WO2023279601A1 - 基于多维度评价因子的智能吹灰控制方法、系统及存储介质 - Google Patents
基于多维度评价因子的智能吹灰控制方法、系统及存储介质 Download PDFInfo
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- WO2023279601A1 WO2023279601A1 PCT/CN2021/128298 CN2021128298W WO2023279601A1 WO 2023279601 A1 WO2023279601 A1 WO 2023279601A1 CN 2021128298 W CN2021128298 W CN 2021128298W WO 2023279601 A1 WO2023279601 A1 WO 2023279601A1
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- 238000011156 evaluation Methods 0.000 title claims abstract description 257
- 239000004071 soot Substances 0.000 title claims abstract description 131
- 238000007664 blowing Methods 0.000 title claims abstract description 85
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000003860 storage Methods 0.000 title claims abstract description 10
- 238000010438 heat treatment Methods 0.000 claims abstract description 52
- 238000011109 contamination Methods 0.000 claims abstract description 35
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 57
- 239000003546 flue gas Substances 0.000 claims description 57
- 239000007789 gas Substances 0.000 claims description 18
- 239000012530 fluid Substances 0.000 claims description 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000005457 optimization Methods 0.000 claims description 10
- 239000000779 smoke Substances 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 239000003245 coal Substances 0.000 claims description 8
- 238000009826 distribution Methods 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000007246 mechanism Effects 0.000 claims description 4
- 108010063499 Sigma Factor Proteins 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 7
- 238000012423 maintenance Methods 0.000 description 4
- 230000001960 triggered effect Effects 0.000 description 4
- 238000013021 overheating Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000003749 cleanliness Effects 0.000 description 2
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000008021 deposition Effects 0.000 description 1
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- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
- 238000010926 purge Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23J—REMOVAL OR TREATMENT OF COMBUSTION PRODUCTS OR COMBUSTION RESIDUES; FLUES
- F23J1/00—Removing ash, clinker, or slag from combustion chambers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
- F23J—REMOVAL OR TREATMENT OF COMBUSTION PRODUCTS OR COMBUSTION RESIDUES; FLUES
- F23J3/00—Removing solid residues from passages or chambers beyond the fire, e.g. from flues by soot blowers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Definitions
- the invention relates to the field of intelligent control of coal-fired boiler equipment, in particular to an intelligent soot blowing control method, system and storage medium based on multi-dimensional evaluation factors.
- the soot blowing control strategy of the boiler is generally based on the operation rules and regularly performs soot blowing in accordance with the sequence of the flue gas flow, which will inevitably lead to frequent or untimely soot blowing. Too frequent soot blowing not only wastes the steam input cost of soot blowing, but also easily causes blowing damage and thinning of the heating surface of the boiler, which increases maintenance costs; if soot blowing is not timely, it will cause excessive ash accumulation on the heating surface, reduce the heat transfer effect of the heating surface, and cause economical damage to the unit. If it is lowered, it will cause the deviation of steam temperature and the overheating of the pipe wall, which will affect the safe operation of the unit.
- Sequential program-controlled sootblowing according to the flue gas flow may lead to overheating of the water wall, low or overheating of the main steam, and other phenomena.
- soot blowers there are many types of soot blowers, and the specific positions and specific times of different types of soot blowers have different effects on the soot blowing effect and the boiler.
- sootblowing optimization system often uses modeling methods to evaluate the degree of contamination of the heating surface, but there are many related factors affecting sootblowing, and each factor has volatility and coupling. actual contamination.
- the above method does not consider the influence of soot blowing on boiler operating parameters.
- the boiler operating parameters deviate from the design value during or after soot blowing, which affects the safe and economical operation of the boiler.
- the present invention provides an intelligent soot blowing control method, system and storage medium based on multi-dimensional evaluation factors, which are used to solve the problem that the existing soot blowing optimization system cannot accurately evaluate the degree of contamination of the heating surface and the impact of soot blowing on the safe and economical operation of the boiler. Influence, technical problems that lead to poor effect of the formulated sootblowing optimization scheme.
- An intelligent soot blowing control method based on multi-dimensional evaluation factors comprising the following steps:
- multiple boiler operating parameters are selected as the evaluation factors for selecting soot blowers.
- Multiple boiler operating parameters reflect the degree of contamination of the corresponding heating surface of the soot blower and the impact of soot blowing on the safety and economy of boiler operation. ;
- each evaluation factor is grouped into priority levels
- the evaluation factors include: furnace outlet flue temperature, water wall temperature, degree of superheat, superheater desuperheating water volume, screen wall temperature, high wall temperature, main steam temperature, reheat steam temperature, denitrification system inlet flue temperature, exhaust gas temperature;
- the evaluation factors include: furnace outlet flue temperature, shield inlet, outlet working fluid temperature rise, superheater desuperheating water volume, flue gas baffle opening, screen wall temperature deviation, The maximum value of screen wall temperature, differential pressure of screen flue gas side, main steam temperature, reheat steam temperature, inlet flue temperature of denitrification system, exhaust gas temperature;
- the evaluation factors include: higher than the inlet and outlet working fluid temperature rise, superheater desuperheating water volume, flue gas baffle opening, higher than the wall temperature deviation, the highest higher than the wall temperature Value, differential pressure above flue gas side, main steam temperature, reheat steam temperature, inlet flue temperature of denitrification system, exhaust flue gas temperature;
- the evaluation factors include: high re-inlet, outlet temperature rise, flue gas baffle opening, reheater desuperheating water volume, high re-wall temperature deviation, high re-wall temperature Maximum value, high reflue gas side differential pressure, main steam temperature, reheat steam temperature, inlet flue temperature of denitrification system, exhaust flue gas temperature;
- the evaluation factors include: low pass outlet pipe wall temperature, low pass inlet, outlet working fluid temperature, low pass inlet, outlet flue gas temperature, air preheater inlet flue temperature, low pass Side flue gas resistance, flue gas temperature at the inlet of the denitrification system, exhaust gas temperature, induced draft fan current;
- the evaluation factors include: low re-exit tube wall temperature, low re-inlet, outlet working fluid temperature, low re-inlet, outlet flue gas temperature, air preheater inlet flue temperature, low re-entry Side flue gas resistance, flue gas temperature at the inlet of the denitrification system, exhaust gas temperature, induced draft fan current;
- the evaluation factors include: economizer inlet and outlet working fluid temperature, economizer inlet and outlet flue gas temperature, air preheater inlet flue temperature, economizer flue gas resistance , Denitrification system inlet flue temperature, exhaust gas temperature, induced draft fan current;
- the evaluation factors include: air preheater inlet flue gas temperature, air preheater outlet flue gas temperature, air preheater flue gas side resistance, air pre The resistance of the secondary air side of the preheater and the current of the induced draft fan.
