CN113722656B - Real-time health evaluation method and system for thermal generator set - Google Patents
Real-time health evaluation method and system for thermal generator set Download PDFInfo
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Abstract
The invention discloses a method and a system for evaluating real-time health of a thermal generator set. The invention adopts the technical scheme that: firstly, dividing all equipment contained in a thermal power generating unit into three logic levels of a unit level, a system level and an equipment level according to a thermal power generating process flow, then determining state variables related to the health degree of each equipment of the equipment level and a reference value or a mathematical model for calculating the reference value of the state variables, setting weight coefficients of health degree components of each level, and then calculating the health degree of the equipment level and the system level from bottom to top step by step; and finally, weighting calculation is carried out to obtain the real-time health index of the unit. The real-time health index obtained by the invention is a comprehensive index reflecting the real-time state of the unit, and the real-time running state of the unit can be accurately mastered by the index, so that the monitoring efficiency of the thermal power unit is improved, and the working intensity of operators is reduced.
Description
Technical Field
The invention belongs to the technical field of power generation, and particularly relates to a method and a system for evaluating real-time health of a thermal generator set.
Background
Currently, thermal power generation (thermal power) is still a main electric energy production mode in China, and according to national statistical office data, the national thermal power unit capacity is 11.9 hundred million kilowatts by 2019, and the total unit capacity specific gravity is 59.2%; when the generating capacity of 2019 thermal power is 51654.3 hundred million, the generating capacity accounts for 72.3 percent of the total generating capacity. Along with the gradual transformation of thermal power generation into an efficient, clean and environment-friendly power generation mode, the thermal power generation will still occupy an important position in the power industry in China for a long time in the future.
The thermal power generating unit is a huge system, and besides a boiler, a steam turbine and a generator, the thermal power generating unit also comprises a plurality of auxiliary equipment such as a coal mill, an induced draft fan, a deaerator and the like, and the thermal power generating unit has the advantages of complex process flow, severe production environment, complex and changeable working conditions and needs a large number of operators and overhaulers to ensure long-term stable operation of the unit. The thermal power generating unit generally adopts a centralized control mode based on a Distributed Control System (DCS), all the systems and equipment are in an automatic control state, operators monitor the running state of the unit through a human-computer interface, and execute operations according to parameter changes, external scheduling instructions and the like so as to ensure that the unit runs safely, economically and environmentally. Because of large equipment scale and complex production flow of the thermal power generating unit, operators need to monitor mass operation parameters for 7×24 hours without interruption, and the working strength is extremely high. The traditional monitoring disc adopts an indiscriminate alarm-check mode, and prompts an operator to operate through alarm information. For safety, the setting of the upper limit value and the lower limit value of the alarm is generally conservative, the alarm is triggered very frequently, the effective alarm information is less, so that operators need to spend a great deal of effort to cope with various meaningless alarms, and the monitoring efficiency is lower. The existing monitoring mode only provides monitoring information about the operation parameters of the equipment, and cannot give comprehensive indexes reflecting the overall operation condition of the unit. In addition, although the factors influencing the operation of the thermal power generating unit are more, operators can monitor only a few important pictures or operation parameters, so that local minor degradation of unimportant systems and equipment cannot be found in time.
In recent years, the construction of intelligent power plants has become a common knowledge in the thermal power industry, wherein intelligent monitoring panels are important application scenes of intelligent power plants. The intelligent monitoring disc is used for deep mining based on mass production data and high-quality operation experience, realizing deep detection and diagnosis of the state of the unit, providing high-efficiency operation guidance for operators, and promoting high self-adaption and optimization of an adjusting system.
The real-time health degree of the thermal power generating unit is accurately estimated to be a basic condition for realizing the intelligent monitoring board, and operators can master the comprehensive and accurate running state of the unit through the real-time health degree comprehensive index, so that preconditions are provided for quickly and accurately executing various pre-control operations.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method and a system for evaluating the real-time health degree of a thermal power generating unit, which can enable operators to intuitively, comprehensively and accurately know and master the running state of the thermal power generating unit without monitoring massive running parameters.
