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

The paper presents a generic model of typical coastal distribution network developed for power quality studies. Its composition is based on number of measurements performed over long period of time at different connection points in existing distribution network. All relevant elements of the network are fully described and discussed. The generic model can be used for benchmarking results of power system studies, in particular harmonic studies, for coastal regions network including specific types of consumers like hotels, resorts, marinas, airports, apartment buildings, market centres and moderate size industrial plants. Streszczenie. W artykule zaprezentowano typową nadmorską siec przesylową z przeznaczeniem do analizy jakości energii. Opisano dokladnie wszystkie elementy sieci oraz dlugookresowe wyniki badan. Celem jest analiza takiej sieci z uwzglednieniem jej typowych odbiorcow takich jak hotle, centra handlowe, apartamentowce itp. (Model generyczny nadmorskiej sieci przesylowej oraz analiza jakości energii w takiej sieci)

Generic Model of Coastal Distribution Network for Power System Harmonics Studies

The recently introduced concept of virtual storage plant (VSP) focuses on optimal coordination of a large number of distributed storage devices (of the same or different technology) to provide multiple network services, e.g., primary frequency response (PFR), voltage regulation. In this paper, the potential benefits that VSPs can bring about are discussed from different perspectives, including the potential to reduce network investment needs, improve reliability at the distribution and transmission network levels, and provide service to the grid. VSPs are expected to provide the grid with flexible operation to facilitate high penetration of renewable generation. In order to achieve this thought, mathematical tools are to be developed, which optimally coordinate the storage units comprised in a VSP. This aspect is particularly explored in this paper. An illustrative case study is discussed, which shows how VSPs comprising battery storage systems can effectively support frequency regulation when the storage units are optimally managed.

Towards Optimal Management and Control of Virtual Storage Plants for Flexible Operation in Future Power Networks

This paper presents some of the results from an international working group on voltage-dip immunity. The working group has made a number of recommendations to reduce the adverse impact of voltage dips. Specific recommendations to researchers and manufacturers of powerelectronic equipment are considering all voltage dip characteristics early in the design of equipment; characterize performance of equipment by means of voltage-dip immunity curves; and made equipment with different immunity available.

https://biblio.ugent.be/publication/1937908/file/1937925.pdf

Voltage dip immunity aspects of power-electronic equipment : recommendations from CIGRE/CIRED/UIE JWG C4.110

The new approach to increase the amount of inertia in power system with dispersed renewable generation and energy storage inverter is proposed. The inverter capability to deliver frequency support is presented and analyzed. The basics of the new method are in the inverter modulation scheme related to the generation of the inverter reference voltage trough a specific delay line. Derived results are verified by MatlabSimulink simulation results, comparing two cases, one with increased generator inertia and the other with energy storage inverter employed as proposed. Key-Words: energy storage integration, power-frequency control, grid inertia, inverter control

Grid inertia supporting by energy storage inverters

Voltage control zones are commonly used within power systems, for applications like Secondary Voltage Control and localized reactive power markets. The idea is to partition a power system into zones that are weakly coupled in terms of voltage control. Conventionally, these control zones are defined with a deterministic approach and are fixed. However, recent research has demonstrated that they may be altered due to variation of the network structure and therefore suggested on-line reconfiguration of the zones. This paper goes a further step to probabilistically asses how the zoning could be affected by the uncertainties of operating condition (i.e., how the zones vary within a certain amount of time). Forced network outage, as the uncertain factor that affects the electrical distance between buses, is modeled in a realistic manner. Choropleth map is used to visualize the results. The 39-bus New England Test System (NETS) is used for demonstration.

Jovica V. Milanovic

Papers

Generic Model of Coastal Distribution Network for Power System Harmonics Studies

Towards Optimal Management and Control of Virtual Storage Plants for Flexible Operation in Future Power Networks

Voltage dip immunity aspects of power-electronic equipment : recommendations from CIGRE/CIRED/UIE JWG C4.110

Grid inertia supporting by energy storage inverters

Probabilistic assessment of voltage control zones and visualization using choropleth map