- each evaluation factor is prioritized into groupings, including the following steps:
- the evaluation factor on boiler safety is less than the preset first safety impact threshold and greater than the preset second safety impact threshold, wherein the first safety impact threshold is greater than the second safety impact threshold and any of the following conditions is met , then it is judged that the evaluation factor is the second priority level:
- the impact value of the evaluation factor on the degree of economic operation exceeds the preset economic impact threshold
- the value of the correlation degree between the evaluation factor and the contamination of the heating surface exceeds the preset threshold value of the contamination degree of the heating surface
- the evaluation factor is judged to be the third priority level:
- the impact value of the evaluation factor on the degree of economic operation does not exceed the preset economic impact threshold
- the value of the correlation degree between the evaluation factor and the contamination of the heating surface does not exceed the preset threshold value of the contamination degree of the heating surface.
- each evaluation factor is grouped in a priority level, which is achieved by looking up the following table:
- judging whether the value of each evaluation factor is within the normal state range specifically includes the following steps:
- ⁇ is the average value of the historical data distribution of the type evaluation factor
- ⁇ is the standard deviation of the historical data distribution of the type evaluation factor
- x is the optimization saturation factor of the sootblower, which is determined according to the condition of the unit
- the value of the evaluation standard is not greater than the alarm value of the corresponding evaluation factor, the value of the evaluation standard should be within the operating safety margin of the corresponding evaluation factor, and the deviation of the normal operation average value of the corresponding evaluation factor should be considered.
- judging whether a sootblower is required to perform sootblowing is determined according to the number of evaluation factors in each group of priority levels whose values are not within the normal state range, including the following steps:
- S is the comprehensive evaluation value
- a is the weight of the first priority level
- n (in) is the number of evaluation factors in the first priority level whose value is not in the normal state
- b is the weight of the second priority level
- n(j n ) is the number of evaluation factors whose value is not in the normal state in the second priority level
- c is the weight of the third priority level
- n(k n ) is the number of evaluation factors in the third priority level whose value is not in the normal state
- the number of evaluation factors of the state value where a>b>c, and 0 ⁇ a, 0 ⁇ b, 0 ⁇ c;
- the Y value of the evaluation standard can be updated rollingly according to the historical data of the previous period to adapt to the changes of different coal types.
- the length of the time period can be determined according to the actual situation of the unit, and the update range of the Y value of the evaluation standard can be adjusted.
- the magnitude factor preferably ranges from 0.1 to 1.0.
- a computer system includes a memory, a processor, and a computer program stored in the memory and operable on the processor, and the steps of the method are implemented when the processor executes the computer program.
- a computer storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the above method are realized.
- the plurality of boiler operating parameters reflect the soot blower Corresponding to the degree of contamination of the heating surface and the impact of soot blowing on the safety and economy of boiler operation; according to the impact of each evaluation factor on the safety of the boiler, the degree of impact on economic operation, and the degree of correlation with the contamination of the heating surface Group the priority levels of each evaluation factor separately; judge whether the value of each evaluation factor is within the normal state range, count the number of evaluation factors within each group’s priority level and whose value is not within the normal state range, and The number of evaluation factors whose values are not within the normal range is used to determine whether a soot blower is required for soot blowing.
- the invention can accurately judge the degree of contamination of the selected soot blower corresponding to the heating surface and the influence of soot blowing on the safety and economy of boiler operation, and determine whether to use the soot blower for soot blowing according to the determined influence. Thereby reducing the soot blowing steam loss of the power plant, reducing the risk of blowing damage to the heating surface, reducing the maintenance of soot blowing equipment and the labor of soot blowing, and improving the safety and economy of boiler operation.
- the present invention uses a big data self-defined algorithm to combine soot blowing optimization with boiler operating parameter optimization and heating surface cleanliness for multi-dimensional evaluation, thereby realizing refined and on-demand soot blowing.
- the evaluation standard Y in the present invention is updated rollingly according to the historical data of the previous n days to realize the self-learning function to adapt to the changes of different coal types.
- the multi-dimensional evaluation factors of each heating surface of the present invention are divided into different levels according to the degree of influence on the safe and economical operation of the boiler and the degree of correlation between the contamination of the heating surface and the evaluation factors, and different levels adopt different judgment conditions. Realize the prioritization and differentiation of evaluation factors, so as to more accurately evaluate the influence of sootblower soot blowing on boiler operation status.
