JPH06257702A - Discrete-time model type device for controlling steam temperature in boiler for steam power plant - Google Patents

Abstract

(57) [Summary] [Purpose] In controlling the steam temperature of a boiler for thermal power generation,
Apply the discrete-time model reference adaptive control method and measure its stable operation. [Structure] A control calculation function for generating a control signal that achieves a control target and a parameter adjustment function for adjusting a parameter for control signal calculation are incorporated in a distributed digital calculation control element, and this distributed digital calculation control element is normally used. The whole adaptive control system is constructed by adding in parallel to the thermal power plant control target.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a boiler for thermal power generation in which the dynamic characteristics are difficult to model and the dynamic characteristics change depending on the size of the load, the type and properties of the fuel used, the state of the furnace, etc. The present invention relates to a model reference adaptive steam temperature control device.

[0002]

2. Description of the Related Art An adaptive control method has been attracting attention as a control method suitable for a boiler for thermal power generation in which it is difficult to model the dynamic characteristics of a controlled object, and the dynamic characteristics change depending on the magnitude of load and operating conditions. The adaptive control methods are roughly classified into model reference adaptive control methods (MRACS: Model Referenc
ce Adaptive Control System) and self-tuning control system (STC: Self Tuning Control System). The present invention relates to MRACS of the above two methods.

[0003]

[Problems to be Solved by the Invention] MRACS research was conducted in 1970.
It is said that it has been actively carried out since the 1980s, and it was theoretically reached the stage of completion in the beginning of 1980, and there are numerous related domestic and foreign documents, and it is regarded as a well-known control theory. There are few examples, and there are almost no reports of applications to large-scale industrial processes such as boilers for large-capacity thermal power generation.

The biggest reason for this is concern about the robustness of MRACS, that is, the stability of the control system when applied to an actual plant. Parasitic elements, that is, instability phenomenon due to modeling error, control signal runaway phenomenon (ran away) phenomenon due to accumulation of numerical calculation error in the control arithmetic unit, are the largest factors that bring about reduction in robustness.
Continuous excitation condition of input and output signals used for parameter estimation
The drift phenomenon of the estimation parameter caused by the lack of (PE: Persistent Ex-citation Condition) can be mentioned.

In addition, when the MRACS is applied to an actual plant, an appropriate parameter (ex. Θ) for reducing the fluctuation of the control signal after the control system is started until the parameter estimation system operates stably. ) It is indispensable to solve problems that do not appear in theory, such as initialization problems and implementation of specific measures to deal with unexpected failures in control devices.

The present invention is based on a model reference adaptive control method (MRA).
Solved various problems encountered in applying (CS) to steam temperature control of a boiler, which reduce the robustness of the control system,
The purpose is to provide a control device that enables stable operation to realize the original control performance of MRACS.

[0007]

In the steam temperature control of a boiler for thermal power generation, a control target value called a reference model (ex. O) is set and a control error (ex. e ₁ ), that is, the control signal (ex. u ₁ ) that achieves the control target based on the difference between the reference model and the actual steam temperature (ex. y)
Control calculation function to generate, boiler load command (ex. W),
Boiler input signals such as superheater spray flow rate command (ex. U ₂ (sh)), gas recirculation amount operation command (ex. U ₂ (rh)) and sampling values of actual steam temperature (ex. Y) The parameters (ex. A, b) related to the dynamic characteristics of are constantly estimated, and the parameter adjustment function that adjusts the parameters for control signal calculation based on the estimated parameters is incorporated in the distributed digital arithmetic control element, and the boiler and turbine are An overall adaptive control system is constructed by adding the above distributed digital arithmetic control elements in parallel to a normal thermal power plant control object that is a closed loop consisting of a process and a distributed PID (proportional, integral, derivative) controller. It was done like this.

According to a second aspect of the present invention, in addition to the first aspect of the present invention, the distributed digital arithmetic control element is operated when the plant is started or stopped or when the plant operation mode is adaptive control. In the range that is significantly out of the condition,
Update the internal parameters (ex. Θ) with the control output set to 0, suspend the data acquisition for the parameter update, and update the parameter if the plant condition returns to the range suitable for the use of adaptive control. It has means to prevent smooth start-up of control and deterioration of control characteristics by automatically restarting adaptive control using the value before suspension as an initial value. Even if the control signal (ex. U ₁ ) from the plant may increase abnormally, it is limited to the upper limit value and the rate of change specified in advance, and the function to continue the safe operation of the plant is provided. is there.

