With the rapid growth of power systems measurements in terms of size and complexity, discovering statistical patterns for a large variety of real-world applications such as renewable energy prediction, demand response, energy disaggregation, and state estimation is considered a crucial challenge In recent years, deep learning has emerged as a novel class of machine learning algorithms that represents power systems data via a large hypothesis space that leads to the state-of-the-art performance compared to most recent data-driven algorithms This study explores the theoretical advantages of deep representation learning in power systems research We review deep learning methodologies presented and applied in a wide range of supervised, unsupervised, and semi-supervised applications as well as reinforcement learning tasks We discuss various settings of problems solved by discriminative deep models including stacked autoencoders and convolutional neural networks as well as generative deep architectures such as deep belief networks and variational autoencoders The theoretical and experimental analysis of deep neural networks in this study motivates long- term research on optimizing this cutting-edge class of models to achieve significant improvements in the future power systems research

Deep learning in power systems research: A review

Assessment on power systems non-deterministic state estimation algorithms

Distribution System State Estimation (DSSE) infers the states (e.g., voltage phasors) using measurements from the electric distribution grid. As the distribution network becomes more active due to the increasing penetration of Distributed Energy Resources (DER), a deeper investigation into conventional DSSE approaches is needed. This review paper focuses on state-of-the-art techniques and challenges for DSSE in the context of emerging concepts, such as new sensor technologies, data-driven approaches, and high DER penetration. 

/pdf/review-of-emerging-concepts-in-distribution-system-state-3pma61p0.pdf

Review of Emerging Concepts in Distribution System State Estimation: Opportunities and Challenges

/pdf/deep-learning-enabled-mcmc-for-probabilistic-state-2332g1cf.pdf

Deep Learning-enabled MCMC for Probabilistic State Estimation in District Heating Grids

Forecasting-aided state estimation based on deep learning for hybrid AC/DC distribution systems

The increasing complexity of distribution grids due to widespread deployment of renewable resources and/or power electronic devices, e.g., voltage source converters, has necessitated the needs of distribution system state estimation (DSSE) for efficient control relying on an accurate picture of the system states. This paper therefore explores the application of using the deep belief network (DBN) for pseudo measurements modeling in the context of DSSE. Two DBNs are trained respectively, for active and reactive power injection outputs, with load profiles and limited number of real measurements. Given the non-Gaussian behavior of the estimated pseudo measurements, we model the error by Gaussian mixture distribution and accordingly design a state estimator based on the Gaussian component combination method (GCCM). This method is able to handle the non-Gaussian measurement uncertainty while retaining the framework of the classic weighed least square (WLS) algorithm. The effectiveness of the proposed DSSE is demonstrated on a modified US PG&E69 distribution network in terms of the accuracy for both the estimated quantities and the associated uncertainty distributions.

Probabilistic State Estimation Approach for AC/MTDC Distribution System Using Deep Belief Network With Non-Gaussian Uncertainties

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Probabilistic State Estimation Approach for AC/MTDC Distribution System Using Deep Belief Network With Non-Gaussian Uncertainties