I hope that posterity will judge me kindly, not only as to the things which I have explained, but also for those which I have intentionally omitted so as to leave others the pleasure of discovery.
RENE DESCARTES La Geometrie QFT has a wide variety of applications in different fields. Power system stabilisation has been a topic under consideration for quite some time now.
The focus of using QFT to solve power system stability problems has oc- curred quite recently.
The merging of these engineering design methods has given a remarkable insight on how to produce results that satisfies the client's expectations, yet still uses sound engineering principles. There are other control design methods such asHec" model-predictive control, and root locus, that can be applied to power system stabiliser design, but their solutions are either more complex, or only solves the problem at a particular operating point.
Chapter 2 has shown-the pioneering work carried out to produce stabilisers that improves system performance. More importantly, the stability phe- nomenon was identified, and research done to find an simple and robust solution to the power system stability issue. All the design methods re- quired an analytical model of the power system. Generally, power systems are difficult to analyse and the models are quite complex. Often, simplified models are used, but this simplification, unless done wisely, leads to a loss of accuracy in terms of representing the power system as a whole.
The increase in computer processing power means that nonlinear simulation of high order systems has become more practical. An understanding of the
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CHAPTER 9. CONCLUSIONS 144 underlying theory of power systems is still essential, and Chapter 3 provided the background necessary to understand how power systems work and how they are modelled. It was shown that the equations that describe the com- ponents of a power system can be tedious and difficult to solve without a computational tool.
Chapter 4 introduced the emerging technology of computer aided design to·
solve complex engineering problems. Using CAD technology, and under- standing how the CAD system models a particular piece of equipment, is paramount to generating accurate simulations and predicting real-life be- haviour of the power system. Once the model of the power system was available for analysis and observation, Chapter 5 introduced the tool that would enable the power system stability problem to be solved.
The basics of QFT was discussed, as well as its historical development.
Reasons for why QFT has evolved into what it is today were offered. The uncertainty concept was introduced and examples were given that showed how this frequency domain technique used uncertainty to design robust con- trollers. A CAD environment offered the benefits of ease of use and reduction of manual computational effort. This freed the designer to concentrate on engineering design rather than mathematical calculations. QFT is an iter- ative process, and the QFT toolbox allows a number of designs to be done quickly, each design hopefully improving on the previous one. QFT is a practical control design tool, and Chapter 6 introduced the vehicle on which the designed power system stabiliser would be tested.
The laboratory power system was designed to accurately represent an indus- trial power system many times its rating. It has the necessary power system components such as a steam turbine prime mover simulator, a distributed mass shaft to account for shaft twist in real applications, and a synchronous generator whose parameters in per unit are equal to a full sized machine.
All the mechanical and electrical parameters for the laboratory electrical machinery are available, allowing accurate simulation of the physical system before practical work is carried out. Due to concerns about machine sta- bility and equipment lifespan in the early days (1970), when the laboratory was first put together, the power system controllers (AVR and governor) designs are quite conservative. This resulted in a power system setup that is over damped.
However, Chapter 7 took the results of a practical test on the laboratory system and compared it to a Matlab simulation of the laboratory system.
The matching results gave the confidence necessary to trust the simulation model. The nonlinear simulation model was converted to a linear model suitable for QFT design. Chapter 6 contained two case studies that used the laboratory simulation models. The first Case Study used a first order AVR model instead of the conservative laboratory AVR model. Its purpose
CHAPTER 9. CONCLUSIONS 145 was twofold. Firstly to show the laboratory power system response if the existing AVR circuit had to be changed. Secondly, to perform QFT design on a lightly damped system.
Case Study 2 used the laboratory AVR model. The aim of this case study was to show how QFT is used to design a power system stabiliser for an existing system, and how to implement the controller.
Chapter 8 made the link between theory and practice. The effectiveness and performance of the designed power system stabiliser is tested in the labora- tory under different conditions. The results show that QFT design of power system stabilisers results in a controller that is simple to implement. It also proves that power system stabilisers can be designed using QFT, and that the designs do work.
Further work should be done to test the robustness of the PSS in the lab- oratory. The test at operating point 4 in the previous chapter will have to be carried out again. The method of obtaining the frequency response data from the nonlinear PSB model, instead of measurement in the laboratory, should also be investigated.
The conclusion is that QFT is a most suitable tool for power system stabiliser design.