CN105134386B - On-line monitoring method for gas turbine combustion system based on measuring-point weighted value - Google Patents

Abstract

The invention discloses an on-line monitoring method for a gas turbine combustion system based on a measuring-point weighted value, belonging to the field of monitoring of the gas turbine combustion system. A current combustion monitoring system cannot judge the changing trend of a combustion state. According to the on-line monitoring method for the gas turbine combustion system based on the measuring-point weighted value, n temperature measuring points are uniformly and circumferentially arranged on the turbine outlet of a gas turbine, so that exhaust gas temperature data Ti during normal operation at a tm time period is obtained; the correlation factor alpha i,1 of Ti and T1 is added, and prediction values of Ti are separately obtained according to the relation function between Ti and T1; the exhaust gas temperature theoretic value T1＜'＞ of a fault-free temperature measuring point 1 is calculated; the difference between the theoretical value T1＜'＞ of the temperature measuring point 1 and the measured value T1 of the temperature measuring point 1 is defined as delta T1, and delta T1 satisfies normal distribution with the mean value 0 and standard deviation delta1; and monitoring on the temperature measuring point is performed through the relationship between delta T1 and the range from minus 3 delta1 to 3 delta1. By adopting the method disclosed by the invention, on-line monitoring of the exhaust gas temperature of the gas turbine is realized, and an abnormal evolution process can be accurately detected by fully utilizing the correlation among the measuring points of the exhaust gas temperature.

Description

Gas turbine combustion system on-line monitoring method based on measuring point weighted value

Technical field

The present invention relates to a kind of gas turbine combustion system on-line monitoring method based on measuring point weighted value.

Background technology

Gas turbine as new power-equipment, with compact conformation, operate steadily, it is safe and reliable, can quickly open Dynamic and band dynamic load, has the advantages that the higher thermal efficiency, is widely used at aspects such as aviation, ground and naval vessels, Therefore the abnormality detection of gas turbine is to produce reality important in inhibiting.In gas turbine unit running, for combustion Gas-turbine combustion system abnormality detection aspect, more than 50% failure is all relevant with combustor.Due to portions such as combustion chambers burn cylinders High-temperature area of the part long-term work at 1600 DEG C, work under bad environment, defect once occurs in equipment will likely be to the spray in downstream Mouth and movable vane piece safety constitute a threat to.Therefore, it is necessary to be monitored to the working condition of combustor.

Combustion system once breaks down, and combustor exit temperature occurs can abnormal.Therefore we can be by detection Combustor exit temperature is monitoring the operation conditions of combustion system.But, conventional temperature-measuring element cannot be in such high temperature Region long-term work, therefore, several aerofluxuss temperature thermocouples have been arranged circumferentially in unit turbine exhaust passage, heat The temperature that galvanic couple is surveyed is exactly row's temperature of gas turbine.Combustor as shown in Figure 1 and thermocouple deployment scenarios schematic diagram.By row Whether the situation of temperature there is exception come the working condition for judging combustion barrel.

The MARK VI combustion monitorings system of GE companies exploitation defines S for the warm dispersion of permission row of delivery temperature, it is believed that S It is average exhaust T of gas turbine outlet₄ ^*, compressor delivery temperature T₄ ^*Function, concrete function is empirical equation：

In the formula, temperature Shi is with ℉ as measurement unit.The 100 of right of formula carry bracket, represent variable working condition Under the conditions of just add this.

Additionally, MARK VI combustion monitoring systems are also defined：S1 is the highest reading and minimum reading of delivery temperature thermocouple Between difference；S2 is the difference between the highest reading and the 2nd low scale of delivery temperature thermocouple；S3 is delivery temperature thermocouple Highest reading and the 3rd low scale between difference.

