scispace - formally typeset
Search or ask a question
Author

Jovica V. Milanovic

Bio: Jovica V. Milanovic is an academic researcher from University of Manchester. The author has contributed to research in topics: Electric power system & Voltage sag. The author has an hindex of 48, co-authored 422 publications receiving 8215 citations. Previous affiliations of Jovica V. Milanovic include Endesa & Newcastle University.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, the authors investigated the contribution of doubly fed induction generator (DFIG) to system frequency responses and investigated the impact of different governor settings and system inertia on frequency regulation.
Abstract: The paper investigates contribution of doubly fed induction generator (DFIG) to system frequency responses. Impact of different governor settings and system inertia are investigated. Three distinct cases are simulated in order to illustrate the influence of DFIG penetration on frequency regulation. Provision of inertial response by DFIG through artificial speed coupling is also presented. The effects of the inertial response on the machine behavior and its significance for frequency regulation are discussed. The influence of converter current limits and auxiliary loop parameters on the inertial response are illustrated and a novel control algorithm is developed for extracting maximum energy from the turbine in a stable manner. The results of the study are illustrated on the example of an isolated power system consisting of a diesel generator and a DFIG.

413 citations

Journal ArticleDOI
TL;DR: This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.
Abstract: Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.

345 citations

Journal ArticleDOI
TL;DR: In this paper, the performance of alternative voltage control strategies applied to doubly fed induction generator (DFIG) is investigated for voltage control purposes, using detailed models built in DIgSILENT PowerFactory to illustrate the influence of controllers on transient stability and steady-state operation of the DFIG-based wind plant.
Abstract: This paper explores and compares the performance of alternative voltage control strategies applied to doubly fed induction generator (DFIG) Different combinations of reactive power control of rotor- and grid-side converters are investigated for voltage-control purposes Simulations are performed using detailed models built in DIgSILENT PowerFactory in order to illustrate the influence of controllers on transient stability and steady-state operation of the DFIG-based wind plant This paper also proposes appropriate control strategies for different sets of network operating conditions and topologies Operational limits, such as current margins and pulse-width modulation limits, are also taken into account

329 citations

Journal ArticleDOI
TL;DR: In this article, the CIGRE Working Group C4.605 (Modeling and aggregation of loads in flexible power networks) established a working group to identify current international industry practice on load modeling for static and dynamic power system studies.
Abstract: Power system load modeling is a mature and generally well researched area which, as many other in electrical power engineering at the present time, is going through a period of renewed interest in both industry and academia. This interest is fueled by the appearance of new non-conventional types of loads (power electronic-based, or interfaced through power electronics) and requirements to operate modern electric power systems with increased penetration of non-conventional and mostly intermittent types of generation in a safe and secure manner. As a response to this renewed interest, in February 2010 CIGRE established working group C4.605: “Modelling and aggregation of loads in flexible power networks”. One of the first tasks of the working group was to identify current international industry practice on load modeling for static and dynamic power system studies. For that purpose, a questionnaire was developed and distributed during the summer/autumn of 2010 to more than 160 utilities and system operators in over 50 countries on five continents. This paper summarizes some of the key findings from about 100 responses to the questionnaire received by September 2011 and identifies prevalent types of load models used as well as typical values of their parameters.

249 citations

Journal ArticleDOI
TL;DR: In this paper, a comparative analysis and an overview of various models of gas turbines published in different literature is presented, with a focus on the dynamics of the gas turbines in a combined cycle setup.
Abstract: Gas turbines have become increasingly popular in the different power systems, due to their lower greenhouse emission as well as the higher efficiency, especially when connected in a combined cycle setup. With increasing installations of gas turbines scheduled in different countries, the dynamics of the gas turbines become increasingly more important. In order to study such dynamics, accurate models of gas turbines are needed. This paper presents a comparative analysis and an overview of various models of gas turbines published in different literature.

199 citations


Cited by
More filters
Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2002

9,314 citations