Implementation of Adaptive Critic-Based Neurocontrollers for Turbogenerators in a Multimachine Power System

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller\u27s training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in system operating conditions and configurations. This paper also shows that it is possible to design and implement optimal neurocontrollers for multiple turbogenerators in real time, without having to do continually online training of the neural networks, thus avoiding risks of instability

Indirect Adaptive Control for Synchronous Generator: Comparison of MLP/RBF Neural Networks Approach with Lyapunov Stability Analysis

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method

Issues on Stability of ADP Feedback Controllers for Dynamical Systems

This paper traces the development of neural-network (NN)-based feedback controllers that are derived from the principle of adaptive/approximate dynamic programming (ADP) and discusses their closed-loop stability. Different versions of NN structures in the literature, which embed mathematical mappings related to solutions of the ADP-formulated problems called “adaptive critics” or “action-critic” networks, are discussed. Distinction between the two classes of ADP applications is pointed out. Furthermore, papers in “model-free” development and model-based neurocontrollers are reviewed in terms of their contributions to stability issues. Recent literature suggests that work in ADP-based feedback controllers with assured stability is growing in diverse forms

Real-Time Implementation of a Dual Function Neuron Based Wide Area SVC Damping Controller

The use of wide area measurements for power system stabilization is recently given a lot of attention by researchers and the power industry to avoid cascading failures and blackouts such as the August 2003. This paper presents the design of a nonlinear external damping controller based on wide area measurements as inputs to a dual function neuron (DFN). This DFN controller is specifically designed to enhance the damping characteristics of a power system considering the nonlinearity in the system. The major advantage of the DFN controller is that it is simple in structure with less development time and hardware requirements for real-time implementation. The DFN controller is implemented on a digital signal processor and its performance is evaluated on the IEEE 12 bus FACTS benchmark power system implemented on a real time platform - real time digital simulator (RTDS). Experimental results show that the DFN controller provides better damping than a conventional linear controlle

Dual-Function Neuron-Based External Controller for a Static Var Compensator

The use of wide-area measurements for power system stabilization has recently been given a lot of attention by researchers and the power industry to avoid cascading failures and blackouts, such as the one in North America in August 2003. This paper presents the design of a nonlinear external damping controller based on wide-area measurements as inputs to a single dual-function neuron (DFN)-based controller. This DFN controller is specifically designed to enhance the damping characteristics of a power system over a wide range of operating conditions using an existing static var compensator (SVC) installation. The major advantage of the DFN controller is that it is simple in structure with less development time and hardware requirements for real-time implementation. The DFN controller presented in this paper is realized on a digital signal processor and its performance is evaluated on the 12-bus flexible ac transmission system benchmark test power system implemented on a real-time platform-the real-time digital simulator. Experimental results show that the DFN controller provides better damping than a conventional linear external controller and requires less SVC reactive power. The damping performance of the DFN controller is also illustrated using transient energy calculations

Ph.D. Research Proposal: Biomimetic Characteristics of an Active Deployable Structure

Biomimetic structures are structures that demonstrate increased functionality through mimicking qualities of biological organisms. Self-repair and adaptation mechanisms are examples of biological qualities that can be adapted in structural engineering. Over the last decades, great strides have been made in advancing theory and practice of active structural control. However, little scientific progress has been made on biomimetic structures. Advances in sensor, actuator, and microprocessor technologies provide increasing possibilities for implementing active control systems in the built environment. Intelligent control methodologies such as self-diagnosis, self-repair and learning could be integrated into structural systems to provide innovative solutions. The general goal of this thesis is to study biomimetic characteristics of an active and deployable tensegrity bridge. Building on previous research carried out at EPFL, this thesis proposal includes the following objectives: 1) design an active control system in order to ensure damage tolerance of a deployable tensegrity pedestrian bridge; 2) extend existing strategies for self-diagnosis of the deployable tensegrity bridge to avoid ambiguous results; 3) extend existing strategies in order to achieve a more robust self-repair scheme; 4) develop algorithms that allow the active control system to learn efficiently using case-based reasoning; 5) validate the methodologies developed with experiments on a near full-scale (1/3) model. A literature survey of biomimetics, structural control, tensegrity structures, deployable structures, deployable tensegrity structures, active tensegrity structures, case-based reasoning, system identification, and multi-objective search has identified that these objectives are original. Results obtained from the preliminary studies demonstrate the potential of this research strategy. A research plan containing 19 subtasks that will be completed by the end of April 2012 leaves sufficient buffer time before the official end of this Ph.D. research on September 30, 2012

