A deep peak-shaving unit last blade humidity safety active fuzzy control method

Abstract

The present application relates to a kind of deep peak regulation unit end blade humidity safety initiative fuzzy control method, comprising the following steps: 1) deep unit operating state parameter implementation acquisition;2) economic back pressure optimization fuzzy control;3) safety back pressure optimization fuzzy control: step 2) economic back pressure optimization calculation result and control strategy, as one of the decision conditions of unit safety back pressure optimization, combined with shafting stability criterion, and according to the difference between actual back pressure and safety boundary back pressure, fuzzy judgment is carried out, and the final safety back pressure circulating water pump optimization operation guidance is provided.4) economic safety back pressure circulation optimization.This method can avoid the unit to run back pressure too low and lead to deviate from the optimal operating back pressure when deep peak regulation and ambient temperature is lower, while ensuring the safety of shafting vibration when back pressure optimization, and fundamentally initiatively ensures that operating back pressure is always above the root water erosion safety back pressure, ensures the long-term operation safety of end blade.

Description

Technical Field

[0001] This invention relates to the field of steam turbine generator set technology, and in particular to an active fuzzy control method for the humidity safety of the terminal blades of a deep peak-shaving unit. Background Technology

[0002] With the rapid development of energy transition and new energy installations across the country, deep peak shaving of steam turbine units, especially thermal power units, has become a normalized trend. The units deviate significantly from their design operating conditions, increasing the pressure on operational safety.

[0003] In China, the typical design of large steam turbine units takes into account base load, peak load, and partial peak load. However, the current demand for deep peak load in China, especially the problem of thermoelectric decoupling under heating conditions, often leads to the occurrence of low flow rate and ultra-low back pressure operation of steam turbines. Moreover, the inlet steam parameters of the unit are often difficult to control under low load, resulting in underheating and increasing water erosion pressure at the exhaust end blades of the unit.

[0004] For turbine blade tip erosion, controlling the inlet steam parameters to ensure superheat can alleviate the problem to some extent, and there are relatively many closed-loop automatic methods available. However, the problem of controlling water erosion at the root of the terminal blade under deep peak shaving, especially how to ensure the joint active control of safe and economical back pressure of the terminal blade, remains a challenge in the industry. Summary of the Invention

[0005] The purpose of this invention is to provide a method for active fuzzy control of humidity safety in the terminal blades of a deep peak-shaving unit. This method aims to ensure that the terminal blades of the unit are always within the safe back pressure for root water erosion while simultaneously achieving economic back pressure operation, thereby ensuring the long-term safe operation of the terminal blades.

[0006] This invention provides an active fuzzy control method for the safety of humidity at the end blades of a deep peak-shaving unit, comprising the following steps: Step 1, acquiring real-time operating status parameters of the deep unit, including:

[0007] The system collects real-time parameters such as unit power, back pressure, exhaust steam temperature, inlet steam temperature, inlet steam pressure, and circulating water pump power to ensure real-time monitoring and identification of relevant state parameter characteristic values ​​related to the unit's optimal and safe operating back pressure. These state parameter characteristic values ​​include:

[0008] A 进汽参数 =A{Main steam pressure, Main steam temperature, Reheat steam pressure, Reheat steam temperature, Low-pressure cylinder inlet steam pressure, Low-pressure cylinder inlet steam temperature}

[0009] B 冷端参数 =B{back pressure, exhaust steam temperature, exhaust steam flow rate, hot water well temperature, condenser outlet and inlet circulating water temperatures}

[0010] C 功率参数=B{Unit input power, unit electrical power, heating power, circulating water pump electrical power, unit incremental output}

[0011] D 性能参数 =D{Coal consumption rate, Heat consumption rate, Plant power consumption rate}

[0012] E 环境参数 =E{Atmospheric pressure, atmospheric temperature, atmospheric humidity, atmospheric dryness};

[0013] Step 2, fuzzy control for economic back pressure optimization, includes:

[0014] Based on the identification of unit state characteristic parameters in step 1, the economic back pressure Ptm of the unit is calculated according to the principle of slight increase in net output. Furthermore, the optimal back pressure fuzzy control strategy provides guidance for the optimized operation of the circulating water pumps. The economic back pressure optimization fuzzy control strategy includes:

[0015] δ(Nt, Np)>0 means that the back pressure Pt of the unit is changed by adjusting the power of the circulating water pump Np to change the circulating water volume, which in turn changes the unit output Nt; when the output is slightly increased to zero, the benefit is positive; at this time, the power of the circulating water pump Np is increased to increase the circulating cooling water flow to further reduce the unit back pressure.

