IJE_Vol 07 N 02

Permanent URI for this collectionhttps://archives.univ-eloued.dz/handle/123456789/23075

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ANFIS Models for Fault Detection and Isolation in the Drive Train of a Wind Turbine
(جامعة الوادي - university of el oued, 2022-12-25) Zemal, Zakariai; Cherroun, Lakhmissi; Hadroug, Hafaifa
The paper aims to improve the fault detection and isolation process in wind turbine systems by developing intelligent systems that can effectively identify and isolate faults. Specifically, the paper focuses on the drive train part of a horizontal axis wind turbine machine. The proposed fault diagnostic strategy is designed using an adaptive neural fuzzy inference system (ANFIS), which is a type of artificial neural network that combines the advantages of both fuzzy logic and neural networks. The ANFIS is used to generate residuals that occur after faults have been detected, and to determine the appropriate thresholds needed to correctly detect faults. The simulation results show that the proposed fault diagnostic strategy is effective in detecting faults in the drive train part of the wind turbine system. By using intelligent systems such as ANFIS, the fault detection process can be automated and streamlined, potentially reducing maintenance costs and improving the overall performance and efficiency of wind turbine systems.
Effective Modeling of Photovoltaic Modules Using Sailfish Optimizer
(جامعة الوادي - university of el oued, 2022-06-22) Danoune, M.B; Djafour, A; Rehouma, Y; Degla., .A
The current study proposes a novel meta-heuristic technique called sailfish optimizer (SFO) to design reliable photovoltaic (PV) modeling models. Unlike others, the proposed technique employs two populations (prey and predator) instead of one to effectively reach the desired solution. This unique propriety can substantially augment the probability of locating the global optimum as well as accelerating the search process. Moreover, to show the efficacy of the algorithm, the results are compared with some literature techniques such as Salp-Swarm-Optimizer (SSA), Whale Optimization (WOA), Artificial-Bee-Colony (ABC), and Particle-Swarm Optimization (PSO) methods. Eventually, the proposed SFO algorithm demonstrated a remarkable amelioration in terms of accuracy with Root-Mean-Square-Error of 13E-3 A
Enhancing the potential of smart building for general hospital: a case study in Malaysian hospital
(جامعة الوادي - university of el oued, 2022-12-20) Baharuddin, Mohd Faisal; Fazlizan, Chin Haw Lim; Fazlizan, Ahmad
Hospital Pulau Pinang is the general hospital in Malaysia which targeting energy savings of 10% within five years from 2015 and other sustainability targets such as 3-star Energy Management Gold Standard and Green Building Certification. The targets are beneficial for the hospital itself to establish the Smart Building Program to improve its energy efficiency concurrent with the green policy of the Ministry of Health Malaysia and Sustainable Development Goals by the United Nations. This paper reviews the background of Hospital Pulau Pinang energy data , energy consumption trending, energy-saving trending, and energy conservation measures taken for the hospital from 2015 to December 2021.The yearly energy consumption baseline taken in 2016 was 27,496,731.00 kWh. It reduced significantly to 21,356,063 kWh in 2021 due to energy conservation measures. As a result, Hospital Pulau Pinang has achieved energy-saving about 16% at approximately RM7.3 million reduction in operational expenditure. The main objective of this paper is to provide further potential energy savings by studying the energy reduction by implementing solar photovoltaics using the simulation method. The simulation method can predict that Hospital Pulau Pinang can achieve another 5,130,000 kWh energy savings annually. This type of simulation has never been done before at a public hospital, and it will give further enhancing strategies to the Smart Building Program itself. Furthermore, the potential of smart building can be maximized to the next level by simulation, which helps the hospital energy committee make the potential decision on the energy-saving investment.
Generating temperature cycle profiles of different solar photovoltaic module technologies from in-situ conditions for accurate prediction of thermomechanical degradation
(جامعة الوادي - university of el oued, 2022-12-05) Sampah, Bebeto Nii Sampa; Frank, K; Nyarko, A; Asaaga, Benjamin Atribawuni; Aggor, Jefferson
The IEC61215 TC200 is a rigorous approval thermal cycling test process that assesses the reliability of solar photovoltaic modules and offers a 25-year lifetime guarantee. However, previous research has shown that installed solar photovoltaic modules experience different rates of degradation depending on the location and climate with most research focused on crystalline silicon. In this study, outdoor weathering data obtained from a rig set up in Kumasi, Ghana for the year 2014, is used to generate thermal cycles for 5 different technologies including monocrystalline, polycrystalline, and amorphous silicon, Copper Indium Gallium Selenide (CIGS) and Heterojunction-With-Intrinsic-Thin-Layer (HIT). From the results, the highest yearly average of the maximum and minimum temperatures, and ramp rates of 54.8oC, 26.1oC, and 6.05oC/h respectively are recorded in CIGS. Polycrystalline recorded the least temperatures of 45.2°C and 23.9°C while HIT recorded the least ramp rate of 4.45°C /h. A comparison between the 2014 and the IEC61215 thermal cycles show extremely wide differences which could explain the higher degradation rates and shorter life of installed solar photovoltaic modules. The procedure adopted in this research can be repeated at different locations to obtain technology-specific thermal cycling profiles to evaluate the thermomechanical damage and predict the life of different solar photovoltaic modules
