Abstract:
A method for controlling a power plant which delivers electrical power to an electrical grid. The grid has a grid frequency which fluctuates around a nominal frequency. A power output of the power plant is controlled as a function of a control frequency, in such a manner that the power output is increased when the control frequency decreases below the nominal frequency. On the other hand the power output is decreased when the control frequency rises above the nominal frequency. The grid frequency is continuously measured. The measured grid frequency is averaged to give, as a moving average, a slowly varying averaged trend frequency which is characteristic of the long term behavior of the grid frequency. The averaged grid frequency is used as the control frequency, if the measured grid frequency lies within a predetermined band around the averaged grid frequency. The measured grid frequency is used as the control frequency, if the measured grid frequency lies outside the predetermined band.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates generally to the field of electrical power plants, and specifically to methods for controlling an output of a power plant providing electrical power to a power grid to a method for controlling a power plant according to the preamble of claim 1. 
     2. Background Information 
     The deregulation of the power generation market in the past years has resulted in an increased competition among power suppliers. In particular, independent power suppliers (IPPS) are increasingly influencing the market rules. The increased competition and the high investments required to operate a power station have led to high expectations in terms of reliability and availability of power stations. In particular, countries which are now in a phase of industrial built-up, are characterized by a continuously growing power demand and, therefore, comparatively unstable grids. In such a situation, the capability of the power plants to provide frequency response is of utmost importance for sustaining a reliable grid operation. In order to make use of the demand of cheap, clean and reliable power supply for the gas turbine business, it is therefore important to enable gas turbine driven power plants to provide frequency response in grids that may experience high and fast frequency fluctuations. The recent blackout in Malaysia underscores the urgency of this requirement. 
     SUMMARY OF THE INVENTION 
     Technically, frequency response operation of a power plant can be summarized by three basic demands: The ability to 
     1) automatically provide a certain amount of power per given drop of the grid frequency, 
     2) provide this power within a given time, i.e. at a certain power gradient, and 
     3) provide the aforementioned capabilities in a wide range of ambient conditions and a wide range of grid frequencies. 
     For gas turbines the satisfaction of these demands find their limitations in terms of temperature limits, gradient limits (power and temperature), and limits of the number of load cycles that certain parts can withstand. These limits have to be pushed by constructive methods and by the gas turbine control system such that the market requirements for frequency response can be met or even surpassed. In the following, a data processing scheme for grid measurements is described that generates signals to the GT power controller such that the grid requirements with respect to dynamic GT response are satisfied, and, at the same time, the detrimental effects of this operation mode on the life time consumption of the GT are minimized. 
     Advantageous and expedient further development of the solution according to the invention are given in the dependent claims. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     A more complete appreciation of the invention and many of the attendant advantages thereof will be apparent upon reference to the following detailed description in connection with the accompanying drawings, wherein: 
     FIG. 1 shows a frequency operation mode. 
     FIG. 2 shows a static characteristic. 
     FIG. 3 shows a Dead Band-static characteristic. 
     FIG. 4 shows a typical grid-development. 
     FIG. 5 shows signal classes: Definition. 
     FIG. 6 shows signal classes: Example. 
     FIG. 7 shows signal classes: Static Dead Band. 
     FIG. 8 shows signal classes: Trend. 
     FIG. 9 shows signal classes: Dynamic Dead Band. 
     FIG. 10 shows GSP Output with constant Dynamic Dead Band. 
     FIG. 11 shows Dead Band Deflation/Inflation. 
     FIG. 12 shows Dead Band Inflation/Inflation. 
     FIG. 13 shows Block Diagram of GSP. 
     FIG. 14 shows Dead Band Shift. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Frequency operation mode of a gas turbine is characterized by the automatic variation of the set point P c  of the GT power controller as a function of the frequency error Δf according to a given (linear) characteristic, which is called the droop characteristic. The basic set-up of the control loop is depicted in FIG. 1: The measured grid frequency (f m ) is compared with the frequency set point (f c  ; normally 50 Hz or 60 Hz). The resulting frequency error 
     
         Δf=f.sub.m -f.sub.c                                  (2.1) 
    
     is then converted to a demanded power signal ΔP c  based on a droop characteristic which is prescribed by the local grid code. In the following, the associated signal processing algorithms will be referred to as Grid Signal Processor (GSP, see FIG. 1). The output of the GSP is added to the power set point P c  (selected by the operator) yielding the total demanded power output P ct . This signal is subsequently forwarded to the power controller which in turn acts on the fuel mass flow (m f ) and/or the variable inlet guide vanes (VIGV) of the gas turbine 100 in order to adjust the measured power output P m  to the total power demand P ct . The capability of this loop to provide reliable and fast frequency response is crucially dependent on the process dynamics (GT process, measurements, actuators), the dynamic of the power controller, and the quality of the GSP. 