- Fig. 1 is the flowchart of the intelligent soot blowing control method based on multidimensional evaluation factors of the present invention
- Fig. 2 is a flowchart of an intelligent sootblowing control method based on multi-dimensional evaluation factors in a preferred embodiment of the present invention.
- this embodiment discloses an intelligent soot blowing control method based on multi-dimensional evaluation factors, including the following steps:
- multiple boiler operating parameters are selected as the evaluation factors for selecting soot blowers.
- Multiple boiler operating parameters reflect the safety and economical operation of the boiler for the corresponding area of the soot blower, the corresponding type of ash layer, and the corresponding heating surface. The influence of the correlation degree of contamination;
- each evaluation factor is grouped into priority levels
- a computer system including a memory, a processor, and a computer program stored in the memory and operable on the processor, and the steps of the method are realized when the processor executes the computer program.
- a computer storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the above method are realized.
- Factors are grouped according to the priority level; judge whether the value of each evaluation factor is within the normal range, count the number of evaluation factors within the priority level of each group, and whose value is not within the normal state range, and based on the values within the priority level of each group
- the number of evaluation factors within the normal state range determines whether a sootblower is required for sootblowing.
- the invention can accurately judge the degree of contamination of the selected soot blower corresponding to the heating surface and the influence of soot blowing on the safety and economy of boiler operation, and determine whether to use the soot blower for soot blowing according to the determined influence. Thereby reducing the soot blowing steam loss of the power plant, reducing the risk of blowing damage to the heating surface, reducing the maintenance of soot blowing equipment and the labor of soot blowing, and improving the safety and economy of boiler operation.
- Embodiment 2 is a preferred embodiment of Embodiment 1. It differs from Embodiment 1 in that it refines the specific steps of the intelligent sootblowing control method based on multi-dimensional evaluation factors, including the following steps:
- Sootblowers at different positions are used to remove soot deposits on the heating surfaces in different areas of the boiler.
- Different boiler operating parameters can directly or indirectly reflect the degree of soot contamination on different heating surfaces and the safe and economical operation of the boiler before and after soot blowing. Therefore, if In order to accurately judge the degree of soot accumulation on the heating surface, at the same time ensure the safety and economy of boiler operation before and after sootblower sootblowing, and control the refinement of sootblower to perform sootblowing efficiently as needed, it is necessary to accurately select the corresponding sootblower.
- the boiler operating parameter group is necessary to accurately select the corresponding sootblower.
- an intelligent soot blowing control method based on multi-dimensional evaluation factors including the following steps:
- Step 1 Group the boiler soot blowers into groups.
- the specific grouping methods can be as follows: 1) Furnace short blowing: the first layer of short blowing, the second layer of short blowing, the third layer of short blowing, and the fourth layer of short blowing; 2) Flame folding area: screen pass, high pass, high pass; 3) Shaft flue: low pass, low pass, economizer; 4) Air preheater soot blower.
- Each sootblower in the above-mentioned sootblower group can be further grouped to realize refined sootblowing in the area of the heating surface.
- Step 2 Select the multi-dimensional evaluation factors of the sootblower group based on expert experience and mechanism analysis.
- the multi-dimensional evaluation factors of sootblower group include but not limited to the following parameters:
- furnace outlet flue temperature pan bottom flue gas temperature
- water-cooled wall temperature degree of superheat, desuperheating water volume of superheater
- screen wall temperature high wall temperature
- main steam temperature Hot steam temperature
- flue gas temperature at the inlet of the denitrification system exhaust gas temperature
- Low pass sootblower group evaluation factors low pass outlet pipe wall temperature, low pass inlet and outlet working fluid temperature, low pass inlet and outlet flue gas temperature, air preheater inlet flue temperature, low pass side flue gas resistance, Inlet smoke temperature of denitrification system, exhaust gas temperature, induced draft fan current;
- Economizer sootblower group evaluation factors economizer inlet and outlet working fluid temperature, economizer inlet and outlet flue gas temperature, air preheater inlet flue temperature, economizer flue gas resistance, denitration system inlet smoke Temperature, exhaust gas temperature, induced draft fan current;
- the evaluation factors of each sootblower group are divided into three levels according to the degree of influence on the safe and economical operation of the boiler and the degree of correlation between the contamination of the heating surface and the evaluation factors, as shown in Table 1.
- Table 1 is only an example of a preferred evaluation factor grading, and is not intended as a limitation. factor, and other instances can be classified differently according to the actual situation of the unit.
- Step 3 Establish evaluation criteria.
- the standard deviation of the distribution of historical data, x is the optimal saturation factor of the sootblower, which is determined according to the condition of the unit, and the value ranges from 1.0 to 3.0.
- the preferred evaluation standard Y should be determined with reference to the alarm value of the wall temperature.
- the preferred evaluation standard Y should be determined with reference to the alarm value of steam temperature.
- the preferred evaluation standard Y should be determined with reference to the upper limit of the allowable operating flue gas temperature of the denitrification system.
- the preferred evaluation standard Y should be determined with reference to the upper limit of the allowable operating smoke temperature of the air preheater.
- the value of w is 0.7, and when S>0.7, it is considered that the soot blowing condition is triggered.
- the weight factors a, b, and c of each level are respectively set to 1, 0.5, and 0.3 as a preferred example, but not as a limitation, and other weight values can be assigned according to the situation of the unit. See Table 2 for the number of evaluation factors at each priority level that meet the corresponding evaluation criteria Y and whether the sootblowing conditions are triggered.
