Sukumar Kamalasadan

A Neural Network Parallel Adaptive Controller for Dynamic System Control

A neural network (NN)-based intelligent adaptive controller that introduces a new concept of intelligent supervisory loop is proposed. The scheme consists of an online radial basis-function NN (RBFNN) in parallel with a model reference adaptive controller (MRAC) and uses a growing dynamic RBFNN to augment MRAC. Updating of the RBFNN width, center, and weight characteristics is performed such that error reduction and improved tracking accuracy are accomplished. The RBFNN architecture adapts its centers and radii and tunes the relevant parameters dynamically. These adaptations effectively address the issues that are related to initial error and dimensional growth that are inherent in static NN design. The strength of the proposed scheme is in its ability to perform effectively, even when the plant mode swings and functional changes occur. Theoretical results are validated by simulation studies based on a nonlinear single-link flexible robotic manipulator position tracking of changing reference pattern. Compared to single and multiple fuzzy reference adaptive control approaches, the proposed intelligent controller produced better tracking with reduced tracking error in the event of functional changes and is capable of delivering plant output to track the reference precisely.

Doubly Fed Induction Generator (DFIG)-Based Wind Farm Control Framework for Primary Frequency and Inertial Response Application

This paper presents a new wind farm control framework for inertial and primary frequency response for a high wind integrated power system. The proposed architecture is unique in the sense that the methodology can be used for frequency regulation support during subsynchronous and super-synchronous operation of the wind turbines (farm). The architecture work with existing wind farm controllers thus avoiding any additional replacement or tuning. The methodology depends on reduced order modeling based on model order reduction (MOR) and subsequent online controller design. The approach is tested on a smaller wind farm and further evaluated on a larger reduced power grid with 39 buses and ten generators. The results show that the proposed architecture provides greater flexibility in wind farm control towards frequency oscillations.

An Effective Power Management Strategy for a Wind–Diesel–Hydrogen-Based Remote Area Power Supply System to Meet Fluctuating Demands Under Generation Uncertainty

This paper addresses power management strategies, including technical issues and control methodologies, for a wind-dominated hybrid remote area power supply (RAPS) system. The system consists of a doubly fed induction generator, a diesel generator, a hydrogen-based generation scheme, and mains loads. The goal is to maximize power extraction from the wind generator under generation uncertainty. For active power management, a hydrogen-based generation scheme consisting of an electrolyser and a fuel cell system is integrated to the RAPS system. Developed control strategies contribute to achieve the following objectives: 1) load side voltage and frequency regulation; 2) maximum power extraction from wind; and 3) regulating diesel generator operation at low load conditions. Simulation studies with a real-life data set are used to evaluate the proposed architecture and prove the control objectives, and it has been observed that all the proposed objectives are met within satisfactory limits.

A neural network based intelligent model reference adaptive controller

This paper presents a novel neural network based intelligent model reference adaptive controller. In this scheme the intelligent supervisory loop (ISL) is incorporated into the traditional model reference adaptive controller (MRAC) framework by utilizing an online growing dynamic radial basis function neural network (RBFNN) structure in parallel with it. The idea is to control the plant by a direct MRAC with a suitable single reference model, and at the same time respond to plant multimodal dynamics by on line tuning of an RBFNN controller. This parallel RBFNN controller is designed in order to precisely track the system output to the desired command signal trajectory, regardless of system multimodality and/or unmodeled dynamics. The updating details of the RBFNN width, centers and weights are derived to ensure error reduction and for improved tracking accuracy. The importance of the proposed scheme is in its ability to perform effectively even when the plant mode swings without using multiple model concept or a multiple reference model adaptive controller if a suitable reference model structure can be established. Further, the parallel controller will be able to precisely track the reference trajectory even with system showing unmodeled dynamics. The performance ability of the scheme is confirmed by applying to control the angular position of the robotic manipulator under tip load variations.