According to a third aspect of the present invention, in addition to the first or second aspect of the present invention, the distributed digital arithmetic control element responds to changes in boiler dynamic characteristics depending on boiler output. Instead of changing the model dead time (ex. D) or the order (ex. N, m), a means to make the data sampling interval (ex. ΔT) variable, that is, the low load region where the peculiar movement of the plant is gentle. In the above, the sampling cycle of the data is made large, and conversely, in the high load region where the movement of the plant is relatively fast, the sampling cycle is made small,
It is provided with means for adaptively adjusting the sampling period in response to the change in the dynamic characteristics of the plant due to the change in load.

The present invention according to claim 4 provides the invention according to claims 1 to 3.
Further, in the present invention according to any one of the above items, the distributed digital arithmetic control element is a plant parameter value (e
x. θ) is taken into account in the load dependence of the load command (ex. w) by approximating it with a linear equation, and its coefficient (e
x. θ _a , θ _b ) is estimated to improve the responsiveness of parameter estimation and to improve the adaptive control performance.

The present invention according to claim 5 is based on claims 1 to 4.
Further, in the present invention according to any one of the above items, the distributed digital arithmetic control element is a vector (ex consisting of a proportional coefficient, which is a term proportional to a load command in a linear expression approximating a parameter change (ex. Θ). If the change in .θ _b ) is within a small range, the vector element is fixed to a converged value, the number of parameters to be estimated is halved, and the parameter correction speed is increased to improve adaptive control performance. It is provided with a means for performing the eclectic method of the original adaptive control and the conventional gain schedule control.

The present invention as defined in claim 6 is any one of claims 1 to 5.
In the present invention according to any one of the above, further, the distributed digital arithmetic control element is a plant input (ex. U ₁ , w, u ₂ ) for parameter update, output (ex. Y) signal change width Under the condition that the rate of change remains small, a certain threshold is set for the estimation error in order to avoid the parameter drift phenomenon due to insufficient excitation signal (information amount).
When the change of the input and output signals stays within the threshold value over the designated time, a means for suspending the parameter update is provided.

[0013]

In MRACS, a so-called direct digital control (DDC) method is generally used in which the operating end of the process input is directly operated by the output signal of the digital controller.

As described in claim 1, the system of the present invention is applied to a conventional thermal power plant controlled by a PID control system composed of distributed PID (proportional, integral, derivative) control elements. As a control target, a configuration is added in which a MRACS controller, which is also incorporated in the distributed digital arithmetic control element, is added in parallel. As a result, the plant to be controlled becomes a stable closed-loop type, and even if the digital arithmetic unit becomes unstable, the control signal (ex.
By limiting u ₁ ), the safe operation of the plant can be continued only by the conventional PID control system. The cause of the MRACS abnormality is investigated and recovered during this period, thus maintaining the robustness of the control system.

Further, by adopting a so-called DCS (Distributed Control System) in which a control system is constituted by distributed digital arithmetic control elements instead of the mainframe computer, risk dispersion based on the failure of the control elements becomes possible. This can be counted as one of the features.

[0016]

1 is a schematic diagram showing the configuration of an adaptive steam temperature system according to the present invention.

In the model reference adaptive control system (MRACS) for controlling the boiler steam temperature of the present invention, the steam temperature of the superheater outlet of the boiler (hereinafter referred to as the main steam temperature) and the steam temperature of the reheater outlet (hereinafter referred to as the reheated steam) are used. is referred to as temperature) as a control target, these steam temperature (ex. the set value of y) (ex. deviation from r ₁₎ (ex.
The objective is to always control e ₁ ) to 0 regardless of changes in boiler load, boiler operating conditions, and the presence of disturbance inputs. Therefore, the reference model is set to 0 in this MRACS.