Based on above-mentioned formula and definition, the discrimination principles of the combustion monitoring protection systems of MARK VI are shown in Fig. 2.In Fig. 2, K₁, K₂,K₃It is three parameters for empirically defining.In typical case：

K₁=1.0；K₂=5.0；K₃=0.8

The discrimination principles of combustion monitoring are as shown in Figure 2；Find in actual applications, the detection of this kind of method exists serious " afterwards " phenomenon is diagnosed, i.e., combustion system has damaged more serious when detecting system sends warning.

The content of the invention

The invention aims to solving existing combustion monitoring system cannot pass through the detection of abnormal evolution process, it is right The problem that fired state variation tendency is judged, occurs to detect in early days in failure, and proposes a kind of based on measuring point The gas turbine combustion system on-line monitoring method of weighted value.

A kind of gas turbine combustion system on-line monitoring method based on measuring point weighted value, it is characterised in that：It is described to be based on The gas turbine combustion system on-line monitoring method of measuring point weighted value is realized by following steps：

Step one, gas turbine turbine outlet it is circumferential equably arrange n temperature point, gas turbine is in t_mPeriod Interior normal operation, obtains t_mRow temperature data T=[T in period₁,T₂,…,T_i,…T_n]；Wherein, T_iRepresent i-th temperature point In t_mRow's temperature data that each time point is measured in period,

Step 2, warm data T of row are respectively obtained using method of least square_iWith warm data T of row₁Relation function, i.e.,:

T₁=f_i,1(T_i) i=2,3,4 ..., n

Wherein, f_i,1Warm data T of the row of expression_iWith warm data T of row₁Relation function；Meet：T₁=k_i,1T_i+b_i,1, i=2, 3,4 ..., n, wherein k_i,1And b_i,1Can be drawn by method of least square, i.e.,：

Step 3, warm data T of row are represented using Pearson product-moment coefficient_iWith warm data T of row₁Between dependency, arranged Warm data T_iWith warm data T of row₁Correlation factor α_i,1, and draw：

T₁With T₂Between correlation factor be α_2,1,

T₁With T₃Between correlation factor be α_3,1,

T₁With T_nBetween correlation factor be α_n,1；

Step 4：According to warm data T of the row of obtaining that step 2 is obtained_iWith warm data T of row₁Relation function, when respectively by t Row's temperature data T that quarter measures₂To T_nIt is brought into relation function:T₁=f_i,1(T_i) in, the row's of respectively obtaining temperature data T₁Predictive value T_2,1,T_3,1,…T_i,1…,T_n,1：

T_i,1=f_i,1(T_i), i=2,3,4 ..., n

Wherein, f_i,1Represent row's temperature data T that step 2 draws_iWith warm data T of row₁Relation function；T refers to t₁Arrive t_mIn a moment；

Step 5：Row's temperature theoretical value T of temperature point 1 when calculating t gas turbine fault-free₁'；

Step 6：Theoretical value T of temperature point 1 when defining each moment unit fault-free₁' with the measured value T of temperature point 1₁ Difference be Δ T₁：ΔT₁=T₁'-T₁；By gas turbine t_mRow temperature data T=[T in period₁,T₂,…,T_i,…T_n] substitute into step Rapid four and step 5, obtain Δ T₁It is σ to meet average for 0, standard deviation₁Normal distribution, i.e. Δ T₁～N (0, σ₁)；

Step 7：The monitoring of operation phase temperature point 1 to be measured is entered to gas turbine：Row's temperature that each moment is measured Data substitute into step 4 and step 5, if obtaining theoretical value T of temperature point 1 during unit failure-free operation₁'；When monitoring each Theoretical value T of temperature point 1 when carving unit fault-free₁' with the measured value T of temperature point 1₁Difference be Δ T₁If, Δ T₁[- 3 σ₁,3σ₁] in the range of, then unit fault-free is illustrated, if exceeding [- 3 σ₁,3σ₁] scope, then illustrate unit break down；

Step 8：The monitoring of operation phase temperature point 2 to be measured to n is entered to gas turbine：Repeat step two is to step Six, if when respectively obtaining unit fault-free temperature point 2 to n theoretical value T₂',T₃',…,T_n', and theoretical value and measured value Difference Δ T₂,ΔT₃,…,ΔT_nStandard deviation sigma₂,σ₃,…,σ_n；Correspondingly, if Δ T₂,ΔT₃,…,ΔT_nAll respectively [- 3 σ_n,3σ_n] scope, then illustrate unit fault-free, if exceed [- 3 σ_n,3σ_n] scope, then illustrate unit break down.