- Step 5 When the set evaluation condition is triggered, the corresponding sootblower group performs sootblowing.
- Step 6 Judgment criteria are updated rollingly every day using the operation data of the previous period.
- the length of the time period can be determined according to the actual situation of the unit.
- the preferred value range is 1.0 to 10.0 days.
- Y 0 is the evaluation standard before the update
- Y n is the history of n days before the update
- the evaluation standard for data calculation, ⁇ is the update range factor of the evaluation standard, and the preferred value range is 0.1-1.0.
- the update range factor of the evaluation standard When the quality of the coal fired by the boiler changes frequently, a larger value should be selected for the update range factor of the evaluation standard to adapt to the change of coal quality. When the quality of the incoming coal burned by the boiler is stable for a long time, the update range factor of the evaluation standard should be selected with a smaller value to reduce the fluctuation range of the evaluation standard.
- Each evaluation factor is judged in real time to meet the demand for real-time soot blowing on demand. After a certain sootblower group blows soot, judge the evaluation factor of the sootblower group one hour later.
- soot blower group that has not triggered the soot blowing condition for 3 consecutive days performs soot blowing once, to avoid that some soot blower groups have less influence on the evaluation factors and cannot obtain effective soot blowing.
- the soot blower at the slope of the flame angle should be blown regularly to avoid the collapse of the flame angle, which will affect the combustion stability in the furnace, and even cause the unit to stop abnormally.
- the intelligent sootblowing control method, system and storage medium based on multi-dimensional evaluation factors in the present invention by selecting multiple boiler operating parameters as the evaluation factors for selecting sootblowers, multiple boiler operating parameters reflect the sootblowing According to the degree of contamination of the heating surface and the impact of soot blowing on the safety and economy of boiler operation; according to the degree of impact of each evaluation factor on boiler safety, the degree of impact on economic operation, and the degree of correlation with the contamination of the heating surface Prioritize each evaluation factor into high and low groupings; judge whether the value of each evaluation factor is within the normal state range, count the number of evaluation factors within each group's priority level and whose value is not within the normal state range, and base the priority level of each group The number of evaluation factors whose value is not within the normal range is used to determine whether a soot blower is required for soot blowing.