     The balance of this exposition deals with a GSP that converts the frequency signal Δ f  into an appropriate signal ΔP c  such that the grid requirements are fulfilled and such that the gas turbine is operated with a minimum effect on its life time consumption. 
     Definition 1 The droop characteristic is a function ΔP (Δf) that determines the stationary change of power output in terms of the frequency error of a power station that runs in frequency response operation. 
     An ideal, linear characteristic is shown in the FIG. 2. 
     Definition 2 The droop determines the slope of the linear, ideal characteristic shown and defined in the FIG. 2. The lower the droop the higher the slope of the droop characteristic. 
     The implementation of the ideal droop characteristic in power plants is not practicable because it will result in frequency response for any frequency error, in particular also for errors that are due to measurement and/or grid noise. This noise will be mapped into a noisy command signal of the plant&#39;s power controller and thus produce output noise of the plant. This is undesired for both, the plant operator and the grid operator. For the plant operator the excitation of the plant with noisy signals results in unnecessary consumption of life time. This is particularly important for a gas turbine power plant. For the grid operator noisy plant output is undesired because it increases the noise level of the grid. Notice that the grid noise is increasingly amplified the lower the droop setting of the grid is. For this reason, the ideal characteristic is modified with a dead band which is centered around the nominal frequency f o . FIG. 3 shows, for example, a deadband 302. Both the droop and the dead band are assigned by the grid operator. It turns out, however, that this characteristic is unable to produce the results that it is designed for. This problem will be analyzed in the following sections, and an optimized GSP will be presented that ensures the requirements of both the grid and the plant operator. 
     The basis for the optimization of the GSP for gas turbine driven power plants is an analysis of the grid dynamics. A typical trace of the British Grid is shown in FIG. 4, as an illustrative example. These data can be classified with respect to their dynamic characteristic in terms of frequencies and amplitudes with a focus on their effect on the life time of a gas turbine. The result of this analysis is summarized in FIG. 5. Essentially, the dynamic signal content of the grid can be divided into three classes of superimposed signals: 
     Class 1-signals describe the long-term behavior of the grid with a horizon which is of the order of magnitude of minutes (see FIG. 4). This type of motion is also referred to as trend. It is generated by fluctuations of the power consumption and by the grid self dynamics. Therefore the grid owner wants to receive frequency response for Class 1-signals. Due to their very low gradients and their restricted frequency of occurrence Class 1-signals have only a low impact on the GT life time. 
     Class 2-signals are low-amplitude signals in the entire frequency spectrum with an extremely high rate of occurrence (on the order of 10 5  occurrences per year). Due to their high rate of occurrence, they have a massive impact on GT life consumption. On the other hand, they are generated basically by stochastic effects (measurement noise, grid noise) and hence are not relevant for frequency response. It is therefore in the interest of the grid owner and the plant operator to suppress frequency response for this type of signals. 
     Class 3-signals are characterized by a very high frequency content, high amplitudes (typically pulses or steps), and a comparatively low frequency of occurrence. These signals are generated by sudden grid events like the trip of a power plant. For the grid operator it is of utmost importance to have reliable frequency response for this type of events. Their impact of GT life consumption is relatively low since these events are very seldom compared to the other signal classes. 
     A typical trace showing the signal types described above is shown in FIG. 6. Based on the above analysis a GSP is constructed that provides frequency response for the signal classes 1 and 3 according to the specified droop, and suppresses frequency response for Class 3-signals. The key to the construction of such a GSP is a way to differentiate the three classes from the available frequency measurement. 
     Concept and mechanism of the Dynamic Dead band: The heart of the new GSP is the replacement of a static dead band, such as that shown in FIG. 7, with a dynamic dead band. The motivation for this becomes clear by analyzing the trace shown in FIG. 7. It is evident from FIG. 7 that the static dead band cannot discriminate the signal class 3 from the life time is the Class 3-signal and not the Class 2-signal (because of the higher amplitude of Class 3). A typical trace that is obtained with this mechanism is shown in the FIG. 10. 
