Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study

Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study

Hepatoprotective Effect of Captopril on Liver Toxicity Induced by High and Low Dose of Paracetamol in Rats:Histological Study

Many patients may administered medications like captopril (ACE inhibitor) for treatment of chronic diseases and may also take Paracetamol as an Over The Counter (OTC) drug which may interact with captopril. Therefore, the aim of this study is to evaluate of the hepatoprotective effect of captopril on liver toxicity induced by low and high dose of paracetamol in rats. This study was conducted in two phases: first study for low dose of paracetamol (300 mg/kg); animals were divided into 4 groups of 6 rats each (n = 6); all groups were treated orally either 0.9 % Normal Saline (NS), captopril 20 mg/kg, paracetamol 300 mg/kg or captopril 20 mg/kg plus paracetamol 300 mg/kg for 10 consecutive days. Second study for single high dose of paracetamol (3000 mg/kg); animals were divided into 4 groups of 6 rats each (n = 6); all groups were pretreated orally either 0.9 % Normal Saline (NS) or captopril 20 mg/kg for 7 consecutive days followed by single oral administration of Paracetamol 3000 mg/kg or normal saline. The administration of Paracetamol or normal saline was performed 24 hours after the last administration of captopril. After 48 hours of hepatic injury induction, the animals were then sacrificed and the liver was removed for histopathological studies. Low dose (300 mg/kg) for 10 days and high single dose (3000 mg/kg) of paracetamol produced hepatotoxic effects. While captopril 20 mg/kg showed marked protection against changes induced by low and high dose of paracetamol on the liver

Multi-port converter for medium and high voltage applications

This work presents a multi-port converter (MPC) that is well-suited for use as a hybrid hub in complex multi-terminal high-voltage direct current (MTDC) networks. The proposed MPC generates several and controllable DC voltages from a constant or variable input DC voltage or AC grid. Its operating principle is explained and corroborated using simulations and experimentations

The adoption of ChatGPT marks the beginning of a new era in educational platforms

Technology has significantly transformed knowledge, education, and access to information by introducing online learning platforms, interactive games, and virtual reality simulations in traditional classrooms, creating a dynamic, engaging, and inclusive learning environment. The ChatGBT project (a pre-developed transformer for training) is a remarkable achievement in artificial intelligence technology. It allows students tailored and efficient learning experiences by providing individual feedback and explanations. ChatGPT e-learning platform has been extensively studied for its adoption and acceptance, but there is a significant gap in research on its acceptability and use, highlighting the need for further exploration. The goal of this work is to bridge this disparity by introducing a comprehensive model that includes three basic elements: performance expectation, expected effort, and social impact. A total of 241 graduate students were surveyed and their data were analyzed using structural equation modeling techniques. The results indicate that “expectation of performance and expected effort” have the greatest impact and importance in determining students’ intentions to use learning platforms via ChatGPT, while social influence does not play an important role. This study enhances the current body of knowledge related to artificial intelligence and environmental sustainability, and provides important insights for professionals, policymakers, and producers of artificial intelligence products. These observations may provide guidance for creating and implementing artificial intelligence technologies to match consumers’ needs and preferences more effectively, while also taking into account broader environmental conditions

New analysis of VSC-based modular multilevel DC-DC converter with low interfacing inductor for hybrid LCC/VSC HVDC network interconnections

The integration of multiterminal hybrid HVDC grids connecting LCC- and VSC-based networks faces several technical challenges such as DC fault isolation, ensuring multi-vendor interoperability, managing high DC voltage levels, and facilitating high-speed power reversal without interruptions. The two-stage DC-DC converter emerges as a key solution to address these challenges. By implementing the modular multilevel converter (MMC) structure, the converter's basic topology includes half-bridge sub-modules on the VSC side and full-bridge sub-modules on the LCC side. However, while this topology has been discussed in the literature, its connection to an LCC-based network with controlled current magnitude lacks detailed analysis regarding operational challenges, control strategies under various scenarios, and design considerations. This paper fills this gap by providing comprehensive mathematical analysis, design insights, and control strategies for the modular DC-DC converter to regulate DC voltage on the LCC-HVDC side. Additionally, the proposed control scheme minimizes the interfacing inductor between the two bridges, ensuring uninterrupted power flow during reversal and effective handling of DC faults. Validation through Control-Hardware-in-the-Loop testing across diverse operational and fault scenarios, along with a comparative analysis of different converters, further strengthens the findings