With the increasing integration of renewable energy sources into distribution and transmission networks, the efficiency of cascade H-bridge multilevel inverters (MLIs) in power control applications has become increasingly significant for sustainable electricity generation. Traditionally, obtaining optimal switching angles of MLIs to minimize total harmonic distortion (THD) requires solving the selective harmonic elimination equations. To this end, this research aims to use two recently developed intelligent optimization algorithms, dandelion optimizer and gold rush optimizer, to solve this problem. To evaluate the effectiveness of the proposed algorithms, an eleven-level cascaded H-bridge MLI (CHB-MLI) was considered in the study. Simulation results for different modulation indices were obtained, and the accuracy and solution quality were compared with genetic algorithm and particle swarm optimization algorithms. MATLAB/Simulink-based models were used to verify numerical computations, ensuring the reliability of the findings. This research contributes to the field by providing insights into obtaining optimal switching angles and minimizing THD in MLIs by applying intelligent optimization algorithms.

This paper proposed a red deer algorithm (RDA)-based selective harmonic elimination (SHE) method for multilevel inverters (MLIs). To eliminate the desired harmonic orders, the optimum switching angles of the MLI have been calculated using the proposed RDA. The calculated switching angles have been applied to the 3-phase cascaded H-bridged 11-level inverter. In addition, the performance of the proposed RDA method was compared with the results of methods such as the Newton-Raphson (NR) method, LSHADE/EpSin technique (LSHADE), whale optimization algorithm (WOA), and particle swarm optimization (PSO) used for the SHE problem in the literature. The results obtained prove that the proposed RDA optimization solves the SHE problems more effectively than other methods. It has also been observed that RDA produces good solutions in different modulation indexes.

Sanayileşmenin hızla artması enerji ihtiyacını günümüzde artmıştır. Bununla birlikte ortaya çıkan bu enerji ihtiyacını karşılayabilmek için ön görülebilecek enerji tahminlerini yapabilmek için optimizasyon ve makine öğrenme algoritmaları ön plana çıkmıştır. Parçacık sürü optimizasyonu (PSO), Lineer Regresyon (LR) ve Gauss Süreç Regresyonu (GSR) bu algoritmalar içerisinde yer almaktadır. Bu çalışmada PSO, LR ve GSR algoritmaları kullanılarak Türkiye’nin 2020-2040 yılları arasındaki enerji talep tahmini yapılmıştır. Bu tahmin işlemlerinin yapılabilmesi için 1980-2019 yılları arasında geçmiş nüfus, ihracat, ithalat, gayri safi yurtiçi hâsıla (GSYH) giriş verileri olarak kullanılırken enerji tüketimi çıkış verisi olarak kullanılmıştır. PSO, LR ve GSR yöntemlerinin performans sonuçlarını değerlendirebilmek için regresyon kare (R2) değeri, kök ortalama kare hatası (RMSE), ortalama kare hatası (MSE) ve ortalama mutlak hata (MAE) hata metrikleri kullanıldı. R2, RMSE, MSE ve MAE değerleri göz önünde bulundurulduğunda bütün yöntemlerin başarılı sonuçlar verdiği gözlemlenmiştir.

The number of missing children and kidnapping is on the rise in recent years. Every parent wills definitely going through an agonizing experience to have their children missing. Therefore, there are many safety measurements to prevent this incident from happening. The help of modern technologies is one of the ways to reduce children missing and kidnapping. A child can be tracked by using the global positioning system (GPS) and global system for mobile communication (GSM) technology. Advanced child monitoring systems are expensive. Not all families have the same living standards. For this purpose, a low-cost child tracking system is proposed in this study. The implementation of the proposed approach is reported in real-time.

This paper compares the performance of a Sliding Mode Control (SMC) and Proportional-Integral (PI) controller under different voltage and load conditions. The study's findings underscore the need to account for load variations in system designs and to continuously seek system optimization irrespective of the controller type chosen. PI controller demonstrates effectiveness under certain circumstances. However, significant drops in voltage and current during abrupt load changes are obtained. On the other hand, SMC enables superior adaptability, efficiently managing voltage transients and load variations without any electrical disturbances, thereby maintaining system stability. A comparative analysis further emphasizes the SMC's superior time response and robustness against reference voltage changes. Consequently, SMC is proven to be a preferable choice over the PI controller in systems experiencing frequent voltage and load variations. However, both controllers achieve the potential for further response time optimization and stability.