[0016] δ(Nt, Np) < 0, meaning that when the incremental output is less than zero, the benefit is negative; do not continue to increase the circulating water pump power Np, and stop adjusting to reduce the circulating cooling water flow rate;

[0017] When δ(Nt, Np) increases positively and approaches zero, that is, when positive returns no longer increase, the back pressure is the economic back pressure Ptm.

[0018] Step 3, fuzzy control for optimized safety back pressure, includes:

[0019] The calculation results and control strategies of economic back pressure optimization in step 2) are used as one of the decision conditions for unit safety back pressure optimization. Based on the difference between the actual back pressure and the safety boundary back pressure, fuzzy judgment is performed to provide guidance for the optimized operation of the circulating water pump at the final safe back pressure. The fuzzy control strategy for safety back pressure optimization includes:

[0020] △(Pt,Pt0)>0, that is, the fuzzy control result Pt after step 2 economic back pressure optimization is compared and fuzzy analyzed with the unit safety boundary back pressure Pt0 given by the manufacturer. When the former is higher than the latter, it is within the safe back pressure operating boundary, which can prevent water erosion of the end blade.

[0021] △(Pt,Pt0)<0, meaning that the fuzzy control result Pt of the economic back pressure optimization is outside the back pressure boundary Pt0 given by the manufacturer. At this time, it is an unsafe operating back pressure and is prone to water erosion of the end blade.

[0022] When the low-pressure cylinder shaft vibration Fx,y>20um or the low-pressure cylinder bearing vibration Fw>10um, i.e. when the stability of the low-pressure cylinder shaft system changes significantly, stop adjusting the economic back pressure and safety back pressure in the previous direction and keep a close watch.

[0023] Based on the above judgment of the safety back pressure boundary and shaft stability boundary, the circulating water volume will be adjusted to control the back pressure within the safe range, so as to prevent water erosion or shaft vibration deterioration.

[0024] Step 4, cyclical optimization of economic security back pressure, including:

[0025] Considering optimal back pressure and shaft stability, and ensuring the unit operates within the safe back pressure Pt0 operating boundary, the safe back pressure optimization fuzzy control strategy from step 3 is fed back to the economic back pressure optimization fuzzy control from step 2, thereby achieving cyclic optimization control. The economic and safe back pressure cyclic optimization fuzzy control strategy includes:

[0026] δ(Nt, Np) & △(Pt, Pt0) → Cyclic optimal control, that is, based on the coupled synchronous cyclic optimization control strategy of safe back pressure Pt0 and economic back pressure Ptm, to ensure active safety control of water erosion at the root of the terminal blade;

[0027] △The low-pressure cylinder shaft vibration Fx, y < 20um or △The low-pressure cylinder bearing vibration Fw < 10um indicates that the shaft system is stable and safe. Continue to optimize and adjust the back pressure.

[0028] Furthermore, step 1 also includes:

[0029] Extract the vibration data of the low-pressure cylinder rotor shaft system, specifically the vibration parameters F, including shaft vibration and bearing vibration operation data. When the vibration suddenly deteriorates during the unit's back pressure optimization control, stop the optimization operation to ensure the safe control of the shaft system stability under back pressure optimization operation.