Therefore, the invention adopts the following technical scheme: a real-time health evaluation method of a thermal generator set comprises the following steps:
Step 1, dividing all equipment contained in one unit into three logic levels, namely a unit level, a system level and an equipment level according to the thermal power generation process flow;
Step 2, determining all state variables related to the health degree of each device of the device level;
Step 3, determining a mathematical model for calculating a 1-level reference value upper limit H 1, a 1-level reference value lower limit L 1, a 2-level reference value upper limit H 2, a 2-level reference value lower limit L 2 or reference values of each state variable;
Step 4, setting weight coefficients of health degree components of all state variables in the total health degree of equipment to which the state variables belong; determining a weight coefficient of the health degree component of each device in the total system health degree to which the health degree component belongs; determining a weight coefficient of the health degree component of each system in the total unit health degree;
Step 5, calculating the health degree of each device at the device level: integrating four dimensions from the deviation amount, the change rate, the overrun time and the deviation amount of the state variable, and obtaining the health degree component of the state variable through weighted calculation; the health degree of each device is calculated by weighting health degree components of state variables contained in the device according to weight coefficients;
step 6, calculating the health degree of the system level: the health degree of each system is calculated by the health degree components of each device contained by each system according to the weighting coefficient;
Step 7, computer group level health: the health degree of the unit is calculated by weighting health degree components of each system according to weight coefficients.
Further, in step 1, for some complex systems included in the unit, a subsystem level is added between the system level and the equipment level, so that the logic relationship is clearer.
Further, in step 2, the state variable selects a state variable affecting at least one of the safety, the running cost, the environmental protection and the automatic control performance of the unit.
Further, in step 3, the reference values are classified into the following two types according to the characteristics of the state variables:
1) Constant reference value
The reference value is constant and cannot be changed along with the change of the operation condition, and the reference value is set according to a set threshold value or specific operation requirements of equipment;
2) Variable reference value
Such reference values are variables that change as the operating conditions change, requiring the prior determination of mathematical models and dependent variables that calculate the reference values: firstly, determining one or more dependent variables related to the reference value through data correlation analysis, and then obtaining a mathematical model of the change of the reference value along with the dependent variables by using a modeling algorithm; when the health degree of a certain state variable is evaluated, the real-time measured value of the related dependent variable is input into a mathematical model, and the reference value of the state variable can be calculated.
Further, in step 4, the weight coefficient reflects the importance degree of each health degree component in the total amount of the superior health degrees to which the weight coefficient belongs, and the weight coefficient is assigned according to the following three methods;
1) Subjective weighting method
Assigning weight coefficients of each health degree component;
2) Objective weighting method for determining weight coefficient by entropy weighting method
According to the variation degree of each health degree component, determining an objective weight coefficient of each component;
3) Combined weighting method combining subjective and objective
Firstly, determining the weight coefficient of each health degree component by using an entropy weight method, and then, for some important state variables, equipment or systems, adjusting the corresponding weight coefficient by using a subjective weighting method.
Further, the specific content of step 5 is as follows:
1) Calculating the health of all the state variables contained by the respective devices
① Calculating a deviation normalized value delta' ijk between each state variable and the reference value, including
Where p ijk is the kth state variable value contained by the jth device of the ith system, H 1ijk is the upper level 1 reference value limit for the state variable, L 1ijk is the lower level 1 reference value limit for the state variable, H 2ijk is the upper level 2 reference value limit for the state variable, and L 2ijk is the lower level 2 reference value limit for the state variable;
the state variable value p ijk is a state real-time measured value, is obtained from a set DCS system, and is subjected to smoothing treatment or takes the value as an average value of a plurality of continuous real-time measured values in the last period of time for eliminating noise;
For the constant reference value, under different operation conditions, the value is a preset fixed value, and calculation is not needed; for a variable type reference value, the value of the variable type reference value changes along with the change of the operation condition, a dependent variable measurement value related to the reference value is required to be collected, input into a corresponding mathematical model and calculated to obtain the reference value;
② Calculating a change rate normalized value v' ijk of each state variable, including:
Where v ijk is the rate of change of the kth state variable contained by the jth device of the ith system, and there is:
wherein T is a sampling period, V ijk is the upper limit of the change rate, nT represents the current sampling time, (n-1) T represents the last sampling time;
③ Calculating normalized value tau' ijk of overrun time of each state variable, including
Where τ ijk is the overrun time of the kth state variable contained by the jth device of the ith system and t ijk is the upper limit of the overrun time;
④ A normalized integral value χ' ijk for calculating the deviation between each state variable and the reference value, including
Wherein χ ijk is an integrated value of the deviation amount between the kth state variable and the reference value contained in the jth device of the ith system, and X ijk is an upper limit of the integrated value;
⑤ The health of the state variable is obtained by weighting calculation by integrating four dimensions of the deviation amount, the change rate, the overrun time and the deviation amount of the state variable
Hijk＝(wijk1(1-δ′ijk)+wijk2(1-δ′ijk)(1-v′ijk)+wijk3(1-τ′ijk)+wijk4(1-χ′ijk))×100 (6)
Wherein w ijk1、wijk2、wijk3 and w ijk4 are respectively the deviation amount, the change rate, the overrun time and the weight coefficient of the deviation amount integration;
2) The health degree of each device at the device level is calculated by weighting the health degree components of each state variable contained by the device level according to weight coefficients, namely:
Where H ij is the health of the jth device of the ith system, H ijk is the health component of the kth state variable contained in the device, w ijk is the weight coefficient of the kth state variable contained in the device, and N ijk is the number of state variables contained in the device.
