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

Abstract

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.

Artificial Intelligence Techniques for Dynamic Security Assessments – A Survey

“…Frequency stability refers to the ability of a power system to maintain a steady frequency following a disturbance that results in imbalances between generation and load (Kundur et al, 2004). Models for evaluating frequency stability were proposed in (Alizadeh & Amraee, 2014;Bo et al, 2014;Chang et al, 2005;Karapidakis, 2007;Nakas et al, 2023;Ortiz-Villalba et al, 2020;Song et al, 2020;Tang et al, 2017;C. Wang et al, 2021;Q.…”

“…Zhang et al, , 2023bZhao et al, 2023b), Graph Neural Network (Y. Luo et al, 2023R. Zhang et al, 2023b), and Recurrent Neural Network (RNN) (Bahbah & Girgis, 2004;Nakas et al, 2023;Song et al, 2020). In the latter, unlike FFNN, each neuron is fed by the input and by its own response in a recursive way.…”

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