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Rao Muhammad Asif

Other affiliations: Capital University
Bio: Rao Muhammad Asif is an academic researcher from Government College University. The author has contributed to research in topics: Computer science & Photovoltaic system. The author has an hindex of 6, co-authored 26 publications receiving 98 citations. Previous affiliations of Rao Muhammad Asif include Capital University.

Papers
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Proceedings ArticleDOI
01 Sep 2017
TL;DR: In this paper, the authors present the idea of automatic irrigation method and the following research sustains this idea, which is done through assistance of soil moisture sensors, apart from soil moisture sensor, Humidity and temperature sensors are also used to make the process more advance.
Abstract: The economy being highly based on agriculture demands innovative and reliable methods of irrigation. The shortcomings of manual methods of irrigation can be rectified using automated process. This paper presents the idea of automatic irrigation method and the following research sustains this idea. The task of automatic irrigation is done through assistance of soil moisture sensors. In the project, apart from soil moisture sensor. Humidity and temperature sensors are also used to make the process more advance. The proposed design also has the feature of GSM which makes this system wireless. The electricity required by components is provided through solar panels hence this liberates us from interrupted power supply due to load shedding. The water content is constantly judged and whenever moisture level of soil gets low, the system sends a signal to motors asking them to turn on. The motors automatically stop after soil reaches its maximum upper threshold value which is decided by user. Every time the motor starts or stops automatically, the user will get a SMS about the status of operation. The major advantages of the project include avoidance from water wastage, growth of plants to their maximum potential, less chances of error due to less labor and uninterrupted supply of water due to solar energy.

35 citations

Journal ArticleDOI
03 Sep 2020
TL;DR: The control system is proposed by the GA genetic algorithm that optimizes the output energy of the PV system by adjusting the spatial angles of the solar panel in both vertical and horizontal axes using the Matlab software to capture the most sun and maximize output energy.
Abstract: In recent years, because of the limitations of fossil fuels and emissions resulting from the use of photovoltaic cells increase. Due to the changing state of the sun, solar cells must follow the sun's radiation to receive more energy. But, in this research, the modeling and analysis of the solar tracking system were carried out to obtain the optimal angle in photovoltaic systems for generating maximum power using genetic algorithm (GA). In this paper, the control system is proposed by the GA genetic algorithm that optimizes the output energy of the PV system by adjusting the spatial angles of the solar panel in both vertical and horizontal axes. In this method, without the need for additional hardware, the optimal panel position angles are calculated by using the Matlab software to capture the most sun and maximize output energy. The main advantage is that the system operates discretely during operation and losses are reduced, as well as in the clouds, solar radiation is received and the output energy rises. The important results of this study can be the system is optimized, the output power of the photovoltaic system in a fixed array mode increases by 15.85%.

23 citations

Journal ArticleDOI
TL;DR: This paper presents a rapid, effective, and linear incremental conductance (IC) algorithm for chasing the maximum power point of the grid-tied photovoltaic (PV) array.
Abstract: This paper presents a rapid, effective, and linear incremental conductance (IC) algorithm for chasing the maximum power point (MPP) of the grid-tied photovoltaic (PV) array. A technique has been pr...

22 citations

Journal ArticleDOI
TL;DR: An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed that improves the LF by optimizing the mean absolute percentage error (MAPE).
Abstract: Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.

19 citations


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Journal ArticleDOI
TL;DR: In this paper, different available MPPT algorithms are described for extracting maximum power which are classified according to the power measurement i.e. direct or indirect power controller and compared in terms of complexity, wind speed requirement, prior training, speed responses, etc.
Abstract: Wind power is the most reliable and developed renewable energy source over past decades. With the rapid penetration of the wind generators in the power system grid, it is very essential to utilize the maximum available power from the wind and to operate the wind turbine (WT) at its maximal energy conversion output. For this, the wind energy conversion system (WECS) has to track or operate at the maximum power point (MPP). A decent variety of publication report on various maximum power point tracking (MPPT) algorithms for a WECS. However, making a choice on an exact MPPT algorithm for a particular case require sufficient proficiency because each algorithm has its own merits and demerits. For this reason, an appropriate review of those algorithms is essential. However, only a few attempts have been made in this concern. In this paper, different available MPPT algorithms are described for extracting maximum power which are classified according to the power measurement i.e. direct or indirect power controller. Merits, demerits and comprehensive comparison of the different MPPT algorithms also highlighted in the terms of complexity, wind speed requirement, prior training, speed responses, etc. and also the ability to acquire the maximal energy output. This paper serves as a proper reference for future MPPT users in selecting appropriate MPPT algorithm for their requirement.

408 citations

01 Jul 2015
TL;DR: The results from different realistic case studies show the effectiveness of the proposed controller in minimizing the household's daily electricity bill while preserving comfort level, as well as preventing creation of new least-price peaks.
Abstract: This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer�€™s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user�€™s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer�€™s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller to minimize the household�€™s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks.

277 citations

Journal ArticleDOI
01 Jan 2020
TL;DR: This study gives a concise classification and evaluation review of all the applied Maximum Power Point Tracking methods and provides an accessible reference to undertake mass research works in MPPT in the near future.
Abstract: Photovoltaic (PV) systems have witnessed a rapid increment, the yield mainly relies on the working condition. In most cases, it is hard to obtain the optimal yield. Therefore, Maximum Power Point Tracking (MPPT) controllers witness much attention as an important optimization field of PV systems. These controllers employ different algorithms and they vary in their efficiency, performance, modernity, complexity, and tracking speed. MPPT controllers have witnessed a rapid improvement, they can be generally classified as conventional and advanced methods. Conventional methods are relatively simple but they can't distinguish between the local and global peaks if partial shading occurs, therefore, their efficiency is relatively low. Advanced tracking methods are widely used due to their superior efficiency. Due to the limitation of the singular conventional and advanced methods, hybrid methods find their way to solve these limitations. Selecting the finest MPPT method is still an open issue, this issue can be solved by implementing a survey of the applied methods. This study gives a concise classification and evaluation review of all the applied MPPT methods. This study also provides an accessible reference to undertake mass research works in MPPT in the near future.

91 citations

Journal ArticleDOI
TL;DR: How digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control is discussed.
Abstract: Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

53 citations