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

Abstract Geomagnetic disturbances have affected electrical systems on the ground for over 150 years. The first effects were noted on the early telegraph in the 1840s and in this century magnetic storms have caused power system blackouts and phone system outages. Affected systems include all those that use electrical conductors: whether for transmission of power or signals or where the conducting properties are incidental to their use such as with pipelines and railway tracks. In power systems geomagnetically induced currents cause partial saturation of power transformers producing transformer heating and distortion of the ac waveform leading to misoperation of relays and other equipment. On pipelines, induced currents may contribute to corrosion but also present a problem with the electrical surveys of the pipe performed to monitor the corrosion prevention systems. Severity of these effects depends on disturbance size, proximity to the auroral zone, and the conductivity structure of the Earth. Also significant are system parameters such as the use of higher resistance coatings on pipelines and the linking of power systems into larger networks. In this paper we have attempted to catalogue all the published reports of geomagnetic effects on electrical systems and show their occurrence in the context of the solar cycle and geomagnetic activity variations for the years 1844 to 1996.

The Effects of Geomagnetic Disturbances on Electrical Systems at the Earth's Surface - An Update

Differential Evolution: A review of more than two decades of research

The introduction of microgrids in distribution networks based on power electronics facilitates the use of renewable energy resources, distributed generation (DG) and storage systems while improving the quality of electric power and reducing losses thus increasing the performance and reliability of the electrical system. The hierarchical control structure, which consists of primary, secondary, and tertiary levels for microgrids that mimic the behavior of the mains grid, is reviewed. The main objective of this article is to give a description of state of the art for distributed power generation systems (DPGS) based on renewable energy and explore the power converters connected in parallel to the grid which are distinguished by their contribution to the formation of the grid voltage and frequency and are accordingly classified in three classes. This analysis is extended focusing mainly on the three classes of configurations: grid-forming, grid-feeding, and grid-supporting. The article ends up with an overview and a discussion of the control structures and strategies to control distribution power generation system (DPGS) units connected to the network.

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A Survey on Control of Electric Power Distributed Generation Systems for Microgrid Applications

Cyber-Physical System (CPS) is a new kind of digital technology that increases its attention across academia, government, and industry sectors and covers a wide range of applications like agriculture, energy, medical, transportation, etc. The traditional power systems with physical equipment as a core element are more integrated with information and communication technology, which evolves into the Cyber-Physical Power System (CPPS). The CPPS consists of a physical system tightly integrated with cyber systems (control, computing, and communication functions) and allows the two-way flows of electricity and information for enabling smart grid technologies. Even though the digital technologies monitoring and controlling the electric power grid more efficiently and reliably, the power grid is vulnerable to cybersecurity risk and involves the complex interdependency between cyber and physical systems. Analyzing and resolving the problems in CPPS needs the modelling methods and systematic investigation of a complex interaction between cyber and physical systems. The conventional way of modelling, simulation, and analysis involves the separation of physical domain and cyber domain, which is not suitable for the modern CPPS. Therefore, an integrated framework needed to analyze the practical scenario of the unification of physical and cyber systems. A comprehensive review of different modelling, simulation, and analysis methods and different types of cyber-attacks, cybersecurity measures for modern CPPS is explored in this paper. A review of different types of cyber-attack detection and mitigation control schemes for the practical power system is presented in this paper. The status of the research in CPPS around the world and a new path for recommendations and research directions for the researchers working in the CPPS are finally presented.

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Cyber-Physical Power System (CPPS): A Review on Modeling, Simulation, and Analysis With Cyber Security Applications

Synthetic inertia control based on fuzzy adaptive differential evolution

The increasing integration of renewables into the grid based on power electronics converters interfaces is affecting the power systems dynamics, requiring effective monitoring and visualisation to provide appropriate assessment during extreme events. The coherent group identification in power systems is of importance for dynamic studies and transmission capability improvement. This study analyses the coherency based on the mode shapes, the application of the Koopman mode analysis (KMA) and a Prony analysis (PA) on-line variation for the identification of coherent groups in power systems. KMA and PA use the voltage angle measurements obtained from simulation. The clustering of the coherent groups are evaluated in two test systems. The coherency methods are also evaluated taking into account the impact of a large gradual scale inclusion of non-synchronous generation under different penetration level cases. From the comparison and visualisation of the different methods it is possible to observe the impact of the large inclusion of non-synchronous generation on the coherency.

Non-synchronous generation impact on power systems coherency

Future plans for integration of large non-synchronous generation and the expansion of the power system in the Nordic countries are a concern to transmission system operators due to the common interconnections and electricity exchanges among these operative areas. The expected reduction in the inertia anticipates an alteration of the frequency response, provoking a high Rate of Change of Frequency (RoCoF) slopes that can jeopardise the security of the interconnected systems. Since power generation in the Nordic countries such as Sweden, Finland and Norway is hydro-dominated, here, the authors propose a novel solution to tackle this problem including wide area measurements to monitor and share the RoCoF in remote areas with lower inertia to enhance their primary frequency control. To demonstrate the effectiveness of the proposed solution, first a test benchmark control with optimised parameters is developed and later compared against the proposed method. Additionally, since the proposed solution is based on measurements from remote locations in order to guarantee the stability of the system the impact of delays in the communication channels is also included in the problem formulation.

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Innovative primary frequency control in low-inertia power systems based on wide-area RoCoF sharing

The use of high power electronics in the large scale integration of wind power in the transmission and distribution systems can affect the system inertia response and the ability to recover frequency stability after large disturbances. Different approaches have been presented to show the system dynamic behaviour, and to quantify the wind power impact on the system inertial and frequency response. This paper gives a short overview of studies performed regarding the system inertia issues under high penetrations of wind power. Also, it presents the results of a case study to show how the system inertia can be affected by high penetrations of wind power.

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Wind power impact on power system frequency response

Microgrids have become in an important research field in power electronics and play a key role in the Distributed Generation (DG) concept. In this paper, a fuzzy logic controller is proposed for Low Voltage (LV) microgrids, based on Voltage Source Converter (VSC) technology and DG. The control strategy designed aims the active and reactive power flow with the grid connection mode and in isolated operation, where local controllers are located with a central command which is in charge to fixing the setpoints in both cases. The VSC topology provides bidirectional energy flow by managing the SPWM signals, the modulation index and phase shift. The measurements based on Instantaneous power theory gives better accuracy in the selected feedback signals. Simulation results demonstrate the coordination and adaptability control in different study cases.

Harold R. Chamorro

Papers

Synthetic inertia control based on fuzzy adaptive differential evolution

Non-synchronous generation impact on power systems coherency

Innovative primary frequency control in low-inertia power systems based on wide-area RoCoF sharing

Wind power impact on power system frequency response

Microgrid central fuzzy controller for active and reactive power flow using instantaneous power measurements