Heavy duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: speed and exhaust temperature control
- Nadji Hadroug^{1},
- Ahmed Hafaifa^{1}Email author,
- Mouloud Guemana^{2},
- Abdellah Kouzou^{1},
- Abudura Salam^{2} and
- Ahmed Chaibet^{3}
https://doi.org/10.1186/s40929-017-0017-8
© The Author(s) 2017
Received: 2 July 2016
Accepted: 5 October 2017
Published: 17 October 2017
Abstract
Gas turbines are currently a popular power generation technology in countries with access to natural gas resources. However they are very complex systems the operation of which at peak performance is challenging. This paper proposes the use of a hybrid approach based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the control of the speed and the exhaust temperature of a gas turbine. The main aim is to maintain turbine operation at optimum performance. The results obtained, based on the use of the Rowen model, clearly show the effectiveness of the proposed hybrid speed/exhaust temperature control approach for the gas turbine.
Keywords
Background
In recent years, gas turbines have become important and widespread devices for heavy industrial applications including electrical power generation and service in the oil and gas industries. Keeping these turbines operating at optimal efficiency is an important research question for the manufacturer and operator of these devices. Turbines are very complex systems that require advanced control techniques to ensure the proper control of their operating parameters. Of particular interest is the control of the speed and the exhaust temperature. Note that the control of the exhaust temperature is affected by ambient environmental parameters.
Recently several studies have been performed to ensure the modelling and the control of gas turbine. Benyounes et al. have proposed a fuzzy logic approach to modelling and controlling vibrations in gas turbines used for pipeline gas transportation [1, 2]. Balamurugan et al. have studied the control of the load frequency of an operating gas turbine plant based on signal processing analysis, via both large and small signal models [3]. Asgari et al. introduced the Nonlinear Autoregressive Exogeneous (NARX) model for the simulation of single shaft gas turbine startup operation [4]. Zaidan et al. have proposed a prognostics system to predict gas turbine behavior using a Bayesian hierarchical model based on a variational approach [5]. Zhu et al. developed a mathematical model to study steam turbine operation phases based on an optimization approach [6] and Onabanjo et al. have modelled the degradation of gas turbine components [7].
The development of gas turbine control systems is important in the oil and gas industries, where turbines are used both in power generation and pipeline gas transportation plants [2, 8–17]. In this context an adaptive neuro-fuzzy inference system (ANFIS)- based hybrid control approach is proposed in this paper. The main objective of the ANFIS control is to ensure the proper control of the speed and the exhaust temperature of a gas turbine based on the Rowen model [18]. It is important to note that the control system proposed in this paper is able to cope with changes in environmental parameters such as ambient temperature, not only by reducing the controlled parameter errors, but also by improving the quality of the response time and reducing the maximum overshoot of the proposed control system.
In 1983 Rowen developed a model of a heavy duty gas turbine plant based on a transfer function block diagram. His main idea was to build a simulation model to aid in the control of three main parameters of the gas turbine: the speed, the exhaust temperature, and the acceleration. Rowen succeeded in validating the system gains using coefficients and time constants estimated for his model based on test and field experience accumulated from numerous installations for many different applications [6]. The decisions taken during the control of a gas turbine profoundly impact the operating cost of the installation. The gas turbine system is a complex nonlinear system featuring strong interactions between operating variables, so it is important to account for the impact of monitoring system behavior on the system behavior. For this reason traditional approaches to turbine control are less effective at ensuring the required, reliable, level of control of parameter dynamic behavior.In this study, a dynamical analysis of the control of a representative gas turbine is carried out based on real operating conditions. The proposed neuro-fuzzy control approach is implemented by combining a neural network approach and a fuzzy systems approach in a homogeneous architecture. This paper demonstrates that the proposed hybrid approach improves the control of both speed and gas exhaust temperature for the gas turbine considered.
Case description
Several models have been developed in many applications for the control of the dynamic behavior of gas turbines. However, the complexity of the dynamic behavior of the gas turbine systems increases the difficulty of obtaining a reliable monitoring system for such devices.
This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) approach to ensure the control of the speed and the exhaust temperature of a gas turbine based on Rowen model [18], in order to maintain its optimum performance.
