Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system

Particle Swarm Optimization (PSO) is widely used in maximum power point tracking (MPPT) of photovoltaic (PV) energy systems. Nevertheless, this technique suffers from two main problems in the case of partial shading conditions (PSCs). The first problem is that PSO is a time invariant optimization technique that cannot follow the dynamic global peak (GP) under time variant shading patterns (SPs) and sticks to the first GP that occurs at the beginning. This problem can be solved by dispersing the PSO particles using two new techniques introduced in this paper. The two new proposed PSO re-initialization techniques are to disperse the particles upon the SP changes and the other one is upon a predefined time (PDT). The second problem is regarding the high oscillations around steady state, which can be solved by using fuzzy logic controller (FLC) to fine-tune the output power and voltage from the PV system. The new contribution of this paper is the hybrid PSO-FLC with two PSO particles dispersing techniques that is able to solve the two previous mentioned problems effectively and improve the performance of the PV system in both normal and PSCs. A detailed list of comparisons between hybrid PSO-FLC and original PSO using the two proposed methodologies are achieved. The results prove the superior performance of hybrid PSO-FLC compared to PSO in terms of efficiency, accuracy, oscillations reduction around steady state and soft tuning of the GP tracked

Modeling and control of single-stage quadratic-boost split source inverters

This paper aims to develop the recently introduced Spilt-Source Inverter (SSI) topology to improve its boosting characteristics. New SSI topologies with high voltage gain are introduced in this paper. The proposed converters square the basic SSI’s boosting factor by utilizing an additional inductor, capacitor, and two diodes. Thus, the proposed converters are called Quadratic-Boost (or Square-Boost) SSIs (QBIs or SBIs). Four different QBI topologies are presented. One with continuous input current (CC-QBI), and the other draws a discontinuous input current (DC-QBI) but with reduced capacitor voltage stresses. This paper also introduces the small-signal model of the CC-QBI using state variables perturbance. Based on this model, the closed-loop voltage and current control approach of the dc-boosting factor are designed. Moreover, a modified space vector modulation (MSVM) scheme is presented to reduce the input current ripples. To evaluate the performance of the proposed topologies, a comparative study between them and the other counterpart from different perspectives is introduced. It can be found that the CC-QBI topology has superior boosting characteristics when operating with low input voltage compared with their counterparts. It has a higher boosting capability, lower capacitor voltages, and semiconductor stresses, especially when high voltage gains are required. These merits make the proposed topologies convenient to the Photovoltaic and Fuel-Cell systems. Finally, the feasibility of the suggested topology and the introduced mathematical model is verified via simulation and experimental results, which show good accordance with the theoretical analysis. AuthorScopu

Mitigation Voltage Sag Using DVR with Power Distribution Networks for Enhancing the Power System Quality

The fast developments in power electronic technology have made it possible to mitigate voltage disturbances in power system. Among the voltage disturbances challenging the industry, the voltage sags are considered the most important problem to the sensitive loads. The Dynamic Voltage Restorer (DVR) is mainly used in a utility grid to protect the sensitive loads from power quality problems, such as voltage sags and swells. However, the effectiveness of the DVR can wane under unbalanced grid voltage conditions. DVR is recognized to be the best effective solution to overcome this problem. The primary advantage of the DVR is keeping the users always on-line with high quality constant voltage maintaining the continuity of production. In this paper, the usefulness of including DVR in distribution system for the purpose of voltage sag and swell mitigation is described. This paper describes the DVR operation strategies and control. The DVR operation with the distribution networks is found very efficient for detecting and clearing any power quality disturbance in distribution systems. Results of simulation using MATLAB/Simulink are demonstrated to prove the usefulness of this DVR design and operation to enhance the power system quality

Comparative study of back-stepping controller and super twisting sliding mode controller for indirect power control of wind generator

© 2021 Springer. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s13198-019-00905-7This paper presents the application nonlinear control to regulate the rotor currents and control the active and reactive powers generated by the Doubly Fed Induction Generator used in the Wind Energy Conversion System (WECS). The proposed control strategies are based on Lyapunov stability theory and include back-stepping control (BSC) and super-twisting sliding mode control. The overall WECS model and control scheme are developed in MATLAB/Simulink and the simulation results have shown that the BSC leads to superior performance and improved transient response as compared to the STSMC controller.Peer reviewe

