Michael Negnevitsky

Bio: Michael Negnevitsky is an academic researcher from University of Tasmania. The author has contributed to research in topics: Electric power system & Wind power. The author has an hindex of 36, co-authored 380 publications receiving 7722 citations. Previous affiliations of Michael Negnevitsky include Queensland University of Technology & Hobart Corporation.

Artificial Intelligence: A Guide to Intelligent Systems

Abstract: From the Publisher: Virtually all the literature on artificial intelligence is expressed in the jargon of commuter science, crowded with complex matrix algebra and differential equations. Unlike many other books on computer intelligence, this one demonstrates that most ideas behind intelligent systems are simple and straightforward. The book has evolved from lectures given to students with little knowledge of calculus, and the reader needs no prerequisites associated with knowledge of any programming language. The methods used in the book have been extensively tested through several courses given by the author. The book provides an introduction to the field of computer intelligence, covering rule-based expert systems, fuzzy expert systems, frame-based expert systems, artificail neural networks, evolutionary computation, hybrid intelligent systems, knowledge engineering, data mining. In a university setting the book can be used as an introductory course within computer science, information systems or engineering departments. The book is also suitable as a self-study guide for non-computer science professionals, giving access to the state of the art in knowledge-based systems and computational intelligence. Everyone who faces challenging problems and cannot solve them using traditional approaches can benefit

A Novel Control Strategy for a Variable-Speed Wind Turbine With a Permanent-Magnet Synchronous Generator

Abstract: This paper presents a novel control strategy for the operation of a direct-drive permanent-magnet synchronous-generator-based stand-alone variable-speed wind turbine. The control strategy for the generator-side converter with maximum power extraction is presented. The stand-alone control is featured with output voltage and frequency controller that is capable of handling variable load. The potential excess of power is dissipated in the dump-load resistor with the chopper control, and the dc-link voltage is maintained. Dynamic representation of dc bus and small-signal analysis are presented. Simulation results show that the controllers can extract maximum power and regulate the voltage and frequency under varying wind and load conditions. The controller shows very good dynamic and steady-state performance.

Very short-term wind forecasting for Tasmanian power generation

Abstract: This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach-the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output.

A Novel Control Strategy for a Variable Speed Wind Turbine with a Permanent Magnet Synchronous Generator

Abstract: This paper presents a novel control strategy for the operation of a direct drive permanent magnet synchronous generator (PMSG) based stand alone variable speed wind turbine. The control strategy for the generator side converter with maximum power extraction is discussed. The stand alone control is featured with output voltage and frequency controller capable of handling variable load. The potential excess of power is dissipated in the damp resistor with the chopper control and the dc link voltage is maintained. Dynamic representation of dc bus and small signal analysis are presented. Simulation results show that the controllers can extract maximum power and regulate the voltage and frequency under varying wind and load conditions. The controller shows very good dynamic and steady state performance.

Pool-Based Demand Response Exchange—Concept and Modeling

Abstract: In restructured power systems, there are many independent players who benefit from demand response (DR). These include the transmission system operator (TSO), distributors, retailers, and aggregators. This paper proposes a new concept-demand response eXchange (DRX)-in which DR is treated as a public good to be exchanged between DR buyers and sellers. Buyers need DR to improve the reliability of their own electricity-dependent businesses and systems. Sellers have the capacity to significantly modify electricity demand on request. Microeconomic theory is applied to model the DRX in the form of a pool-based market. In this market, a DRX operator (DRXO) collects DR bids and offers from the buyers and sellers, respectively. It then clears the market by maximizing the total market benefit subject to certain constraints including: demand-supply balance, and assurance contracts related to individual buyer contributions for DR. The DRX model is also tested on a small power system, and its efficiency is reported.

The Analysis of Time Series: An Introduction

Abstract: The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

A particle swarm optimization for reactive power and voltage control considering voltage security assessment

Abstract: Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.

Electrical energy storage systems: A comparative life cycle cost analysis

Abstract: Large-scale deployment of intermittent renewable energy (namely wind energy and solar PV) may entail new challenges in power systems and more volatility in power prices in liberalized electricity markets. Energy storage can diminish this imbalance, relieving the grid congestion, and promoting distributed generation. The economic implications of grid-scale electrical energy storage technologies are however obscure for the experts, power grid operators, regulators, and power producers. A meticulous techno-economic or cost-benefit analysis of electricity storage systems requires consistent, updated cost data and a holistic cost analysis framework. To this end, this study critically examines the existing literature in the analysis of life cycle costs of utility-scale electricity storage systems, providing an updated database for the cost elements (capital costs, operational and maintenance costs, and replacement costs). Moreover, life cycle costs and levelized cost of electricity delivered by electrical energy storage is analyzed, employing Monte Carlo method to consider uncertainties. The examined energy storage technologies include pumped hydropower storage, compressed air energy storage (CAES), flywheel, electrochemical batteries (e.g. lead–acid, NaS, Li-ion, and Ni–Cd), flow batteries (e.g. vanadium-redox), superconducting magnetic energy storage, supercapacitors, and hydrogen energy storage (power to gas technologies). The results illustrate the economy of different storage systems for three main applications: bulk energy storage, T&D support services, and frequency regulation.