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.

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

Object-oriented techniques have been applied to power system software development for quite long time. However, object-oriented modeling has several disadvantages for power system stability assessment software. Aspect-oriented modeling (AOM) has become an extension of object-oriented modeling by capitalizing on its advantages while improving the disadvantages. This paper addresses the disadvantages encountered by object-oriented modeling in power system stability assessment software, and proposes using aspect-oriented modeling for power system stability assessment software development so as to solve the problems encountered by object- oriented techniques. A framework based on the aspect-oriented technique for stability assessment software is presented.

Aspect-Oriented Modeling for Power System Stability Assessment

During normal operation Doubly Fed Induction Generator (DFIG) wind turbine drivetrains are exposed to transients in mechanical and electromagnetic torque which excite drivetrain torsional modes. This can cause twisting in the drivetrain which over time can weaken the system and eventually result in failure. This paper presents an active damping method to smooth drivetrain transients that may arise due to turbulent winds or faults in the power system. The proposed method utilizes the generator and turbine speeds to maintain the drivetrain twist angle at a stable value and is realized through sliding mode control. The efficacy of the proposed method is verified through subjecting the wind generator to rapid changes in wind speed and power system transients. Simulations are carried out using the detailed DFIG model in the RTDS® simulation platform.

A Novel Torsional Vibration Mitigation Strategy for DFIG Based Wind Turbines

Several large scale failures of power system took place due to extreme weather disaster recent years, which aroused the consideration of power network security operation. Considering that the line failure events caused by natural disaster presented random characteristic, using Poisson distribution theory to depict the probability of line failure, a stochastic power system optimal dispatch model based on chance constraints theory was also proposed. We adopted the Differential Evolution algorithm to calculate the total loss based on Monte-Carlo simulation. The results of IEEE 9-bus case study imply that the dispatch model will give full consideration of weather effects, and provide a more reasonable dispatch plan for power system disaster prevention and reduction.

Stochastic Optimal Dispatch of Power System under Extreme Weather Disaster

A multi-resolution neural network model for electricity market place forecasting

Since the power industry has entered into the deregulated era, and no participants in the market want them to be exposed completely to the price fluctuations, the risk management has become an essential tool to help power utilities to manage the risks. This paper reviews the risks in the context of electricity market, proposing a risk management framework with major instruments from energy derivative market. The attached case study illustrates the work flow for a generation company, with relevant discussions and analyses

Zhao Yang Dong

Papers

Aspect-Oriented Modeling for Power System Stability Assessment

A Novel Torsional Vibration Mitigation Strategy for DFIG Based Wind Turbines

Stochastic Optimal Dispatch of Power System under Extreme Weather Disaster

A multi-resolution neural network model for electricity market place forecasting

Risk hedging in electricity generation planning