[0030] By employing the above scheme and the active fuzzy control method for humidity safety of the terminal blades of deep peak-shaving units, it is possible to achieve coupled active optimization of the overall economic back pressure and safety back pressure based on the water erosion safety of the terminal blades of deep peak-shaving units. On the one hand, this avoids the drawbacks of inadequate safety or inadequate economy when optimizing only economic back pressure or only safety back pressure. On the other hand, it proposes a back pressure safety fuzzy control strategy that takes into account shaft stability, making it more comprehensive and applicable to engineering.

[0031] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0032] Figure 1 This is a flowchart of the active fuzzy control method for humidity safety of the terminal blades of the deep peak shaving unit according to the present invention. Detailed implementation manners

[0033] The following further describes in detail the specific implementation manners of the present invention with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention but not to limit the scope of the present invention.

[0034] Refer Figure 1 As shown, this embodiment provides a safety active fuzzy control method for the last-stage blade humidity of a deep peak shaving unit, including the following steps:

[0035] 1) Real-time acquisition of the operating state parameters of the deep unit: Collect the operating parameters such as the real-time power, back pressure, exhaust steam temperature, inlet steam temperature, inlet steam pressure, and circulating water pump power of the unit to ensure that the characteristic values of the relevant state parameters related to the optimal operating back pressure and safe operating back pressure of the unit can be monitored and identified in real time. The main characteristic values of the state parameters are as follows:

[0036] A 进汽参数 = A{main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, low-pressure cylinder inlet steam pressure, low-pressure cylinder inlet steam temperature}

[0037] B 冷端参数 = B{back pressure, exhaust steam temperature, exhaust steam flow, hot well temperature, condenser outlet and inlet circulating water temperature, ambient temperature}

[0038] C 功率参数 = B{unit input power, unit electric power, heating power, circulating water pump electric power, unit incremental output}

[0039] D 性能参数 = D{coal consumption rate, heat consumption rate, plant power consumption rate}

[0040] E 环境参数 = E{atmospheric pressure, atmospheric temperature, atmospheric humidity, atmospheric dryness}

[0041] In addition, the characteristic values of the unit state parameters in step 1) are not limited to the above range. For example, to ensure the safety control of the shafting stability under the optimized operation of the back pressure of the unit, the shaft vibration and bearing vibration operation data F 振动参数 of the low-pressure cylinder rotor shafting should also be extracted. When the vibration suddenly deteriorates during the optimized control of the unit back pressure, the optimized operation should be stopped.

[0042] F 振动参数 = F{low-pressure cylinder shaft vibration, low-pressure cylinder bearing vibration}

[0043] 2) Fuzzy control for economic back pressure optimization: Through the identification of the unit state characteristic parameters in step 1), calculate the economic back pressure Ptm of the unit according to the principle of incremental net output method, and provide guidance for the optimized operation of the circulating water pump through the optimal back pressure fuzzy control strategy. The main fuzzy control strategies are as follows:

[0044] δ(Nt, Np) > 0, meaning that adjusting the circulating water pump power Np changes the circulating water flow, thereby altering the unit's back pressure Pt and consequently the unit's output Nt. When the slight increase in output is greater than zero, the benefit is positive. At this point, the circulating water pump power Np can be further increased to increase the circulating cooling water flow and further reduce the unit's back pressure.

[0045] δ(Nt, Np) < 0, meaning that when the incremental output is less than zero, the benefit is negative. Therefore, the circulating water pump power Np should not be increased further, and it is advisable to stop adjusting and reduce the circulating cooling water flow rate.

[0046] When δ(Nt, Np) increases positively and approaches zero, that is, when positive returns no longer increase, the back pressure is the economic back pressure Ptm.

[0047] 3) Fuzzy Control for Safe Back Pressure Optimization: The calculation results and control strategies for economic back pressure optimization in step 2) serve as one of the decision conditions for unit safe back pressure optimization. Based on the difference between the actual back pressure and the safe boundary back pressure, fuzzy judgment is performed to provide guidance for the optimized operation of the circulating water pump at the final safe back pressure. The main fuzzy control strategies include:

[0048] △(Pt, Pt0)>0, that is, the fuzzy control result Pt after step 2) economic back pressure optimization is compared and fuzzy analyzed with the unit safety boundary back pressure Pt0 given by the manufacturer. When the former is higher than the latter, it is within the safe back pressure operating boundary, which can prevent water erosion of the end blade.

