep peak-shaving capacity assessment and analysis device for coal-fired power generating units, or it can be a terminal or a server; the specific implementation is not limited here. This embodiment of the invention will be described using a server as an example.

[0018] Specifically, by using flame detectors, pressure transmitters, and temperature sensors installed in the boiler combustion system, furnace pressure fluctuation sequences, combustion temperature distribution maps, and flame intensity signals are collected in real time to generate a set of boiler combustion stability parameters; by using flow meters, temperature sensors, and pressure sensors installed in the steam-water system, main steam flow sequences, feedwater temperature change sequences, and steam drum pressure fluctuation sequences are collected synchronously to generate a set of dynamic parameters for the thermodynamic cycle; by using flue gas analyzers and oxygen meters installed in the flue gas system, flue gas oxygen content change sequences, carbon monoxide concentration sequences, and nitrogen oxide emission sequences are continuously collected to generate a set of combustion product composition parameters; by using vibration sensors, current transformers, and voltage transformers in the generator monitoring system, rotor shaft vibration trajectory sequences, stator winding temperature distribution sequences, and excitation current fluctuation sequences are collected synchronously to generate a set of generator operating status parameters; and by using protection and control devices and frequency monitoring units at the power grid interface, system frequency deviation sequences, tie-line power fluctuation sequences, and node voltage amplitude sequences are collected in real time to generate a set of power grid environmental disturbance parameters. The boiler combustion stability parameter set, thermodynamic cycle dynamic parameter set, combustion product composition parameter set, generator operating status parameter set, and power grid environmental disturbance parameter set are fused to generate a multi-source heterogeneous operating database. All parameter sequences in the multi-source heterogeneous operating database are processed using a time-stamp alignment algorithm based on GPS clock synchronization to eliminate timestamp differences between different sampling devices and generate a multi-physics operating data table with unified timestamps. For a multi-physics field operation data table with unified timestamps, an anomaly data detection method based on physical mechanisms is adopted. By comparing the rate of change of physical quantities of adjacent sampling points with a preset reasonable change threshold, anomaly data points that do not conform to the laws of thermodynamics and electromagnetics are identified and removed, and an anomaly label dataset is generated. For the anomaly-labeled dataset, a data repair method based on the Laida criterion is adopted. The statistical characteristics of the normal data segments are used to interpolate and repair the anomaly data segments, generating a complete and continuous standardized multiphysics operation database.

[0019] 102. The physical parameters in the standardized multiphysics operation database are normalized without dimension. Based on Takens' embedding theorem, the normalized boiler combustion parameter sequence is reconstructed in phase space. By calculating the delay time and embedding dimension, a high-dimensional phase space trajectory is constructed to generate the phase space evolution map of the boiler combustion dynamic system.

[0020] Specifically, key parameter sequences related to boiler combustion are extracted from a standardized multi-physics field operation database, including furnace pressure fluctuation sequences, combustion temperature distribution sequences, and main steam pressure change sequences. These sequences are then processed using a physical mechanism-based normalization algorithm to generate dimensionless boiler combustion parameter sequences. Using the dimensionless boiler combustion parameter sequence, the optimal delay time of each parameter sequence is calculated by the autocorrelation function method. At the same time, the false nearest neighbor method is used to determine the embedding dimension of the system and generate a phase space reconstruction parameter set. Based on the phase space reconstruction parameter set, the dimensionless boiler combustion parameter sequence is reconstructed in high dimension phase space. By constructing the delayed coordinate vector, the trajectory point set of the system in the reconstructed phase space is constructed, and the phase space trajectory matrix of the boiler combustion system is generated. For the phase space trajectory matrix of the boiler combustion system, the evolution characteristics of the trajectory are extracted by geometric topology analysis, including trajectory divergence rate, attractor distribution density and phase space filling degree, to generate a dynamic feature set of boiler combustion. Based on the dynamic feature set of boiler combustion, a three-dimensional projection map of phase space trajectory is constructed using nonlinear dynamics visualization technology. The trajectory distribution characteristics under different load conditions are distinguished by color coding, and a phase space evolution map of the boiler combustion dynamic system is generated.

