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 …

I and i

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.).

Machine learning

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.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

This paper presents a newly developed voltage and reactive power control device, the coordinated Q -V controller (CQVC), for a multimachine steam power plant (SPP). The requirements for the new controller are defined based on identified inadequacies of manual Q-V control. The controller uses a feed-forward correction signal based on the application of the sensitivity matrix at a power plant level. It is designed and its performance initially assessed using the Matlab/Simulink environment. The performed simulations show that all the primary design objectives are met. The Q-V controller maintains the voltage at the SPP busbar with a certain droop characteristic during slow voltage changes in the power system whilst also maintaining equal per unit reactive power sharing among the SPP generators. This ensures that the power system fully benefits from all generating units during system perturbations. The performance of the designed CQVC is validated by comparing simulation results with measured responses obtained from a real CQVC installed at a SPP.

Coordinated Reactive Power-Voltage Controller for Multimachine Power Plant

The paper proposes an adaptive supervisory controller (ASC) to enhance the damping of inter-area oscillations using Wide Area Measurement (WAM) and accounting for the effects of signal transmission delays. The damping controller based on the mixed-sensitivity linear matrix inequality (LMI) approach with pole-placement restriction is designed. Pade approximation approach is used to model the time delays. The time delays are regarded as parametric uncertainty in the design of the ASC. The effects of time delays involved in signal transfers (to and from the controller) and variable number of ASC outputs on the effectiveness of the controller are clearly illustrated. The design of the ASC is explained in detail and its performance validated by time domain simulations on a 4-machine 2-area test system for various fault scenarios.

The Effectiveness of WAM Based Adaptive Supervisory Controller for Global Stabilization of Power Systems

Economic Framework for Power Quality

Guidelines for power quality monitoring– measurement locations, processing and presentation of data

This paper summarizes some of the results of an extensive survey and monitoring of quality of electricity supply conducted over period of one year in Tasmania. The survey and monitoring was performed as a part of a collaborative research project between the Department of Electrical Engineering and Computer Science of the University of Tasmania from one side, and Hydro Electric Corporation (HEC) and major electricity users in Tasmania from the other. The survey and monitoring were performed in entire state and included power system utility and a number of the major electricity users in the state. The major results of the survey and monitoring are presented and discussed in the paper.

Jovica V. Milanovic

Papers

Coordinated Reactive Power-Voltage Controller for Multimachine Power Plant

The Effectiveness of WAM Based Adaptive Supervisory Controller for Global Stabilization of Power Systems

Economic Framework for Power Quality

Guidelines for power quality monitoring– measurement locations, processing and presentation of data

Power quality problems and solutions: current understanding