Mahmoud Elsisi

Bio: Mahmoud Elsisi is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Model predictive control & Computer science. The author has an hindex of 14, co-authored 36 publications receiving 447 citations. Previous affiliations of Mahmoud Elsisi include Banha University.

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.

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

Abstract: The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition‐based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.

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

A Comparative Study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in Estimating the Heating Load of Buildings’ Energy Efficiency for Smart City Planning

Abstract: Energy-efficiency is one of the critical issues in smart cities. It is an essential basis for optimizing smart cities planning. This study proposed four new artificial intelligence (AI) techniques for forecasting the heating load of buildings’ energy efficiency based on the potential of artificial neural network (ANN) and meta-heuristics algorithms, including artificial bee colony (ABC) optimization, particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and genetic algorithm (GA). They were abbreviated as ABC-ANN, PSO-ANN, ICA-ANN, and GA-ANN models; 837 buildings were considered and analyzed based on the influential parameters, such as glazing area distribution (GLAD), glazing area (GLA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), relative compactness (RC), for estimating heating load (HL). Three statistical criteria, such as root-mean-squared error (RMSE), coefficient determination (R2), and mean absolute error (MAE), were used to assess the potential of the aforementioned models. The results indicated that the GA-ANN model provided the highest performance in estimating the heating load of buildings’ energy efficiency, with an RMSE of 1.625, R2 of 0.980, and MAE of 0.798. The remaining models (i.e., PSO-ANN, ICA-ANN, ABC-ANN) yielded lower performance with RMSE of 1.932, 1.982, 1.878; R2 of 0.972, 0.970, 0.973; MAE of 1.027, 0.980, 0.957, respectively.

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Abstract: Recycled aggregate concrete is used as an alternative material in construction engineering, aiming to environmental protection and sustainable development. However, the compressive strength of this concrete material is considered as a crucial parameter and an important concern for construction engineers regarding its application. In the present work, the 28-days compressive strength of recycled aggregate concrete is investigated through four artificial intelligence techniques based on a meta-heuristic search of sociopolitical algorithm (i.e. ICA) and XGBoost, called the ICA-XGBoost model. Based on performance indices, the optimum among these developed models proved to be ICA-XGBoost model. Namely, findings demonstrated that the proposed ICA-XGBoost model performed better than the other models (i.e. ICA-ANN, ICA-SVR, and ICA-ANFIS models) in estimating compressive strength of recycled aggregate concrete. The suggested model can be used in construction engineering in order to ensure adequate mechanical performance of the recycled aggregate concrete and allow its safe use for building purposes.

Critical research areas on load frequency control issues in a deregulated power system: A state-of-the-art-of-review

Abstract: In this paper, an extensive literature survey on critical research areas of various load frequency control (LFC) methods in a deregulated power system with conventional and distributed power generators are emphasized. The main goal of LFC is to ensure zero steady state error for frequency and tie-line power deviations. This paper provides an overview of different types of deregulated power system structures, market models, contracts agreements and various control methodologies/techniques for mitigating the various LFC issues in a deregulated power system. Detailed analysis of various control methodologies based on classical control, robust and self tuning control and various soft computing control techniques are discussed. Finally, the investigations on incorporating fast acting energy storage devices such as Battery energy storage system (BESS), superconducting magnetic energy storage (SMES), Redox flow batteries (RFB) and Flexible AC transmission systems (FACTS) devices for mitigating the LFC problems in a deregulated power system are also addressed.

A review on rapid responsive energy storage technologies for frequency regulation in modern power systems

Abstract: A paradigm shift in power generation technologies is happening all over the world. This results in replacement of conventional synchronous machines with inertia less power electronic interfaced renewable energy sources (RES). The replacement by intermittent RES, i.e., solar PV and wind turbines, has two-fold effect on power systems: (i) reduction in inertia and (ii) intermittent generation, lead to the degradation of the frequency stability. In modern power system, the frequency regulation (FR) has become one of the most crucial challenges compared to conventional system because the inertia is reduced and both generation and demand are stochastic. The fast responsive energy storage technologies, i.e., battery energy storage, supercapacitor storage technology, flywheel energy storage, and superconducting magnetic energy storage are recognized as viable sources to provide FR in power system with high penetration of RES. The important aspects that are required to understand the applications of rapid responsive energy storage technologies for FR are modeling, planning (sizing and location of storage), and operation (control of storage). This paper comprehensively reviews these important aspects to understand the applications of fast responsive storage technologies more effectively for FR services. In addition, based on the real world experiences this paper highlights the gaps and limitations in the state-of-the-art practices. Moreover, this study also provides recommendations and future directions for researchers working on the applications of storage technologies providing FR services.