Gas turbine modeling
The data of heavy duty gas turbines
The metric Units | |
---|---|
Model | 7001B |
The speed of turbine | 3000 rpm |
Nominal-Temperature | 510 °C |
Max torque | 16.231 Kg.M |
Inertia | 7.834 Kg.M2 |
Power Rating | 157.7 MW |
Turbine parameters in nominal operating conditions [22]
Parameter | Symbol | Unit | Value |
---|---|---|---|
Compressor pressure ratio | PR | / | 11.3 |
Fuel mass flow | / | kg/s | 9.1 |
Lower heating value of fuel | LHV | kJ/kg | 42,532 |
Exhaust mass flow | \( {\dot{m}}_n \) | kg/s | 539 |
Exhaust temperature | TR | °C | 510 |
Nominal frequency | F | Hz | 50-60 |
Electrical power | P _{ Gn } | MW | 157.7 |
The inside compressor temperature | T _{2(oc)} | °C | 401.54 |
Efficiency (simple cycle) | η | % | 34.7 |
Turbine speed | N | rpm | 3000 |
Where T _{3} is the combustion chamber temperature, T _{4}is the temperature of the exhaust gas of the gas turbine (HP) section and γ _{ g } = 1.333 is the specific heat ratio of the gas.
with: \( {\eta}_t=\frac{T_3-{T}_{4(oc)}}{T_3-{T}_{4s(oc)}}=\frac{1060-510}{1060-471.69}=0.935 \) and \( {Z}_g={R}^{\left(\frac{\gamma_g-1}{\gamma_g}\right)}={11.3}^{\left(\frac{1.33-1}{1.33}\right)}=1.824 \).
Where \( {\dot{m}}_a \) is the air mass flow, \( {\dot{m}}_g \) is the gas mass flow, C _{ pg } is the specific heat at constant gas pressure, C _{ pa } is the specific heat at constant air pressure, with C _{ pa } = 1.015 and C _{ pg } = 1.149.
The turbine parameters are calculated for nominal operating conditions according to Table 2.
It is obvious that when the HDGT is operating at nominal speed, the output torque (p.u) and the mechanical power (PG) are almost the same (\( A=-0.223;\kern0.75em B=1.221;\kern0.75em {\dot{m}}_{fn}=5.129 kg/s \)).
Fuel valve positioner system modeling
p(t) and w _{ f }(t) are functions of (t) (i.e., functions of time domain), p(s) and w _{ f }(s) are functions of (s) (i.e., functions of frequency domain).
The PID controller is used to ensure the control of the installed gas turbine in oil and gas plants, where they are used to ensure the most important gas turbine parameters, especially speed and exhaust temperature, remain within desired bounds. While PID controllers are very simple, they are based on system linearization and so suffer from the major drawback of being non robust for nonlinear systems, with the control constants Ki, Kp, and Kd changing dramatically as process parameters change. To overcome this drawback, the ANFIS technique suggested in this paper may easily and rapidly be updated as gas turbine parameters and ambient environmental variables change. The optimization considers the performance of the entire process.
Adaptive neuro-fuzzy inference system (ANFIS)
The principle of fuzzy approaches in the sense that the variables are not treated as logical variables but as linguistic variables close to human language. Furthermore, these linguistic variables are processed using rules that refer to some knowledge of the system behavior [18].
A whole series of fundamental concepts are developed in fuzzy logic. These concepts are used to justify and demonstrate the basic principles. The structure of a conventional fuzzy controller is shown in Fig. 6, it is composed of four separate blocks whose definitions are given below. The fuzzy controller is designed to automatically run a process based on a set by acting on the control variables, and it has some characteristics that are intrinsic advantages.
A rule-base (a set of IF-THEN rules) contains a fuzzy logic quantification of the linguistic description of the expert for how to achieve good control. An inference mechanism (also called “inference engine”) emulates the interpretation and application of knowledge on how best to control the plant. A fuzzification interface converts the information into control signals for the inference mechanism which can be used to activate and apply rules. A defuzzification interface converts the results of the inference mechanism into the real process inputs. The rule-base is constructed so that a human expert is “in the loop.” The information in the rules of the rule-base can come from a real human expert who has spent a long time learning the best way to control the process. The fuzzification process plays an important role in the relationship between the soft information that can be objective or subjective.
Where x _{1} and x _{2} are the inputs, A _{1} and B _{2} are the fuzzy sets, y _{1} and y _{2} are the outputs of all defuzzification of neurons, p _{ i }, r _{ i } and q _{ i } are the parameters of the i ^{ th } rule determined during the learning process.
The structure of the proposed adaptive neuro-fuzzy network is shown in Fig. 7.