     The Dead Band Strategies are as follows: 
     a) In light of the Deflation/Inflation: The trace depicted in FIG. 10 reveals a problem that arises when the measured frequency signal stays close to the dead band. In this situation there may be frequent exits and re-entries into the dead band (caused by Class 2 signals) which each time trigger a jump from the trend signal to the frequency measurement and back to the trend. This effect is called chattering. Chattering can be avoided by the use of a deflating dead band. The idea is to reduce the dead band to a very small value as soon as the measured frequency leaves the dead band. In this way chattering cannot be triggered by Class-2 signals. The dead band is inflated again as soon as the measured frequency reenters the deflated dead band. This mechanism is schematically shown in the FIG. 11 and depicted for a simple frequency trace in FIG. 12. 
     As shown in FIG. 11, when the measured grid frequency 1102 leaves the dead band 1104, by rising above the upper bound 1106 of the dead band 1104, the bandwidth of the dead band 1104 deflates. In other words, a frequency distance between the upper bound 1106 of the dead band 1104 and the lower bound 1108 of the dead band 1104 decreases. When the measured grid frequency reenters the dead band 1104, the bandwidth of the dead band 1104 reinflates. 
     As a consequence of the dead band deflation the GSP output will contain Class 2-signals until it enters the deflation dead band. This does not pose any problem to GT life time because of the relative rareness of the Class 3 events (and hence Dead Band deflation/inflation), and because of the relatively short time (and hence low number of others because the trend (Class 1-signal) resides outside the static dead band for most of the time. However, since the trend is defined as the long term behavior of the grid, it can be constructed by appropriate filtering of the grid frequency measurement (see FIG. 8) with a trend filter. Remembering that the Class 3-signals occupy the entire frequency range, it is obvious that they cannot be discriminated via dynamic filters. On the other hand, FIG. 5 and FIG. 8 suggest that they can be discriminated by their amplitude with respect to the Class 1-signals. In other words: Any small deviation from the trend is, by definition, a Class 3-signal and hence should be suppressed for frequency response. This can be easily accomplished by implementing a dead band which is centered around the trend rather than the nominal frequency f o . Since the location of this band varies with the trend it is called a dynamic dead band. 
     The trend signal together with the dynamic dead band also provides the mechanism to detect the Class 3-signals: By definition, any grid signal that leaves the dynamic dead band is a Class 3-signal (high amplitude, high frequency). The principle is illustrated in FIG. 9. 
     The basic mechanism of operation of the GSP can now be summarized as follows: The signal that is forwarded to the ideal characteristic (FIG. 2) is the trend signal as long as the measured grid signal is within the dynamic dead band, and it is the measured grid signal for those time intervals where the grid signal leaves the dynamic dead band. In this way the frequency response is restricted to those events that are relevant to the grid and the grid noise is suppressed for most of the time. Notice that the grid noise cannot be suppressed when responding to Class 3-signals. This is, however, of no concern to the GT life time, because Class 3-events are seldom and during these events the dominant effect on Class 2 events) that is required until dead-band re-entry occurs. FIG. 13 shows the block diagram of the grid signal processing. This block diagram is understandable to a person skilled in the art. 
     b) In light of the Dead Band Shift: Another way to deal with the chattering problem without the necessity for Dead Band deflation is the dead band shift. With this mechanism, the trend filter is reinitialized each time the measured frequency reenters the dead band. At this point of time, the trend filter is initialized with the measured frequency at the moment of reentry. As a consequence, the dead band (which is centered around the trend) is shifted by half its total width. In this way a smooth reentry into the dead band is established for isolated Class 3-events, and chattering is eliminated in the case of reentries which are triggered by Class 2-signals. The latter is achieved because a Class 2 reentry will now result in a dead band shift that subsequently rules out any more Class 2-events with respect to the new trend. A sample trace is shown in FIG. 14. 
     c) In light of the prescribed Out-of-Dead Band-Time: Yet another strategy could be to prescribe the maximum time that the measured frequency may reside outside the dead band. In conjunction with the dead band shift described in the previous section, this mechanism provides the possibility of minimizing the number of Class 2-events that are passed through the droop characteristic.