[0049] △(Pt,Pt0)<0, meaning that the fuzzy control result Pt of the economic back pressure optimization is outside the back pressure boundary Pt0 given by the manufacturer. This is an unsafe operating back pressure and is prone to water erosion of the end blades.

[0050] When the vibration of the low-pressure cylinder shaft (Fx,y) is greater than 20µm or the vibration of the low-pressure cylinder bearing (Fw) is greater than 10µm, indicating a significant change in the stability of the low-pressure cylinder shaft system, the previous adjustment of the economic back pressure and safety back pressure should be stopped and close observation should be maintained.

[0051] The safety back pressure optimization fuzzy control strategy will adjust the circulating water volume based on the judgment of the above safety back pressure boundary and shaft stability boundary to control the back pressure within a safe range, thereby preventing water erosion or shaft vibration deterioration.

[0052] 4) Cyclic optimization of economic and safe back pressure: While ensuring the unit operates within the safe back pressure Pt0 boundary, the safe back pressure operation strategy 3) is fed back to the economic back pressure optimization fuzzy control in step 2), thereby achieving cyclic optimization control. The main fuzzy control strategies include:

[0053] δ(Nt, Np) & △(Pt, Pt0) → Cyclic optimal control, that is, based on the coupled synchronous cyclic optimization control strategy of safe back pressure Pt0 and economic back pressure Ptm, to ensure active safety control of water erosion at the root of the terminal blade.

[0054] △The low-pressure cylinder shaft vibration Fx, y < 20um or △The low-pressure cylinder bearing vibration Fw < 10um indicates that the shaft system is stable and safe, and the back pressure can be further optimized and adjusted.

[0055] The method incorporates cyclic optimization control in step 4), combining the fuzzy control of economic back pressure in step 2) and the shaft stability control in step 3), further integrating optimal back pressure and shaft stability considerations to ensure the inherent safety of the unit under end-blade water erosion conditions. By incorporating cyclic optimization control in step 4), combined with the fuzzy control of economic back pressure in step 2) and the fuzzy control of safety back pressure in step 3), the method achieves coupled active optimization of overall economic back pressure and safety back pressure.

[0056] In steps 2) and 3) of this method, economic back pressure fuzzy control and safety back pressure fuzzy control are set respectively to solidify expert experience into actual operation strategy optimization. In step 4), cyclic optimization control is set. By combining the optimal back pressure and shaft system stability, the overall economic back pressure and safety back pressure can be coupled and actively optimized, avoiding the drawbacks of poor safety or poor economy when optimizing only economic back pressure or only safety back pressure.

[0057] This method can prevent the unit from deviating from the optimal operating back pressure due to excessively low operating back pressure during deep peak shaving and low ambient temperature. It also ensures shaft vibration safety during back pressure optimization and fundamentally and proactively guarantees that the operating back pressure is always above the root water erosion safety back pressure, ensuring the long-term safe operation of the terminal blades.

