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

Identifying critical, failure prone areas in a power system network are often a difficult and computationally intensive task. Artificial Immune System (AIS) algorithms have been shown to be capable of generalization and learning to identify previously unseen patterns. In this paper, a method is developed that uses artificial immune system classification and clustering algorithms to identify critical areas in the network. The algorithm identifies areas of the power system network that are prone to voltage collapse and areas with overloaded lines. The applicability of AIS for this particular task is demonstrated on test electrical power system networks. Its accuracy is compared with an optimised support vector machine (SVM) algorithm and k nearest neighbours algorithm (kNN) across 3 different power system networks.

An immune system inspired clustering and classification method to detect critical areas in electrical power networks

This paper addresses the problem of identifying the optimal location for voltage sags monitors for reliable estimation of the sags performance of the network when only partial monitoring is applied. The method is based on a very simple and intuitive approach that only requires knowledge about basic network data, i.e., the system impedance matrix $\mathrm {Z}_{\mathrm {B}\mathrm {U}\mathrm {S}}$. The approach allows to determine the optimal location of monitors as well as to estimate the expected number of sags at non-monitored sites. The performance of the method is illustrated on several cases studies applied to the IEEE 24-busbar reliability test system.

Efficient Monitor Placement and Voltage Sag Estimation Using System Impedance Matrix

Probabilistic assessment of the influence of transformer rating on power quality indices in future residential networks

Assessment of Customer Damage Costs due to Voltage Sags

The paper presents development of mathematical model of steam power plant for studies of reactive power - voltage (Q-V) control. The model of realistic six generator power plant is developed in Matlab-Simulink environment. All relevant components of the system studied are modeled in sufficient detail including synchronous generators, turbines and governors, exciters and AVRs, transformers, and the network. Several case studies are used to investigate potential problems that could occur if manual control was performed by the plant operator to restore the voltage at a point of common coupling or to produce the required reactive power set by the system operator. Results of simulations are compared with recorded responses at the power plant and confirmed the accuracy of developed mathematical model.

Jovica V. Milanovic

Papers

An immune system inspired clustering and classification method to detect critical areas in electrical power networks

Efficient Monitor Placement and Voltage Sag Estimation Using System Impedance Matrix

Probabilistic assessment of the influence of transformer rating on power quality indices in future residential networks

Assessment of Customer Damage Costs due to Voltage Sags

Mathematical model of steam power plant for voltage and reactive power control