The adaptive control system has one input and one output (SISO: Single) for each of the main steam temperature control and the reheat steam temperature control.
Input Single Output) Configures the system. The superheater spray flow rate is used as the main operation amount for controlling the main steam temperature, and the opening of the gas damper that controls the combustion gas recirculation amount in the boiler furnace is used as the main operation amount for controlling the reheat steam temperature. Also,
In order to remove the mutual interference between the main steam temperature control system and the reheat steam temperature control system, the load instruction (ex. Operation command for gas recirculation amount in main steam temperature control system
(ex. u ₂ (rh)) and the superheater spray flow rate operation command (ex. u ₂ (sh)) in the reheat steam temperature control system are considered to be known disturbances to the respective control systems, and the disturbance compensation circuit is considered. Make up.
The adaptive control controller for MRACS is connected in parallel with the PID controller of the plant, takes in plant data at every predetermined sampling period, and calculates the parameter estimation adaptive control algorithm prepared in advance in the controller. The so-called discrete-time sampled value control is performed in which the control signal (ex. U ₁ ) calculated according to the above is sent to the plant every predetermined control cycle.

FIG. 2 is a schematic diagram showing the relationship between the controller for adaptive control and the plant model. In FIG. 2, w is the disturbance due to the boiler load, and u ₂ is the disturbance compensation element for preventing mutual interference. As mentioned earlier, the plant contains a PID controller. The diagonal arrow in the calculation block of the adaptive controller indicates that this part is adjusted by the adaptive control law.

The outline of the parameter estimation and control signal calculation method is as follows. A controlled plant with one input and one output is expressed in the form of equation (1) called the autoregressive moving average (ARMA) model. A (z ^-1 ) y (k) ＝ z ^-d B (z ^-1 ) u (k)… (1)

Where y (k) and u (k) respectively represent the plant output at time k, ie the steam temperature and its corresponding control input, and A (z ^-1 ) and B (z ^-1 ) Is a polynomial with respect to z ^-1 expressed by equation (2).

[Equation 1]

In equation (2), z ⁻ⁱ is the time i · ΔT
It represents an operator that delays by (ΔT is a sampling period). Therefore, z ^{−d in the} equation (1) represents the dead time of d · ΔT.

Considering the physical meaning of the equation (1), the plant output y (k) at time k is the past plant output y (k-1), ..., y (k-1 to kn). kn) and kd
Past control input up to kdm time u (kd),…, u (kdm)
Is expressed by a so-called linear combination, which is obtained by multiplying by an appropriate coefficient (ex. Θ _a , θ _b ), and these coefficients a ₁ ,…,
It can be seen that the values of a _n , b _o , b ₁ , ..., b _m are the parameters (ex. θ) that represent the plant dynamics. That is, the polynomial A (z ^-1 ) represents the unique dynamics of the plant,
B (z ^-1 ) is a polynomial that represents the influence of the control input on the plant output. In Fig. 2, A (z ^-1 ), z ^-d1 B ₁ (z ^-1 ) in the part that represents the plant model is shown in Eq. (1).
Corresponds to A (z ^-1 ) and z ^-d B (z ^-1 ), and also z ^-dw C (z ^-1 ), z ^-d2 B
₂ (z ^-1 ) indicates the effect of measurable disturbance (ex. W, u ₂ ) on the plant output, and is used to compensate for the deterioration of control performance due to disturbance. Model reference adaptive control (M
The goal of RAC), the steam temperature caused by a boiler load changes and other disturbances (ex. Y) of the set value (ex. Deviation from r ₁₎ (e
x. e ₁ ) is converged to zero according to the characteristics specified by the designer of the control system. In the following, e ₁ (k) is used instead of y (k) in Eq. (1).

The polynomial R ₁ regarding appropriate ^{z -1 (z -1), S} (z
^-1 ), the parameter (ex.θ) that gives the convergence characteristics specified by Eq. (1) and the plant input (control input and disturbance (e.
x. u ₁ , w, u ₂ )) and output (ex. y) are rewritten in the form of a product, and constitute the part of the adaptive controller of FIG.

In the adaptive controller,

[Equation 2] Is the time k that is constantly estimated using plant data.
R ₁ (z ^{-1) in, S (z - 1),} B 1 (z -1, k), B 2 (z -1, k),
This represents the parameter estimation value of C (z ^-1 , k), and in this adaptive controller, the adaptive control signal (ex.u ₁ ) is calculated according to the specified convergence characteristic while considering the characteristics of the disturbance (ex.w, u ₂ ). Indicates that is calculated.

In FIG. 2, this adaptive control signal (ex. U ₁ ) is MR
ACS is shown to be processed by a switching circuit for "ON" and "OFF", and an upper limit value and a change rate limiting circuit, and then output to the plant. This relates to the second claim and will be described below.

