Excitation control of a power‐generating system based on fuzzy logic and neural networks

Abstract

This paper presents a practical design of an intelligent controller (using fuzzy logic and neural network concepts) f o r the excitation control of an isolated power-generating system. The controller is suitable f o r realtime operation, with the aim of improving the dynamic characteristics of the generating unit by acting properly on the exciter input. Atfirst, digital simulations of high-and reduced-order models of the above system are performed using conventional control techniques on five system cases which are based on previous work. Then, a fuzzy logic proportional-plus-derivative controller (FPDC) is designed and the dynamic performances of the rnentionedfive system cases and the FPDCs are presented by comparison. Finally, an enhanced controller is designed which involves a pre-trained neural network as a model dynamics capture. The computer simulation results obtained clearly demonstrate that the performance of the developed fuzzy logic and neural network controller offers better damping effects on the generator oscillations over a bvider range of operating conditions, than the associated ones of the conventional excitation controller designs with output feedback applied to high-and reduced-order system models.

Experimental verification of P&O MPPT algorithm with direct control based on Fuzzy logic control using CUK converter

“…The defuzzification is the last step in fuzzy controller, which interprets the membership degrees of the fuzzy sets into the crisp real value outputs or specific decision. Using the common centroid method, also known as the centre of gravity method . where S t is the output of FLC, S ti is the center of max‐min, μ ( S ti ) is the membership function and N is related to the maximum number of the effective rules, where in this case N = 3 .…”

“…The defuzzification is the last step in fuzzy controller, which interprets the membership degrees of the fuzzy sets into the crisp real value outputs or specific decision. Using the common centroid method, also known as the centre of gravity method [61].…”

Virtual Inertia Adaptive Control of a Doubly Fed Induction Generator (DFIG) Wind Power System with Hydrogen Energy Storage

“…However, the increasing of the virtual inertia has less impact on maintaining the frequency of the electrical grid at a certain constant, such as 50 Hz. For example, when the frequency of the electrical grid once restores, the continual increasing virtual inertia will prolong the recovery time of the frequency fluctuation [33]. Thus, the effect of the increasing virtual inertia of the energy storage system is related to the frequency of the electrical grid at the specific fluctuating stage.…”

Robust synchronous generator excitation regulator based on stabilizing feedback action

“…Also, it is shown that an increased level of robust performance can be achieved by the proposed controlling structure, comparable with ones achieved by much more complex, robust controllers designed for SG voltage control . For example, when compared to the fuzzy based excitation controllers and the nonlinear solutions the following two improvements are introduced by the novel stator voltage based stabilization feedback action: (i) it reduces the range of the equivalent plant model (that includes the SG and novel stabilizing feedback action) parameter variations, consequently enabling the more robust design of any applied control algorithm superimposed to the applied novel feedback action; and (ii) the novel feedback action moves the pole of the equivalent plant model in the higher frequency range, consequently achieving the higher excitation speeds and increased level of stability (larger phase margin, for example). The stabilizing feedback and accompanying sequential PI controller are designed by means of frequency domain techniques , and by using the simplified first order SG model .…”