Study and Analysis of Power System Stability Based on FACT Controller System

Energy framework soundness is identified with standards rotational movement and the swing condition administering electromechanical unique conduct. In the exceptional instance of two limited machines, the basis of equivalent territory security can be utilized to ascertain the basic clearing point in the force framework, It is important to look after synchronization, in any case the degree of administration for customers won't be accomplished. This term steadiness signifies "looking after synchronization." This paper is an audit of three kinds of consistent state. The main sort of adjustment, consistent state steadiness clarifies the most extreme consistent state quality and force point chart. The transient solidness clarifies the wavering condition and the idleness steady while dynamic soundness manages the transient security time frame. There are a few different ways to improve framework soundness a portion of the techniques are clarified. Versatile AC Transmission Frameworks (FACTS) Flexible AC Transmission System (FACTS) regulators have been utilized frequently to comprehend the different issues of a non-variable force structure. Versatile AC Transmission Frames or FACTS are devices that permit versatile and dynamic control of intensity outlines. Improving casing respectability has been explored with FACTS regulators. This examination focuses to the upsides of utilizing FACTS apparatuses with the explanation behind improving electric force tire activity. There has been discussion of an execution check for different FACTS regulators

Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower-top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response

Measurements, Models, Systems and Design

531 s.

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships

Memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively Memetic Gravitational Search Algorithm (MGSA) and Memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.El algoritmo de retropropagación (BP) es un algoritmo basado en gradientes que se utiliza para entrenar una red neuronal feedforward (FNN). A pesar de que BP todavía se usa hoy en día cuando se entrenan las FNN, tiene algunas desventajas, incluidas las siguientes: (i) falla cuando se abordan funciones no diferenciables, (ii) puede quedar atrapada en mínimos locales y (iii) ) tiene convergencia lenta. Para resolver algunos de estos problemas, se han utilizado algoritmos metaheurísticos para entrenar FNN. Aunque tienen buenas habilidades de exploración, no son tan buenos como los algoritmos basados ​​en gradientes en las tareas de explotación. La principal contribución de este artículo radica en la aplicación de nuevos enfoques meméticos basados ​​en los algoritmos Gravitational Search Algorithm (GSA) y Chaotic Gravitational Search Algorithm (CGSA), llamados respectivamente Algoritmo de búsqueda gravitacional memético (MGSA) y Algoritmo de búsqueda gravitacional caótico memético (MCGSA), para entrenar FNN en tres problemas de referencia clásicos: el problema XOR, la aproximación de una función continua y tareas de clasificación. Los resultados muestran que ambos enfoques constituyen alternativas adecuadas para el entrenamiento de FNN, incluso mejorando el rendimiento de otros algoritmos metaheurísticos de última generación como ParticleSwarm Optimization (PSO), el Algoritmo Genético (GA), el algoritmo de Evolución Diferencial Adaptativa con Tasa de cruce reparada (Rcr-JADE) y el algoritmo de evolución diferencial (COBIDE) de configuración de parámetros de distribución bimodal y aprendizaje de matriz de covarianza. Optimización de enjambre, el algoritmo genético, el algoritmo de evolución diferencial adaptativo con tasa de cruce reparada

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area

Novel Algorithms for Modern Power Systems

The restructuring of the electric power industry has brought about many interesting and new problems to be solved by researchers. Some of the problems have been considered and suitable intelligent techniques that have been developed are described in this thesis. A new technique using wavelet transform and neural networks for fault location and protection of a practical tee-circuit has been developed. Fault simulation is carried out using EMTP software. The waveforms obtained from the simulation are then used in wavelet analysis to generate patterns for training and validation, which is carried out using Radial Basis Function network. Dynamic Protection Modelling (DPM) software developed by the University of Strathclyde, Centre for Electrical Power Engineering is used for the evaluation of relay settings. A new approach is proposed to use object oriented techniques and improved genetic algorithms in developing software to estimate generator excitation control system parameters. Simulation studies are carried using data representing a generator, with its transformer connected to an infinite busbar. Application of evolutionary programming to Optimal Power Flow is another technique that is proposed in this work. The objective is to minimize fuel cost keeping the system secure under both normal and contingent states. Studies are carried out using the IEEE - 30 bus test system. A short-term load-forecasting model using Artificial Neural Networks and Genetic Algorithm has been developed and tested on data obtained from a power company. It is found that the time taken to obtain a satisfactory solution is long, as the problem is very complex. This points in the direction of evolutionary computing being integrated with parallel processing techniques to solve such practical problems. A novel approach to the formulation and evaluation of transmission loss and line flow through a set of new loss coefficients and distribution factors respectively which are efficient, exact and robust and suitable for real time application is proposed. These loss coefficients and distribution factors are generated from the available load flow solution with trivial computational burden. Results on IEEE Test systems show that the coefficients need not be re-evaluated for wide changes in system operating condition

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words