Further, in step 6, the calculation formula of the health degree of each system at the system level is as follows:
Wherein H i is the health degree of the ith system, H ij is the health degree component of the jth device contained in the system, w ij is the weight coefficient of the jth device, and N ij is the number of devices contained in the system.
Further, if the system belongs to a subsystem level, the health degree calculation form is shown as a formula (8), and health degree components of all subsystems contained by the system are weighted and calculated according to weight coefficients; if the system does not include a device or subsystem, the health calculation is performed as step 5, in which the health components of the state variables included therein are weighted by weight coefficients.
Further, in step 7, the calculation formula of the unit health degree is as follows:
Wherein H is the health degree of the unit, H i is the health degree component of the ith system, w i is the weight coefficient of the ith system, and N is the number of systems contained in the unit.
The invention adopts another technical scheme that: a real-time health evaluation method of a thermal generator set comprises the following steps:
a classification unit: according to the thermal power generation process flow, all the equipment contained in one unit is divided into three logic levels, namely a unit level, a system level and an equipment level;
All state variable determining units: determining all state variables related to the health of each device at the device level;
State variable reference value determining unit: determining a 1-level reference value upper limit H 1, a 1-level reference value lower limit L 1, a 2-level reference value upper limit H 2, a 2-level reference value lower limit L 2 or a mathematical model for reference value calculation of each state variable;
Weight coefficient unit: setting weight coefficients of health degree components of all state variables in the total health degree of equipment to which the state variables belong; determining a weight coefficient of the health degree component of each device in the total system health degree to which the health degree component belongs; determining a weight coefficient of the health degree component of each system in the total unit health degree;
a device-level health degree calculation unit: integrating four dimensions from the deviation amount, the change rate, the overrun time and the deviation amount of the state variable, and obtaining the health degree component of the state variable through weighted calculation; the health degree of each device is calculated by weighting health degree components of state variables contained in the device according to weight coefficients;
a system-level health degree calculation unit: the health degree of each system is calculated by the health degree components of each device contained by each system according to the weighting coefficient;
Unit level health degree calculating unit: the health degree of the unit is calculated by weighting health degree components of each system according to weight coefficients.
The real-time health index calculated by the invention is a comprehensive index reflecting the real-time running state of the unit, and operators can scientifically, comprehensively and accurately grasp the current running state of the unit by means of the index, so that the monitoring efficiency can be improved, and the working intensity can be reduced.
Drawings
Fig. 1 is a flow chart of a real-time health evaluation method of a thermal power generating unit according to embodiment 1 of the present invention;
Fig. 2 is a schematic diagram of a unit level of a real-time health evaluation algorithm of a thermal power unit according to embodiment 1 of the present invention;
FIG. 3 is a graph showing changes in the real-time value of the outlet air temperature of the coal mill, the upper limit of the 1-stage reference value, the lower limit of the 1-stage reference value, the upper limit of the 2-stage reference value and the lower limit of the 2-stage reference value according to the embodiment 1 of the present invention;
FIG. 4 is a graph of the variation between the current and the input coal amount of the coal pulverizer of example 1 of the present invention, and the upper 1-stage reference value limit, the lower 1-stage reference value limit, the upper 2-stage reference value limit, the lower 2-stage reference value limit, and the input coal amount calculated based on the unitary linear regression model;
Fig. 5 is a block diagram of a real-time health evaluation system of a thermal power generating unit according to embodiment 2 of the present invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Example 1
As shown in the attached figure 1, the real-time health evaluation method of the thermal generator set comprises the following steps:
Step 1, as shown in fig. 2, according to the thermal power generation process flow, all the devices contained in one unit are divided into 3 logic levels, namely a unit level, a system level and a device level. For a certain 600MW supercritical thermal power generating unit, the system level comprises a water supply system, a heat recovery system, a condensate system, a pulverizing system, a flue gas system, a turbine bypass system, a denitration system, a coal conveying system and other systems; the equipment level is arranged below the system level, for example, the water supply system comprises A, B side steam feed pumps, electric feed pumps and other equipment; the subsystem level can be arranged below the complex system, for example, the pulverizing system comprises 6 subsystems, namely No. 1-6 pulverizing subsystems, and each subsystem comprises a coal mill, a coal feeder, a cyclone separator and other devices.
