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Arsalan Pervez

Bio: Arsalan Pervez is an academic researcher. The author has contributed to research in topics: Maximum power principle & Maximum power point tracking. The author has an hindex of 3, co-authored 4 publications receiving 11 citations.

Papers
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Journal ArticleDOI
TL;DR: A novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability is proposed.
Abstract: When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such as perturb and observe (P&O) and hill-climbing (HC)—are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms—such as an artificial neural network (ANN) and fuzzy logic control (FLC)—have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB® is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.

31 citations

Book ChapterDOI
01 Jan 2021
TL;DR: A modified PSO algorithm known PSO with constriction factor (PSO-CF) is used for tracking maximum power point (MPP) of a solar PV array with better convergence time and efficiency of convergence to the maximum power.
Abstract: In today’s competitive world, the advancements in technology are taking place at a very rapid rate. Due to this advancement in technology and higher population growth, the existing energy-producing sources are depleting very fast. In order to eliminate such issues, there comes into consideration the use of non-renewable energy systems. But the adoption of these systems requires their proper and efficient utilization. In this paper, we have proposed a method to utilize a solar PV system efficiently with a better convergence time and efficiency of convergence to the maximum power. A modified PSO algorithm known PSO with constriction factor (PSO-CF) is used for tracking maximum power point (MPP) of a solar PV array.

6 citations

Book ChapterDOI
01 Jan 2021
TL;DR: In this paper, a nature-inspired algorithm Coyote Optimization Algorithm (COA) is used to track maximum power at the output terminal of the solar PV cell, which is considered one of the most valuable renewable energy sources.
Abstract: The energy crisis due to population growth and advancements in technology made it mandatory for the researchers to develop some methods that can be helpful in prevention of these crisis. In order to eliminate the day by day growing energy issues, making use of renewable energy sources is considered one of the alternatives. Among these energy sources, solar PV systems are considered one of the most valuable. But the issue with these cells is that they cannot work smartly and cannot detect the optimal power at their output. Hence, in this paper a nature-inspired algorithm Coyote Optimization Algorithm (COA) is used to track maximum power at the output terminal of the solar PV cell.

4 citations

Proceedings ArticleDOI
01 Nov 2019
TL;DR: To track and determine the maximum power amongst all the available power peaks in this paper a Nature-Inspired algorithm (PSODTVAC) is employed and it was observed that the proposed algorithm outperformed these existing algorithms.
Abstract: Solar PV systems are increasingly becoming an indispensable source of energy owing to their simple construction, pollution free operation, and comparatively low operating costs. The solar PV systems provide an eco-friendly, cheap and almost omnipresent availability of energy throughout the world. Although, seemingly a very lucrative and easily available source of energy, these systems are not very widely implemented as Solar PV systems are not very efficient and major power losses occur. Due to clouds, skyscrapers in metropolitan cities, huge trees in rural areas etc. the sunlight is inhibited this leads to the sun rays striking the surface of the PV cell in an uneven fashion thereby leading to high power losses where the intensity of light is low and high power peaks where the intensity of light is high. Bypass diodes are employed to mitigate the effect of partial shading, but their application causes multiple peaks of power at the output. To track and determine the maximum power amongst all the available power peaks in this paper a Nature-Inspired algorithm (PSODTVAC) is employed. This algorithm is compared with other established algorithms like the gravitational search algorithm (GSA), Standard PSO (PSOSTD) and PSO with time varying acceleration (PSOTVAC) and it was observed that the proposed algorithm outperformed these existing algorithms.
Journal ArticleDOI
01 Oct 2022
TL;DR: In this article , the impact of sustainable supply chain management in the Pakistani construction industry and how it helps with the performance and development of the construction industry is discussed, and the results derived from the test show that SCM has a moderate impact leading to leading to increased performance.
Abstract: This paper aims to introduce the impact of sustainable supply chain management in the Pakistani construction industry and how it helps with the performance and Development of the construction industry. It aims to elaborate on and emphasize how a sustainable supply chain will be more effective the traditional construction management and its supply chains. A quantitative research method with a deductive approach was used to test research hypotheses. A sample of 154 respondents using a questionnaire responded for data analysis. In Pakistan, the market for a construction company is primarily local based and highly volatile. The assessment is carried out to determine the level of significance and degree of impact of supply chain management (SCM) and sustainability on the construction industry's performance. The results derived from the test show that SCM has a moderate impact leading to leading to increased performance. The lack of time and budget limited the focus to Karachi-based construction businesses, leaving other construction businesses in Pakistan as not relevant. The paper primarily focused on the Pakistan-based construction industry, and the articles that are a source for this study generated knowledge regarding various issues and opportunities associated with supply chain management in the Karachi-based construction industry.