Investigations of Different Approaches for Controlling the Speed of an Electric Motor with Nonlinear Dynamics Powered by a Li-ion battery - Case study

This research investigated different nonlinear models, state estimation techniques and control strategies applied to rechargeable Li-ion batteries and electric motors powered and adapted to these batteries. The finality of these investigations was achieved by finding the most suitable design approach for the real-time implementation of the most advanced state estimators based on intelligent neural networks and neural control strategies. For performance comparison purposes, was chosen as case study an accurate and robust EKF state of charge (SOC) estimator built on a simple second-order RC equivalent circuit model (2RC ECM) accurate enough to accomplish the main goal. An intelligent nonlinear autoregressive with exogenous input (NARX) Shallow Neural Network (SSN) estimator was developed to estimate the battery SOC, predict the terminal voltage, and map the nonlinear open circuit voltage (OCV) battery characteristic curve as a function of SOC. Focusing on nonlinear modeling and linearization techniques, such as partial state feedback linearization, for “proof concept” and simulations purposes in the case study, a third order nonlinear model for a DC motor (DCM) drive was selected. It is a valuable research support suitable to analyze the performance of state feedback linearization, system singularities, internal and zero dynamics, and solving reference tracking problems

A state-of-the-art assessment of active structures

A state-of-the-art assessment of active structures with emphasis towards the applications in aeronautics and space is presented. It is felt that since this technology area is growing at such a rapid pace in many different disciplines, it is not feasible to cover all of the current research but only the relevant work as relates to aeronautics and space. Research in smart actuation materials, smart sensors, and control of smart/intelligent structures is covered. In smart actuation materials, piezoelectric, magnetostrictive, shape memory, electrorheological, and electrostrictive materials are covered. For sensory materials, fiber optics, dielectric loss, and piezoelectric sensors are examined. Applications of embedded sensors and smart sensors are discussed

Aeronautical engineering: A continuing bibliography with indexes (supplement 301)

This bibliography lists 1291 reports, articles, and other documents introduced into the NASA scientific and technical information system in Feb. 1994. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

The Modeling and Advanced Controller Design of Wind, PV and Battery Inverters

Renewable energies such as wind power and solar energy have become alternatives to fossil energy due to the improved energy security and sustainability. This trend leads to the rapid growth of wind and Photovoltaic (PV) farm installations worldwide. Power electronic equipments are commonly employed to interface the renewable energy generation with the grid. The intermittent nature of renewable and the large scale utilization of power electronic devices bring forth numerous challenges to system operation and design. Methods for studying and improving the operation of the interconnection of renewable energy such as wind and PV are proposed in this Ph.D. dissertation.;A multi-objective controller including is proposed for PV inverter to perform voltage flicker suppression, harmonic reduction and unbalance compensation. A novel supervisory control scheme is designed to coordinate PV and battery inverters to provide high quality power to the grid. This proposed control scheme provides a comprehensive solution to both active and reactive power issues caused by the intermittency of PV energy. A novel real-time experimental method for connecting physical PV panel and battery storage is proposed, and the proposed coordinated controller is tested in a Hardware in the Loop (HIL) experimental platform based on Real Time Digital Simulator (RTDS).;This work also explores the operation and controller design of a microgrid consisting of a direct drive wind generator and a battery storage system. A Model Predictive Control (MPC) strategy for the AC-DC-AC converter of wind system is derived and implemented to capture the maximum wind energy as well as provide desired reactive power. The MPC increases the accuracy of maximum wind energy capture as well as minimizes the power oscillations caused by varying wind speed. An advanced supervisory controller is presented and employed to ensure the power balance while regulating the PCC bus voltage within acceptable range in both grid-connected and islanded operation.;The high variability and uncertainty of renewable energies introduces unexpected fast power variation and hence the operation conditions continuously change in distribution networks. A three-layers advanced optimization and intelligent control algorithm for a microgrid with multiple renewable resources is proposed. A Dual Heuristic Programming (DHP) based system control layer is used to ensure the dynamic reliability and voltage stability of the entire microgrid as the system operation condition changes. A local layer maximizes the capability of the Photovoltaic (PV), wind power generators and battery systems, and a Model Predictive Control (MPC) based device layer increases the tracking accuracy of the converter control. The detail design of the proposed SWAPSC scheme are presented and tested on an IEEE 13 node feeder with a PV farm, a wind farm and two battery-based energy storage systems