Developed Algorithm Based on Supply-Demand-Based Optimizer for Parameters Estimation of Induction Motor

Ibrahim, Safaa Abdo; Kamel, Salah; Hassan, Mohamed H.; Elsayed, Salah K.; Nasrat, Loai

doi:10.1109/icaacca51523.2021.9465231

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…SDO has also been used for proton exchange membrane fuel cell parameter extraction, which is vital for improving the efficiency and reliability of fuel cell systems [34]. Also, it has been applied to estimating the parameters of induction motors [35]. Additionally, SDO has been applied to estimating the breakdown voltage of silicon rubber insulators, which is important for ensuring the safety and reliability of electrical power systems [36].…”

Section: Introductionmentioning

confidence: 99%

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

Hassan

Kamel

Shaikh

et al. 2023

IET Generation Trans & Dist

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The Economic and Emission Dispatch (EED) method is widely used to optimize generator output in a power system. The goal is to reduce fuel costs and emissions, including carbon dioxide, sulphur dioxide, and nitrogen oxides, while maintaining power balance and adhering to limit constraints. EED aims to minimize emissions and operating costs while meeting power demands. To solve the multi‐objective EED problem, the supply‐demand optimization (SDO) algorithm is proposed, which employs a price penalty factor approach to convert it into a single‐objective function. The SDO algorithm uses a swarm‐based optimization strategy inspired by supply‐demand mechanisms in economics. The algorithm's performance is evaluated on seven benchmark functions before being used to simulate the EED problem on power systems with varying numbers of units and load demands. Established algorithms like the Grey Wolf Optimizer (GWO), Moth‐Flame Optimization (MFO), Transient Search Optimization (TSO), and Whale Optimization Algorithm (WOA) are compared to the SDO algorithm. The simulations are conducted on power systems with different numbers of units and load demands to optimize power generation output. The numerical analyses demonstrate that the SDO technique is more efficient and produces higher quality solutions than other recent optimization methods.

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Section: Introductionmentioning

confidence: 99%

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

Hassan

Kamel

Shaikh

et al. 2023

IET Generation Trans & Dist

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, the SDA adds the distinguishing features of exploratory and exploitative strategies, which are discriminated by controlling the supply and demand weights. The SDA has been applied effectively for different engineering optimization tasks, such as the performance evaluation of the proton-exchange membrane fuel cells [32], the sizing of hybrid energy systems [33], the photovoltaic cells' design [34], and parameter estimations of an induction motor [35].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

et al. 2022

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The parameter extraction of parameters for Li-ion batteries is regarded as a critical topic for assessing the performance of battery energy storage systems (BESSs). The supply–demand algorithm (SDA) is used in this work to identify a storage system’s unknown parameters. The parameter-extracting procedure is represented as a nonlinear optimization task in which the state of charge (SOC) is approximated using nonlinear features related to the battery current and the initial SOC condition. Furthermore, the open-circuit voltage is approximated using the resulting SOC, which is performed in a nonlinear formula, as well. When used in the dynamic nonlinear BESS model, the SDA was used to verify the fitness values and standard deviation error. Furthermore, the results that were acquired using SDA are compared to recently developed approaches, which are the gradient-based, tuna swarm, jellyfish, heap-based, and forensic-based optimizers. Simulated studies were paired with experiments for the 40 Ah Kokam Li-ion battery and the ARTEMIS driving-cycle pattern. The numerical outcomes showed that the proposed SDA is an approach which is excellent at identifying the parameters. Furthermore, when compared to the other current optimization techniques, for both the Kokam Li-ion batteries and the ARTEMIS drive-cycle pattern, the suggested SDA exhibited substantial precision.

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Parameters Tuning of Fractional-Order Proportional Integral Derivative in Water Turbine Governing System Using an Effective SDO with Enhanced Fitness-Distance Balance and Adaptive Local Search

Zhao

Zhang

et al. 2022

Water

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Supply-demand-based optimization (SDO) is a swarm-based optimizer. However, it suffers from several drawbacks, such as lack of solution diversity and low convergence accuracy and search efficiency. To overcome them, an effective supply-demand-based optimization (ESDO) is proposed in this study. First, an enhanced fitness-distance balance (EFDB) and the Levy flight are introduced into the original version to avoid premature convergence and improve solution diversity; second, a mutation mechanism is integrated into the algorithm to improve search efficiency; finally, an adaptive local search strategy (ALS) is incorporated into the algorithm to enhance the convergence accuracy. The effect of the proposed method is verified based on the comparison of ESDO with several well-regarded algorithms using 23 benchmark functions. In addition, the ESDO algorithm is applied to tune the parameters of the fractional-order proportional integral derivative (FOPID) controller of the water turbine governor system. The comparative results reveal that ESDO is competitive and superior for solving real-world problems.

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Developed Algorithm Based on Supply-Demand-Based Optimizer for Parameters Estimation of Induction Motor

Cited by 3 publications

References 15 publications

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

Supply‐demand optimizer for economic emission dispatch incorporating price penalty factor and variable load demand levels

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

Parameters Tuning of Fractional-Order Proportional Integral Derivative in Water Turbine Governing System Using an Effective SDO with Enhanced Fitness-Distance Balance and Adaptive Local Search

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