A Neural Network Parallel Adaptive Controller for Fighter Aircraft Pitch-Rate Tracking

A fighter aircraft pitch-rate command-tracking controller based on a neural network parallel controller is proposed. The scheme consists of an online radial basis function neural network (RBFNN) in parallel with a model reference adaptive controller (MRAC) and uses a growing dynamic RBFNN to augment MRAC. Updating the RBFNN width, the center and weight characteristics are performed such that the error reduction and improved tracking accuracy are accomplished. The RBFNN architecture adapts its centers and radii and tunes the relevant parameters, dynamically addressing the issues related to initial error and dimensional growth inherent in static neural network design. The total control signal is used to change the elevator deflection, keeping the other control surface deflections at random values, even when the aircraft operates at different maneuvers. Moreover, a suitable reference model structure is used for all aircraft operating modes, and the system is then fully tuned by the parallel controller. The strength of the proposed scheme is in its ability to effectively perform, even when plant mode swings and functional changes occur. Theoretical results are validated by conducting simulation studies on a nonlinear F16 fighter aircraft model operating at different modes created by a randomly changing parameter set.

A New Intelligent Controller for the Precision Tracking of Permanent Magnet Stepper Motor

A novel intelligent control scheme for the precise tracking of permanent magnet step motor (PMSM) under wide range of motor system parameters is proposed. The scheme consists of an online growing radial basis neural network (RBFNN) controller in parallel with an implicit model adaptive controller for the precise tracking of motor position and speed. The proposed intelligent controller operates in parallel with a fixed parameter PID control law to compensate for the drastic system functional changes. The main advantage of this algorithm is that it is precise, feasible and more effective than other nonlinear adaptive controllers reported to-date. Simulation results are presented in order to show that, positional tracking errors are reduced when the motor parameters are changed drastically while using the proposed control scheme.

Integrated PV Capacity Firming and Energy Time Shift Battery Energy Storage Management Using Energy-Oriented Optimization

In this paper, we propose a complete active-power-management scheme for the control of battery energy-storage systems (BESSs) for two main applications: 1) photovoltaic (PV) capacity firming and 2) energy time shift (ETS). In the proposed approach, first two control algorithms are designed to provide active-power set points to BESS for the above applications. Then, an optimization routine for integrating these controllers is designed. The proposed approach uses an energy-conservation method to integrate these two applications of energy-storage system. The designed algorithm was tested on a transient simulation platform and then implemented on a 720-node actual power distribution feeder. The main advantage of the proposed method is that the algorithm can be used to optimize multiple functions and perform simultaneous control of BESS.

Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning

In this paper, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested. The algorithm is an optimal wide-area system-centric controller and observer based on a hybrid reinforcement learning and temporal difference framework. It allows the system to deal with major concerns of wide-area monitoring problem: delays in signal transmission, the uncertainty of the communication network, and data traffic. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory of cost function through temporal difference method. The control algorithm is evolved from adaptive critic design (ACD) and performed online at a finite horizon through backward and forward view. The ACD controllers training and testing are implemented on the Innovative Integration Picolo card integrated to TMS320C28335 processor. Results on a real experimental test bed using a real power system feeder shows that this architecture provides better stability compared with conventional schemes.

Multiple Fuzzy Reference Model Adaptive Controller Design for Pitch-Rate Tracking

A multiple fuzzy reference model adaptive controller (MFRMAC) is proposed for nonlinear aircraft pitch-rate tracking. The controller is developed using a direct model reference adaptive control (MRAC) scheme with variable fuzzy logic reference model. The proposed controller provides a soft reference model switching for the multiple modes of operation of the aircraft without any prohibitive computation or explicit system identification. The effectiveness of the technique is assessed by simulation studies based on a 6-DOF high-performance fighter aircraft model undergoing pitch-rate control at a wide range of operating contingencies. It is demonstrated that the scheme performs extremely well when controlling multimodal dynamic systems, as opposed to conventional MRAC designs.

Electricity markets: an overview and comparative study

Purpose – The electric power industry has been moving from a regulated monopoly structure to a deregulated market structure in many countries. The purpose of this study is to comprehensively review the existing markets to study advantages, issues involved and lessons learnt to benefit emerging electricity markets.Design/methodology/approach – The paper employs a comprehensive review of existing competitive electricity market models in USA (California), UK, Australia, Nordic Countries (Norway), and developing country (Chile) to analyze the similarities, differences, weaknesses, and strengths among these markets based on publically available data, literature review and information.Findings – Ongoing or forthcoming electricity sector restructuring activities in some countries can be better designed based on lessons learnt from existing markets and incorporating their own political, technical and economical contexts. A template for design of successful electricity market has also been presented.Research limit...