Draz Abdelmonem, Ashraf Hossam, El Shamy Ahmed R, El-Fergany Attia A
Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt.
Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo, 113-8656, Japan.
Sci Rep. 2025 Sep 12;15(1):32421. doi: 10.1038/s41598-025-14233-3.
Accurate modelling of distribution transformers (TXs) is crucial to identify their operating characteristics across several power system applications. Consequently, this paper employs an improved version of the artificial ecosystem optimizer (called IAEO) in parameters estimation of distribution TXs with different four sizes (i.e. 4, 15, 112.5 and 167 kVA ratings). Comparison with well-known literature optimizers, including genetic algorithm, particle swarm optimizer, coyote optimization algorithm, artificial hummingbird optimizer, and others, validates the performance of the proposed IAEO. The IAEO demonstrates its superiority by getting the lowest possible value of the sum of absolute errors (SAEs) between measured and calculated values, which serves as the objective function (OF) to be optimized. Moreover, three additional optimizers are employed and compared to IAEO for all study cases: firefly algorithm, political optimizer, and exponential distribution optimizer. It is found that IAEO attains the minimum SAEs values of 1.12e-5 and 0.0322, outperforming the best competitors for 4 kVA and 15 kVA TXs, respectively. Furthermore, IAEO accurately captures the steady state fingerprint of all studied TXs in terms of efficiency and voltage regulation (VR). This way, the peak efficiency occurs at 36.2% loading in 112.5 kVA TX while the negative VR may reach -8% when the 167 kVA TX is loaded with its rated leading power factor. Finally, all executed optimizers are analyzed using several statistical indices, including t-test, where the proposed IAEO gets the smoothest and fastest OF minimization trend.
准确建模配电变压器（TXs）对于识别其在多个电力系统应用中的运行特性至关重要。因此，本文采用了人工生态系统优化器的改进版本（称为IAEO）来估计不同四种容量（即额定容量为4、15、112.5和167 kVA）的配电TXs的参数。与包括遗传算法、粒子群优化器、郊狼优化算法、人工蜂鸟优化器等在内的知名文献优化器进行比较，验证了所提出的IAEO的性能。IAEO通过获得测量值与计算值之间绝对误差总和（SAEs）的尽可能低的值来证明其优越性，该值作为要优化的目标函数（OF）。此外，还采用了另外三种优化器并与IAEO针对所有研究案例进行比较：萤火虫算法、政治优化器和指数分布优化器。结果发现，IAEO分别在4 kVA和15 kVA的TXs中实现了1.12e - 5和0.0322的最小SAEs值，优于最佳竞争对手。此外，IAEO在效率和电压调节（VR）方面准确地捕捉了所有研究TXs的稳态特征。这样，112.5 kVA的TX在36.2%负载时出现峰值效率，而当167 kVA的TX以其额定超前功率因数加载时，负VR可能达到 - 8%。最后，使用包括t检验在内的几个统计指标对所有执行的优化器进行分析，所提出的IAEO获得了最平滑和最快的目标函数最小化趋势。