Novel Hybrid Mexican Axolotl Optimization with Fuzzy Logic for Maximum Power Point Tracker of Partially Shaded Photovoltaic Systems

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms

A Novel Design and Optimization Software for Autonomous PV/Wind/Battery Hybrid Power Systems

This paper introduces a design and optimization computer simulation program for autonomous hybrid PV/wind/battery energy system. The main function of the new proposed computer program is to determine the optimum size of each component of the hybrid energy system for the lowest price of kWh generated and the best loss of load probability at highest reliability. This computer program uses the hourly wind speed, hourly radiation, and hourly load power with several numbers of wind turbine (WT) and PV module types. The proposed computer program changes the penetration ratio of wind/PV with certain increments and calculates the required size of all components and the optimum battery size to get the predefined lowest acceptable probability. This computer program has been designed in flexible fashion that is not available in market available software like HOMER and RETScreen. Actual data for Saudi sites have been used with this computer program. The data obtained have been compared with these market available software. The comparison shows the superiority of this computer program in the optimal design of the autonomous PV/wind/battery hybrid system. The proposed computer program performed the optimal design steps in very short time and with accurate results. Many valuable results can be extracted from this computer program that can help researchers and decision makers

Upgrading Conventional Power System for Accommodating Electric Vehicle through Demand Side Management and V2G Concepts

The continually increasing fossil fuel prices, the dwindling of these fuels, and the bad environmental effects which mainly contribute to global warming phenomena are the main motives to replace conventional transportation means to electric. Charging electric vehicles (EVs) from renewable energy systems (RES) substantially avoids the side effects of using fossil fuels. The higher the increase in the number of EVs the greater the challenge to the reliability of the conventional power system. Increasing charging connections for EVs to the power system may cause serious problems to the power system, such as voltage fluctuations, contingencies in transmission lines, and loss increases. This paper introduces a novel strategy to not only replace the drawbacks of the EV charging stations on the power system’s stability and reliability, but also to enhance the power system’s performance. This improvement can be achieved using a smart demand side management (DSM) strategy and vehicle to grid (V2G) concepts. The use of DSM increases the correlation between the loads and the available generation from the RES. Besides this, the use of DSM, and the use of V2G concepts, also helps in adding a backup for the power system by consuming surplus power during the high generation period and supplying stored energy to the power system during shortage in generation. The IEEE 30 bus system was used as an example of an existing power system where each load busbar was connected to a smart EV charging station (SEVCS). The performance of the system with and without the novel DSM and V2G concepts was compared to validate the superiority of the concepts in improving the performance of the power system. The use of modified particle swarm optimization in optimal sizing and optimal load flow reduced the cost of energy and the losses of the power system. The use of the smart DSM and V2G concepts substantially improved the voltage profile, the transmission line losses, the fuel cost of conventional power systems, and the stability of the power system

Upgrading Conventional Power System for Accommodating Electric Vehicle through Demand Side Management and V2G Concepts

The continually increasing fossil fuel prices, the dwindling of these fuels, and the bad environmental effects which mainly contribute to global warming phenomena are the main motives to replace conventional transportation means to electric. Charging electric vehicles (EVs) from renewable energy systems (RES) substantially avoids the side effects of using fossil fuels. The higher the increase in the number of EVs the greater the challenge to the reliability of the conventional power system. Increasing charging connections for EVs to the power system may cause serious problems to the power system, such as voltage fluctuations, contingencies in transmission lines, and loss increases. This paper introduces a novel strategy to not only replace the drawbacks of the EV charging stations on the power system’s stability and reliability, but also to enhance the power system’s performance. This improvement can be achieved using a smart demand side management (DSM) strategy and vehicle to grid (V2G) concepts. The use of DSM increases the correlation between the loads and the available generation from the RES. Besides this, the use of DSM, and the use of V2G concepts, also helps in adding a backup for the power system by consuming surplus power during the high generation period and supplying stored energy to the power system during shortage in generation. The IEEE 30 bus system was used as an example of an existing power system where each load busbar was connected to a smart EV charging station (SEVCS). The performance of the system with and without the novel DSM and V2G concepts was compared to validate the superiority of the concepts in improving the performance of the power system. The use of modified particle swarm optimization in optimal sizing and optimal load flow reduced the cost of energy and the losses of the power system. The use of the smart DSM and V2G concepts substantially improved the voltage profile, the transmission line losses, the fuel cost of conventional power systems, and the stability of the power system

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six