Motivated by the progress in artificial intelligence and edge computing, this paper proposes a real-time distributed computing model for low-voltage flow data in digital power grids under autonomous and controllable environments. The model utilizes edge computing through wireless offloading to efficiently process and analyze data generated by low-voltage devices in the power grid. Firstly, we evaluate the performance of the system under consideration by measuring its outage probability, utilizing both the received signal-to-noise ratio (SNR) and communication and computing latency. Subsequently, we analyze the system’s outage probability by deriving an analytical expression. To this end, we utilize the Gauss-Chebyshev approximation to provide an approximate closed-form expression. The results of our experimental evaluation demonstrate the effectiveness of the proposed model in achieving real-time processing of low-voltage flow data in digital power grids. Our model provides an efficient and practical solution for the processing of low-voltage flow data, making it a valuable contribution to the field of digital power grids.
Motivated by the progress in artificial intelligence such as deep learning and IoT networks, this paper presents an intelligent flink framework for real-time voltage computing systems in autonomous and controllable environments. The proposed framework employs machine learning algorithms to predict voltage values and adjust them in real-time to ensure the optimal performance of the power grid. The system is designed to be autonomous and controllable, enabling it to adapt to changing conditions and optimize its operation without human intervention. The paper also presents experimental results that demonstrate the effectiveness of the proposed framework in improving the accuracy and efficiency of voltage computing systems. Simulation results are provided to verify that the proposed intelligent flink framework can work well for real-time voltage computing systems in autonomous and controllable environments, compared with the conventional DRL and cross-entropy methods, in terms of convergence rate and estimation result. Overall, the intelligent flink framework presented in this paper has the potential to significantly improve the performance and reliability of power grids, leading to more efficient and sustainable energy systems.
This paper proposes a real-time task fault-tolerant scheduling algorithm for a dynamic monitoring platform of distribution network operation under overload of distribution transformers. The proposed algorithm is based on wireless communication and mobile edge computing to address the challenges faced by distribution networks in handling the increasing load demand. For the considered system, we evaluate the system performance by analyzing the communication and computing latency, from which we then derive an analytical expression of system outage probability to facilitate the performance evaluation. We further optimize the system design by allocating computing resources for multiple mobile users, where a greedy-based optimization scheme is proposed. The proposed algorithm is evaluated through simulations, and the results demonstrate its effectiveness in reducing task completion time, improving resource utilization, and enhancing system reliability. The findings of this study can provide a basis for the development of practical solutions for the dynamic monitoring of distribution networks.
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