Abstract
This paper presents a novel application of a fuzzy logic controller (FLC) driven by an adaptive fuzzy set (AFS) for a power system stabilizer (PSS).The proposed FLC, driven by AFS, is compared with a classical FLC, driven by a fixed fuzzy set (FFS). Both FLC algorithms use the speed error and its rate of change as input vectors. A single generator equipped with FLC-PSS and connected to an infinite bus bar through double transmission lines is considered. Both FLCs, using AFS and FFS, are simulated and tested when the system is subjected to different step changes in the reference value. The simulation results of the proposed FLC, using the adaptive fuzzy set, give a better dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with classical FLC using FFS. The proposed FLC using AFS also reduces the computational time of the FLC as the number of rules is reduced.
Highlights
The design of a power system stabilizer is an important issue from the viewpoint of power system stability, since it damps out local plant modes and inter area modes of oscillation without compromising the stability of other modes
The performance of power system stabilizer (PSS) with both static and adaptive fuzzy logic controllers is compared through the simulation results, when the system is subjected to various disturbances
The dynamic response when using adaptive fuzzy logic controller (AFLC) is superior to the three other controllers as regards the rising time, settling time and damping coefficient of the overall system
Summary
The design of a power system stabilizer is an important issue from the viewpoint of power system stability, since it damps out local plant modes and inter area modes of oscillation without compromising the stability of other modes. The static fuzzy rules are usually based on operator experience, as fuzzy logic can encode linguistic information [11]. This is the main advantage of the fuzzy logic controller over neural networks. The linguistic information captured from operator experience can be used to initialize the fuzzy set. This helps to reduce the number of iterations in the training [12]. The third controller is proposed as an adaptation scheme, based on the back propagation (BP) algorithm [12], which dynamically varies the fuzzy sets to achieve better dynamic performance. The performance of PSS with both static and adaptive fuzzy logic controllers is compared through the simulation results, when the system is subjected to various disturbances
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