[0021] 103. Based on the phase space evolution map of the boiler combustion dynamic system, the maximum Lyapunov exponent and correlation dimension of the system are calculated using nonlinear dynamic analysis. By analyzing the divergence characteristics of the phase space trajectory and the attractor structure, the stability boundary of the combustion system is identified, and quantitative evaluation indexes of boiler combustion stability are generated.

[0022] Specifically, the evolution characteristics of adjacent trajectory point pairs are extracted from the phase space evolution map of the boiler combustion dynamic system, and the divergence rate of adjacent trajectory point pairs in phase space is calculated using the small data quantity method to generate a trajectory point pair divergence rate sequence. Based on the divergence rate sequence of trajectory point pairs, the least squares method is used to fit the logarithmic growth curve of the divergence rate over time. The maximum divergence exponent in the reconstructed phase space is calculated by the slope of the logarithmic growth curve, and the maximum Lyapunov exponent value is generated. The distribution density characteristics of phase space points are extracted from the phase space evolution map of the boiler combustion dynamic system. By calculating the correlation function values ​​of phase space point pairs under different neighborhood radii, the relationship curve of the correlation function as a function of the neighborhood radius is generated. Based on the relationship curve of the correlation function with the neighborhood radius, the slope of the curve is calculated by linear fitting under double logarithmic coordinates. The fractal characteristics of the system are determined by the trend of slope change, and the correlation dimension estimate is generated. By comprehensively analyzing the relationship curves between the calculated maximum Lyapunov exponent and the correlation function as a function of the neighborhood radius, and combining the estimated correlation dimension, a combustion stability criterion based on nonlinear dynamics theory is constructed, generating a quantitative assessment index for boiler combustion stability that includes stability level, instability risk, and critical load.

[0023] 104. Using the generator parameters in the standardized multiphysics operating database, establish the generator rotor motion equation considering shaft torsional vibration and electromagnetic transients. Construct the system's energy function through Hamiltonian system theory, analyze the changes in the system's potential energy surface under different load levels, and generate the generator dynamic stability boundary feature set.

[0024] Specifically, generator rotor angular displacement sequence, angular velocity fluctuation sequence and electromagnetic power oscillation sequence are extracted from a standardized multiphysics operation database. Combined with generator shaft system structural parameters, a rotor motion state vector considering multi-mass coupling is constructed to generate a generator electromechanical transient state parameter set. Based on the generator electromechanical transient state parameter set, an extended rotor motion equation considering the torsional vibration mode of the shaft system and the influence of grid disturbance is established. By introducing the damping torque coefficient and the synchronous torque coefficient, the energy dissipation and exchange process of the system is described, and a set of nonlinear dynamic equations of the generator is generated. Using the set of nonlinear dynamic equations of the generator, the total energy function of the system is constructed based on Hamiltonian system theory. The kinetic energy term is defined as the rotor kinetic energy related quantity and the potential energy term is defined as the system potential energy related quantity, thus generating the Hamiltonian energy function expression of the generator system. Based on the Hamiltonian energy function expression of the generator system, the distribution of extreme points of the potential energy surface under different load levels is calculated by numerical scanning method, the variation law of potential energy well depth and potential barrier height is identified, and a set of characteristic parameters of potential energy surface is generated. The variation trend of the potential energy surface characteristic parameter set with load level is analyzed. By identifying the critical point where the potential energy trap disappears, the boundary conditions for the system to lose stability are determined. The accuracy of the boundary conditions is verified by combining the generator nonlinear dynamic equation set. A generator dynamic stability boundary feature set containing the stable operating domain boundary, critical instability point and safety margin is generated.

[0025] 105. Perform multi-timescale coupling analysis between the quantitative evaluation index of boiler combustion stability and the boundary feature set of generator dynamic stability. Establish a fast and slow variable interaction model based on singular perturbation theory. Verify the stability of the system under deep peak shaving conditions by constructing a composite Lyapunov function. Generate a final evaluation report on the deep peak shaving capability that comprehensively considers boiler combustion stability and generator dynamic stability.