As described above, the target plant of the present adaptive controller is a thermal power plant controlled by a PID control system composed of distributed PID (proportional, integral, derivative) control elements. Here, in order to secure the required load, the plant usually uses a PID control system to give its operation output command (ex. U) to the boiler operating end, and at the same time, these controls are usually fully automatic. Has been achieved by the control of. This adaptive control device controls the normal state where the plant is operated by the above-mentioned fully automatic control, and for example, it is a distributed type of the main steam temperature control system.
If a failure occurs in the PID control system, the characteristics of the plant to be controlled will change significantly.
This adaptive control system constantly monitors the operating mode of the plant and promptly outputs the control output (ex.u ₁ ) when the plant starts / stops or when the operating mode of the plant deviates from the operating conditions of adaptive control. Is set to 0, and the system waits for the restoration of the plant operation form. At the same time, internal parameter update (ex.
θ) is paused. However, if data acquisition for parameter updating is always continued, and if the operation mode of the plant is restored later and it returns to the range suitable for the operating conditions of adaptive control, the parameter values before the suspension of updating will be automatically controlled as the initial values. By restarting the transmission of the signal (ex. U ₁ ), a means is adopted to prevent smooth start-up of control and deterioration of control characteristics. In addition, giving an excessively large control signal (ex. U ₁ ) to the plant as the control output (ex. U ₁ ) is not preferable in terms of control, and also hinders plant safe operation.

Therefore, in the system of the present invention, the control signal from the adaptive operation element should be changed due to a modeling error or other factors.
Even if (ex. u ₁ ) is a temporarily large value, it is specified in advance as an upper limit value (absolute value limit) or rate of change (speed value limit).
It has a function to limit to.

Specifically, assuming that the control signal from the adaptive arithmetic element is u _1c (k), and the control signal processed by the limiting circuit and actually input to the plant is u ₁ (k), the following is given. It has a limiting circuit.

[Equation 3] However, uL is related to the size of the estimation error e ^* (k), and uses the following function that makes the change rate limit variable.

[Equation 4] By specifying R, u _min , u _max , and k, the above-mentioned limiting circuit can be provided.

FIG. 3 is a schematic diagram showing the relationship between the signal flow of the adaptive control program and the conventional control system.

The thermal power plant to be controlled has the characteristic that the response (dead time, settling time) to the manipulated variable (ex. U) changes depending on the magnitude of the boiler load. For example, it is empirically known that the dead time (ex. D) is shorter and the model order (ex. N, m) is higher in a higher load region. In fact, in the discrete time representation used for adaptive control, the dead time and the model order are taken up as the main factors related to the control characteristics.

On the other hand, it can be said that it is very difficult to change the dead time and the order of the model during the on-line control because of the design of the adaptive control. Therefore, performing adaptive control for a controlled object whose dynamic characteristics change with load changes at a constant sampling time causes a difference between the dead time, the model order, and the plant characteristics. In the online modeling, it is expected that an unfavorable state such as difficulty in convergence of the estimation parameter due to modeling error will occur. Therefore, in the system of the present invention, as described in claim 3, the load level (ex.
It has a function that can deal with the difference in dead time and model order corresponding to w) by changing the sampling time corresponding to the load level. That is, the distributed digital arithmetic control element corresponds to a change in the boiler dynamic characteristic depending on the boiler output, and the dead time (ex.
Instead of changing d) or the order (ex. n, m), the data sampling interval (ex. ΔT) is made variable, that is, in the low load region where the plant's unique movement is gentle, the data sampling period , And conversely, in the high load region where the plant movement is relatively fast, the sampling period is made small, and a means for adaptively adjusting the sampling period in response to the change in the dynamic characteristics of the plant due to the load change is provided. It was done like this.

The calculation cycle (ex. ΔT) of the adaptive control is selected to be an appropriate value depending on the dynamic characteristics of the plant, and it is on the order of several tens of seconds for a large boiler.

Data u ₂ (k) taken in every calculation cycle,
The adaptive control is calculated from w (k), u ₁ (k), and e ₁ (k) in the parameter adjustment rules in the figure, and a new adaptive control signal u ₁ (k) is output to the plant. Here, (k) represents the time point k. After outputting the adaptive control signal, the pre-calculation necessary for the control at the next control time is performed, and the matrix calculation for updating the parameters occupies the main part of the calculation. As an example, a matrix operation of size 42 × 42 is performed for each of the main steam temperature control and the reheat steam temperature control.