- the invention can accurately judge the degree of contamination of the selected soot blower corresponding to the heating surface and the influence of soot blowing on the safety and economy of boiler operation, and determine whether to use the soot blower for soot blowing according to the determined influence. Thereby reducing the soot blowing steam loss of the power plant, reducing the risk of blowing damage to the heating surface, reducing the maintenance of soot blowing equipment and the labor of soot blowing, and improving the safety and economy of boiler operation.
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Abstract
基于多维度评价因子的智能吹灰控制方法、系统及存储介质，通过选取多个反映吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性影响的锅炉运行参数作为选取吹灰器的评价因子，根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰，从而减少电厂的吹灰蒸汽损耗，降低受热面吹损风险，提高锅炉运行的安全性和经济性。
Description
本发明涉及燃煤锅炉设备智能化控制领域，尤其涉及基于多维度评价因子的智能吹灰控制方法、系统及存储介质。
火电机组锅炉在煤粉燃烧过程中，由于不可燃矿物的沉积，各受热面会产生不同程度的积灰结渣。受热面积灰结渣会降低受热面的传热性能，是严重影响锅炉出力和锅炉效率的主要原因。所以在锅炉运行中，及时采用吹灰器进行受热面吹扫对锅炉的高效、安全运行显得尤为重要。
目前锅炉的吹灰控制策略一般基于操作规程定期按照烟气流程顺序进行吹灰，这势必造成吹灰频繁或者不及时。吹灰太频繁不仅浪费吹灰的蒸汽投入成本而且易造成锅炉受热面吹损减薄，增加维护费用；吹灰不及时将造成受热面过度积灰，降低受热面传热效果，导致机组经济性降低，更为严重的会引起汽温偏差和管壁超温，影响机组安全运行。而按照烟气流程进行顺序程控吹灰，可能会导致水冷壁超温、主蒸汽温度过低或者超温等现象发生。此外，吹灰器的种类众多，不同种类的吹灰器所应用的具体位置和具体时间对吹灰效果、以及锅炉的影响也有差异。
现阶段的吹灰优化系统常用建模的方法来评估受热面的沾污程度，但是影响吹灰的相关因素较多，各因素存在波动性和耦合性，建立的模型难以准确反映锅炉受热面的实际沾污情况。此外，上述方法没有考虑吹灰对锅炉运行参数的影响，锅炉运行参数在吹灰过程中或吹灰后偏离设计值较多，影响锅炉安全经济运行。
因此，现有的吹灰优化系统由于不能准确评估受热面的沾污程度以及吹灰对锅炉安全经济运行的影响，导致制定的吹灰优化方案效果差已成为本领域技术人员亟待解决的技术问题，研究基于锅炉运行参数优化和受热面洁净程度相结合的精细化按需吹灰非常必要。
发明内容
本发明提供了基于多维度评价因子的智能吹灰控制方法、系统及存储介质，用于解决现有的吹灰优化系统由于不能准确评估受热面的沾污程度以及吹灰对锅炉安全经济运行的影响，导致制定的吹灰优化方案效果差的技术问题。
为解决上述技术问题，本发明提出的技术方案为：
一种基于多维度评价因子的智能吹灰控制方法，包括以下步骤：
根据专家经验和机理分析选取多个锅炉运行参数作为选取吹灰器的评价因子，多个锅炉运行参数反映吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响；
根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；
判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰，若需要，则开启吹灰器进行吹灰操作。
优选的，当吹灰器为炉膛短吹灰器时，评价因子包括：炉膛出口烟温、水冷壁壁温、过热度、过热器减温水量、屏过壁温、高过壁温、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
当吹灰器为屏过吹灰器时，评价因子包括：炉膛出口烟温、屏过进、出口工质温升、过热器减温水量、烟气挡板开度、屏过壁温偏差、屏过壁温最大值、屏过烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
当吹灰器为高过吹灰器时，评价因子包括：高过进、出口工质温升、过热器减温水量、烟气挡板开度、高过壁温偏差、高过壁温最大值、高过烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
当吹灰器为高再吹灰器时，评价因子包括：高再进、出口工质温升、烟气挡板开度、再热器减温水量、高再壁温偏差、高再壁温最大值、高再烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
当吹灰器为低过吹灰器时，评价因子包括：低过出口管壁温、低过进、出口工质温度、低过进、出口烟气温度、空预器入口烟温、低过侧烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；
当吹灰器为低再吹灰器时，评价因子包括：低再出口管壁温、低再进、出口工质温度、低再进、出口烟气温度、空预器入口烟温、低再侧烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；
当吹灰器为省煤器吹灰器时，评价因子包括：省煤器进、出口工质温度、省煤器进、出口烟气温度、空预器入口烟温、省煤器烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；
当吹灰器为空预器吹灰器组，评价因子包括：空预器进口烟气温度、空预器出口烟气温度、空预器烟气侧阻力、空预器一次风侧阻力、空预器二次风侧阻力、引风机电流。
优选的，根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组，包括以下步骤：
对于每一个评价因子均执行以下步骤：
分析评价因子对锅炉安全影响的大小，将评价因子对锅炉安全影响的大小与预设的第一安全影响阈值进行比较，若评价因子对锅炉安全影响的大小大于预设的第一安全影响阈值，则判断评价因子为第一优先层；
若评价因子对锅炉安全的影响值小于预设的第一安全影响阈值，且大于预设的第二安全影响阈值，其中，第一安全影响阈值大于第二安全影响阈值，且满足以下任一条件，则判断评价因子为第二优先层级：
评价因子对经济运行程度的影响值超过预设的经济影响阈值；
评价因子与受热面沾污之间关联程度值超过预设的受热面沾污程度阈值；
若评价因子对锅炉安全的影响值小于预设的第二安全影响阈值或同时满足以下条件，则判断评价因子为第三优先层级：
评价因子对经济运行程度的影响值未超过预设的经济影响阈值；
评价因子与受热面沾污之间关联程度值未超过预设的受热面沾污程度阈值。
优选的，根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组，通过查找下表实现：