[0026] Specifically, the time-scale characteristics of the combustion process are extracted from the quantitative evaluation index of boiler combustion stability, and the time-scale characteristics of the electromechanical process are extracted from the dynamic stability boundary characteristic set of the generator. The singular perturbation theory is used to separate the two types of dynamic processes with different time scales, and a time-scale separation parameter set of the boiler-generator system is generated. Based on the time-scale separation parameter set, a dynamic correlation model of the slow-varying boiler combustion subsystem and the fast-varying generator subsystem is established. By designing boundary layer correction terms, the energy exchange process between the fast and slow subsystems is described, generating a set of dynamic interaction equations for the boiler-generator coupled system. Using the set of dynamic interaction equations for the boiler-generator coupled system, Lyapunov functions for the fast and slow subsystems are constructed respectively. A composite Lyapunov function is constructed through a weighted combination method, generating a stability verification function for the coupled system. Based on the stability verification function of the coupled system, the energy dissipation characteristics and potential energy surface changes of the system under deep peak shaving conditions are analyzed. The system stability is judged by verifying the negative qualitative time derivative of the composite Lyapunov function, and a stability verification report of the boiler-generator coupled system is generated. By combining quantitative evaluation indicators of boiler combustion stability, dynamic stability boundary feature set of generator, and stability verification report of boiler-generator coupled system, a set of characteristic parameters of deep peak shaving capability is constructed, which includes minimum technical output, stable operation boundary, risk warning level and regulation capability indicators, and a final evaluation report of deep peak shaving capability with engineering guidance significance is generated. Based on the final evaluation report of deep peak shaving capability, key stability boundary parameters are extracted, and a verification mechanism based on historical operating data is established. By comparing the conformity between the actual operating state and the predicted boundary, an evaluation result verification report is generated. Using the evaluation result verification report and real-time monitoring data in a standardized multiphysics operating database, a dynamic boundary correction model is constructed. The stability boundary parameters are adjusted through a real-time feedback mechanism to generate an adaptive boundary correction parameter set. Based on an adaptive boundary correction parameter set, the final assessment report of deep peak shaving capacity is dynamically updated. By introducing real-time operational status feedback, the parameter settings of the assessment model are adjusted to generate an optimized deep peak shaving capacity assessment report. Using the optimized deep peak shaving capacity assessment report, a preventive control strategy library for different load conditions is established. Through preset control logic, response schemes for different stability risks are generated, resulting in a deep peak shaving preventive control strategy set. Based on the deep peak shaving preventive control strategy set and combined with real-time operational status monitoring, an online decision support system is constructed. The assessment results and control suggestions are displayed through a visual interface, generating a deep peak shaving operation decision support report with real-time guidance capabilities.

[0027] In this invention, a multi-physics coupled deep peak-shaving capability assessment system is constructed, achieving a significant breakthrough in accurately assessing peak-shaving capability from the perspective of the unit's inherent dynamic characteristics. Through phase space reconstruction and Lyapunov stability analysis, the system's stability boundary is accurately identified; operational safety is effectively enhanced, providing early warning of combustion instability and generator oscillation risks; simultaneously, significant economic benefits are brought by accurately determining minimum technical output and optimizing operating strategies, thereby increasing peak-shaving revenue and reducing energy consumption; it possesses strong engineering practicality, can be implemented based on existing monitoring systems, and is easily applied in the field through visual decision support, providing innovative technical support for the construction of smart power plants and the flexible operation of units under the background of new power systems.

[0028] Figure 2 This is a schematic diagram of a deep peak-shaving capacity assessment and analysis device for coal-fired power generating units provided in an embodiment of the present invention. This deep peak-shaving capacity assessment and analysis device 200 for coal-fired power generating units can vary considerably due to differences in configuration or performance. The device 200 includes a transmitter 201, a receiver 202, and a processor 203. The processor 203 can also be a controller. Figure 2 The device is referred to as "controller / processor 203". Optionally, the device 200 may also include a modem processor 205, wherein the modem processor 205 may include an encoder 206, a modulator 207, a decoder 208, and a demodulator 209.