Beneficial effects of the present invention are：

When combustion barrel occurs abnormal, the result for arranging temperature also occurs exception, and by the situation of row's temperature combustion is judged Whether the working condition for burning cylinder there is exception, so as to the delivery temperature for monitoring gas turbine carrys out the indoor work of indirect monitor burning Situation, obtains monitoring the operation conditions of combustion system.

The present invention considers the dependency between different temperatures measuring point, logical when the dependency between different temperatures measuring point is calculated The addition of correlation factor is crossed, the weights of those measuring points strong with certain measuring point dependency are enhanced, is reduced related to the measuring point The weights of those weak measuring points of property.Compared with prior art, the inventive method realizes the on-line monitoring of gas turbine row's temperature, fully Using the dependency between warm each measuring point of row, abnormal evolution process is accurately detected.The present invention can be better achieved combustion engine The exception monitoring of row's temperature, timely finds that failure even finds earlier failure, so as to reduce because gas turbine produces failure The probability for causing can not in time be found.

Description of the drawings

Combustor and thermocouple deployment scenarios schematic diagram that Fig. 1 is related to for background of invention；

The discrimination principles figure of the combustion monitoring that Fig. 2 is related to for background of invention；

Fig. 3 is the flow chart of the present invention；

Specific embodiment

Specific embodiment one：

The gas turbine combustion system on-line monitoring method based on measuring point weighted value of present embodiment, with reference to described in Fig. 3 Gas turbine combustion system on-line monitoring method based on measuring point weighted value is realized by following steps：

Step one, gas turbine turbine outlet it is circumferential equably arrange n temperature point, gas turbine is in t_mWhen Normal operation, obtains t in section_mRow temperature data T=[T in period₁,T₂,…,T_i,…T_n]；Wherein, T_iRepresent i-th temperature Measuring point is in t_mRow's temperature data that each time point is measured in period,

Step 2：Warm data T of row are respectively obtained using method of least square₂、T₃...T_i...、T_nWith warm data T of row₁Relation Function, i.e.,:

T₁=f_i,1(T_i) i=2,3,4 ..., n

Wherein, f_i,1Warm data T of the row of expression_iWith warm data T of row₁Relation function；Meet, T₁=k_i,1T_i+b_i,1, i=2, 3,4 ..., n, wherein k_i,1And b_i,1Can be drawn by method of least square, i.e.,：

Step 3：Warm data T of row are represented using Pearson product-moment coefficient₂、T₃...T_i...T_nWith warm data T of row₁Between Dependency, warm data T of the row of obtaining₂、T₃、...T_i...T_nWith warm data T of row₁Correlation factor α_i,1, and draw：

T₁With T₂Between correlation factor be α_2,1,

T₁With T₃Between correlation factor be α_3,1,

T₁With T_nBetween correlation factor be α_n,1；

Step 4：According to warm data T of the row of obtaining that step 2 is obtained₂、T₃、...T_i...T_nWith warm data T of row₁Relation letter Number, row's temperature data T for respectively measuring t₂To T_nIt is brought into relation function:T_i,1=f_i,1(T_i), i=2,3,4 ..., n In, the row's of respectively obtaining temperature data T₂、T₃、...T_i...T_nTo T₁Predictive value T_2,1,T_3,1,…T_i,1…,T_n,1：

Wherein, f_i,1Represent row's temperature data T that step 2 draws_iWith warm data T of row₁Relation function；T refers to t₁Arrive t_mIn a moment；

Step 5：Row's temperature theoretical value T of temperature point 1 when calculating t gas turbine fault-free₁'；