The adaptive control is performed according to the configuration and algorithm of the control system described above. While the control is in progress, the operating condition of the plant, the magnitude of the control output signal (ex. U ₁ ) and the operation of the control device are controlled. Control is performed in preparation for the operating environment of adaptive control and unexpected occurrence of abnormalities by constantly monitoring the state and the like and constantly transferring and saving the values of parameters in the control device to a spare memory.

Next, a load command (MWD: Megawatt Dem) which is a measurable disturbance described in claims 4 and 5 is claimed.
and) Explain measures for improving control performance by actively using (ex. w).

As described above, in the MRACS according to the present invention, a polynomial that represents the dynamic characteristics of the plant using the sampling values of the input (ex. U ₁ , w, u ₂ ) and output (ex. Y) signals of the plant. A control signal (ex. U ₁ ) is generated that asymptotically shifts the control deviation (ex. E ₁ ) of the steam output, which is the plant output, to 0 according to the convergence characteristics specified in advance.

The expression of the plant dynamic characteristics for achieving this purpose takes the form of the following expression. D (z ^-1 ) y (k + d) = θ ^T ζ (k)

Where D (z ^-1 ) is a polynomial expressing the asymptotic convergence property of the control deviation, k is time, d is a symbol representing the dead time of the plant, y is the plant output, and thus y (k + d) is k + d
Output of the plant at time, θ is a parameter vector that has MRACS parameters as elements, ^T is a symbol indicating transposition of the vector, and ζ (k) is a data vector at time k.

In a general one-input one-output MRACS,
The data vector ζ is the input (ex. U ₁ ) and output (e.
x. y) and the order of the dead time (ex. n, m, d) and the number of data, and the parameter vector is a vector of the same size including the corresponding number of parameters.

Now, for the purpose of disturbance compensation, the control input (e
x. u ₁ ) and the known disturbance input (ex.w, u ₂ ), the number of data contained in the data vector ζ,
That is, the size of the data vector increases by the order of the disturbance input, and the size of the parameter vector increases correspondingly.

Generally, the value of the parameter of the boiler for thermal power generation changes depending on the plant load, so the value of this parameter θ is a linear expression θ = θ _a + w (kd) θ _b related to the load command (ex. W). , The plant parameters will consist of a load-dependent steady-state change component θ _a and a load command-proportional component θ _b , and the parameter vector and data vector sizes will be approximated by a linear equation. Will be twice the size if you do not.

However, by adopting this linear equation approximation, it is not the value of the parameter θ that greatly changes depending on the load but the components θ _a and θ that change relatively slowly.
_Since it is sufficient to estimate _b as a parameter, as a result, the parameter estimation speed is increased and the estimation accuracy is improved. Claim 4 above relates to improvement of control performance utilizing this advantage.

Further, in the system in which the parameter is approximated by the linear expression relating to the load command (ex. W), as the parameter estimated value converges, the portion θ _b of the elements of the parameter vector proportional to the load command changes. Since the width becomes smaller, when these elements can be regarded as almost constant, fix these values near the convergence value,
By switching the parameter update to only the load-independent component θ _a , the number of parameters to be estimated can be halved, and the parameter estimation speed can be further increased. This measure can reduce the load on the control arithmetic unit and improve the control performance.

Claim 5 relates to improvement of control performance by the above measures.

Next, the characteristics of the calculation algorithm employed in the present invention in the control parameter update calculation process will be described. Generally, in MRACS, when the dead time d is not considered, the parameter estimation calculation based on the recursive least squares method as described below is performed.

[Equation 5]

Where

[Equation 6] Is an estimated parameter vector, z is a measured value of the output signal of the plant, and corresponds to y or e ₁ described above. ζ is a data vector whose elements are input and output data of the plant, k is time, and ^T is a symbol that represents the transpose of the vector. The meaning of equation (3) is as follows.

When the input and output data u (k) and z (k) of the plant are obtained at time k, the data vector ζ (k) is updated using this, and this data vector and at time k-1 are obtained. Estimated value of the obtained parameter vector

[Equation 7] Dot product with

[Equation 8] The plant output estimate at time k is calculated by and the difference between this estimate and the plant output data z (k) actually obtained at time k, that is, the pre-estimation error

[Equation 9] Ask for this

[Equation 10] A modified term obtained by multiplying by the modified gain matrix K (k)

[Equation 11] Is the estimated parameter vector at k-1

[Equation 12] By adding to

[Equation 13] That is, the estimated value (post-estimated value) of the parameter at time k is obtained.