各吹灰器组评价因子分级表
优选的，判断各个评价因子的值是否处于正常状态范围内，具体包括以下步骤：
对于每一个评价因子均执行以下步骤：
获取种类评价因子的历史数据，计算历史数据的均值和标准差，并通过以下公式计算种类的评价因子正常值的取值范围作为评价标准Y:
Y＝μ±xσ；
其中，μ为种类评价因子的历史数据分布的平均值，σ为种类评价因子的历史数据分布的标准差，x是吹灰器优化饱和因数，根据机组情况确定；
将评价因子的值与评价标准Y进行比较，若评价因子的值在评价标准Y内，则判断评价因子处于正常状态范围内，若评价因子的值未在评价标准Y内，则判断评价因子的值不处于正常状态范围内。
优选的，评价标准的值不大于对应的评价因子的报警值，评价标准的值应在对应的评价因子的运行安全裕量之内，并考量对应的评价因子的正常运行平均值的偏差。
优选的，根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰，包括以下步骤：
根据以下公式计算评价因子的综合评价值：
S＝an(i
n)+bn(j
n)+cn(k
n)；
其中，S为综合评价值，a为第一优先层级的权重，n(i
n)为第一优先层级中的、值不处于正常状态值的评价因子数量；b为第二优先层级的权重，n(j
n)为第二优先层级中的、值不处于正常状态值的评价因子数量；c为第三优先层级的权重，n(k
n)为第三优先层级中的、值不处于正常状态值的评价因子数量，其中，a＞b＞c，且0＜a，0＜b，0＜c；
将综合评价值与预设的评价阈值进行比较，当综合评价值大于预设的评价阈值时，则判断需要吹灰器进行吹灰，当综合评价值小于或等于预设的评价阈值时，则判断不需要吹灰器 进行吹灰。
优选的，评价标准Y值可以根据前面一段时间的历史数据进行滚动更新，以适应不同煤种的变化，时间段的长短可以根据机组实际情况来定，评价标准Y值更新幅度可以进行调整，更新后的Y值为Y＝Y
0+λ(Y
n-Y
0)，其中，Y
0为更新前的评价标准，Y
n为更新前n天的历史数据计算的评价标准，λ为评价标准更新幅度因数，优选的取值范围为0.1～1.0。
一种计算机系统，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时实现方法的步骤。
一种计算机存储介质，其上存储有计算机程序，程序被处理器执行时实现上述方法中的步骤。
本发明具有以下有益效果：
1、本发明中的基于多维度评价因子的智能吹灰控制方法、系统及存储介质，通过选取多个锅炉运行参数作为选取吹灰器的评价因子，多个锅炉运行参数反映所述吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响；根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰。本发明能准确判断出选取的吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响大小，并根据判断出影响大小确定是否采用该吹灰器进行吹灰，从而减少电厂的吹灰蒸汽损耗，降低受热面吹损风险，减小吹灰设备检修维护及吹灰人工劳动量，提高锅炉运行的安全性和经济性。
2、在优选方案中，本发明利用大数据自定义算法将吹灰优化与锅炉运行参数优化和受热面洁净程度相结合进行多维度评价，从而实现精细化按需吹灰。
3、在优选方案中，本发明中的评价标准Y根据前面n天的历史数据进行滚动更新，实现自学习功能，以适应不同煤种的变化。
4、在优选方案中，本发明各受热面多维度评价因子根据影响锅炉安全经济运行程度和受热面沾污情况与评价因子的关联程度分为不同的层级，不同层级采用不同的判断条件，以实现评价因子的优先等级化和差异化，从而能更加准确的评价吹灰器吹灰对锅炉运行状态的影响。
除了上面所描述的目的、特征和优点之外，本发明还有其它的目的、特征和优点。下面将参照附图，对本发明作进一步详细的说明。
构成本申请的一部分的附图用来提供对本发明的进一步理解，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：
图1是本发明基于多维度评价因子的智能吹灰控制方法的流程图；
图2是本发明优选实施例中的基于多维度评价因子的智能吹灰控制方法的流程图。
以下结合附图对本发明的实施例进行详细说明，但是本发明可以由权利要求限定和覆盖的多种不同方式实施。
实施例一：
如图1所示，本实施例中公开了一种基于多维度评价因子的智能吹灰控制方法，包括以下步骤：
根据专家经验和机理分析选取多个锅炉运行参数作为选取吹灰器的评价因子，多个锅炉运行参数反映吹灰器对应区域、对应种类的灰层对锅炉的安全、经济运行程度以及对应受热面沾污的关联程度的影响；
根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；
判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰，若需要，则开启吹灰器进行吹灰操作。
此外，在本实施例中，还公开了一种计算机系统，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时实现方法的步骤。
此外，在本实施例中，还公开了一种计算机存储介质，其上存储有计算机程序，程序被处理器执行时实现上述方法中的步骤。
本发明中的基于多维度评价因子的智能吹灰控制方法、系统及存储介质，通过选取多个锅炉运行参数作为选取吹灰器的评价因子，多个锅炉运行参数反映吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响；根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰。本发明能准确判断出选取的吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响大小，并根据判断出影响大小确定是否采用该吹灰器进行吹灰，从而减少电厂的吹灰蒸汽损耗，降低受热面吹损风险，减小 吹灰设备检修维护及吹灰人工劳动量，提高锅炉运行的安全性和经济性。
实施例二：
实施例二是实施例一的优选实施例，其与实施例一的不同之处在于，对基于多维度评价因子的智能吹灰控制方法的具体步骤进行了细化，包括以下步骤：
不同位置的吹灰器用于去除锅炉不同区域受热面的积灰，不同的锅炉运行参数可以直接或者间接反映不同受热面的积灰沾污程度以及吹灰前后锅炉安全、经济运行情况，因此，若要准确判断受热面的积灰沾污程度，同时保证吹灰器吹灰前后锅炉运行的安全经济性，控制吹灰器精细化按需高效的进行吹灰，就要准确的选取吹灰器对应的锅炉运行参数组。
如图2所示，在本实施例中公开了一种基于多维度评价因子的智能吹灰控制方法，包括以下步骤：
步骤1：将锅炉吹灰器进行分组，具体分组方式可以采用下述方式：1)炉膛短吹：第一层短吹、第二层短吹、第三层短吹、第四层短吹；2)折焰角区域：屏过、高过、高再；3)竖井烟道：低过、低再、省煤器；4)空预器吹灰器。
上述吹灰器组内各吹灰器还可以进行进一步分组，以实现受热面区域的精细化吹灰。
步骤2：根据专家经验和机理分析选择吹灰器组多维度评价因子。吹灰器组多维度评价因子包括但不限于下列参数：