Heat exchanger design for the production of synthesized gold nanoparticles
(جامعة الوادي - university of el oued, 2020-12-10) Erlangga, Thyta Medina Salsabila; Nandiyanto, Bayu Dani; Ragadhita, Risti; Kurniawan, Teguh
This study aims to develop and analyze the design of heat exchangers in the production of gold nanoparticles (AuNPs) by the biosynthesis method using Sargassum horneri (SH) extract. The simple design of this heat exchanger (HE) uses the shell and tube type, the one-pass tube, and the fluids are water. These specifications pertain to the design of a heat exchanger (HE). The tube length of 4.267 m, shell diameter of 254 mm, outer tube diameter of 22.225 mm, inner tube diameter of 21.184 mm, and wall thickness of 2.1082 mm describe the physical dimensions of the tubes in the heat exchanger. The pitch tube of 31.75 mm refers to the distance between the centers of adjacent tubes in the heat exchanger. Based on manual calculations using Microsoft Excel, the results show that this design has laminar flow as indicated by the Reynolds value. In addition, the HE designs has an effectiveness value of 98.98% with an NTU value of 11.50. In this study, the HE designs results have a high effectiveness value, so it can be considered effective for use in producing gold nanoparticles with SH extract. Therefore, this HE designs analysis can be used as a learning medium in the HE designs process, the operating mechanism, and the performance analysis of the HE.
Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study
(جامعة الوادي - university of el oued, 2022-11-06) Dahmani, Abdennasser; Ammi, Yamina; Hanini, Salah
Global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the western region of Algeria. The neural network-optimal model was trained and tested using 80 %, and 20 % of the whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feed forward neural network with Regularization Bayesienne (trainbr) training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and a coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions. Keywords: Prediction, Global Solar Radiation, Artificial Neural Networks
Optimal Controller Design and Dynamic Performance Enhancement of High Step-up Non-Isolated DC-DC Converter for Electric Vehicle Charging Applications
(جامعة الوادي - university of el oued, 2022-12-05) Narasipuram, Rajanand Patnaik; Muthukuri , Narendra Kumar; Mopidevi, Subbarao
Ideally, traditional boost converters can achieve a high conversion ratio with a high-duty cycle. But, in regular practice, due to low conversion efficiency, RR reverse-recovery, and EMI (electromagnetic interference) problems, the high voltage gain cannot be performed, whereas CIBC (coupled inductor-based converters) can achieve high voltage gain by re-adjusting the turn ratios. Even though the leakage inductor of the CI (coupled inductor) makes some problems like voltage spikes on the main connectivity switch, high power dissipation, and voltage pressure can be minimized by voltage clamp. In this paper, a non-isolated DC-DC converter with high voltage gain is demonstrated with 3 diodes, 3 capacitors, 1-inductor, and a coupled inductor. The main inductor is connected to the input to decrease the current ripple. The voltage stress at main switch S is shared by diode D1 and capacitor C1 and the main switch is turned ON under zero current, hence it turns to low switching losses. This paper proposes two controllers like proportional-integral (PI) controller and fuzzy logic (FLC) for dc-dc converter. Furthermore, it demonstrates the operation, design, mathematical analysis, and performance of DC-DC converter using controllers for efficient operation of the system is performed using simulations in MATLAB 2012b
Structural analysis of wind blades with and without power control
(جامعة الوادي - university of el oued, 2022-12-05) Kouadria, B; Debbache., M
–The blade is the principal element in the wind rotor mechanism. the efficiency of the wind turbine depends on the optimal geometry of this element, as well as its structural configuration. This work presents a contribution to wind blade structural design. the blade structure was evaluated without the control power operating case and with the power control case. In this case, an 80KW horizontal axis wind turbine design was proposed. the process begins with design and aerodynamic analysis based on blade element momentum theory by using Qblade software to determine the blade geometry. The blade structure was defined by the NuMad package, it is composed of two parts. the shell part is four layers of composite materials and the rib part has a sandwich panel shape. The evolution of structure was done by the Co-Blade package. The results show a decreasing in displacement decreased to 64% at the tip of the blades which leads to the stress at the leading and trailing edge being negligible. That proves the importance of a control power system in the protection of the blade structure and turbine generator in the operating case under high wind velocity and ensures the stability of the power output value.

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