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Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach | | Xinan Wang
; Yishen Wang
; Di Shi
; Jianhui Wang
; Zhiwei Wang
; | | Date: |
8 Nov 2019 | | Abstract: | With the increasing complexity of modern power systems, conventional dynamic
load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to
address the current load characteristic transitions. In recent years, the WECC
composite load model (WECC CLM) has shown to effectively capture the dynamic
load responses over traditional load models in various stability studies and
contingency analyses. However, a detailed WECC CLM model typically has a high
degree of complexity, with over one hundred parameters, and no systematic
approach to identifying and calibrating these parameters. Enabled by the wide
deployment of PMUs and advanced deep learning algorithms, proposed here is a
double deep Q-learning network (DDQN)-based, two-stage load modeling framework
for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for
more efficient identification and does not require explicit model details. In
the first stage, the DDQN agent determines an accurate load composition. In the
second stage, the parameters of the WECC CLM are selected from a group of
Monte-Carlo simulations. The set of selected load parameters is expected to
best approximate the true transient responses. The proposed framework is
verified using an IEEE 39-bus test system on commercial simulation platforms. | | Source: | arXiv, 1911.4894 | | Services: | Forum | Review | PDF | Favorites | |
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