Nowadays, there is an increased concern for stability improvement in modern electrical systems, especially due to the use of distributed generation into the grids. stability of power system is one of the main challeng...
详细信息
ISBN:
(纸本)9781665412810
Nowadays, there is an increased concern for stability improvement in modern electrical systems, especially due to the use of distributed generation into the grids. stability of power system is one of the main challenges to improve the power system efficiency. A Power System stabilizer (PSS) is a cost-effective controller and efficient device to increase the stability and reliability of power systems. In this paper, the transient stability of ieee 9-bus system is improved with the integration of excitation system and PSS. The relative power angle is considered to evaluate the transient stability. The simulation results show that the existing system has oscillated. However, when the system is integrated with the exciter st1A and PSS1A, the peak power angle decreases from 132.1 degrees to 103.7 degrees, and the settling time is 5.53 s. Additionally, the speed deviation is improved when PSS connected to st1A.
The Internet of Things (IoT) is the most popular technology exist due to its huge number of application. One of the components of IoT is wireless sensor network (WSN). The WSN is the combination of huge amount of sens...
详细信息
This paper presents a load demand management of interconnected distributed generation (DG) working on islanded mode. The development of technology related to network integration and the use of energy renewable source ...
详细信息
Knitwear is the second largest product category in the traditional clothing industry. The traditional clothing product supply chain is difficult to meet consumer's demand of fast fashion. AI and industrial Interne...
详细信息
User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cl...
详细信息
ISBN:
(数字)9798350363999
ISBN:
(纸本)9798350364002
User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cloud to facilitate DL model learning causes extensive overhead and privacy concerns. distributed edge cloud-based federated learning solves these issues, but it faces challenges in handling out-of-distribution data from decentralized edges at the network periphery, and the model biases due to data heterogeneity. This paper addresses these limitations by proposing a fully distributed Collaborative User Mobility Prediction (CUMP) framework that mitigates the out-of-distribution data issue through collaboration among initial layers of DL models in edges that are selected using inter-edge mobility rates. The remaining part of each model only trains on local data, preserving biases towards their respective edges. This enhances the generalization, robustness, and predictive performance of the DL models. Results show that CUMP outperforms conventional global learning and state-of-the-art distributed personalized federated learning and cyclic incremental institutional learning by 63%, 12%, and 10% in predicting the next Point of Attachment (PoA) of a user and by 70%, 22%, and 28% in predicting user dwell time in current PoA, respectively. Thus, CUMP improves prediction performance while reducing network and storage overheads while preserving privacy.
The increasing power demand with energy management is turning out to be an important aspect for electrical power system. Integration of Renewable Energy Source (RES) has indeed minimized the carbon emission and succee...
详细信息
As distributed generation (DG) supported by renew-able energy become more and more prevalent in the distribution network (DN), the coordination of directional overcurrent relays (DOCRs) in their presence needs to be a...
详细信息
ISBN:
(数字)9798350384246
ISBN:
(纸本)9798350384253
As distributed generation (DG) supported by renew-able energy become more and more prevalent in the distribution network (DN), the coordination of directional overcurrent relays (DOCRs) in their presence needs to be addressed. Numerous meta-heuristic optimization strategies have been used to address this problem in order to determine the optimal relay settings and to achieve the best possible coordination of the protective relays considering coordination constraints. This study presents an adaptive protection coordination strategy for DNs with inter-connected DG units. The scheme utilizes Chicken swarm opti-mization (CSO) to achieve optimal coordination. It dynamically adjusts protective device (DOCR) settings in response to changes in the system, such as fluctuations in DG output or modifications to the network topology. The effectiveness of the proposed adaptive scheme is demonstrated through its application to two benchmark systems: the 9-bus Canadian system and the ieee 30-bus system. The results are presented and analyzed, showcasing the scheme's capabilities. This approach significantly enhances power system protection and enhances reliability and efficiency of power DNs.
The ethical and philosophical problems concerning the cooperation of AI systems and human artists are also examined in this study. In addressing authorship, agency, and the very essence of creation, the changing posit...
详细信息
In this paper, we propose a symbolic framework to analyze and debug communicating distributed models. We implement dedicated symbolic execution techniques for such models and use them to compute interaction scenarios ...
详细信息
暂无评论