The two passes in the hybrid learning
Forward Pass | Backward Pass | |
---|---|---|
Premise Parameters | Fixed | Back-propagation |
Consequent Parameters | Least Squares Estimate | fixed |
Note that in this algorithm parameters corresponding both to the premises and of the consequences are optimized.
Discussion and evaluation
Dynamic model parameters
Parameter | Value |
---|---|
Radiation shield parameter G_{SH} | 0.85 |
Radiation shield time constant T_{SH} | 12.2 s |
Exhaust Temperature | 537.3 °C |
Fuel Demand signal Max Limit (maxK _{ NL }) | 1.5 |
Fuel Demand signal Min Limit (minK _{ NL }) | -1 |
K _{1} = 25; K _{2} = K _{3} = 1 | |
\( F1:{T}_R-D\left(1-{\dot{m}}_{fP\cdot U}\right)+0.6\left(1-N\right) \) | |
\( F2:A+B\cdot {\dot{m}}_{fp\cdot u}+0.5\left(1-N\right) \) | |
TI = 15.64 |
The proposed based ANFIS controller has two inputs (Tx error, error n) and one output. Each input has three fuzzified fuzzy set of Gaussian types. Figure 8 shows the ANFIS controller surface of the studied gas turbine variables.
The obtained results of the high-efficiency gas turbine system control during startup using the ANFIS approach are shown in Figs. 9 and 10. Figure 9 shows the speed variation per unit based on the Rowen model and Fig. 10 shows the measured exhaust temperature variation of the studied gas turbine.
In order to validate the ANFIS approach, a comparison with a PID controller has been performed for the both responses of the exhaust temperature and the speed as shown in Figs. 11 and 12 respectively. It is clear to see that, in both responses, the ANFIS controller responds more rapidly than does the PID controller. Indeed, the time response of the ANFIs is 5 s, whereas the time response of the PID is 34 s, which means that a gain of 29 s can be ensured by the use of the ANSIS controller. On the other hand the peak presented in the response of the PID controller response is avoided totally with the ANFIS controller. Therefore the ANFIS controller is more efficient and obtains a faster response time than the PID controller, suggesting that a reduced cost may be obtained when the ANFIS controller is used for complex industrial gas turbine systems.
This improved response is achieved by imposing the desired performance by limiting the maximum overshoot value at around 15% as shown in Figs. 13 and 14. From these two figures, it can be noted clearly that the proposed controller is working accurately with acceptable performances.
The results obtained in Figs. 13 and 14 describe the control of HDGT of 157.7 MW type of the 7001B turbine model, based on the ANFIS approach using the Rowen model parameters reported in [18]. These results clearly show that the ANFIS controller achieves better performance for the control of the speed and the exhaust temperature of the gas turbine studies, which allows an efficiency improvement to be obtained for the entire system.
For this particular gas turbine, to understand the effect of the speed control on other gas turbine system parameters, simulations have been performed based on the use of a PID controlled to ensure the control of a Model 7001B gas turbine used in the present study. Indeed, two tests have been achieved using two distinct sets of PID controller parameters in order to check their impact on the controlled gas turbine parameters. The simulations results so obtained are shown in Figs. 15, 16, and 17 for test 01 and in Figs. 18, 19 and 20 for test 02.
In this case, two factors affect the speed and the exhaust temperature of the gas turbine, as depicted in Figs. 15 and 16. The first factor is the change of the load at t = 100 s and the second factor is the linear decrease of the reference speed beginning at t = 130 s and ending t = 200 s. Figure 15 makes it clear that the PID controller can control the gas turbine speed only very slowly at the initial transient peak around t = 30s when it returns to its reference value.
First test 01: K _{ p } = 10.5, Ki = 1, Kd = 1
In this case, there are two factors that are affecting the speed and the exhaust temperature of the gas turbine as shown in Figs. 15 and 16, the first factor is the change of the load at \( t=100s \), and the second factor is the linear decrease of the reference speed during the interval of [130s 200s]. it can be seen clearly that the PID controller can achieve the control of the gas turbine speed but slowly along \( 30s \)where it rejoins its reference value (1 p.u). Furthermore, Fig. 15 shows that an important overshoot of the developed speed over the reference speed, implying that control is not accurate. At the same time the control of the exhaust temperature is not accurately achieved, as an overshoot above the allowed limit of 960F is observed. Such temperature overshoots can damage the turbine if they last for more than a very short time. However there is a difference between the responses with and without the use of the PID controller, as shown in Fig. 16. Figure 16 shows that the PID controller is not doing a good job, due to a poor choice of PID controller constants. The same outcome is visible in Fig. 17 for torque behavior dynamics. The perturbation in the system set point at t = 100 is caused by the external operating constraints and between t = 130 and t = 200 is due to the air leakage at the compressor level.
