Brushless excitation of synchronous generators: study of models and control optimization

Transcription

1 Brushless excitation of synchronous generators: study of models and control optimization Nicolau, Nuno, IST Abstract Brushless excitation systems have been largely applied in recent years. Nonetheless models used to simulate them show a few problems that originate imprecise results. This work has the objective to program and simulate the AC7B model for brushless excitation systems. This model is an update on the ACA model from IEEE. The simulation will, first, compare the performances of both models in a shortcircuit fault situation. After that new values for the AC7B s controller will be calculated in order to improve its response to the shortcircuit fault. systems have an almost instantaneous response. DC excitation systems have been less used in favor of AC systems that avoid bushes and rings. Excitation systems are, normally, composed of: Automatic voltage regulator (AVR), an exciter, measuring elements, a power system stabilizer (PSS), and a limitation and protection unit, as shown in figure. Index Terms Excitation systems, ACA, AC7B, PID controller S I. INTRODUCTION synchrouns generator are, today, the base element of Electric Energy Systems []. These generators have a excitation system that produces a excitation current necessary for the generator to work. Moreover, excitation systems can control the generators voltage, making them an important element in system stability. To ensure that generators operate rightly it is crucial to understand excitation systems. For that simulation is essential. Today must of the operation and control of Electric Energy Systems is performed through simulation. Excitation systems are modeled and simulates with computational models. Those models have been used for a number of years. In 968, IEEE published a series of models for excitation systems. These models were updated in 98, and became industry standards for the test of excitation systems [2]. Still, some problems are encountered when using these models. Brushless excitation systems are some of the most used and worse simulated. It becomes then necessary to develop models that allow for a better simulation of excitation systems. II. EXCITATION SYSTEMS As mentioned before, excitation systems are responsible for the excitation current of generators, and are able to control its terminal voltage. The basic requirement for excitation systems is to provide and adjust the excitation current of generators to maintain the terminal voltage in the right levels. Over the years, different types of excitation systems have been applied. The first ones had to be adjusted manually. Automatic regulators started to gain visibility, and today excitation Fig.. Excitation system. The models are usually divided in three groups: static systems (ST); dc systems (DC); and ac systems (AC). A. Static Systems Every component of this excitation system is static. The current is provided to the generator through transformers or auxiliary generators winding. The rectifiers use brushes and deliver the current directly on the generator s field winding. By acting directly on the generator s field winding the response time is shortened. The current is controlled by an AVR. Some of the disadvantages of these systems are the use of brushes and the deformations in the excitation current caused by the semiconductors. Despite this, the response of static systems is practically instantaneous and these systems are less costly, and because of that they have a satisfactory performance for generators in big networks.

2 Fig. 4. Static AC excitation system. B. DC Systems Fig. 2. Static excitation system. For DC excitation systems dc generators are used as the source for the excitation, which is delivered in the rotor of the main generator using rings. If the dc generator is excited using an external source, this is made with a permanent magnet generator (PMG). These systems were highly used between 92 e 96 [3]. In the mid 96 dc systems usage slows down. DC systems show some disadvantages with the increase of generators nominal power. The great number of brushes necessary for high currents e low voltages is one of them. Systems with rotary rectification eliminate brushes and rings. They re used for big generator where the field winding can reach MW. An excitation system like this has the following elements: ) Insideout auxiliary ac generator. 2) Noncontrolled rectifier. 3) An exciter. 4) Controlled rectifier. 5) An AVR. A brushless excitation system is, essentially, an insideout ac generator that delivers its ac voltage to the rotor of the main generator, and receives its excitation from the stator of that same generator. The ac voltage of the insideout generator is transformed to dc by a noncontrolled rectifier, this way producing the excitation current of the main generator. An exciter, in the stator of the main generator, produces a dc current that controls the output voltage of the insideout generator. The dc current of the exciter comes from a controlled rectifier, either manually or by an AVR. The exciter can be powered by a PMG, this way avoiding any external voltage source, figure 5. Fig. 3. DC excitation system. C. AC Systems In these systems auxiliary ac generators, mounted on the main generator s shaft, are used as the source for the excitation current. The output of the auxiliary generator is rectified by rectifiers, that can be controlled or noncontrolled, that produced the current necessary for the main generator. The rectifiers can be stationary or rotary. The AC systems with stationary rectifiers need rings to deliver the current to the main generator, figure 4. The use of rings is a major disadvantage that led to the search for new alternatives to eliminate them. Fig. 5. Rotary AC excitation system. Some of the advantages of brushless excitation system are: easy manual control; reduced maintenance. III. ACA MODEL The ACA model from IEEE represents a brushless excitation system, with noncontrolled rectifiers, figure 6.