Here, the gain matrix K for obtaining the correction term
(k) is generally called Kalman gain and is calculated by the following equation.

[Equation 14] Where P (k) is the covariance matrix that gives the variance of the data at time k, and its size depends on the order of the plant model. Equation (5) shows that if the data vector ζ (k) is measured at time k, the data covariance matrix P (k) at that time is
It is shown that it can be calculated by the recurrence formula using the data covariance matrix P (k-1) at time k-1. This P (k)
Then, the Kalman gain K (k) is obtained by Eq. (6), and this K (k) is used by Eq. (3) to estimate the parameter estimation vector at time k.

[Equation 15] Is calculated. Therefore, the parameter vector

[Equation 16] The estimation calculation of is performed in the order of (4) → (5) → (6) → (3).

In practice, considering the dead time d,
Parameter estimation will be performed using ζ (kd) instead of ζ (k) in Eqs. (3) to (6).

In the above-mentioned parameter update calculation process, since the matrix of the equation (5) is subtracted, a rounding error occurs due to the cancellation of digits in the numerical calculation. In particular, when the size of the matrix is large as in the present embodiment, and the change width and the change rate of the data for updating the parameters are small, the positive definite symmetry of P (k), which should originally be a positive definite symmetric matrix, is accumulated due to the accumulation of rounding errors. This causes a problem such that the control signal (ex. U ₁ ) is biased or the control output signal runs away in an extreme case. In order to avoid such a situation, MRACS of the present embodiment uses a so-called UD factorization method for calculating the parameter P (k) into a unit upper triangular matrix and a diagonal matrix.

Details of this method can be found, for example, in the reference “Niu, S., Fisher DG, and Xiao, D. An augmented.
UD identification algorithm, International Journa
lof Control, Vol.56 No.1 pp 193-211, 1992. ”, and enumerating the advantages of using this method, (i) there is no need to perform matrix subtraction in the calculation process, so the covariance The positive definite symmetry of the matrix is maintained, (ii) the optimum order of the polynomial model that gives the plant representation can be determined from the process of matrix operation, and (iii) the data vector used for parameter updating is the continuous excitation condition (PE It is possible to obtain a threshold value for determining whether or not (conditions) are satisfied, etc. These features obtained by the UD decomposition method are used for the purpose of maintaining the accuracy of the parameter update operation, and It is also used for the purpose of providing a threshold value for carrying out the means described in claim 6, that is, the means for suspending the parameter update when the excitation condition of the input and output data is insufficient and for preventing the parameter drift phenomenon. It is possible.

In the present embodiment, the above equation (4) s (k) indicates the information measure for the input (ex. U ₁ ) and output (ex. Z) signals of the plant,

[Equation 17] Is the parameter vector at time k-1

[Equation 18] Paying attention to the points indicating the pre-estimation error using

[Formula 19] However, if an appropriate threshold value is set in advance and the change in the input and output signals of the plant stays within this threshold value for the specified time or longer, the parameter update is suspended and measures are taken to prevent the parameter drift phenomenon. There is.

FIGS. 4 to 7 show the results of actual machine tests conducted on a 375 MW subcritical drum type boiler in order to compare the control characteristics of the conventional PID controller and the model reference adaptive control according to the present invention. Deviation from In this test, the same operating conditions, under the same load change of the main steam temperature and reheat steam temperature by both control methods described above setpoint (ex. R ₁₎
We compared (ex. e ₁ ). As shown in the figure, the adoption of the adaptive control method reduces the steam temperature deviation (ex. E ₁ ) compared to the conventional PID control method, and allows the fluctuation to change quickly to the set value (ex. It can be seen that it converges to r ₁ ).

[0055]

The following effects can be obtained by applying the adaptive control device according to the present invention. Supports changes in plant dynamic characteristics due to changes in operating conditions due to boiler output, type of fuel used, mill / burner switching, etc., and changes in heat absorption distribution of heat transfer surfaces due to furnace fouling and aging. Then, the parameters of the controller are adaptively adjusted, and control is always performed to keep the deviation from the set value of the steam temperature near 0 even if there is a load change or other disturbance due to the power supply command. Maintenance and efficiency can be achieved, and at the same time, the output of the thermal power plant can promptly follow the power supply command, which can contribute to smooth operation of the power system.