Step 2, determining all state variables related to the health degree of each device of the device level. For the 600MW thermal power generating unit in the step 1, the state variables corresponding to the coal mill equipment include current, oil pressure, inlet-outlet differential pressure, outlet pressure, inlet air powder temperature, outlet air powder temperature, motor bearing temperature, coil temperature and the like.
And 3, determining a mathematical model for 1-level reference value upper limit H 1, 1-level reference value lower limit L 1, 2-level reference value upper limit H 2, 2-level reference value lower limit L 2 or reference value calculation of each state variable. The reference values are classified into the following two types according to the characteristics of the state variables:
(1) Constant reference value
The standard value is constant and can not be changed along with the change of the operation condition, and the standard value can be generally set according to the threshold value or the equipment operation requirement given by the units of manufacturers, design houses, electric departments and the like.
Fig. 3 shows the change curves of the outlet air temperature real-time value, the upper level 1 reference value limit H 1, the lower level 1 reference value limit L 1, the upper level 2 reference value limit H 2 and the lower level 2 reference value limit L 2 of a certain coal mill, wherein the reference value of the outlet air temperature of the coal mill is constant.
(2) Variable reference value
Such reference values are variables that change as the operating conditions change, and mathematical models and dependent variables that calculate the reference values need to be determined in advance. One or more dependent variables related to the reference value are determined through data correlation analysis, and then a mathematical model of the change of the reference value along with the dependent variables is obtained through modeling algorithms such as linear regression, a neural network and the like. When the health degree evaluation is carried out, the real-time measured value of the related dependent variable is input into a mathematical model, and the reference value of the state variable can be calculated.
Fig. 4 shows the curves of the current and the input coal quantity of a certain coal mill, and the 1-level reference value upper limit H 1, the 1-level reference value lower limit L 1, the 2-level reference value upper limit H 2, the 2-level reference value lower limit L 2 and the input coal quantity calculated based on the unitary linear regression model.
Step 4, setting weight coefficients of health degree components of all state variables in the total health degree of equipment to which the state variables belong; determining a weight coefficient of the health degree component of each device in the total system health degree to which the health degree component belongs; and determining the weight coefficient of the health degree component of each system in the total unit health degree. The weight coefficients of all levels can be assigned according to three methods of a subjective weighting method, an objective weighting method and a combined weighting method, and for a certain 600MW thermal power unit, some unimportant devices can adopt the subjective weighting method, and important devices and systems adopt the objective weighting method or the combined weighting method.
Step 5, calculating the health degree of each device at the device level:
(1) Calculating the health of all the state variables contained by the respective devices
① Calculating a deviation normalized value delta' ijk between each state variable and the reference value, including
Where p ijk is the value of the kth state variable contained in the jth device of the ith system, H 1ijk is the upper level 1 reference value limit, L 1ijk is the lower level 1 reference value limit, H 2ijk is the upper level 2 reference value limit, and L 2ijk is the lower level 2 reference value limit for that state variable.
The state variable p ijk, namely a state real-time measured value, can be generally obtained from a set DCS system, and can be subjected to smoothing treatment for eliminating noise, or can be an average value of a plurality of continuous real-time measured values in a last period of time.
② Calculating the change rate normalized value v' ijk of each state variable, including
Where v ijk is the rate of change of the kth state variable contained by the jth device of the ith system, there is
Wherein T is a sampling period, V ijk is the upper limit of the change rate, nT represents the current sampling time, (n-1) T represents the last sampling time;
③ Calculating normalized value tau' ijk of overrun time of each state variable, including
Where τ ijk is the overrun time of the kth state variable contained by the jth device of the ith system and t ijk is the upper limit of the overrun time.
④ A normalized integral value χ' ijk for calculating the deviation between each state variable and the reference value, including
Where χ ijk is the integral value of the deviation between the kth state variable and the reference value contained in the jth device of the ith system, and X ijk is the upper limit of the integral value.
⑤ The health of the state variable is obtained by weighting calculation from 4 dimensions of the deviation amount, the change rate, the overrun time and the deviation amount integral of the state variable, namely
Hijk＝(wijk1(1-δ′ijk)+wijk2(1-δ′ijk)(1-v′ijk)+wijk3(1-τ′ijk)+wijk4(1-χ′ijk))×100 (6)
Where w ijk1、wijk2、wijk3 and w ijk4 are weight coefficients for each dimension.
(2) The health of each device at the device level is weighted by the health components of each state variable contained therein by weight coefficients, i.e
Where H ij is the health of the jth device of the ith system, H ijk is the health component of the kth state variable contained in the device, w ijk is the weight coefficient of the kth state variable, and N ijk is the number of state variables contained in the device.
