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Amari Alpha Divergence-Based Frequency Estimation Algorithm for Power Systems

Harmonic analysis

Interference (wave propagation)

Divergence (linguistics)

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The Amari-Alpha divergence-based frequency estimation algorithm for power systems is a novel approach proposed in the research papers. The Amari-Alpha LMS (AALMS) algorithm utilizes the Amari-Alpha information theoretic divergence to enhance adaptive filtering . Additionally, an advanced frequency estimation algorithm compensates for harmonic interference through a new model, improving accuracy and convergence speed . Furthermore, the Alpha-GAN model introduces the alpha divergence as an objective function, offering flexibility in balancing training stability and image quality . These innovative algorithms demonstrate improved performance in frequency estimation, noise robustness, and image generation, showcasing the potential of divergence-based techniques in enhancing various signal processing applications in power systems.

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Proceedings Article•DOI An Enhanced Algorithm for Frequency Estimation in Power Systems Xiaoqing Chen, Manus Henry, Stephen R. Duncan - Show less +2 more 31 Oct 2018 3 Citations	Not addressed in the paper.
Open access•Journal Article•DOI An Advanced Frequency Estimation Algorithm Based on Analytic Compensation of Effects of Dominant Harmonic in Power Systems Kyou Jung Son, Gi Sung An, Kyung Deok Nam, Tae-Gyu Chang - Show less +3 more 01 Jan 2021-IEEE Access 2 Citations	Not addressed in the paper.
Journal Article•DOI A Straightforward and Robust Algorithm for Accurate Estimation of Power System Frequency Moein Abedini, Seyed-Alireza Ahmadi, Majid Sanaye-Pasand - Show less +2 more 01 Dec 2021-IEEE Transactions on Industrial Electronics 6 Citations	Not addressed in the paper.
Journal Article•DOI Development of Amari Alpha Divergence based Gradient-Descent Least Mean Square Algorithm Parth Sharma, Pyari Mohan Pradhan - Show less +1 more 01 Jan 2023-IEEE Transactions on Circuits and Systems Ii-express Briefs	Not addressed in the paper.

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•What advancements in AI have enabled its application in the modeling and management of power system frequency dynamics?5 answersAdvancements in AI have enabled its application in the modeling and management of power system frequency dynamics. One approach is the use of multi-agent deep reinforcement learning (MA-DRL) methods, which can adapt control strategies for load frequency control (LFC) through off-policy learning. Specifically, the multi-agent twin delayed deep deterministic policy gradient (TD3) algorithm has been proposed to adjust and refine control system parameters considering variational load and source behavior. Another advancement is the use of self-adaptive algorithms based on AI to effectively understand and manage the massive data sets generated by IoT devices in power systems. These algorithms, such as the Improved Random Energy Optimization Algorithm (IIRBEOA), can distribute power levels to small portable devices and optimize energy consumption in IoT networks. Overall, these advancements in AI have improved the modeling and management of power system frequency dynamics by enabling adaptive control strategies and efficient energy management.

Different mathematical models that have been used to analyze and predict frequency dynamics in power system?5 answersDifferent mathematical models have been used to analyze and predict frequency dynamics in power systems. One approach is to use statistical-model-based methods to analyze the propagation characteristics of frequency dynamic behavior. Another approach is to use fundamental dynamic simulation principles to identify key changes in the dynamic behavior of the system due to the high penetration of renewable energy sources (RES). Additionally, properties of power grid frequency trajectories can be analyzed using statistical and stochastic analysis, and compared with synthetic models for frequency dynamics. Furthermore, power system scheduling models can incorporate frequency dynamics by formulating frequency security constraints that consider the details of frequency response dynamics. Finally, a precise frequency-dependent model has been proposed that takes into account spatial variations of the frequency in the network during a transient, leading to more accurate simulation of transient conditions.

Different mathematical and computational models that have been used to analyze and predict frequency dynamics in power system?5 answersDifferent mathematical and computational models have been used to analyze and predict frequency dynamics in power systems. One approach is to establish a frequency response model based on the full-state model of the power system, which allows for the quantitative relationship between system frequency-related characteristic quantities and system parameters to be obtained. Another method involves using a physics-inspired machine learning model that integrates stochastic differential equations and artificial neural networks to construct a probabilistic model of power grid frequency dynamics. Additionally, dynamic state and parameter estimation methods such as the Kalman filter, extended Kalman filter, unscented Kalman filter, and moving horizon estimation have been compared for their accuracy and computational time in estimating the states and parameters for frequency dynamics. These various models and methods provide valuable tools for analyzing and predicting frequency dynamics in power systems.

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