1)炉膛短吹组评价因子：炉膛出口烟温(屏底烟气温度)、水冷壁壁温、过热度、过热器减温水量、屏过壁温、高过壁温、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
2)屏过吹灰器组评价因子：炉膛出口烟温(屏底烟气温度)、屏过进、出口工质温升、过热器减温水量、烟气挡板开度、屏过壁温偏差、屏过壁温最大值、屏过烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
3)高过吹灰器组评价因子：高过进、出口工质温升、过热器减温水量、烟气挡板开度、高过壁温偏差、高过壁温最大值、高过烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
4)高再吹灰器组评价因子：高再进、出口工质温升、烟气挡板开度、再热器减温水量、高再壁温偏差、高再壁温最大值、高再烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；
5)低过吹灰器组评价因子：低过出口管壁温、低过进、出口工质温度、低过进、出口烟 气温度、空预器入口烟温、低过侧烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；
6)低再吹灰器组评价因子：低再出口管壁温、低再进、出口工质温度、低再进、出口烟气温度、空预器入口烟温、低再侧烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；
7)省煤器吹灰器组评价因子：省煤器进、出口工质温度、省煤器进、出口烟气温度、空预器入口烟温、省煤器烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；
8)空预器吹灰器组评价因子：空预器进口烟气温度、空预器出口烟气温度、空预器烟气侧阻力、空预器一次风侧阻力、空预器二次风侧阻力、引风机电流。
各吹灰器组评价因子根据影响锅炉安全经济运行程度和受热面沾污与评价因子的关联程度分为三个层级，如表1所示。表1仅是一种优选的评价因子分级实例，并不作为限制。因子，其他实例可以根据机组实际情况进行不同的分级。
表1 各吹灰器组评价因子分级表
步骤3：建立评价标准。各吹灰器组评价因子在各个机组负荷下的历史运行数据分布，采用Y＝μ±xσ作为各负荷下的评价标准，μ为种类评价因子的历史数据分布的平均值，σ为种类评价因子的历史数据分布的标准差，x是吹灰器优化饱和因数，根据机组情况确定，取值范围为1.0～3.0。
当壁温作为评价因子时，优选的评价标准Y应参考壁温的报警值进行确定。
当主蒸汽温度、再热蒸汽温度作为评价因子时，优选的评价标准Y应参考汽温的报警值进行确定。
当脱硝系统入口烟温作为评价因子时，优选的评价标准Y应参考脱硝系统允许运行烟温的上限值进行确定。
当空预器入口烟温作为评价因子时，优选的评价标准Y应参考空预器允许运行烟温的上限值进行确定。
步骤4：获取实时运行数据，进行判断是否触发设定的评价条件。判断各层级的评判因子i
n、j
n、k
n是否达到评价标准Y值，第一优先级评价因子i
n中有n个因子达到评价标准Y值计为n(i
n)，第二优先级评价因子j
n中有n个因子达到评价标准Y值为n(j
n)，第三优先级评价因子k
n中有n个因子达到评价标准Y值影响吹灰的权重计为n(k
n)，计算各层级的评价因子达到各自评价标准Y值的总数S＝an(i
n)+bn(j
n)+cn(k
n)，其中，a、b、c为各层级的权重因子，其中，a＞b＞c，且0＜a，0＜b，0＜c。
将综合评价值与预设的评价阈值进行比较，当综合评价值大于预设的评价阈值时，则判断需要吹灰器进行吹灰，当综合评价值小于或等于预设的评价阈值时，则判断不需要吹灰器进行吹灰。设评价阈值为w，则有a＞w,2b＞w,3c＞w；
在本实施例中w取值为0.7，当S>0.7，则认为触发了吹灰条件。
根据上述规则，以各层级权重因子a、b、c分别设为1、0.5、0.3作为优选的实例，但并不作为限制，可以根据机组情况赋予其他权重值。具体各优先层级的评价因子达到相应的评价标准Y的个数与吹灰条件是否触发情况见表2。
表2 触发吹灰条件情况汇总表
步骤5：当触发设定评价条件时，对应的吹灰器组进行吹灰。
步骤6：判断标准每天采用前面一段时间的运行数据进行滚动更新，时间段的长短可以根据机组实际情况来定，优选的取值范围为1.0～10.0天。
评价标准Y值更新幅度可以进行调整，更新后的Y值为Y＝Y
0+λ(Y
n-Y
0)，其中，Y
0为更新前的评价标准，Y
n为更新前n天的历史数据计算的评价标准，λ为评价标准更新幅度因数，优选的取值范围为0.1～1.0。
当锅炉燃烧的入炉煤质变换频繁，评价标准更新幅度因数宜选取较大的值，以适应煤质变化。当锅炉燃烧的入炉煤质长时间稳定，评价标准更新幅度因数宜选取较小的值，以降低评价标准波动的幅度。
各评价因子进行实时判断，以满足按需实时吹灰需求。当某吹灰器组吹灰动作后，1小时后再进行该吹灰器组的评价因子判断。
如果连续3天未触发吹灰条件的吹灰器组执行吹灰一次，以避免部分吹灰器组对评价因子影响较少而得不到有效的吹灰。尤其是折焰角斜坡处吹灰器应定期进行吹灰，避免折焰角垮灰，影响炉内燃烧稳定，甚至造成机组非异停。
综上所述，本发明中的基于多维度评价因子的智能吹灰控制方法、系统及存储介质，通过选取多个锅炉运行参数作为选取吹灰器的评价因子，多个锅炉运行参数反映吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响；根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于 正常状态范围内的评价因子数量判断是否需要吹灰器进行吹灰。本发明能准确判断出选取的吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响大小，并根据判断出影响大小确定是否采用该吹灰器进行吹灰，从而减少电厂的吹灰蒸汽损耗，降低受热面吹损风险，减小吹灰设备检修维护及吹灰人工劳动量，提高锅炉运行的安全性和经济性。
以上所述仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
Claims (10)
- 一种基于多维度评价因子的智能吹灰控制方法，其特征在于，包括以下步骤：根据专家经验和机理分析选取多个锅炉运行参数作为选取吹灰器的评价因子，所述多个锅炉运行参数反映所述吹灰器对应受热面沾污程度以及吹灰对锅炉运行的安全性和经济性的影响；根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组；判断各个评价因子的值是否处于正常状态范围内，统计各组优先层级内的、值不处于正常状态范围内的评价因子数量，并根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要所述吹灰器进行吹灰，若需要，则开启所述吹灰器进行吹灰操作。