Step 6, the health degree of each system of the system level is calculated by weighting the health degree components of each equipment contained by each system by weight coefficients, namely
Wherein H i is the health degree of the ith system, H ij is the health degree component of the jth device contained in the system, w ij is the weight coefficient of the jth device, and N ij is the number of devices contained in the system.
If a system belongs to a subsystem level, the health degree calculation form is shown as a formula (8), and health degree components of all subsystems contained by the system are weighted and calculated according to weight coefficients. If the system does not include a device or subsystem, the health is weighted by the health components of the state variables it includes according to the weight coefficients, as in step 5.
Step 7, the health degree of the unit is calculated by weighting the health degree components of each system according to weight coefficients, namely
Wherein H is the health degree of the unit, H i is the health degree component of the ith system, w i is the weight coefficient of the ith system, and N is the number of systems contained in the unit. The thermal power generating unit is a system which runs continuously, so that the real-time health index of the unit needs to be continuously evaluated, and after the calculation of the step is completed, a certain sampling period is arranged, and the step returns to the step 5 to be repeatedly executed.
Example 2
The embodiment provides a real-time health evaluation system of a thermal generator set, which is composed of a grading unit, an all-state variable determining unit, a state variable reference value determining unit, a weight coefficient unit, a health calculating unit of each equipment of the equipment level, a system level health calculating unit and a unit level health calculating unit as shown in fig. 5.
A classification unit: according to the thermal power generation process flow, all the equipment contained in one unit is divided into three logic levels, namely a unit level, a system level and an equipment level;
All state variable determining units: determining all state variables related to the health of each device at the device level;
State variable reference value determining unit: determining a 1-level reference value upper limit H 1, a 1-level reference value lower limit L 1, a 2-level reference value upper limit H 2, a 2-level reference value lower limit L 2 or a mathematical model for reference value calculation of each state variable;
Weight coefficient unit: setting weight coefficients of health degree components of all state variables in the total health degree of equipment to which the state variables belong; determining a weight coefficient of the health degree component of each device in the total system health degree to which the health degree component belongs; determining a weight coefficient of the health degree component of each system in the total unit health degree;
a device-level health degree calculation unit: integrating four dimensions from the deviation amount, the change rate, the overrun time and the deviation amount of the state variable, and obtaining the health degree component of the state variable through weighted calculation; the health degree of each device is calculated by weighting health degree components of state variables contained in the device according to weight coefficients;
a system-level health degree calculation unit: the health degree of each system is calculated by the health degree components of each device contained by each system according to the weighting coefficient;
Unit level health degree calculating unit: the health degree of the unit is calculated by weighting health degree components of each system according to weight coefficients.
Specifically, in the grading unit, for some complex systems included in the unit, a subsystem level is added between a system level and a device level, so that a logic relationship is clearer.
Specifically, in the all state variable determining units, the state variable selects a state variable affecting at least one of the operation safety, the operation cost, the environmental protection performance and the automatic control performance of the unit.
Specifically, in the state variable reference value determination unit, the reference values are classified into the following two types according to the characteristics of the state variables:
1) Constant reference value
The reference value is constant and cannot be changed along with the change of the operation condition, and the reference value is set according to a set threshold value or specific operation requirements of equipment;
2) Variable reference value
Such reference values are variables that change as the operating conditions change, requiring the prior determination of mathematical models and dependent variables that calculate the reference values: firstly, determining one or more dependent variables related to the reference value through data correlation analysis, and then obtaining a mathematical model of the change of the reference value along with the dependent variables by using a modeling algorithm; when the health degree of a certain state variable is evaluated, the real-time measured value of the related dependent variable is input into a mathematical model, and the reference value of the state variable can be calculated.
Specifically, in the weight coefficient unit, the weight coefficient represents the importance degree of each health degree component in the total amount of the superior health degrees to which the weight coefficient unit belongs, and the weight coefficient unit assigns values according to the following three methods;
1) Subjective weighting method
Assigning weight coefficients of each health degree component;
2) Objective weighting method for determining weight coefficient by entropy weighting method
According to the variation degree of each health degree component, determining an objective weight coefficient of each component;
3) Combined weighting method combining subjective and objective
Firstly, determining the weight coefficient of each health degree component by using an entropy weight method, and then, for some important state variables, equipment or systems, adjusting the corresponding weight coefficient by using a subjective weighting method.
