Superheater Steam Temperature Control for a 300MW Boiler Unit with Inverse Dynamic Process Models

1 Superheater Steam Temperature Control for a 300MW Boiler Unit with Inverse Dynamic Process Models Liangyu Ma, Yongjun Lin and Kwang Y. Lee, Life Fellow, IEEE to achieve good control results under different loading Abstract--An Inverse Dynamic Neuro-Controller (IDNC) is condition and changing environment, which often requires developed to improve the superheater steam temperature control much effort and time [1]. Therefore, it is highly desirable to of a 300MW boiler unit. A recurrent neural network was used adopt intelligent control algorithms in order to improve the for building the Inverse Dynamic Process Models (IDPMs) for superheater steam temperature control. the superheater system. Two inverse dynamic neural network (NN) models referring to the first-stage and the second-stage Artificial neural network is an attractive method for water-spray attemperators are constructed separately. To identifying nonlinear processes, due to its good modeling achieve highly accurate approximation of the superheater system, capability and its ability to learn complex dynamic behaviour the NN models are trained with sufficient historical data in a of a physical system. Applications of neural networks have wide operating range, which consists of both different steady- been widespread in process control, both in simulation and state conditions and dynamic transients. Then the IDNCs are on-line implementation, which includes model predictive designed based on the well-trained IDPMs and applied to superheater steam temperature control. In order to eliminate the control, inverse model-based control, and adaptive control [2]. steady-state control error arisen by the model error, a simple Recently, the concept of inverse dynamic neuro-controller was feedback PID compensator is added to an inverse controller. used for superheater and reheater steam temperature control of Detailed control tests are carried out on a full-scope simulator for a large-scale ultra-supercritical boiler unit [3,4]. Feedforward a 300MW coal-fired power generating unit. It is shown that the and recurrent networks are the two commonly used neural temperature control is greatly improved with the IDNCs network structures for modeling, prediction and control of compared to the original cascaded PID control scheme. nonlinear dynamic systems. A feedforward network is a static Index Terms—Boiler control, feedforward control, inverse mapping that models a steady-state condition of a plant. It can dynamic neuro-controller, superheater steam temperature also be used to model dynamic behavior of the plant by control, neural network control, PID compensator. including delayed input and output values as additional inputs. On the other hand, a recurrent network possesses internal I. INTRODUCTION memory by including feedbacks for past values, either from UPERHEATER steam temperature is one of the most network output into hidden units or from hidden units in one Simportant variables for a boiler system in a large-capacity layer into hidden units in another layer [5-8]. fossil-fuel power generating unit. It must be controlled to be The main focus of this paper is to use a recurrent neural within given upper and lower limits to ensure high efficiency network to model the inverse dynamic relationship of the and safety of the power unit. However, often it is not easily superheater steam temperature in a 300MW boiler unit and controlled with a simple PID controller since the boiler is a use the trained inverse dynamic neural network models as multi-input multi-output (MIMO) nonlinear system consisting feedforward controllers to improve the superheater steam of many strongly-coupled sub-systems, which leads to a large temperature control. Detailed control tests are made on a full- time delay and big inertia to the superheater steam scope simulator of a 300MW power generating unit. temperature response. Therefore, cascaded PID controllers are generally used for superheater steam temperature control and II. SYSTEM DESCRIPTION the control logic is getting more complex with increasing The power plant under investigation is a 300MW boiler capacity. Moreover, even for a cascaded control scheme subcritical coal-fired boiler-turbine-generator unit. The rated with at least 2 PID control modules, the gains and time steam temperature for superheater and reheater under full load constants of these PID controllers have to be tuned frequently condition is 540℃. The superheater is mainly made of 4 parts: (1) Primary Superheater (PSH), which is arranged above the L. Y. Ma and Y. J. Lin are with the Automation Department, School of economizer, including both horizontal-type and vertical-type Control and Computer Engineering, North China Electric Power University, ones; (2) Divided Platen Superheater (DPSH), which is Baoding, Hebei 071003, China (e-mail: [email protected]; [email protected]). installed over furnace combustion area and between the front K. Y. Lee is with the Department of Electrical and Computer