On the other hand, for a linear decrease in reference speed, the PID controller has better dynamics. It can be said the control is achieved and these results can be accepted as shown in Figs. 15, 16, and 17. For the start up operation of the turbine the PID controller has very results and presents a real danger as shown in Figs. 15, 16 and 17. Again, this can be explained by poor choice of PID parameters.
Second test 02: K _{ p } = 10.5, Ki = 0, Kd = 0
In this case, the same factors as in the first case are presented. Here there is a change of the load at t = 100 s and a linear decrease of the reference speed during the time interval [130 s, 200 s]. Figure 18 makes it clear that the controller under the new parameters has achieved better control of the gas turbine speed when it rejoins its reference value of 1 per unit at t = 100 s. At the same time the gas turbine develops excess torque to compensate for the drop in developed speed and to ensure stable operation at the reference speed, as shown in Fig. 20. However, due to this sudden perturbation the exhaust temperature rapidly increases as shown in Fig. 19. To clarify the main role of the controller used here, the exhaust temperature dynamics are presented both with and without the use of the controller. It can be seen that the difference is very clear when the exhaust temperature increases sharply. Indeed, the level exceeds the maximum allowed level of 960F very rapidly to reach a huge overshoot value of 1160F which can cause damage to the whole system. However, when the controller is used, the increase in exhaust temperature happens only slowly and never reaches the maximum limit, for a safe operation.
The second factor here is the linear speed decrease after t = 130 s representing a very soft braking of the turbine. In this case Figs. 18 and 19 show that the controller can achieve a very accurate and rapidly compensating control of both speed and exhaust temperature. At the same time, as shown in Fig. 20, torque dynamics respond well. We can conclude that, as long as the choice of PID parameters remains adequately accurate things are well even though the PID controller cannot support the proper control of gas turbine parameters over a wide range.
The proposed controller based on ANFIS approach
In order to validate the improved performance of the proposed ANFIS controller over the classical PID control, we performed a simulation test in which the same conditions as described in Section 3.2 were implemented. The results obtained are presented in Figs. 21, 22, and 23. It can be seen that the ANFIS controller eliminates the three parametric peaks in the speed, the exhaust temperature, and the torque at start-up, avoiding the turbine damage risk associated with the classical PID controller. In particular, Fig. 23 shows a reduction in torque peak to about half the previous PID peak. On the other hand, controller performance in ensuring stability of speed and exhaust temperature is very satisfactory in that disturbances caused either by load change or speed reference change are rapidly brought under control, allowing speed and exhaust temperatures to rejoin their respective references within a very short time without substantial overshoot values. In conclusion, we can report that the proposed ANFIS controller allows the smooth control of complex gas turbines even under parameter variation.
Conclusion
The present work deals with the use of a Neuro-Fuzzy Adaptive Inference System (ANFIS) controller designed to ensure adequate control of speed and exhaust temperature for a Heavy Duty Gas Turbine (HDGT) based on Rowen Model equations. The results obtained clearly demonstrate the high performance of the proposed controller and its validity under varying operating conditions, in particular in comparison with the classical PID controller. In future work, more control parameters and different new constraints could be added to the study.
Declarations
Acknowledgements
We would like to express our gratitude and acknowledgements to the staff of the Applied Automation and Industrial Diagnostics Laboratory of the University of Djelfa for his endless guidance and encouragement during the realization of this work.
Funding
This work is carried out by the Automation and Industrial Diagnostics Laboratory of the University of Djelfa, Algeria.
Authors’ contributions
All authors read and approved the final manuscript.
Competing interests
This work proposes the integration of the artificial intelligence tools based on adaptive neuro-fuzzy inference system to ensure the heavy duty gas turbine monitoring, this fuzzy approach has the advantage of no need to the use of the analytical models to control the speed and the exhaust temperature in this equipment and make the gas turbine performance monitoring improved. This fuzzy method proposed in this paper permits based on the obtained gas turbine data to obtain information on system status, which will be useful for real time supervision.
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