Step 6：Theoretical value T of temperature point 1 when defining each moment unit fault-free₁' with the measured value T of temperature point 1₁ Difference be Δ T₁：ΔT₁=T₁'-T₁；By gas turbine t_mRow temperature data T=[T in period₁,T₂,…,T_i,…T_n] substitute into step Rapid four and step 5, obtain Δ T₁It is σ to meet average for 0, standard deviation₁Normal distribution, i.e. Δ T₁～N (0, σ₁)；

Step 7：The monitoring of operation phase temperature point 1 to be measured is entered to gas turbine：Row's temperature that each moment is measured Data substitute into step 4 and step 5, if obtaining theoretical value T of temperature point 1 during unit failure-free operation₁'；When monitoring each Theoretical value T of temperature point 1 when carving unit fault-free₁' with the measured value T of temperature point 1₁Difference be Δ T₁If, Δ T₁[- 3 σ₁,3σ₁] in the range of, then unit fault-free is illustrated, if exceeding [- 3 σ₁,3σ₁] scope, then illustrate unit break down；

Step 8：The monitoring of operation phase temperature point 2 to be measured to n is entered to gas turbine：Repeat step two is to step Six, if when respectively obtaining unit fault-free temperature point 2 to n theoretical value T₂',T₃',…,T_n', and theoretical value and measured value Difference Δ T₂,ΔT₃,…,ΔT_nStandard deviation sigma₂,σ₃,…,σ_n；Correspondingly, if Δ T₂,ΔT₃,…,ΔT_nAll respectively [- 3 σ_n,3σ_n] scope, then illustrate unit fault-free, if exceed [- 3 σ_n,3σ_n] scope, then illustrate unit break down.

Specific embodiment two：

From unlike specific embodiment one, the gas turbine combustion system based on measuring point weighted value of present embodiment On-line monitoring method, the warm data (T of row described in step 3₂、T₃、...)T_i(...、T_n) and warm data T of row₁Correlation factor α_i,1's Computational methods are as follows：

Wherein, j is represented in time period t_mInterior 1st time point is to m-th time point, j=t₁...t_m。

Specific embodiment three：

From unlike specific embodiment one or two, the gas turbine combustion based on measuring point weighted value of present embodiment System on-line monitoring method,

Row's temperature theoretical value T of temperature point 1 when t gas turbine fault-free is calculated described in step 5₁' process be, According to the warm theoretical value computing formula of row：

Row's temperature theoretical value T of temperature point 1 when calculating t gas turbine fault-free₁'。

Claims (1)

1. a kind of gas turbine combustion system on-line monitoring method based on measuring point weighted value, it is characterised in that：It is described based on survey The gas turbine combustion system on-line monitoring method of point weighted value is realized by following steps：

Step one, gas turbine turbine outlet it is circumferential equably arrange n temperature point, gas turbine is in t_mWhen Normal operation, obtains t in section_mRow temperature data T=[T in period₁,T₂,…,T_i,…T_n]；Wherein, T_iRepresent i-th it is warm Degree measuring point is in t_mRow's temperature data that each time point is measured in period,

Step 2, warm data T of row are respectively obtained using method of least square_iWith warm data T of row₁Relation function, i.e.,:

T₁=f_i,1(T_i) i=2,3,4 ..., n

Wherein, f_i,1Warm data T of the row of expression_iWith warm data T of row₁Relation function；Meet：T₁=k_i,1T_i+b_i,1, i=2,3, 4 ..., n, wherein k_i,1And b_i,1Can be drawn by method of least square, i.e.,：

$k_{i,1}=\frac{\underset{a=t_1}{\overset{t_m}{\Σ}}(T_{ia}-{\stackrel{\‾}{T}}_i)(T_{1a}-{\stackrel{\‾}{T}}_1)}{\underset{a=t_1}{\overset{t_m}{\Σ}}{(T_{ia}-{\stackrel{\‾}{T}}_i)}^2}$

