Traditional network switching involves modifying the distribution network’s topology to minimize the dissipated power based on consumer demand. Multiple kinds of loads and their stochastic nature introduce variations in power dissipation owing to the uncertain reliance on the precision of the load forecast. Despite this, some existing models overlook consumer diversity and uncertainty. Even studies that account for these factors often neglect the impact of temperature fluctuations on power usage and network structure. To address this gap, this present article proposes a novel network reconfiguration approach that simultaneously considers load type, stochastic behavior, ambient temperature, linear modeling, and line limit calculations. By incorporating ambient temperature effects into stochastic load modeling, the proposed method enhances analysis accuracy. Additionally, the use of a linear model ensures computational efficiency and precise results, while explicit line limit calculations guarantee reliable network operation. The findings reveal that small systems maintain stable configurations with temperature increases up to 50%, but experience significant power losses and voltage drops when temperatures rise by 60%–100%, leading to topology adjustments. In larger networks, reconfiguration becomes ineffective for temperature increases beyond 20%, requiring line capacity upgrades. Furthermore, while some actual grids remain stable under temperature variations up to 70%, others show topology changes with only a 10% temperature increase, highlighting the substantial impact of temperature fluctuations on system performance.
To ensure the desired higher voltage conversion ratio (VCR) from fuel cell stacks, nonisolated high-gain dc-dc converters play a crucial role in electric vehicles (EVs). High-gain converters are essential because EVs often require a wide range of higher voltage levels to meet the demand for the dc bus, which drives the traction motor and other modules in a vehicle. To mitigate the use of extreme duty ratios in conventional high-gain converters, a triple-switch, switched-inductor, switched-capacitor (SC) dc-dc converter is proposed. This converter integrates an additional auxiliary switch, a simple resonant cell, and a voltage multiplier cell (VMC). These integrations provide the desired VCR while all switches operate at a nominal duty ratio, ensuring improved efficiency and minimizing switching losses by maintaining soft-switching ability. Furthermore, the proposed converter facilitates zero current switching (ZCS) turn-on operation for all switches through the utilization of an auxiliary inductor, an auxiliary capacitor, and two diodes. The operation of the proposed converter is analyzed in continuous current mode, steady-state gain, and efficiency analysis are discussed in detail. The proposed converter is compared with the existing converters and a 200 W laboratory prototype is developed to validate the theoretical analysis or to prove the effectiveness of the converter.
Correct use of home appliances is intended to avoid property damage and life unsafety. Also, serious fire accidents can affect the safety of one’s neighborhood. Therefore, this study uses the Internet of Things (IoT) platform to build an intelligent system that aims to detect the temperature rise of home appliances. It is employed to monitor the usage states of home appliances in real time. Hereby, the microcontroller, node microcontrol unit (NodeMCU-32S), is used to develop an IoT platform combined with sensors for measuring ac voltage, current, and ambient temperature so that the electrical power and temperature rise of load (e.g., electric motor) can be detected. Meanwhile, the smart measurement system is integrated with mobile devices to upload the detected datasets to the Google cloud database system. Moreover, the verification of feasibility and soundness of a system model is performed by using the Petri net tool, WoPeD, for the purpose of eliminating the improper states to optimize the system performance. Finally, the experimental results show that the proposed IoT-enabled system has a promising precision of 94.17% and a recall of 92.26%, which obviously outperforms other existing state-of-the-art systems.
In this work, a meander line (ML) periodic leaky wave antenna (LWA) is proposed with gain improvement and broadside scanning capability. A longer ML length is taken in a fixed unit cell period to improve the gain. A longer ML brings more space harmonics (SHs) in the radiation region. Therefore, the geometry of the ML is modified to obtain impedance matching at SHs transition frequencies at 8.35 and 10.45 GHz. The antenna covers the frequency range from 8 to 11 GHz in the X-band with the optimized broadside frequency (fB) at 9.4 GHz. A maximum gain of 15.5 dBi is achieved in 6.25λ0 antenna length. This antenna is a potential candidate for use in modern wireless communication where high gain is required in a small footprint area.