[0029] In one example, transmitter 201 modulates (e.g., analog-to-analog conversion, filtering, amplification, and up-conversion, etc.) the output sample and generates an uplink signal, which is transmitted via an antenna to an access network device. On the downlink, the antenna receives the downlink signal transmitted by the access network device. Receiver 202 modulates (e.g., filtering, amplification, down-conversion, and digitization, etc.) the signal received from the antenna and provides an input sample. In modem processor 205, encoder 206 receives traffic data and signaling messages to be transmitted on the uplink and processes (e.g., formatting, encoding, and interleaving) the traffic data and signaling messages. Modulator 207 further processes (e.g., symbol mapping and modulation) the encoded traffic data and signaling messages and provides an output sample. Demodulator 209 processes (e.g., demodulates) the input sample and provides a symbol estimate. Decoder 208 processes (e.g., deinterleaving and decoding) the symbol estimate and provides decoded data and signaling messages to device 200. Encoder 206, modulator 207, demodulator 209, and decoder 208 can be implemented by a combined modem processor 205. These units process data according to the radio access technology used by the radio access network (e.g., LTE and other evolved systems access technologies). It should be noted that when device 200 does not include modem processor 205, the aforementioned functions of modem processor 205 can also be performed by processor 203.

[0030] The processor 203 controls and manages the operation of the device 200, and is used to execute the processing procedures performed by the device 200 in the above embodiments of this disclosure. For example, the processor 203 is also used to execute various steps of the transmitting or receiving device in the above method embodiments, and / or other steps of the technical solutions described in the embodiments of this disclosure.

[0031] Furthermore, the device 200 may also include a memory 204 for storing program code and data for the device 200.

[0032] Understandable, Figure 2 Only a simplified design of device 200 is shown. In practical applications, device 200 can include any number of transmitters, receivers, processors, modem processors, memory, etc., and all devices that can implement the embodiments of this disclosure are within the protection scope of the embodiments of this disclosure.

[0033] The present invention also provides a deep peak-shaving capacity assessment and analysis device for coal-fired power generating units. The deep peak-shaving capacity assessment and analysis device for coal-fired power generating units includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor performs the steps of the deep peak-shaving capacity assessment and analysis method for coal-fired power generating units described in the above embodiments.

[0034] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the method for evaluating and analyzing the deep peak-shaving capacity of a coal-fired power generating unit.

[0035] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0036] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0037] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating and analyzing deep peak shaving capability of a coal-fired power generating unit, characterized in that, The method comprises the following steps: Real-time acquisition of boiler combustion parameters, monitoring of generator parameters, acquisition of parameters through the power grid interface, alignment and elimination of data, and generation of a multi-physical field operation database; Processing the parameters in the multi-physical field operation database, reconstructing the parameter sequence, and generating a phase space evolution map; Based on the phase space evolution map, identify the stability boundary, and generate a quantitative evaluation index of boiler combustion stability; Using the generator parameters in the multi-physical field operation database, establish the motion equation, analyze the potential energy surface change, and generate the dynamic stability boundary feature set of the generator; Analyze the quantitative evaluation index of boiler combustion stability and the dynamic stability boundary feature set of the generator, verify the stability of the system, and generate the final evaluation report.

2. The method for evaluating and analyzing the deep peak shaving capability of a coal-fired power generating unit according to claim 1, characterized in that, It includes: Obtain a stability parameter set, a dynamic parameter set, a component parameter set, a state parameter set, and a disturbance parameter set; Fuse the stability parameter set, the dynamic parameter set, the component parameter set, the state parameter set, and the disturbance parameter set to generate a multi-source heterogeneous operation database; Processing the sequence in the multi-source heterogeneous operation database, generating a multi-physical field operation data table; Comparing the physical quantity change rate with the change threshold value, identifying and eliminating abnormal data points, and generating an abnormal marker data set for the multi-physical field operation data table; Using statistical features to interpolate and repair the abnormal marker data set to generate a multi-physical field operation database.