Specifically, the specific content of the device-level health degree calculating unit is as follows:
1) Calculating the health of all the state variables contained by the respective devices
① Calculating a deviation normalized value delta' ijk between each state variable and the reference value, including
Where p ijk is the kth state variable value contained by the jth device of the ith system, H 1ijk is the upper level 1 reference value limit for the state variable, L 1ijk is the lower level 1 reference value limit for the state variable, H 2ijk is the upper level 2 reference value limit for the state variable, and L 2ijk is the lower level 2 reference value limit for the state variable;
the state variable value p ijk is a state real-time measured value, is obtained from a set DCS system, and is subjected to smoothing treatment or takes the value as an average value of a plurality of continuous real-time measured values in the last period of time for eliminating noise;
For the constant reference value, under different operation conditions, the value is a preset fixed value, and calculation is not needed; for a variable type reference value, the value of the variable type reference value changes along with the change of the operation condition, a dependent variable measurement value related to the reference value is required to be collected, input into a corresponding mathematical model and calculated to obtain the reference value;
② Calculating a change rate normalized value v' ijk of each state variable, including:
Where v ijk is the rate of change of the kth state variable contained by the jth device of the ith system, and there is:
wherein T is a sampling period, V ijk is the upper limit of the change rate, nT represents the current sampling time, (n-1) T represents the last sampling time;
③ Calculating normalized value tau' ijk of overrun time of each state variable, including
Where τ ijk is the overrun time of the kth state variable contained by the jth device of the ith system and t ijk is the upper limit of the overrun time;
④ A normalized integral value χ' ijk for calculating the deviation between each state variable and the reference value, including
Wherein χ ijk is an integrated value of the deviation amount between the kth state variable and the reference value contained in the jth device of the ith system, and X ijk is an upper limit of the integrated value;
⑤ The health of the state variable is obtained by weighting calculation by integrating four dimensions of the deviation amount, the change rate, the overrun time and the deviation amount of the state variable
Hijk＝(wijk1(1-δ′ijk)+wijk2(1-δ′ijk)(1-v′ijk)+wijk3(1-τ′ijk)+wijk4(1-χ′ijk))×100 (6)
Wherein w ijk1、wijk2、wijk3 and w ijk4 are respectively the deviation amount, the change rate, the overrun time and the weight coefficient of the deviation amount integration;
2) The health degree of each device at the device level is calculated by weighting the health degree components of each state variable contained by the device level according to weight coefficients, namely:
Where H ij is the health of the jth device of the ith system, H ijk is the health component of the kth state variable contained in the device, w ijk is the weight coefficient of the kth state variable contained in the device, and N ijk is the number of state variables contained in the device.
Specifically, in the system-level health degree calculation unit, the calculation formula of the health degree of each system at the system level is as follows:
Wherein H i is the health degree of the ith system, H ij is the health degree component of the jth device contained in the system, w ij is the weight coefficient of the jth device, and N ij is the number of devices contained in the system.
More specifically, if the system belongs to a subsystem level, the health degree calculation form is as shown in formula (8), and health degree components of all subsystems contained by the system are weighted and calculated according to weight coefficients; if the system does not include a device or subsystem, the health calculation is performed as step 5, in which the health components of the state variables included therein are weighted by weight coefficients.
Specifically, in the unit health care calculation unit, a calculation formula of the unit health degree is as follows:
Wherein H is the health degree of the unit, H i is the health degree component of the ith system, w i is the weight coefficient of the ith system, and N is the number of systems contained in the unit.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (9)
1. A real-time health evaluation method of a thermal generator set is characterized by comprising the following steps:
Step 1, dividing all equipment contained in one unit into three logic levels, namely a unit level, a system level and an equipment level according to the thermal power generation process flow;
Step 2, determining all state variables related to the health degree of each device of the device level;
Step 3, determining a mathematical model for calculating a 1-level reference value upper limit H 1, a 1-level reference value lower limit L 1, a 2-level reference value upper limit H 2, a 2-level reference value lower limit L 2 or reference values of each state variable;
step 4, setting weight coefficients of the health degrees of all the state variables in the total health degree of the equipment to which the state variables belong; determining a weight coefficient of the health degree of each device in the total system health degree to which the device belongs; determining a weight coefficient of the health degree of each system in the total health degree of the unit;
Step 5, calculating the health degree of each device at the device level: integrating four dimensions from the deviation amount, the change rate, the overrun time and the deviation amount of the state variable, and obtaining the health degree of the state variable through weighted calculation; the health degree of each device is calculated by weighting the health degree of the state variable contained by each device according to a weight coefficient;
Step 6, calculating the health degree of the system level: the health degree of each system is calculated by weighting the health degree of each device contained by each system according to a weight coefficient;
Step 7, computer group level health: the health degree of the unit is calculated by weighting the health degree of each system according to a weight coefficient;
