Shoaib Ahmed Khan

Off-grid plants that supply electricity and hydrogen from shared assets are sized under modelling choices that are rarely tested one at a time. We test three on a PV–hydrogen–biogas plant in Islamabad that serves homes, electricvehicle charging and hydrogen refuelling. First, one aggregate loss-of-power-supply probability (LPSP) is a demandweighted mean of the service indices, so a 3 % limit allows up to 15.9 % hydrogen shortfall when hydrogen is 18.8 % of demand. The optimized designs leave 6.10 % of refuelling demand unserved while meeting the limit. Reserving hydrogen for vehicles cuts hydrogen shortfall to 0.16 %, but electrical shortfall rises to 3.66 %. Under the original dispatch order, separate limits for each service bring hydrogen shortfall to 2.99 % for 1.3 % more total annualizedcost. The other two choices are how the AC and DC buses are coupled and whether a finite biogas budget can move between months. A full factorial design separates them. Modelling the DC coupling gives 88 % of the converter reduction (526 to 209 kW) but makes the fuel cell 8 to 9 % larger. An annual biogas budget shrinks the fuel cell by about 25 % under either coupling and gives 77.5 % of the fall in levelized cost of electricity, which charges the wholeplant cost to electricity. Both reductions hold for five optimizers and in all twenty robustness settings where each step can be tested (2,490 re-optimized runs). The capacities belong to this case, while the mechanisms follow from the formulation.

Electric power supply (EPS) is the heart of any aerospace mission and plays an important role in improving the performance and service lifetime of spacecraft. It generates, converts, stores, and distributes power to different voltage levels. The EPS is composed of solar panels, a power conditioning unit (PCU), batteries, and a power distribution unit (PDU). This paper describes the design and analysis of an efficient power conditioning system for a CubeSat standard small satellite. For this purpose, the aim of this paper is to propose a two-input maximum power point tracker (MPPT)-based interleaved boost converter. The design copes with the fact that when a satellite revolves around the Earth, a single panel or at most two panels face solar radiation at different angles. In order to extract maximum power from the panels, the designed converter drives the solar panels at the maximum power point (MPP). A small signal model is drawn for the converter, and the closed-loop gain of the converter is analyzed using a Bode diagram. To improve the phase margin and gain, a PID compensator is designed and added to the closed loop of the converter. Finally, the performance of the proposed converter is validated by the simulation results.

Many versatile and promising swarm intelligence evolutionary algorithms are being developed to solve engineering optimization problems. Although evolutionary algorithms have been implemented in various optimization fields, there is still potential for enhancement in the domain of complex, electromagnetic, and multimodal objective problems. To effectively address the shortcomings and slow convergence speed observed in both smart quantum particle swarm optimization (QPSO) and differential evolution (DE), a hybrid strategy is proposed. In the proposed QPSODE, apart from the smart strategy of QPSO for improving the exploration as a whole, more additional features such as non-linear adaptive control parameter, the partition of the swarm to apply smart and gaussian mutation mechanism, crossover and selection of best particle using Boltzmann strategy to avoid premature convergence are introduced. Consequently, applying the new design algorithm to several benchmark-constrained, mostly non-convex, and superconducting magnetic energy storage (SMES) electromagnetic problems shows a marked performance improvement. The performances of the QPSODE is compared with those of many other widely recognized population-based swarm intelligence optimizers. Experimental results and statistical analysis using Friedman test show that the search accuracy and the convergence of the hybrid QPSODE strategy are advantageous over other optimization approaches.

Quantum behaved particle swarm optimization (QPSO) has been one of the most widely used algorithm in engineering world. Since its debut in 2004, QPSO has been used for resolving numerous complicated multimodal problems. Moreover, considering the adaptability and versatility, it has resolved a variety of real-world and test problems. To tackle numerical and engineering optimization problems, we introduce novel hybrid algorithm QPSODE. The novel hybrid algorithm integrates Quantum behaved particle swarm optimization (QPSO) with differential evolution (DE) strategy. A crossover and selection (influenced by DE) is used in the QPSODE’s position updating mechanism. During the selection process, the Boltzmann operator is applied to the position vectors of two randomly chosen particles, not to their individual optimum placements. Therefore, unlike the QPSO, a particle is only relocated to a new position if it has a higher fitness value, implying the application of a selection strategy across the whole search space. Additionally, the hybrid algorithm is improved by introducing proper parameters tuning, control parameter, path disparity. The hybrid algorithm enhances the algorithm’s performance by speeding up the convergence and avoiding the premature convergence, the main flaw in the earlier algorithms. The proposed algorithm is put to test, by using 19 well-known benchmark test functions and the engineering optimization problem for superconducting magnetic energy storage (SMES). In terms of the quality of the resulting outputs, QPSODE outperforms various state-of-the-art approaches.

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

A miniaturized attitude control system (ACS), which is capable of attaining a high spin rate and good pointing accuracy, is an integral part of any small spacecraft. In this regard, embedded magnetorquers are gaining significant attention because they are inserted in printed circuit board (PCB) internal layers with no extra space occupation and result in almost no mass and development cost. In this article, an innovative embedded magnetorquer with asymmetric nonuniform geometric structure is presented. The proposed magnetorquer is optimized for various parameters of interest, i.e., magnetic moment, resultant generated torque, power consumed, and heat dissipation for a CubeSat standard nanosatellite by means of employing diverse nonunity track-width-ratio geometrical arrangements. The magnetorquer driver and reconfigurable circuit is implemented with commercial-off-the-shelf (COTS) components which are low cost and easily available from the market. Embedded coils with variable trace width were simulated in order to predict the accuracy and to achieve the maximum torque versus power dissipation ratio of the developed models. Time-varying rotational analyses are performed to evaluate the angular spin rates driven by the asymmetric coils. Due to embedded coil structure, a detailed thermal analysis is conducted for various coil configurations (i.e., series, parallel, hybrid, etc.) to preserve thermal limits and to ensure the efficacy of the system. The simulation results were validated through a comprehensive set of experimental measurements which also establishes the framework for selecting the optimal coils configuration based on specific mission requirements.