Photovoltaic (PV) power generation can considerably reduce the consumption of traditional fossil energy and improve environmental problems. Reliable photovoltaic (PV) cell modelling owns great significance to the foll...
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Photovoltaic (PV) power generation can considerably reduce the consumption of traditional fossil energy and improve environmental problems. Reliable photovoltaic (PV) cell modelling owns great significance to the following output characteristics analysis and optimal operation of the whole PV system, while there are several unknown physical parameters within different PV cell models. Thus, the identification of the internal parameters of the PV cell model is the first and foremost step for PV cell modelling, nevertheless, the intrinsic highly complex and non-linear and multi-modal features make traditional approaches, such as analytical methods hard to achieve satisfactory performance in solving this problem. Hence, this work aims to employ a powerful tool to effectively and efficiently overcome this thorny problem based on the most advanced optimization method. A recently developed meta-heuristic algorithm called peafowl optimization algorithm (POA) is employed in this work for PV cell modelling parameter identification. For comprehensive validation, two different PV cell models, i.e., double diode model (DDM) and triple diode model (TDM) are utilized. Simulation results demonstrate that POA can more accurately identify the unknown parameters of PV cell models in a higher convergence speed compared against other algorithms.
Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance effici...
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Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance efficiency and quality. However, when disassembling industrial equipment, there are many uncertainties that can have a detrimental impact on the disassembly and subsequent maintenance work. Therefore, this paper proposes a multi-objective DSP problem in an uncertain environment that addresses the uncertainties in the disassembly process through stochastic planning, with the objectives of minimizing disassembly time and enhancing responsiveness to priority maintenance components. Due to the complexity of the problem, an improved peafowl optimization algorithm (IPOA) is proposed for efficient problem-solving. The algorithm is specifically designed and incorporates four customized optimization mechanisms: peafowls' courtship behavior, the adaptive behavior of female peafowls in proximity, the adaptive search behavior of peafowl chicks, and interactive behavior among male peafowls. These mechanisms enable effective search for optimal or near-optimal solutions. Through comparisons with a real-world industrial case and other advanced algorithms, the superiority of the IPOA in solving DSP problems is demonstrated. This research contributes to improving maintenance efficiency and quality, bringing positive impacts to industrial equipment maintenance.
The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental conce...
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The purpose of this paper is to address an urgent operational issue referring to optimal power flow (OPF), which is associated with a number of technical and financial aspects relating to issues of environmental concern. In the last few decades, OPF has become one of the most significant issues in nonlinear optimization research. OPF generally improves the performance of electric power distribution, transmission, and production within the constraints of the control system. It is the purpose of an OPF to determine the most optimal way to run a power system. For the power system, OPFs can be created with a variety of financial and technical objectives. Based on these findings, this paper proposes the peafowl optimization algorithm (POA). A powerful meta-heuristic optimizationalgorithm inspired by collective foraging activities among peafowl swarms. By balancing local exploitation with worldwide exploration, the OPF is able to strike a balance between exploration and exploitation. In order to solve optimization problems involving OPF, using the standard IEEE 14-bus and 57-bus electrical network, a POA has been employed to find the optimal values of the control variables. Further, there are five study cases, namely, reducing fuel costs, real energy losses, voltage skew, fuel cost as well as reducing energy loss and voltage skew, and reducing fuel costs as well as reducing energy loss and voltage deviation, as well as reducing emissions costs. The use of these cases facilitates a fair and comprehensive evaluation of the superiority and effectiveness of POA in comparison with the coot optimizationalgorithm (COOT), golden jackal optimizationalgorithm (GJO), heap-based optimizer (HPO), leader slime mold algorithm (LSMA), reptile search algorithm (RSA), sand cat optimizationalgorithm (SCSO), and the skills optimizationalgorithm (SOA). Based on simulations, POA has been demonstrated to outperform its rivals, including COOT, GJO, HPO, LSMA, RSA, SCSO, and SOA. In addition
This paper comprises two studies;the first one provides an advanced and low-cost implementation for a remote astronomical platform applicable to small and medium-sized telescopes. It has been carried out for the 14-in...
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This paper comprises two studies;the first one provides an advanced and low-cost implementation for a remote astronomical platform applicable to small and medium-sized telescopes. It has been carried out for the 14-inch observatory, which includes a Celestron German Equatorial (CGE) telescope at the Kottamia astronomical observatory (KAO) in Egypt. This integrated control system is based on embedded systems, internet of things (IoT) technology, row packets communication procedure, and the Transmission Control Protocol (TCP) based on the Internet Protocol (IP). Using this platform, remote astronomers could control the whole system, observe, receive images and view them efficiently and safely without any human physical intervention. The proposed design has been achieved without dependence on commercial control kits or software. Indeed, many previous studies have focused on this field;however, their area of interest was limited or non-affordable. The excellence in this practical research is revealed and compared with others in terms of cost, inclusiveness, and communication speed. The other contribution of this research is to enhance the performance of the telescope pointing and tracking to be adapted with remote action. It has been achieved based on the mathematical model of the telescope where two fractional controllers have been applied, tilt integral-derivative (TID) and integral derivative-tilted (ID-T) controllers. After that, they have been optimized using a recent optimizationalgorithm called the peafowl optimization algorithm (POA) and compared with one of the well-known algorithms, particle swarm optimization (PSO). Simulation results under the MATLAB/SIMULINK environment reveal that modified ID-T-based POA has minimized the pointing error sharply. Moreover, compared with previous studies, it has significantly improved the telescope system characteristics represented in overshoot, settling, and rising periods.
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