Mohamed M. F. Darwish

Bio: Mohamed M. F. Darwish is an academic researcher from Banha University. The author has contributed to research in topics: Computer science & Dielectric. The author has an hindex of 14, co-authored 39 publications receiving 451 citations. Previous affiliations of Mohamed M. F. Darwish include Aalto University.

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

Abstract: Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT The proposed method is verified and tested through the simulation of a grid-connected PV system model The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions

Robust Design of ANFIS-Based Blade Pitch Controller for Wind Energy Conversion Systems Against Wind Speed Fluctuations

Abstract: Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite–Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings.

Abstract: Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper's innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

Promising MPPT Methods Combining Metaheuristic, Fuzzy-Logic and ANN Techniques for Grid-Connected Photovoltaic.

Abstract: This paper addresses the improvement of tracking of the maximum power point upon the variations of the environmental conditions and hence improving photovoltaic efficiency Rather than the traditional methods of maximum power point tracking, artificial intelligence is utilized to design a high-performance maximum power point tracking control system In this paper, two artificial intelligence-based maximum power point tracking systems are proposed for grid-connected photovoltaic units The first design is based on an optimized fuzzy logic control using genetic algorithm and particle swarm optimization for the maximum power point tracking system In turn, the second design depends on the genetic algorithm-based artificial neural network Each of the two artificial intelligence-based systems has its privileged response according to the solar radiation and temperature levels Then, a novel combination of the two designs is introduced to maximize the efficiency of the maximum power point tracking system The novelty of this paper is to employ the metaheuristic optimization technique with the well-known artificial intelligence techniques to provide a better tracking system to be used to harvest the maximum possible power from photovoltaic (PV) arrays To affirm the efficiency of the proposed tracking systems, their simulation results are compared with some conventional tracking methods from the literature under different conditions The findings emphasize their superiority in terms of tracking speed and output DC power, which also improve photovoltaic system efficiency

Towards Secured Online Monitoring for Digitalized GIS Against Cyber-Attacks Based on IoT and Machine Learning

Abstract: Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 40 However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision–making about the GIS status

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Abstract: Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

An Efficient Fuzzy-Logic Based Variable-Step Incremental Conductance MPPT Method for Grid-Connected PV Systems

Abstract: Recently, solar energy has been intensively employed in power systems, especially using the photovoltaic (PV) generation units In this regard, this paper proposes a novel design of a fuzzy logic based algorithm for varying the step size of the incremental conductance (INC) maximum power point tracking (MPPT) method for PV In the proposed method, a variable voltage step size is estimated according to the degree of ascent or descent of the power-voltage relation For this purpose, a novel unique treatment is proposed based on introducing five effective regions around the point of maximum PV power To vary the step size of the duty cycle, a fuzzy logic system is developed according to the locations of the fuzzy inputs regarding the five regions The developed fuzzy inputs are inspired from the slope of the power-voltage relation, namely the current-voltage ratio and its derivatives whereas appropriate membership functions and fuzzy rules are designed The benefit of the proposed method is that the MPPT efficiency is improved for varying the step size of the incremental conductance method, thanks to the effective coordination between the proposed fuzzy logic based algorithm and the INC method The output DC power of the PV array and the tracking speed are presented as indices for illustrating the improvement achieved in MPPT The proposed method is verified and tested through the simulation of a grid-connected PV system model The simulation results reveal a valuable improvement in static and dynamic responses over that of the traditional INC method with the variation of the environmental conditions Further, it enhances the output dc power and reduce the convergence time to reach the steady state condition with intermittent environmental conditions