The specific content of the step 5 is as follows:
1) Calculating the health of all the state variables contained by the respective devices
① Calculating a deviation normalized value delta' ijk between each state variable and the reference value, including
Where p ijk is the kth state variable value contained by the jth device of the ith system, H 1ijk is the upper level 1 reference value limit for the state variable, L 1ijk is the lower level 1 reference value limit for the state variable, H 2ijk is the upper level 2 reference value limit for the state variable, and L 2ijk is the lower level 2 reference value limit for the state variable;
the state variable value p ijk is a state real-time measured value, is obtained from a set DCS system, and is subjected to smoothing treatment or takes the value as an average value of a plurality of continuous real-time measured values in the last period of time for eliminating noise;
For the constant reference value, under different operation conditions, the value is a preset fixed value, and calculation is not needed; for a variable type reference value, the value of the variable type reference value changes along with the change of the operation condition, a dependent variable measurement value related to the reference value is required to be collected, input into a corresponding mathematical model and calculated to obtain the reference value;
② Calculating a change rate normalized value v' ijk of each state variable, including:
Where v ijk is the rate of change of the kth state variable contained by the jth device of the ith system, and there is:
wherein T is a sampling period, V ijk is the upper limit of the change rate, nT represents the current sampling time, (n-1) T represents the last sampling time;
③ Calculating normalized value tau' ijk of overrun time of each state variable, including
Where τ ijk is the overrun time of the kth state variable contained by the jth device of the ith system and t ijk is the upper limit of the overrun time;
④ A normalized integral value χ' ijk for calculating the deviation between each state variable and the reference value, including
Wherein χ ijk is an integrated value of the deviation amount between the kth state variable and the reference value contained in the jth device of the ith system, and X ijk is an upper limit of the integrated value;
⑤ The health of the state variable is obtained by weighting calculation from 4 dimensions of the deviation amount, the change rate, the overrun time and the deviation amount integral of the state variable, namely
Hijk＝(wijk1(1-δ′ijk)+wijk2(1-δ′ijk)(1-v′ijk)+wijk3(1-τ′ijk)+wijk4(1-χ′ijk))×100 (6)
Wherein w ijk1、wijk2、wijk3 and w ijk4 are respectively the deviation amount, the change rate, the overrun time and the weight coefficient of the deviation amount integration;
2) The health degree of each device at the device level is calculated by weighting the health degree of each state variable contained by the device level according to a weight coefficient, namely:
Where H ij is the health of the jth device of the ith system, H ijk is the health of the kth state variable contained in the device, w ijk is the weight coefficient of the kth state variable contained in the device, and N ijk is the number of state variables contained in the device.
2. The method for evaluating real-time health of a thermal generator set according to claim 1, wherein in step 1, subsystem stages are added between system stages and equipment stages for some complex systems included in the set, so that logic relationships are clearer.
3. The method for evaluating real-time health of a thermal generator set according to claim 1, wherein in step 2, the state variable is selected from the state variables affecting at least one of safety, running cost, environmental protection and automatic control performance of the set.
4. The method for evaluating real-time health of a thermal generator set according to claim 1, wherein in step 3, the reference values are classified into the following two types according to the characteristics of the state variables:
1) Constant reference value
The reference value is constant and cannot be changed along with the change of the operation condition, and the reference value is set according to a set threshold value or specific operation requirements of equipment;
2) Variable reference value
Such reference values are variables that change as the operating conditions change, requiring the prior determination of mathematical models and dependent variables that calculate the reference values: firstly, determining one or more dependent variables related to the reference value through data correlation analysis, and then obtaining a mathematical model of the change of the reference value along with the dependent variables by using a modeling algorithm; when the health degree of a certain state variable is evaluated, the real-time measured value of the related dependent variable is input into a mathematical model, and the reference value of the state variable is calculated.
5. The method for evaluating the real-time health degree of a thermal generator set according to claim 1, wherein in the step 4, the weight coefficient reflects the importance degree of each health degree in the total amount of the superior health degrees to which the weight coefficient belongs, and the weight coefficient is assigned according to the following three methods;
1) Subjective weighting method
Assigning a weight coefficient of each health degree;
2) Objective weighting method for determining weight coefficient by entropy weighting method
Determining objective weight coefficients of the health degrees according to the variation degrees of the health degrees;
3) Combined weighting method combining subjective and objective
Firstly, determining the weight coefficient of each health degree by using an entropy weight method, and then, for some important state variables, equipment or systems, adjusting the corresponding weight coefficient by using a subjective weighting method.
6. The method for evaluating the real-time health of a thermal generator set according to claim 1, wherein in step 6, the calculation formula of the health of each system at the system level is as follows:
Wherein H i is the health degree of the ith system, H ij is the health degree of the jth equipment contained in the system, w ij is the weight coefficient of the jth equipment, and N ij is the number of the equipment contained in the system.
