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deep learning in power system

AU - Mocanu, Elena. This paper presents an innovative method based on compressive sensing (CS), singular spectrum analysis (SSA), wavelet transform (WT) and deep neural network (DNN) for monitoring and classification of PQDs. The impressive performance gains and the time savings when compared to feature engineering signify a paradigm shift.Why is deep learning unequaled among machine learning techniques? The sheer complexity of power systems exceeding a few buses, however, makes it an extremely computationally demanding task.

By continuing you agree to the Copyright © 2020 Elsevier B.V. or its licensors or contributors. Systems such as Siri and Cortana are powered, in part, by deep learning.Several developments are now advancing deep learning:At the same time, human-to-machine interfaces have evolved greatly as well. Adding more data requires you to do it all over again.The new approach with deep learning is to replace the formulation and specification of the model with hierarchical characterizations (or layers) that learn to recognize latent features of the data from the regularities in the layers.The paradigm shift with deep learning is a move from feature engineering to feature representation.The promise of deep learning is that it can lead to predictive systems that generalize well, adapt well, continuously improve as new data arrives, and are more dynamic than predictive systems built on hard business rules. Dive into the research topics of 'Deep learning for power system data analysis'. Unprecedented high volumes of data are available in the smart grid context, facilitated by the growth of home energy management systems and advanced metering infrastructure. Both, their theoretical advantages and limitations are discussed, such as computational requirements, convergence, and stability. Consequently, two applications for building energy prediction using supervised and unsupervised deep learning methods will be presented. View full fingerprint Technology expert Phil Simon suggests considering these ten questions as a preliminary guide.With smart grid analytics, utility companies can control operating costs, improve grid reliability and deliver personalized energy services.From cows to factory floors, the IoT promises intriguing opportunities for business.
His research interests include smart grids, deep and machine learning, power system automation, and hydropower automation.
Consequently, two applications for building energy prediction using supervised and unsupervised deep learning methods will be presented. Both, their theoretical advantages and limitations are discussed, such as computational requirements, convergence, and stability.

In this paper, initially, SSA time-series tool and multi-resolution wavelet transform are introduced to extract the features of PQDs, and then CS technique is used to reduce the dimensionality of the extracted features. The impact that the deep learning has had on the world has been significant – and it’s only getting started. degree from Jiangsu University of Science and Technology, in 2001, the M.S.

Power system state estimation is data-driven in nature as the amount of measurement data is rapidly increasing with emerging sensing technology. If you don't find your country/region in the list, see our For example, deep learning is used to classify images, recognize speech, detect objects and describe content.

Deep learning, which is a branch of artificial intelligence, aims to replicate our ability to learn and evolve in machines. Designed to get results quick, develop and debug code on a basic … These models typically consider a single snapshot of the system without capturing temporal correlations of system states. The proposed chapter will focus on deep learning methods and will be structured as follows: Firstly, as a starting point with respect to the state of the art, the most known deep learning concepts, such as deep belief networks and high-order restricted Boltzmann machine (i.e., conditional restricted Boltzmann machine, factored conditional restricted Boltzmann machine, four-way conditional restricted Boltzmann machine), are presented. The proposed adaptive hybrid deep learning … Still, the power system transition toward the big data era encourages the use of deep learning, as the most advanced solutions for large-scale applications. While deep learning was first theorized in the 1980s, there are two main reasons it has only recently become useful: Deep learning requires large amounts of labeled data. If you are working on complex problems or are a company that leverages deep learning, you should probably build your own deep learning system or use a cloud service. Deep learning is a more effective approach than traditional neural network to solve problems including availability of data, better local optimum, and diffusion of gradients.

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