Cited by
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Journal ArticleDOI
TL;DR: In this article , a hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO-PSO) techniques is proposed to extract the maximum power from the PV array, especially when they operate under partial shading conditions.
Abstract: Abstract One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO–PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO–PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.

19 citations

Posted ContentDOI
TL;DR: An adaptive MPPT of a stand-alone PV system using an updated PI controller optimized by harmony search (HS) and the validity of the simulation results obtained at different irradiance and temperature levels is tested.
Abstract: Solar photovoltaic (PV) energy has met great attention in the electrical power generation field for its many advantages in both on and off-grid applications. The requirement for higher proficiency from the PV system to reap the energy requires maximum power point tracking techniques (MPPT). This paper presents an adaptive MPPT of a stand-alone PV system using an updated PI controller optimized by harmony search (HS). A lookup table is formed for the temperature and irradiance with the corresponding voltage at MPP (VMPP). This voltage is considered as the updated reference voltage required for MPP at each temperature and irradiance. The difference between this updated reference voltage at MPP and the variable PV voltage due to changing the environmental conditions is used to stimulate PI controller optimized by HS to update the duty cycle (D) of the DC-DC converter. The temperature, irradiance, and corresponding duty cycle at MPP are utilized to convert this MPP technique into an adaptive one without the PI controllers' need. An experimental implementation of the proposed adaptive MPPT is introduced to test the simulation results' validity at different irradiance and temperature levels.

13 citations

Journal ArticleDOI
TL;DR: In this paper , the authors proposed an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking, and the experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses.
Abstract: The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Nature-inspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions.

11 citations

Posted ContentDOI
TL;DR: In this article , a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS), and the results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the Particle Swarm Optimization (PSO) based MPPT.
Abstract: Of all the renewable energy sources, solar photovoltaic (PV) power is estimated to be a popular source due to several advantages such as its free availability, absence of rotating parts, integration to building such as rooftops, and less maintenance cost. The nonlinear current-voltage (I–V) characteristics and power generated from a PV array primarily depend on solar insolation/irradiation and panel temperature. The extracted PV output power is influenced by the accuracy with which the nonlinear power–voltage (P–V) characteristic curve is traced by the maximum power point tracking (MPPT) controller. In this paper, a bio-inspired roach infestation optimization (RIO) algorithm is proposed to extract the maximum power from the PV system (PVS). To validate the usefulness of the RIO MPPT algorithm, MATLAB/Simulink simulations are performed under varying environmental conditions, for example, step changes in solar irradiance, and partial shading of the PV array. Furthermore, the search performance of the RIO algorithm is examined on different unconstrained benchmark functions, and it is that realized that the RIO algorithm has improved convergence characteristics in terms of finding the optimal solution than Particle swarm optimization (PSO). The results demonstrated that the RIO-based MPPT performs remarkably in tracking with high accuracy as the PSO-based MPPT.

10 citations

Journal ArticleDOI
TL;DR: In this paper , the authors proposed an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking, and the experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses.
Abstract: The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Nature-inspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions.

10 citations