Search

In this paper, the experimental results obtained in the design and implementation of a self-tunning voltage controller for a micro energy system are analyzed. Two types of indirect adaptive self-tuning schemes are proposed. Both uses a recursive estimation scheme based on the well known Least Squares method. The difference between them is the controller formulation methodology: one uses the pole placement method and the other uses the generalized predictive control. Tests shows the comparison performance of the proposed adaptive controllers versus fixed digital controllers.

This paper investigates the use of self-tuning Linear Quadratic Gaussian control with Loop Transfer Recovery (LQG/LTR), applied in Thyristor Controlled Series Capacitors (TCSC) installed in interconnected power systems. The proposed robust adaptive controller can improve power system stability margins, damping properly the system's dominant electromechanical modes. Although this TCSC controller is designed mainly to identify and damp inter-area modes, it does not affect negatively the local modes damping, therefore enhancing the overall system performance. The proposed controller properties can be verified using non-linear simulations for a four-machine power system, in different operation conditions.

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 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.

This work presents a digital adaptive power system stabilizer (PSS) which operates in a gain scheduling scheme. Its parameters are designed for several differently selected operating points in a PxQ plane (active and reactive powers), and saved offline under the form of a look-up table in the microcomputer that implements the control action. The complete set of all designed PSSs is called a local compensator network (LCN). During working, the a fuzzy logic based supervisor identifies the current operating point, and synthesizes the PSS parameters from the LCN. The test presented shows good performance for this PSS, when compared to a conventional (nonadaptive) 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 P/spl times/Q plane (active and reactive powers) to form a local model network (LMN), and saved off-line under the form of a look-up table in the microcomputer that implements the control action. During working, a fuzzy logic based supervisor identifies the current operating point and determines the plant model from the LMN. Then, using this plant model, the PSS control law is designed based on the generalized predictive control (GPC) method. The tests presented show good performance for this PSS, when compared to a conventional (nonadaptive) one.