Applications of Novel Heuristic Algorithms in Design Optimization of Energy-Efficient Distribution Transformer

Abstract

Transformers are one of the crucial and expensive assets of a power grid. Reducing power losses in power and distribution transformers is important because it increases the efficiency of the transformer, which in turn reduces costs for the utility company and consumers. Losses in the transformer generate heat, which can reduce the lifespan of the transformer and require additional cooling. Additionally, reducing losses can help to decrease greenhouse gas emissions associated with the generation of electricity. This study presents an optimization method for transformer design problem using variables that have a great impact on the performance of a transformer. Due to the non-convex nature of the transformer design problems, the empirical methods fail to find the optimal solution and the design process is very tedious and time-consuming. Considering No Free Lunch (NFL) theorem, the design problem is solved using four novel heuristic optimization algorithms, the Firefly Optimization Algorithm (FA), Arithmetic Optimization Algorithm (AOA), Grey Wolf Optimization Algorithm (GWO), and Artificial Gorilla Troops Optimizer Algorithm (GTO) and the results are compared to an already manufactured 1000 kVA eco-friendly distribution transformer using the empirical methods. The outcome of the optimization shows that the suggested method along with the algorithms mentioned leads to a notable decrease in power losses by up to 3.5%, and a reduction in transformer weight by up to 8.3%. This leads to an increase in efficiency, decreased costs for materials, longer lifespan and a reduction in emissions. The developed model is capable of optimally designing oil-immersed distribution transformers with different power ratings and voltage levels.

A Multi-Objective Improved Hybrid Butterfly Artificial Gorilla Troop Optimizer for Node Localization in Wireless Sensor Groundwater Monitoring Networks

“…The iHBAGTO algorithm was designed to discover the locations of anchor nodes close to the near-optimal target nodes to increase the convergence rate and maximize accuracy in localization while reducing localization errors. The existing Artificial Rabbit Optimization (ARO) [27], Northern Goshawk Optimization (NGO) [28], Hunger Games Search (HGS) [29], and Slime Mould-inspired Algorithm (SMA) [30] approaches were compared with the proposed iHBAGTO algorithm.…”