EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

Abstract:- This paper intends to the modeling of polyphase synchronous generator and minimization of power losses using Particle swarm optimization (PSO) technique with a constriction factor. Usage of Polyphase synchronous generator mainly leads to the total power circulation in the system which can be distributed in all phases. Another advantage of polyphase system is the fault at one winding does not lead to the system shutdown. The Process optimization is the chastisement of adjusting a process so as to optimize some stipulated set of parameters without violating some constraint. Accurate value can be extracted using PSO and it can be reformulated. Modeling and simulation of the machine is executed. MATLAB/Simulink has been cast-off to implement and validate the result

Relative Rate Observer Self-Tuning of Fuzzy PID Virtual Inertia Control for An Islanded microgrid

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids\u27 dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC\u27s success in showing remarkable improvement in the microgrid\u27s dynamic response and enhancement of its stability. Moreover, it handles different contingency conditions successfully by giving the desired frequency support. Ant colony optimization (ACO) technique is used to find the optimal values of the RROSTF-PID based on VIC parameters. Furthermore, using MATLAB TM/Simulink, RROSTF-PID based on VIC response is compared to Optimal Fuzzy PID (OF-PID) based VIC, Fuzzy PID (F-PID) based VIC, PID-based VIC, conventional VIC responses, and the microgrid without VIC response under different operation conditions

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness

2011 - 2012The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]XI n.s

Managing Epistemic Uncertainty in Design Models through Type-2 Fuzzy Logic Multidisciplinary Optimization

Humans have a natural ability to operate in dynamic environments and perform complex tasks with little perceived effort. An experienced ship designer can intuitively understand the general consequences of design choices and the general attributes of a good vessel. A person's knowledge is often ill-structured, subjective, and imprecise, but still incredibly effective at capturing general patterns of the real-world or of a design space. Computers on the other hand, can rapidly perform a large number of precise computations using well-structured, objective mathematical models, providing detailed analyses and formal evaluations of a specfic set of design candidates. In ship design, which involves generating knowledge for decision-making through time, engineers interactively use their own mental models and information gathered from computer-based optimization tools to make decisions which steer a vessel's design. In recent decades, the belief that large synthesis codes can help achieve cutting-edge ship performance has led to an increased popularity of optimization methods, potentially leading to rewarding results. And while optimization has proven fruitful to structural engineering and the aerospace industry, its applicability to early-stage design is more limited for three main reasons. First, mathematical models are by definition a reduction which cannot properly describe all aspects of the ship design problem. Second, in multidisciplinary optimization, a low-fidelity model may incorrectly drive a design, biasing the system level solution. Finally, early-stage design is plagued with limited information, limiting the designer's ability to develop models to inform decisions. This research extends previously done work by incorporating type-2 fuzzy logic into a human-centric multidisciplinary optimization framework. The original framework used type-1 fuzzy logic to incorporate human expertise into optimization models through linguistic variables. However, a type-1 system does not properly account for the uncertainty associated with linguistic terms, and thus does not properly represent the uncertainty associated with a human mental model. This limitation is corrected with the type-2 fuzzy logic multidisciplinary optimization presented in this work, which more accurately models a designer's ability to "communicate, reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information and partiality of truth" (Mendel et al., 2010). It uses fuzzy definitions of linguistic variables and rule banks to incorporate "human intelligence" into design models, and better handles the linguistic uncertainty inherent to human knowledge and communication. A general mathematical optimization proof of concept and a planing craft case study are presented in this dissertation to show how mathematical models can be enhanced by incorporating expert opinion into them. Additionally, the planing craft case study shows how human mental models can be leveraged to quickly estimate plausible values of ship parameters when no model exists, increasing the designer's ability to run optimization methods when information is limited.PHDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145891/1/doriancb_1.pd

Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

Advances in Modelling and Control of Wind and Hydrogenerators

Rapid deployment of wind and solar energy generation is going to result in a series of new problems with regards to the reliability of our electrical grid in terms of outages, cost, and life-time, forcing us to promptly deal with the challenging restructuring of our energy systems. Increased penetration of fluctuating renewable energy resources is a challenge for the electrical grid. Proposing solutions to deal with this problem also impacts the functionality of large generators. The power electronic generator interactions, multi-domain modelling, and reliable monitoring systems are examples of new challenges in this field. This book presents some new modelling methods and technologies for renewable energy generators including wind, ocean, and hydropower systems