3. The method for evaluating and analyzing the deep peak shaving capability of a coal-fired power generating unit according to claim 2, characterized in that, It includes: Extracting and processing the sequence from the multi-physical field operation database to generate a boiler combustion parameter sequence; Using the boiler combustion parameter sequence, calculate the delay time, determine the embedding dimension, and generate a phase space reconstruction parameter set; Based on the phase space reconstruction parameter set, reconstruct the boiler combustion parameter sequence to generate a phase space trajectory matrix; Extracting the evolution features of the trajectory from the phase space trajectory matrix, and generating a boiler combustion dynamic feature set; Based on the boiler combustion dynamic feature set, construct a three-dimensional projection map to distinguish the trajectory distribution features, and generate a phase space evolution map.

4. The method for evaluating and analyzing deep peak shaving capability of a coal-fired power generating unit according to claim 3, characterized in that, It includes: Extracting features from the phase space evolution map, calculating divergence rates, and generating a trajectory point pair divergence rate sequence; Based on the trajectory point pair divergence rate sequence, fitting a logarithmic growth curve to generate a maximum Lyapunov index calculation value; Extracting features from the phase space evolution map, calculating correlation function values, and generating a relationship curve; Based on the relationship curve, calculate the slope to generate a correlation dimension estimate value; Comprehensive analysis of the maximum Lyapunov index calculation value and the relationship curve, constructing a combustion stability criterion, and generating a quantitative evaluation index of boiler combustion stability.

5. The method for evaluating and analyzing deep peak shaving capability of a coal-fired power generating unit according to claim 4, characterized in that, It includes: Extracting sequences from the multi-physical field operation database, constructing state vectors, and generating a generator electromechanical transient state parameter set; Based on the generator electromechanical transient state parameter set, establish an extended rotor motion equation to generate a set of generator nonlinear dynamics equations; Using the generator nonlinear dynamics equation set, based on the total energy function, generate a Hamilton energy function expression; Based on the Hamilton energy function expression, the extreme point distribution is calculated, the change law is identified, and the potential energy surface characteristic parameter set is generated; The trend of the potential energy surface characteristic parameter set is analyzed, the boundary condition is determined, and the generator dynamic stability boundary characteristic set is generated.

6. The method for evaluating and analyzing deep peak shaving capability of a coal-fired power generating unit according to claim 5, characterized in that, It includes: Extracting time scale features from the boiler combustion stability quantitative evaluation index and extracting time scale features from the generator dynamic stability boundary characteristic set, generating a time scale separation parameter set; Based on the time scale separation parameter set, a dynamic correlation model is established, and a dynamic interaction equation set is generated; Using the dynamic interaction equation set, a composite Lyapunov function is constructed, and a coupled system stability verification function is generated; Based on the coupled system stability verification function, the energy dissipation characteristics and the potential energy surface change are analyzed, and a coupled system stability verification report is generated; Comprehensive boiler combustion stability quantitative evaluation index, generator dynamic stability boundary characteristic set and coupled system stability verification report, build depth peak shaving capability characteristic parameter set, generate depth peak shaving capability final evaluation report.

7. The method for evaluating and analyzing deep peak shaving capability of a coal-fired power generating unit according to claim 1, characterized in that, It also includes: Based on the depth peak shaving capability final evaluation report, a verification mechanism is established, the conformity is compared, and an evaluation result verification report is generated; Using the evaluation result verification report, combined with the monitoring data, a dynamic boundary correction model is constructed, and an adaptive boundary correction parameter set is generated; Based on the adaptive boundary correction parameter set, the depth peak shaving capability final evaluation report is updated, and the depth peak shaving capability evaluation report is generated; Using the depth peak shaving capability evaluation report, a preventive control strategy library is established, and a depth peak shaving preventive control strategy set is generated; Based on the depth peak shaving preventive control strategy set, combined with the state monitoring, an online system is constructed, and a depth peak shaving operation decision support report is generated.

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