Engineering, waterwall and the Rear Platen Superheater; (3) Rear Platen Baylor University, Waco, TX 76798-7356, USA (e-mail: Superheater (RPSH) located before the furnace arch; and (4) [email protected]). Final Superhater (FSH) installed at the horizontal gas pass. 978-1-4244-6551-4/10/$26.00 ©2010 IEEE 2 The reheater also includes 3 different parts: (1) Wall-type III. NEURAL NETWORK STRUCTURE AND ITS TRAINING Radiant Reheater, which is arranged near the front and side- SCHEME front waterwall, about 1.5m lower than the DPSH; (2) Rear Recurrent neural network differs from other conventional Platen Reheater (PRH), installed behind the RPSH along the feedforward networks in that it includes recurrent or feedback gas direction; (3) Final Reheater (FRH), installed in the connections [6-8]. The delays in these connections store horizontal gas pass behind the furnace arch and before the values from the previous time step, which makes it sensitive to FSH. the history of input and output data and fit for dynamic system During normal operation, the superheater steam modeling. For convenience, the Elman network is often used temperature is adjusted by regulating the first stage and the for a recurrent neural network, which has tansig neurons in its second stage water-spray attemperators. The first stage hidden (recurrent) layer, and purelin neurons in its output attemperator is installed between the PSH and the DPSH. The layer [9-11]. This combination is special in that a three-layer second stage attemperators are installed between the RPSH network with these transfer functions can approximate any and FSH. The desuperheating water comes from the feedwater function (with a finite number of discontinuities) with pumps’ outlet header. As for the reheater, the reheated steam arbitrary accuracy if the hidden layer has enough neurons. The temperature is mainly controlled by adjusting the angle of the structure of an Elman recurrent network is shown in Fig. 3. tilting burners, either upward or downward under normal operation. The angle adjustment for the tilting burners also exerts influence on the superheater steam temperature. In addition, two paratactic water-spray attemperators are installed at the entrance of the wall-type radiant reheater, mainly for emergency desuperheating use. The reheater desuperheating water comes from the midpoint taps of the running feedwater pumps. The schematic of the above 300MW boiler system is shown in Fig. 1. The modularized flow chart of the superheater system is shown in Fig. 2. Fig. 3. Structure of the Elman recurrent network model. As shown in Fig. 3, the outputs in each layer of an Elman network is given by: MR =+ (1) x jijiiji()kfWukWock (∑∑ 1,, () 3 _ ()) ii==11 =− ckii() xk ( 1) (2) R = y jiji()kgWxk (∑ 2, ()) (3) i=1 where, is the weight that connects node i in the input W1i, j layer to node j in the hidden layer; W 2i, j is the weight that connects node i in the hidden layer to node j in the output layer; is the weight that connects node i in the context ①Economizer；②Steam Drum；③ Primary SH；④Divided platen SH；⑤ W 3i, j Rear Platen SH；⑥Final SH；⑦wall-type radiant reheater；⑧Rear Platen layer to node j in the hidden layer; and f(·) and g(·) are the RH；⑨Final RH transfer functions of hidden layer and output layer, Fig. 1. Schematic of a 300MW boiler unit. respectively. An Elman neural network can be created and trained according to the back-propagation algorithm with MATLAB Neural Network Toolbox. When the entire input sequence is presented to the network, its outputs are calculated and compared with the target sequence to generate an error sequence. For each time step, the error is back propagated to find gradients of errors for each weight and bias. This gradient is actually an approximation, because the contributions of Fig. 2. Modularized structure of the superheater system. weights and biases to errors via the delayed recurrent 3 connection are ignored [9-11]. However, more accurate gradient can be evaluated by including the contributions Since an inverse dynamic process model expressed by the through the recurrent neurons [8]. This gradient is then used to neural network is not a complete inverse but an update the weights with the chosen back-propagation training approximation, it may generate a steady-state error when it is algorithm [12]. Since Levenberg-Marquart method is fast and used as a feedforward controller. Therefore, a supplementary signal E(k) is needed to eliminate the steady-state error has robust convergence property in the off-line training, it is induced by modeling error and other disturbances. The used for training the Elman network in this paper. supplementary signal E(k) may come from the output of a feedback PID compensator. This combined control scheme IV. INVERSE CONTROL SCHEME with an IDPM feedforward controller and a simple PID The concept of inverse control was originated by B. feedback compensator is shown in Fig. 6. Widrow [13-16], and has been widely used in control of different industrial applications [3,17-21].

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