Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool | Zendy

Sungyoon Song | Zendy; Yoongun Jung | Zendy; Changhee Han | Zendy; Seungmin Jung | Zendy; Minhan Yoon | Zendy; Gilsoo Jang | Zendy

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Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool

Author(s) -

Sungyoon Song,

Yoongun Jung,

Changhee Han,

Seungmin Jung,

Minhan Yoon,

Gilsoo Jang

Publication year - 2020

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2020.3028707

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and time-variable frequency data in case of contingency. To capture the regularity and random characteristics of PV power output, a probability power flow-dynamic tool (PPDT) for uncertain power system modeling has been developed. This tool considers all possible combinations of PV power generation patterns, even those with low probability, such as those caused by passing clouds. The results are verified by a comparison of various artificial intelligence methods using case studies from the South Korean power system. An online dispatch algorithm that considers the frequency constraints for a designated contingency can be implemented by using the proposed model.

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