A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement

A Novel, Intelligent control of a Unified Power Quality Conditioner (UPQC), coupled with a Photovoltaic (PV) system, is proposed in this work. It enhances the decarbonizes clean energy generation and maintains Power Quality (PQ) to the grid. In PV integrated UPQC, Crow Search Algorithm (CSA) assisted Perturb and Observation (P&O) Maximum Power Point Tracking (MPPT) technique. A d-q theory-based control is employed with the assistance of a Proportional Integral (PI) controller for controlling the working of UPQC and maintaining the power quality. The dynamic working of the PV-based UPQC is evaluated based on simulation outcomes attained from MATLAB

Positioning Control of an Antagonistic Pneumatic Muscle Actuated System using Feedforward Compensation with Cascaded Control Scheme

This paper presents a feedforward compensation with cascaded control scheme (FFC) for the positioning control of a vertical antagonistic based pneumatic muscle actuated (PMA) system. Owing to the inherent nonlinearities and time varying parameters exhibited by PMA, conventional fixed controllers unable to demonstrate high positioning performance. Hence, the feedforward compensation with cascaded control scheme is proposed whereby the scheme includes a PID controller coupled with nonlinear control elements. The proposed scheme has a simple control structure in addition to its straightforward design procedures. Though there are nonlinear control elements involved, these elements are derived from the open loop system responses that does not requires any accurate known parameters. Performance of the FFC scheme are then evaluated experimentally and compared to a PID controller with feedforward compensation (FF-PID) in point-to-point motion of different step heights. Overall, the experimental results show that the effectiveness of the proposed FFC scheme in reducing the steady state error to zero in comparison to FF-PID controller for all cases of step heights examined

Power quality enhancement in a grid-integrated photovoltaic system using hybrid techniques

In recent years, the photovoltaic (PV) system was designed to supply solar power through photovoltaic arrays. The PV generator exhibits nonlinear voltage–current characteristics and its maximum power point tracking (MPPT), which varies with temperature and radiation. In the event of non-uniform solar insolation, several multiple maximum power points (MPPs) appear in the power–voltage characteristic of the PV module. Thus, a hybrid combination of binary particle swarm optimization (BPSO) and grey wolf optimization (GWO) is proposed herein to handle multiple MPPs. This combination is nowhere found in the literature, so the author chose this hybrid technique; and the main advantage of the proposed method is its ability to predict the global MPP (GMPP) in a very short time and to maintain accurate performance, even under different environmental conditions. Moreover, a 31-level multilevel inverter (MLI) was designed with a lower blocking voltage process to reduce the complexity of the circuit design. The entire system was executed in the MATLAB platform to examine the performance of the PV system, which was shown to extract a maximum power of 92.930 kW. The simulation design clearly showed that the proposed method with a 31-level MLI achieved better results in terms of total harmonic distortion (THD) at 1.60%, which is less when compared to the existing genetic algorithm (GA) and artificial neural networks (ANNs).The authors would like to thank the Spanish Ministerio de Ciencia, Innovación y Universidades (MICINN)‐Agencia Estatal de Investigación (AEI) and the European Regional De‐ velopment Funds (ERDF), by grant PGC2018‐098946‐B‐I00 funded by MCIN/ AEI /10.13039/501100011033/ and by ERDF.ERDF A way of making Europe.Peer ReviewedPostprint (published version

Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf

Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

Recent Trends in Image Encryption: A Review

Security of multimedia data is gaining acceptance owing to the growth and acceptability of images in various applications and in telecommunication. Encryption is one of the ways to ensure high security of images as they are used in many fields such as in secure medical imaging services, military intelligence, internet and intranet communication, e-banking etc. These images are stored or transmitted through a network; hence the security of such image data is important. In this work, recently developed encryption techniques are studied and analyzed to promote further development of more encryption methods to ensure additional security and versatility. All the techniques reviewed came into existence within the last five years (2011-2015) and are found to be useful for the present day encryption applications. Each technique is unique in its own way, which might be suitable for different applications. As time goes on, new encryption techniques are evolving. Hence, fast and secure conventional encryption techniques will always be needed in applications requiring high rate of security