NDLI: Data-driven efficient reliability evaluation of power systems with wind penetration: an integrated GANs and CE method

Content Provider	IET Digital Library
Author	Zhuang, Xinran Ye, Chengjin Ding, Yi Cheng, Lin Song, Yonghua Ye, Shuiquan Tian, Shiming Chen, Rong
Abstract	The conventional Monte Carlo simulation may not be efficient enough for reliability evaluation of composite power systems. The cross-entropy (CE) algorithm is a promising state-of-the-art fast sampling method, while it has not been well developed in this field due to the implicit probability distributions of penetrated renewable energies. Specifically, the CE sampling requires the distributions of interest to be explicit and parametric, while some preconceived probabilistic distribution functions (PDFs) such as the Weibull distribution of wind speed make the results to deviate from the reality sometimes. In this study, a data-driven efficient approach for reliability evaluation of power systems with wind penetration is proposed utilising generative adversarial networks (GANs) and CE sampling. The distributions of wind speeds in multiple wind farms are estimated by GANs considering their spatial correlation without any prior knowledge. With the trained generative network mapping from the explicit Gaussian noise to the raw wind speed data, the CE sampling is successfully enabled to efficiently sample the system states with implicit PDFs, which are associated with wind speeds and component failures. A real wind speed dataset and the RTS testing system are utilised to verify the proposed integrated method, including the accuracy of distribution estimation and reliability evaluation result, as well as the speed-up efficiency of sampling.
Starting Page	577
Ending Page	584
Page Count	8
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 4, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1048
Journal	IET Generation, Transmission & Distribution
Publisher Date	2019-11-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CE Sampling Composite Power System Cross-entropy Algorithm Data-driven Efficient Approach Data-driven Efficient Reliability Evaluation Distribution Estimation Entropy Explicit Gaussian Noise Gaussian Noise Generative Adversarial Network Generative Network Mapping Implicit Probability Distribution Integrated GAN Method Monte Carlo Method Monte Carlo Simulation Multiple Wind Farms Neural Computing Technique Neural Nets Power Engineering Computing Power Generation Reliability Power System Reliability Preconceived Probabilistic Distribution Function Probability Raw Wind Reliability Reliability Evaluation Renewable Energy RTS Testing System Sampling Method Spatial Correlation Weibull Distribution Wind Penetration Wind Power Plant Wind Speed Dataset Wind Speed Distribution
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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