Abstract

Abstract
Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks’ intelligent behaviors during the chasing of the prey. Furthermore, the authors have investigated four stages of DGs. The first stage involves the optimal allocation of one DG. The second stage includes an investigation with two DGs, the third stage considers three DGs, and the fourth stage investigates the integration of four DGs. The effectiveness of the applied HHO is validated on IEEE 33 and 69 bus RDNs, and results are analyzed by comparing with the standard optimization methods. The Big-O test is also executed for statistical analysis with standard algorithms. The simulation results reveal the better performance of the applied HHO under different circumstances than other algorithms. Furthermore, the total active and reactive power losses and bus voltage drops are improved by adding more DGs into IEEE 33 and 69 bus RDNs.

Abstract

Abstract
The reliability of power converters in photovoltaic systems is critical to the overall system reliability. This paper proposes a novel active thermal-controlled algorithm that aims to reduce the rate of junction temperature increase, therefore, increasing the reliability of the device. The algorithm works alongside a normal perturb and observe maximum power point tracking algorithm, taking control when certain temperature criteria are met. In conjunction with a neural network, the algorithm is applied to long-term real mission profile data. This would grant a better understanding of the real-world trade-offs between energy generated and lifetime improvement when using the proposed algorithm, as well as shortening study cycle times. The neural network, when applied to 365 days of data, was 28 times faster than using standard electrothermal modeling, and the lifetime consumption was predicted with greater than 96.5% accuracy. Energy generated was predicted with greater than 99.5% accuracy. The proposed algorithm resulted in a 3.3% reduction in lifetime consumption with a 1.0% reduction in the total energy generated. There is a demonstrated trade-off between lifetime consumption reduction and energy-generated reduction. The results are also split by environmental conditions. Under very variable conditions, the algorithm resulted in a 4.4% reduction in lifetime consumption with a 1.4% reduction in the total energy generated.

Abstract
This paper presents an adaptive protection strategy for multi-microgrid (MMG) systems with inverter-based resources (IBRs) in medium voltage (MV) networks, using the IEEE 33-bus test system. The approach combines overcurrent (OC) and undervoltage (UV) protections through an offline-optimized, clustering-based scheme and real-time selection of setting groups. A metaheuristic algorithm determines optimal relay settings for representative scenarios, ensuring responsive and coordinated protection. Hardware-in-the-loop validation on OPAL-RT confirms the method's effectiveness across varying loads, DER outputs, and fault conditions. Results demonstrate reliable fault isolation, smooth mode transitions, and uninterrupted supply to healthy segments. Identified limitations in high-impedance fault handling suggest future improvements.

Abstract
The growing number of energy consuming devices, together with the increasing demand for energy, has led to an energy crisis in the world today. As electricity is an important factor for the development in the economic growth. At this time, the growing population will bring disaster situations relating to electrical energy and pollution; it will affect both the economy and the citizens and make a barrier in the path to complete with developed countries. In order to overcome the epileptic situation of electrical energy and make our environment clean and green. First, we need to give opportunities for renewable energy to address electricity generation. Second, there is no other way to reduce complexity and eliminate energy problem without using solar energy. The solar energy which is renewable energy, environmental friendly and free of cost but still we face difficulty by complete harnessing of solar energy because of variation of irradiation and temperature to get constant point at the output we will use PI controller.Moreover, there is different classification of inverter we used "PWM inverter" that can take steady DC voltage PWM inverter can take in a steady dc voltage. So as to feed solar voltage to the boost converter to step-up solar voltage we used PI controller to control boost converter and get constant voltage at the output the we feed this boost DC voltage to the 3phase PWM inverter to converter into three phase AC voltage. Finally, the inverter should direct the extent and the recurrence of AC output voltages, and the diode rectifiers are required to settle the line-to-line voltage.