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

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Signal fingerprinting and machine learning framework for UAV detection and identification.

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into restricted or private areas. A UAV detection and identification system is one of the ways of detecting and identifying the presence of a UAV in an area. UAV detection and identification systems employ different sensing techniques such as radio frequency (RF) signals, video, sounds, and thermal imaging for detecting an intruding UAV. Because of the passive nature (stealth) of RF sensing techniques, the ability to exploit RF sensing for identification of UAV flight mode (i.e., flying, hovering, videoing, etc.), and the capability to detect a UAV at beyond visual line-of-sight (BVLOS) or marginal line-of-sight makes RF sensing techniques promising for UAV detection and identification. More so, there is constant communication between a UAV and its ground station (i.e., flight controller). The RF signals emitting from a UAV or UAV flight controller can be exploited for UAV detection and identification. Hence, in this work, an RF-based UAV detection and identification system is proposed and investigated. In RF signal fingerprinting research, the transient and steady state of the RF signals can be used to extract a unique signature. The first part of this work is to use two different wavelet analytic transforms (i.e., continuous wavelet transform and wavelet scattering transform) to investigate and analyze the characteristics or impacts of using either state for UAV detection and identification. Coefficient-based and image-based signatures are proposed for each of the wavelet analysis transforms to detect and identify a UAV. One of the challenges of using RF sensing is that a UAV\u27s communication links operate at the industrial, scientific, and medical (ISM) band. Several devices such as Bluetooth and WiFi operate at the ISM band as well, so discriminating UAVs from other ISM devices is not a trivial task. A semi-supervised anomaly detection approach is explored and proposed in this research to differentiate UAVs from Bluetooth and WiFi devices. Both time-frequency analytical approaches and unsupervised deep neural network techniques (i.e., denoising autoencoder) are used differently for feature extraction. Finally, a hierarchical classification framework for UAV identification is proposed for the identification of the type of unmanned aerial system signal (UAV or UAV controller signal), the UAV model, and the operational mode of the UAV. This is a shift from a flat classification approach. The hierarchical learning approach provides a level-by-level classification that can be useful for identifying an intruding UAV. The proposed frameworks described here can be extended to the detection of rogue RF devices in an environment

Brief Review on Identification, Categorization and Elimination of Power Quality Issues in a Microgrid Using Artificial Intelligent Techniques

Power quality is the manifestation of a disruption in the supply voltage, current or frequency that damages the utility equipment and has become an important issue with the introduction of more sophisticated and sensitive devices. So, the supply power quality issue still remains a major challenge as its degradation can cause huge destabilization of electrical networks. As renewable energy sources have irregular nature, a microgrid essentially needs energy storage system containing advanced power electronic converters which is the root cause of majority of power quality disturbances. Also, the integration of non-linear and unbalanced loads into the grid adds to its power quality problems. This article gives a compact overview on the identification, categorization and mitigation of these power quality events in a microgrid by using various Artificial Intelligence-based techniques like Optimization techniques, Adaptive Learning techniques, Signal Processing and Pattern Recognition, Neural Networks and Fuzzy Logic

Intentional Controlled Islanding in Wide Area Power Systems with Large Scale Renewable Power Generation to Prevent Blackout

Intentional controlled islanding is a solution to prevent blackouts following a large disturbance. This study focuses on determining island boundaries while maintaining the stability of formed islands and minimising load shedding. A new generator coherency identification framework based on the dynamic coupling of generators and Support Vector Clustering method is proposed to address this challenge. A Mixed Integer Linear Programming model is formulated to minimize power flow disruption and load shedding, and ensure the stability of islanding. The proposed algorithm was validated in 39-bus and 118-bus test systems

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

Power system stabilizer (PSS) is applied to dampen system oscillations so that the frequency does not deviate beyond tolerance. PSS parameter tuning is increasingly difficult when dealing with complex and nonlinear systems. This paper presents a novel hybrid algorithm developed from incorporating chaotic maps into the sea-horse optimizer. The algorithm developed is called the chaotic sea-horse optimizer (CSHO). The proposed method is adopted from the metaheuristic method, namely the sea-horse optimizer (SHO). The SHO is a method that duplicates the life of a sea-horse in the ocean when it moves, looks for prey and breeds.  In This paper, The CSHO method is used to tune the power system stabilizer parameters on a single machine system. The proposed method validates the benchmark function and performance on a single machine system against transient response. Several metaheuristic methods are used as a comparison to determine the effectiveness and efficiency of the proposed method. From the research, it was found that the application of the logistics Tent map from the chaotic map showed optimal performance. In addition, the application of the PSS shows effective and efficient performance in reducing overshoot in transient conditions

Optimized Kernel Extreme Learning Machine for Myoelectric Pattern Recognition

Myoelectric pattern recognition (MPR) is used to detect user’s intention to achieve a smooth interaction between human and machine. The performance of MPR is influenced by the features extracted and the classifier employed. A kernel extreme learning machine especially radial basis function extreme learning machine (RBF-ELM) has emerged as one of the potential classifiers for MPR. However, RBF-ELM should be optimized to work efficiently. This paper proposed an optimization of RBF-ELM parameters using hybridization of particle swarm optimization (PSO) and a wavelet function. These proposed systems are employed to classify finger movements on the amputees and able-bodied subjects using electromyography signals. The experimental results show that the accuracy of the optimized RBF-ELM is 95.71% and 94.27% in the healthy subjects and the amputees, respectively. Meanwhile, the optimization using PSO only attained the average accuracy of 95.53 %, and 92.55 %, on the healthy subjects and the amputees, respectively. The experimental results also show that SW-RBF-ELM achieved the accuracy that is better than other well-known classifiers such as support vector machine (SVM), linear discriminant analysis (LDA) and k-nearest neighbor (kNN)