7. The method for evaluating the real-time health degree of a thermal generator set according to claim 6, wherein if the system is subordinate to a subsystem level, the health degree is calculated in a form of formula (8), and the health degree of each subsystem included in the system is calculated by weighting according to a weight coefficient; if the system does not include a device or subsystem, the health degree is calculated as in step 5, i.e., the health degree of the state variables included by the system is weighted by the weight coefficients.
8. The method for evaluating real-time health of a thermal generator set according to claim 1, wherein in step 7, the calculation formula of the health of the set is as follows:
Wherein H is the health degree of the unit, H i is the health degree of the ith system, w i is the weight coefficient of the ith system, and N is the number of systems contained in the unit.
9. A real-time health evaluation system of a thermal generator set is characterized by comprising:
a classification unit: according to the thermal power generation process flow, all the equipment contained in one unit is divided into three logic levels, namely a unit level, a system level and an equipment level;
All state variable determining units: determining all state variables related to the health of each device at the device level;
State variable reference value determining unit: determining a 1-level reference value upper limit H 1, a 1-level reference value lower limit L 1, a 2-level reference value upper limit H 2, a 2-level reference value lower limit L 2 or a mathematical model for reference value calculation of each state variable;
Weight coefficient unit: setting weight coefficients of the health degrees of all the state variables in the total health degree of the equipment to which the state variables belong; determining a weight coefficient of the health degree of each device in the total system health degree to which the device belongs; determining a weight coefficient of the health degree of each system in the total health degree of the unit;
A device-level health degree calculation unit: integrating four dimensions from the deviation amount, the change rate, the overrun time and the deviation amount of the state variable, and obtaining the health degree of the state variable through weighted calculation; the health degree of each device is calculated by weighting the health degree of the state variable contained by each device according to a weight coefficient;
A system-level health degree calculation unit: the health degree of each system is calculated by weighting the health degree of each device contained by each system according to a weight coefficient;
Unit level health degree calculating unit: the health degree of the unit is calculated by weighting the health degree of each system according to a weight coefficient;
the specific content of the equipment-level health degree calculating unit is as follows:
1) Calculating the health of all the state variables contained by the respective devices
① Calculating a deviation normalized value delta' ijk between each state variable and the reference value, including
Where p ijk is the kth state variable value contained by the jth device of the ith system, H 1ijk is the upper level 1 reference value limit for the state variable, L 1ijk is the lower level 1 reference value limit for the state variable, H 2ijk is the upper level 2 reference value limit for the state variable, and L 2ijk is the lower level 2 reference value limit for the state variable;
the state variable value p ijk is a state real-time measured value, is obtained from a set DCS system, and is subjected to smoothing treatment or takes the value as an average value of a plurality of continuous real-time measured values in the last period of time for eliminating noise;
For the constant reference value, under different operation conditions, the value is a preset fixed value, and calculation is not needed; for a variable type reference value, the value of the variable type reference value changes along with the change of the operation condition, a dependent variable measurement value related to the reference value is required to be collected, input into a corresponding mathematical model and calculated to obtain the reference value;
② Calculating a change rate normalized value v' ijk of each state variable, including:
Where v ijk is the rate of change of the kth state variable contained by the jth device of the ith system, and there is:
wherein T is a sampling period, V ijk is the upper limit of the change rate, nT represents the current sampling time, (n-1) T represents the last sampling time;
③ Calculating normalized value tau' ijk of overrun time of each state variable, including
Where τ ijk is the overrun time of the kth state variable contained by the jth device of the ith system and t ijk is the upper limit of the overrun time;
④ A normalized integral value χ' ijk for calculating the deviation between each state variable and the reference value, including
Wherein χ ijk is an integrated value of the deviation amount between the kth state variable and the reference value contained in the jth device of the ith system, and X ijk is an upper limit of the integrated value;
⑤ The health of the state variable is obtained by weighting calculation by integrating four dimensions of the deviation amount, the change rate, the overrun time and the deviation amount of the state variable
Hijk＝(wijk1(1-δ′ijk)+wijk2(1-δ′ijk)(1-v′ijk)+wijk3(1-τ′ijk)+wijk4(1-χ′ijk))×100 (6)
Wherein w ijk1、wijk2、wijk3 and w ijk4 are respectively the deviation amount, the change rate, the overrun time and the weight coefficient of the deviation amount integration;
2) The health degree of each device at the device level is calculated by weighting the health degree of each state variable contained by the device level according to a weight coefficient, namely:
Where H ij is the health of the jth device of the ith system, H ijk is the health of the kth state variable contained in the device, w ijk is the weight coefficient of the kth state variable contained in the device, and N ijk is the number of state variables contained in the device.
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