3 This model is widely used today to simulate brushless excitation systems. However the model leads to some mistakes, and doesn t accurately represent the excitation system. One of the bigger problems with this model is that it doesn t assume the influence of the terminal voltage of the main generator. Fig. 6. ACA model. The model is linear with the exception of the exciter saturation function, SE[VE], and the rectifier regulation function, FEX. The SE[VE] function can be representing by the following expression: S E (V E ) = a e b V E () A. The model IV. AC7B MODEL The AC7B model, figure 8, is an updated version of some of the previous excitation systems, mainly the ACA. Some of its features are an internal feedback loop, which regulates the filed voltage, a faster excitation current limit, and a PID regulator. The a and b variables are calculate from two points of the saturation function, (S, E) e (S2, E2), with: b = ln(e S E2 S2 ) E E 2 (2) a = E S e b E (3) The rectifier regulation function, F EX, receives the value of I N and, based on that, determines one mode of operation. There exist here modes of operation, figure 7. Fig. 8. AC7B model. B. Programming the model in Matlab The main objective of this work was to program the AC7B model in Matlab, with the Simulink environment. This was made like figure 9 shows. The model has 5 inputs, V C, V REF, V S, V UEL, and I FD and one output, E FD. Fig. 7. Rectifier regulation curve. The curve is described in segments through the next expressions: F EX = I N F EX =.577 I N < I N.433 Fig. 9. Programmed model. F EX =.75 I 2 N.433 < I N <.75 (4) F EX =.732( I N ).75 I N F EX = I N >

4 V. SIMULATION OF THE ACA AND AC7B MODELS A. Simulation environment After the model is programmed, it s important to understand how it works. For that, the model was simulated with a generator and a load for a shortcircuit test, figure. It this it s possible to observe the response of the system to a situation that causes a lot of disturbances. The excitation system must be able to limit the overvoltage and guarantee that the voltage goes back to its initial value. TABLE II GENERATOR PARAMETERS Parameter Value P N 2.53E6 V N fn E3 5 Xd.65 Xd.4 Xd.2 Xq.5 Xq.23 Xl.9 Tdo Td Td.245 Tdo.45 Tq.67 Tqo.438 Rs.38 H 6 F. Pole pairs Pole type 2 Salient Fig.. Simulation environment. Both models were tested for a shortcircuit fault of second, with a voltage fall of 2, 5, and 8%. Tables I and II show the parameters used for each model, and for the generator. All of these values were obtained from a real system. TABLE I MODEL PARAMETERS Using the values on the tables, both models were simulated. Figures, 2, and 3 show the responses for the shortcircuit fault. Parameter ACA AC7B T R K PR K IR K DR T DR T C 25 T B K A K P K PA 4 K IA T A.3 K F. K F K F K F3 T F 9999 K I K L V AMAX V AMIN V RMAX V RMIN 5.43 V UEL 5 V OEL V FEMAX V EMIN T E K E S EV E S EV E2 V E V E2 K D K C Fig.. Models response to a shortcircuit, with a 2% voltage fall. Fig. 2. Models response to a shortcircuit, with 5% voltage fall.

5 Fig. 3. Models response to a shortcircuit, with 8% voltage fall. From the figures we can see that both of the models have a difficulty in limiting the overvoltage and have a slow response. After the fault the voltage shouldn t surpass % of the nominal value, although in some cases it can go as high as 4% for a few seconds. Either way, both of the models have values higher than 4% when the voltage fall is around 8%. This situation is in need of improvement. One easy solution is to decrease the voltage regulator upper limit to a value that sets the overvoltage to %. In both models this leads to a situation where the response becomes very slow. This happens because of the windup in the regulator. In the AC7B model it is possible to introduce antiwindup in its PID regulator to fix this problem. The ACA model doesn t allow this solution. Therefore the next simulations will only consider the AC7B model. Figure 4 shows the models response to the same situation as figure 3, with antiwindup. Fig. 5. AC7B responses for two values for the upper regulator limit. By changing V RMAX its possible to limit the overvoltage after the fault. Still the response of the system is a bit slower. To improve this we can find new values for the regulator, instead of just for the upper limit. VI. REGULATIONS OPTIMIZATION OF THE AC7B S CONTROLLER In order to find new values for the controller two methods were used. First the PID Tuner tool in Matlab. The tool provides a simple way to determinate values for a PID controller. Second the ZieglerNichols method. This method provides a formula to calculate the PID parameters. A. PID Tuner Before using this tool the system need to be simplified. For that, the generator has to be replace with a bloc that simulates its behavior, through the transfer function: KG/( + s.tg). For this optimization method, we consider the saturated values of the generator s parameters, and so KG is and TG equals 4,59. The AC7B model has its regulator replaced with a PID Controller bloc, available in Matlab, that allows for the use of the PID Tuner tool. Figure 6 shows the system with these changes. Fig. 4. AC7B response to a shortcircuit, with 8% voltage fall and antiwindup. With the introduction of antiwindup the model s response becomes slower. As said earlier, in some cases the overvoltage limit can be higher, about 4%, as long as it stays higher than % for less than 5 seconds. This can be used to try and get a faster response, altering again the upper limit of the voltage regulator, this time with a value that allows the overvoltage to reach 4%. Figure 5 shows the responses of the AC7B model with different values for the upper regulator limit. Fig. 6. Simplified system used for the PID Tuner tool.

