Search

This paper is concerned with the evaluation of a robust adaptive control technique applied to damp power systems oscillations. This robust technique is based on the LQG/LTR control method, used in a self-tuning way. The stabilizer synthesized with this method is used in a TCSC device to increase the damping of low frequency electromechanical oscillations modes. The method is evaluated in a 4-machine benchmark system and simulation results show that the proposed controller presents a good performance, improving power system stability margins.

This paper presents the design of a Power System Stabilizer synthesized using an Artificial Neural Network. The patterns used in the network training are sets of controller parameters, previously calculated for several system operation points using the pole-placement method. The trained network presents, as its main characteristic, uniform values for all the stabilizers parameters when the system synchronous machine is generating reactive power, but these same parameters suffer great variations when reactive power is being absorbed by the machine. Simulation tests show very good performance for the proposed Neural PSS, when compared with a fixed-parameter stabilizer.

This work presents a digital adaptive control method, where the controller is designed using a self-tuning structure. Parametric models are identified off line for several differently selected operating points of the plant to form a Local Model Network (LMN), and saved as a look-up table in a microcomputer real time control. During working, a supervisor identifies the actual condition of operation and uses a set of logic fuzzy rules to estimate a plant model as a function of the рrе-identified models for the nearest selected operating points. Then, using this plant model, the controller is designed. This method is employed in the design of a Power System Stabilizer (PSS) and the control obtained for a test with two severe faults showed good performance for this PSS, when compared to a conventional (non adaptive) one.

This work presents a digital adaptive Power System Stabilizer (PSS) which operates in a self-tuning structure. The parametric models are identified off line for several differently selected operating points in a PxQ plane (active and reactive powers) to form a Local Model Network (LMN), and saved under the form of a look-up table in a microcomputer real time control. During working, a supervisor identifies the actual condition of operation and uses a set of logic fuzzy rules to estimate a plant model as a function of the pre-identified models for the nearest selected operating points. Then, using this plant model, the PSS is designed. The tests presented show good performance for this PSS, when compared to a conventional (non adaptive) one.

This work presents a laboratory kit designed for the purpose of controlling temperature in liquids. Many different control strategies can be easily evaluated by using this low cost kit. The results of comparative tests with PID controllers designed by two pole placement techniques are presented comprising the following alternatives: a controller with fixed parameters; and an adaptive controller using on-line recursive parameter estimation. These tests show the superior performance obtained with the adaptive strategy. Others interesting experiences comparing alternative control techniques could also be realised by using this inexpensive and useful laboratory kit.