A survey of fuzzy control for stabilized platforms

This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques

A Heuristic Dynamic Programming Based Power System Stabilizer for a Turbogenerator in a Single Machine Power System

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique

Neural Network-Based Stabilizer for the Improvement of Power System Dynamic Performance

This paper develops an adaptive control coordination scheme for power system stabilizers (PSSs) to improve the oscillation damping and dynamic performance of interconnected multimachine power system. The scheme was based on the use of a neural network which identifies online the optimal controller parameters. The inputs to the neural network include the active- and reactive- power of the synchronous generators which represent the power loading on the system, and elements of the reduced nodal impedance matrix for representing the power system configuration. The outputs of the neural network were the parameters of the PSSs which lead to optimal oscillation damping for the prevailing system configuration and operating condition. For a representative power system, the neural network has been trained and tested for a wide range of credible operating conditions and contingencies. Both eigenvalue calculations and time-domain simulations were used in the testing and verification of the performance of the neural network-based stabilizer

A Heuristic-Dynamic-programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System

Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS\u27s performance is presented

AN ADAPTIVE POWER SYSTEM STABILIZER BASED ON FOCUSED TIME DELAY NEURAL NETWORK

In this paper, Power System Stabilizer is designed in Single Machine Infinite Bus (SMIB) and speed control is implemented with a dynamic topology based on Focused Time Delay Neural Network (FTDNN).  In case of prediction and control, two individual strategies are concerned for the current projects. The first is identification the dynamics of system. The other is an optimization unit expected for minimization disturbances. The performance of the system with FTDNN-PSS controller is compared with a Conventional PSS (C-PSS), RNN-PSS and DTDNN PSS. The results show the effectiveness of FTDNN-PSS design, and superior robust performance for enhancement power system stability compared to Conventional PSS with different cases

Design of Power System Stabilizer

A power system stabilizer (PSS) installed in the excitation system of the synchronous generator improves the small-signal power system stability by damping out low frequency oscillations in the power system. It does that by providing supplementary perturbation signals in a feedback path to the alternator excitation system. In our project we review different conventional PSS design (CPSS) techniques along with modern adaptive neuro-fuzzy design techniques. We adapt a linearized single-machine infinite bus model for design and simulation of the CPSS and the voltage regulator (AVR). We use 3 different input signals in the feedback (PSS) path namely, speed variation(w), Electrical Power (Pe), and integral of accelerating power (Pe*w), and review the results in each case. For simulations, we use three different linear design techniques, namely, root-locus design, frequency-response design, and pole placement design; and the preferred non-linear design technique is the adaptive neuro-fuzzy based controller design. The MATLAB package with Control System Toolbox and SIMULINK is used for the design and simulations

Comparisons of an Adaptive Neural Network Based Controller and an Optimized Conventional Power System Stabilizer

Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN\u27s weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design