6 Opening the PID Tuner tool shows the response of the system with the original values and the response with the new values, figure 7. The new values are calculated by the tool. By changing the arrow at the bottom it s possible to see the new response changing, as well as the tuned values. B. ZieglerNichols The ZieglerNichols method was developed by John G. Ziegler and Nathaniel B. Nichols. With a few steps it s possible to determine two values, the ultimate gain, K u, and the oscillation period, T u, with which the PID parameters are calculated. First, the integral gain, K I, and the derivative gain, K D, are set to zero. Then the proportional gain, K P, is increased until the system start to oscillate with a constant amplitude, figure 9. Fig. 7. PID Tuner. The values obtained with the tool were then tested with the complete system. The new values for the PID controller found with the PID Tuner were: K PR = K IR = K DR = Figure 8 show the response to the shortcircuit fault, with 8& voltage fall, with the new and the old values for the PID controller parameters. Fig. 9. Constant oscillation with Tu period. With the values of K u and T u the PID parameters are calculated like: K P = K u/,7 K I = 2*(K P/T u) K D = (K P*T u)/8 Using the ZieglerNichols method for this case, it was obtained a K u and T u of 26 and,8, respectively. With this values the we have: K P = 5.29 K I = K D = 5.85 Fig. 8. AC7B responses with the old and the new values for PID controller parameters. With this values the AC7B system was simulated, this time for a shortcircuit fault that lowers the voltage to zero, figure 2. For the same fault situation, voltage dropping to zero, figure 2 shows the response with the values calculated with the PID tuner tool.

7 Vt (pu) REFERENCES [] Kundur, P. Power System Stability and Control, McGrawHill, 994. [2] Sucena Paiva, José Pedro Redes de Energia Eléctrica : uma Análise Sistémica, IST Press 25. [3] IEEE Power Engineering Society, IEEE Recommended Practice for Excitation System Models for Power System Stability Studies, IEEE Std , Tempo (s) Fig. 2. AC7B response with PID controller parameters from Ziegler Nichols method. Vt (pu) [4] Kabir, S.M.L. and Shutleworth, R. Brushless exciter models, IEEE Proc.Gener. Transm. Dirtib., Vol. 4, No., January 994. [5] Jerkovic, V., Miklosevic, K. e Spoljaric, Z. Excitation System Models of Synchronous Generators. [6] There, M., Chawardol, P. e Badre, D. Excitation System of Alternator, International Journal of Engineering Research & Technology (IJERT), Vol. 2 Issue 2, February 23. [7] Feng, S., Jianbo, X., Guoping, W. e Yonghong, X. Study of a Brushless Excitation System Parameters Estimation Based on Improved Genetic Algorithm, IEEE, [8] Bayram, M., Bulbul, H., Can, C., Bayindir, R. Matlab/GUI Based Basic Design Principles of PID Controller in AVR, Istanbul, Turkey, 37 May Tempo (s) Fig. 2. AC7B response with PID controller parameters from PID Tuner. VII. CONCLUSION In conclusion it can be said that the study of brushless excitation systems is still a topic that needs study, given the crucial importance these systems have in electric energy systems. As far as this particular work goes, the objective of building the AC7B model in Matlab was achieved, and with this it was possible to understand the model s behavior in a situation of a shortcircuit fault. When compared with the ACA model, the AC7B had a faster response. The improvements that the AC7B model has allow for a better simulation of the brushless excitation systems. The PID regulator, in particular, can be used to regulate the systems response better. A number of methods can be used for that purpose. Using the Ziegler Nichols method was simple, but it provided a response far from optimal. The PID Tuner tool was not as direct but it yield values for the PID parameters that improved the original response of the system. It is also important to point out that, as a future work, it would be relevant to have a build in bloc in Matlab for the AC7B model.