[Brief description of drawings]

FIG. 1 is a schematic diagram showing a configuration of an adaptive steam temperature system according to the present invention.

FIG. 2 is a schematic diagram showing a relationship between a controller for adaptive control and a plant model.

FIG. 3 is a schematic diagram showing a relationship between a signal flow of an adaptive control program and a conventional control system.

FIG. 4 is a diagram showing a control characteristic in a main steam lamp load test, where (A) is a conventional example and (B) is an example of the present invention.

FIG. 5 is a diagram showing control characteristics in a main steam M-type load test, where (A) is a conventional example and (B) is an example of the present invention.

FIG. 6 is a diagram showing a control characteristic in a reheat steam lamp load test, in which (A) is a conventional example and (B) is an example of the present invention.

FIG. 7 is a diagram showing control characteristics in a reheat steam M-type load test, where (A) is a conventional example and (B) is an example of the present invention.

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Shiro Matsumura 1 at 20 Kitakanzan, Otaka-cho, Midori-ku, Nagoya-shi, Aichi Chubu Electric Power Co., Inc. Electric Power Technology Laboratory (72) Inventor Hideo Shiotani Shizuoka 511 Haraki, Nirayama-cho, Takata-gun Japan Belay Co., Ltd.

Claims (6)

[Claims] 1. In steam temperature control of a boiler for thermal power generation, a control target value called a reference model is set, and a control error,
That is, based on the difference between the reference model and the actual steam temperature, the control calculation function that generates the control signal that achieves the control target, and the parameters related to the dynamic characteristics of the boiler are constantly estimated from the boiler input signal and the sampling measurement values of the actual steam temperature. Then, the parameter adjustment function that adjusts the parameters for control signal calculation based on the estimated parameters is installed in the distributed digital operation control element, and the boiler, turbine process and distributed PID (proportional, integral, derivative) controller are used. A discrete-time model reference adaptive steam temperature controller for a thermal power generation boiler that constitutes the entire adaptive control system by adding the above-mentioned distributed digital arithmetic control elements in parallel to an ordinary thermal power plant control object configured in a closed loop. . 2. The distributed digital arithmetic control element outputs a control output when the plant is started or stopped, or when the operating mode of the plant is significantly outside the operating conditions of adaptive control.
When the internal parameter update is paused as 0, data acquisition for parameter update is always continued, and if the plant condition returns to a range suitable for the use of adaptive control, the value before the parameter update pause is used as the initial value. A function that automatically restarts adaptive control and limits this to a prespecified upper limit value or rate of change even if the control signal from the adaptive computing element may increase abnormally due to modeling errors or other factors. The discrete time model reference type adaptive steam temperature control device for a thermal power generation boiler according to claim 1, further comprising: 3. A means for changing the sampling interval of the data, instead of changing the dead time or the order of the boiler model in response to a change in the boiler dynamic characteristic depending on the boiler output, by the distributed digital arithmetic control element, That is, in the low load region where the peculiar movement of the plant is gradual, the data sampling period is set large, and conversely, in the high load region where the plant movement is relatively fast, the sampling period is set small and the plant accompanying the load change is 3. A discrete time model reference type adaptive steam temperature control device for a thermal power generation boiler according to claim 1 or 2, further comprising means for adaptively adjusting a sampling cycle in response to a change in dynamic characteristics of the above. 4. The distributed digital arithmetic control element comprises means for approximating a parameter change by a linear expression relating to a load command in consideration of load dependency of a plant parameter value and estimating a coefficient thereof. A discrete time model reference type adaptive steam temperature control device for a thermal power generation boiler according to any one of 1 to 3. 5. When the distributed digital arithmetic control element is within a small range in which there is a term proportional to a load command in a linear expression approximating a parameter change, that is, a vector change consisting of a proportional coefficient falls within a small range. 5. A discrete time model reference form for a thermal power generation boiler according to claim 1, further comprising means for fixing the elements to converged values, halving the number of parameters to be estimated, and accelerating the parameter correction speed. Adaptive steam temperature controller. 6. An insufficient excitation signal (information amount) under the condition that the distributed digital arithmetic control element remains within a range where the change width and change rate of the input and output signals of the plant for parameter updating are small. In order to avoid the parameter drift phenomenon due to
A discrete time model reference form for a thermal power generation boiler according to any one of claims 1 to 5, further comprising means for suspending parameter updating when a change in input and output signals stays within a threshold for a specified time. Adaptive steam temperature controller.