[0058] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A deep peak-shaving unit last leaf humidity safety active fuzzy control method, characterized in that, Includes the following steps: Step 1: Obtain the real-time operating status parameters of the deep-seated generator unit, including: The system collects real-time parameters such as unit power, back pressure, exhaust steam temperature, inlet steam temperature, inlet steam pressure, and circulating water pump power to ensure real-time monitoring and identification of relevant state parameter characteristic values ​​related to the unit's optimal and safe operating back pressure. These state parameter characteristic values ​​include: A 进汽参数＝ A{main steam pressure, main steam temperature, reheat steam pressure, reheat steam temperature, low-pressure cylinder inlet steam pressure, Low cylinder intake temperature B 冷端参数 = B {back pressure, exhaust temperature, exhaust flow, hot well temperature, condenser outlet and inlet circulating water temperature} C 功率参数 = B{unit input power, unit electric power, heating power, circulating water pump electric power, unit micro-increment output} D 性能参数 = D {coal consumption rate, heat consumption rate, auxiliary power rate} E 环境参数 = E{atmospheric pressure, atmospheric temperature, atmospheric humidity, atmospheric dryness}; Step 2, fuzzy control for economic back pressure optimization, includes: Based on the identification of unit state characteristic parameters in step 1, the economic back pressure Ptm of the unit is calculated according to the principle of slight increase in net output. Furthermore, the optimal back pressure fuzzy control strategy provides guidance for the optimized operation of the circulating water pumps. The economic back pressure optimization fuzzy control strategy includes: δ(Nt, Np)>0 means that the back pressure Pt of the unit is changed by adjusting the power of the circulating water pump Np to change the circulating water volume, which in turn changes the unit output Nt; when the output is slightly increased to zero, the benefit is positive; at this time, the power of the circulating water pump Np is increased to increase the circulating cooling water flow to further reduce the unit back pressure. δ(Nt, Np) < 0, meaning that when the incremental output is less than zero, the benefit is negative; do not continue to increase the circulating water pump power Np, and stop adjusting to reduce the circulating cooling water flow rate; When δ(Nt, Np) increases positively and approaches zero, that is, when positive returns no longer increase, the back pressure is the economic back pressure Ptm. Step 3, fuzzy control for optimized safety back pressure, includes: The calculation results and control strategies of economic back pressure optimization in step 2) are used as one of the decision conditions for unit safety back pressure optimization. Based on the difference between the actual back pressure and the safety boundary back pressure, fuzzy judgment is performed to provide guidance for the optimized operation of the circulating water pump at the final safe back pressure. The fuzzy control strategy for safety back pressure optimization includes: △(Pt,Pt0)>0, that is, the fuzzy control result Pt after step 2 economic back pressure optimization is compared and fuzzy analyzed with the unit safety boundary back pressure Pt0 given by the manufacturer. When the former is higher than the latter, it is within the safe back pressure operating boundary, which can prevent water erosion of the end blade. △(Pt,Pt0)<0, meaning that the fuzzy control result Pt of the economic back pressure optimization is outside the back pressure boundary Pt0 given by the manufacturer. At this time, it is an unsafe operating back pressure and is prone to water erosion of the end blade. When the low-pressure cylinder shaft vibration Fx,y>20um or the low-pressure cylinder bearing vibration Fw>10um, i.e. when the stability of the low-pressure cylinder shaft system changes significantly, stop adjusting the economic back pressure and safety back pressure in the previous direction and keep a close watch. Based on the above judgment of the safety back pressure boundary and shaft stability boundary, the circulating water volume will be adjusted to control the back pressure within the safe range, so as to prevent water erosion or shaft vibration deterioration. Step 4, cyclical optimization of economic security back pressure, including: Considering optimal back pressure and shaft stability, and ensuring the unit operates within the safe back pressure Pt0 operating boundary, the safe back pressure optimization fuzzy control strategy from step 3 is fed back to the economic back pressure optimization fuzzy control from step 2, thereby achieving cyclic optimization control. The economic and safe back pressure cyclic optimization fuzzy control strategy includes: δ(Nt, Np) & △(Pt, Pt0) → Cyclic optimal control, that is, based on the coupled synchronous cyclic optimization control strategy of safe back pressure Pt0 and economic back pressure Ptm, to ensure active safety control of water erosion at the root of the terminal blade; △The low-pressure cylinder shaft vibration Fx, y < 20um or △The low-pressure cylinder bearing vibration Fw < 10um indicates that the shaft system is stable and safe. Continue to optimize and adjust the back pressure.

2. The method of claim 1, wherein, Step 1 further includes: Extract the vibration data of the low-pressure cylinder rotor shaft system, specifically the vibration parameters F, including shaft vibration and bearing vibration operation data. When the vibration suddenly deteriorates during the unit's back pressure optimization control, stop the optimization operation to ensure the safe control of the shaft system stability under back pressure optimization operation.

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