- 根据权利要求1所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，当所述吹灰器为炉膛短吹灰器时，所述评价因子包括：炉膛出口烟温、水冷壁壁温、过热度、过热器减温水量、屏过壁温、高过壁温、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；当所述吹灰器为屏过吹灰器时，所述评价因子包括：炉膛出口烟温、屏过进、出口工质温升、过热器减温水量、烟气挡板开度、屏过壁温偏差、屏过壁温最大值、屏过烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；当所述吹灰器为高过吹灰器时，所述评价因子包括：高过进、出口工质温升、过热器减温水量、烟气挡板开度、高过壁温偏差、高过壁温最大值、高过烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；当所述吹灰器为高再吹灰器时，所述评价因子包括：高再进、出口工质温升、烟气挡板开度、再热器减温水量、高再壁温偏差、高再壁温最大值、高再烟气侧差压、主蒸汽温度、再热蒸汽温度、脱硝系统入口烟温、排烟温度；当所述吹灰器为低过吹灰器时，所述评价因子包括：低过出口管壁温、低过进、出口工质温度、低过进、出口烟气温度、空预器入口烟温、低过侧烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；当所述吹灰器为低再吹灰器时，所述评价因子包括：低再出口管壁温、低再进、出口工质温度、低再进、出口烟气温度、空预器入口烟温、低再侧烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；当所述吹灰器为省煤器吹灰器时，所述评价因子包括：省煤器进、出口工质温度、省煤器进、出口烟气温度、空预器入口烟温、省煤器烟气阻力、脱硝系统入口烟温、排烟温度、引风机电流；当所述吹灰器为空预器吹灰器组，所述评价因子包括：空预器进口烟气温度、空预器出口烟气温度、空预器烟气侧阻力、空预器一次风侧阻力、空预器二次风侧阻力、引风机电流。
- 根据权利要求1所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组，包括以下步骤：对于每一个评价因子均执行以下步骤：分析所述评价因子对锅炉安全影响的大小，将所述评价因子对锅炉安全影响的大小与预设的第一安全影响阈值进行比较，若所述评价因子对锅炉安全影响的大小大于预设的第一安全影响阈值，则判断所述评价因子为第一优先层；若所述评价因子对锅炉安全的影响值小于预设的第一安全影响阈值，且大于预设的第二安全影响阈值，其中，第一安全影响阈值大于第二安全影响阈值，且满足以下任一条件，则判断所述评价因子为第二优先层级：所述评价因子对经济运行程度的影响值超过预设的经济影响阈值；所述评价因子与受热面沾污之间关联程度值超过预设的受热面沾污程度阈值；若所述评价因子对锅炉安全的影响值小于预设的第二安全影响阈值或同时满足以下条件，则判断所述评价因子为第三优先层级：所述评价因子对经济运行程度的影响值未超过预设的经济影响阈值；所述评价因子与受热面沾污之间关联程度值未超过预设的受热面沾污程度阈值。
- 根据权利要求2所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，根据各个评价因子对锅炉安全影响的大小、经济运行程度影响的高低以及与受热面沾污之间关联程度的高低分别对各个评价因子进行优先层级分组，通过查找下表实现：各吹灰器组评价因子分级表
- 根据权利要求1所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，判断各个评价因子的值是否处于正常状态范围内，具体包括以下步骤：对于每一个评价因子均执行以下步骤：获取所述种类评价因子的历史数据，计算所述历史数据的均值和标准差，并通过以下公式计算所述种类的评价因子正常值的取值范围作为评价标准Y:Y＝μ±xσ；其中，μ为所述种类评价因子的历史数据分布的平均值，σ为所述种类评价因子的历史数据分布的标准差，x是吹灰器优化饱和因数，根据机组情况确定；将所述评价因子的值与所述评价标准Y进行比较，若所述评价因子的值在所述评价标准Y内，则判断所述评价因子处于正常状态范围内，若所述评价因子的值未在所述评价标准Y内，则判断所述评价因子的值不处于正常状态范围内。
- 根据权利要求5所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，所述评价标准的值不大于对应的评价因子的报警值，所述评价标准的值应在对应的评价因子的运行安全裕量之内，并考量对应的评价因子的正常运行平均值的偏差。
- 根据权利要求1所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，根据各组优先层级内的、值不处于正常状态范围内的评价因子数量判断是否需要所述吹灰器进行 吹灰，包括以下步骤：根据以下公式计算所述评价因子的综合评价值：S＝an(i n)+bn(j n)+cn(k n)；其中，S为综合评价值，a为第一优先层级的权重，n(i n)为第一优先层级中的、值不处于正常状态值的评价因子数量；b为第二优先层级的权重，n(j n)为第二优先层级中的、值不处于正常状态值的评价因子数量；c为第三优先层级的权重，n(k n)为第三优先层级中的、值不处于正常状态值的评价因子数量，其中，a＞b＞c，且0＜a，0＜b，0＜c；将所述综合评价值与预设的评价阈值进行比较，当所述综合评价值大于预设的评价阈值时，则判断需要所述吹灰器进行吹灰，当所述综合评价值小于或等于预设的评价阈值时，则判断不需要所述吹灰器进行吹灰。
- 根据权利要求5所述的基于多维度评价因子的智能吹灰控制方法，其特征在于，所述评价标准Y值根据前面一段时间的历史数据进行滚动更新，以适应不同煤种的变化，时间段的长短根据机组实际情况来定，所述评价标准Y值更新幅度可以进行调整，更新后的Y值为Y＝Y 0+λ(Y n-Y 0)，其中，Y 0为更新前的评价标准，Y n为更新前n天的历史数据计算的评价标准，λ为评价标准更新幅度因数，取值范围为0.1～1.0。
- 一种计算机系统，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，处理器执行计算机程序时实现上述权利要求1至8任一方法的步骤。
- 一种计算机存储介质，其上存储有计算机程序，其特征在于，程序被处理器执行时实现上述权利要求1至8任一项方法中的步骤。
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| CN116713709A (zh) * | 2023-05-29 | 2023-09-08 | 苏州索力伊智能科技有限公司 | 一种连接器自动组装设备控制系统及其方法 |
| CN116713709B (zh) * | 2023-05-29 | 2023-12-19 | 苏州索力伊智能科技有限公司 | 一种连接器自动组装设备控制系统及其方法 |
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| CN113757701A (zh) | 2021-12-07 |
| CN113757701B (zh) | 2022-08-05 |
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