$b_{i,1}={\stackrel{\‾}{T}}_1-k_{i,1}{\stackrel{\‾}{T}}_i;$

Step 3, warm data T of row are represented using Pearson product-moment coefficient_iWith warm data T of row₁Between dependency, the warm number of the row of obtaining According to T_iWith warm data T of row₁Correlation factor α_i,1, computational methods are as follows：

${\mathrm{\α}}_{i,1}=\frac{\underset{j=t_1}{\overset{t_m}{\Σ}}(T_{ij}-{\stackrel{\‾}{T}}_{ij})(T_{1j}-{\stackrel{\‾}{T}}_{1j})}{\sqrt{\underset{j=t_1}{\overset{t_m}{\Σ}}{(T_{ij}-{\stackrel{\‾}{T}}_{ij})}^2}\sqrt{\underset{j=t_1}{\overset{t_m}{\Σ}}{(T_{1j}-{\stackrel{\‾}{T}}_{1j})}^2}}$

Wherein, j is represented in time period t_mInterior 1st time point is to m-th time point, j=t₁...t_m, and draw：

T₁With T₂Between correlation factor be α_2,1,

T₁With T₃Between correlation factor be α_3,1,

T₁With T_nBetween correlation factor be α_n,1；

Step 4：According to warm data T of the row of obtaining that step 2 is obtained_iWith warm data T of row₁Relation function, t is surveyed respectively Row's temperature data T for obtaining₂To T_nIt is brought into relation function:T₁=f_i,1(T_i) in, the row's of respectively obtaining temperature data T₁Predictive value T_2,1, T_3,1,…T_i,1…,T_n,1：

T_i,1=f_i,1(T_i), i=2,3,4 ..., n

Wherein, f_i,1Represent row's temperature data T that step 2 draws_iWith warm data T of row₁Relation function；T refers to t₁To t_mIn A moment；

Step 5：Row's temperature theoretical value T of temperature point 1 when calculating t gas turbine fault-free₁', it is theoretical especially by row's temperature Value computing formula：

$T_1^{\′}=\frac{{\Σ}_{i=2}^n{\mathrm{\α}}_{i,1}{T}_{i,1}}{{\Σ}_{i=2}^n{\mathrm{\α}}_{i,1}}$

Row's temperature theoretical value T of temperature point 1 when calculating t gas turbine fault-free₁'；

Step 6：Theoretical value T of temperature point 1 when defining each moment unit fault-free₁' with the measured value T of temperature point 1₁Difference For △ T₁：△T₁=T₁'-T₁；By gas turbine t_mRow temperature data T=[T in period₁,T₂,…,T_i,…T_n] substitute into step 4 And step 5, obtain △ T₁It is σ to meet average for 0, standard deviation₁Normal distribution, i.e. △ T₁～N (0, σ₁)；

Step 7：The monitoring of operation phase temperature point 1 to be measured is entered to gas turbine：Row's temperature data that each moment is measured Step 4 and step 5 are substituted into, if obtaining theoretical value T of temperature point 1 during unit failure-free operation₁'；Monitor each moment machine Theoretical value T of temperature point 1 during group fault-free₁' with the measured value T of temperature point 1₁Difference be △ T₁If, △ T₁In [- 3 σ₁,3 σ₁] in the range of, then unit fault-free is illustrated, if exceeding [- 3 σ₁,3σ₁] scope, then illustrate unit break down；

Step 8：The monitoring of operation phase temperature point 2 to be measured to n is entered to gas turbine：Repeat step two is divided to step 6 If not obtaining theoretical value T of temperature point 2 to n during unit fault-free₂',T₃',…,T_n', and the difference of theoretical value and measured value △T₂,△T₃,…,△T_nStandard deviation sigma₂,σ₃,…,σ_n；Correspondingly, if △ is T₂,△T₃,…,△T_nAll respectively in [- 3 σ_n,3σ_n] Scope, then illustrate unit fault-free, if exceed [- 3 σ_n,3σ_n] scope, then illustrate unit break down.