Noel Cressie and Christopher WikleHardcover: 624 pagesYear: 2011Publisher: John WileyISBN-13: 978-0471692744Here is the new reference book about spatial and spatio-temporal statistical modeling! No...

Statistics for Spatio-Temporal Data

This paper gives a comprehensive overview on disturbance/uncertainty estimation and attenuation (DUEA) techniques in permanent-magnet synchronous motor (PMSM) drives. Various disturbances and uncertainties in PMSM and also other alternating current (ac) motor drives are first reviewed which shows they have different behaviors and appear in different control loops of the system. The existing DUEA and other relevant control methods in handling disturbances and uncertainties widely used in PMSM drives, and their latest developments are then discussed and summarized. It also provides in-depth analysis of the relationship between these advanced control methods in the context of PMSM systems. When dealing with uncertainties, it is shown that DUEA has a different but complementary mechanism to widely used robust control and adaptive control. The similarities and differences in disturbance attenuation of DUEA and other promising methods such as internal model control and output regulation theory have been analyzed in detail. The wide applications of these methods in different ac motor drives (in particular in PMSM drives) are categorized and summarized. Finally, the paper ends with the discussion on future directions in this area.

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Disturbance/Uncertainty Estimation and Attenuation Techniques in PMSM Drives—A Survey

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Weather data

Whole building energy model for HVAC optimal control: A practical framework based on deep reinforcement learning

Permanent magnet synchronous motors are extensively used in high-performance industrial applications. However, plenty of practical factors (e.g., cogging torques, load torques, friction torques, measurement error effects, dead-time effects, and parameter perturbations) in the closed-loop servo system inevitably bring barriers to the high-performance speed regulation, which can be regarded as generalized disturbances. Most of the existing control approaches only focus on one single kind of disturbances. However, the practical servo system is affected by multiple sources of disturbances simultaneously and these disturbances enter into the system through different channels. To this end, this paper systematically analyzes several representative disturbances, particularly including their features and distribution in the practical servo system, and then, specifically puts forward a novel disturbance rejection framework based on a noncascade structure. Under this framework, a comprehensive disturbance observer is proposed to simultaneously and accurately estimate multiple disturbances such that a composite controller can be designed to correspondingly compensate disturbances. Rigorous analysis of stability is established. Comparative experimental results demonstrate that the proposed method achieves a better speed dynamic response and a higher accuracy tracking performance even in the presence of multiple sources of disturbances.

Robust Speed Regulation for PMSM Servo System With Multiple Sources of Disturbances via an Augmented Disturbance Observer

Volt-VAR control is critical to keeping distribution network voltages within allowable range, minimizing losses, and reducing wear and tear of voltage regulating devices. To deal with incomplete and inaccurate distribution network models, we propose a safe off-policy deep reinforcement learning algorithm to solve Volt-VAR control problems in a model-free manner. The Volt-VAR control problem is formulated as a constrained Markov decision process with discrete action space, and solved by our proposed constrained soft actor-critic algorithm. Our proposed reinforcement learning algorithm achieves scalability, sample efficiency, and constraint satisfaction by synergistically combining the merits of the maximum-entropy framework, the method of multiplier, a device-decoupled neural network structure, and an ordinal encoding scheme. Comprehensive numerical studies with the IEEE distribution test feeders show that our proposed algorithm outperforms the existing reinforcement learning algorithms and conventional optimization-based approaches on a large feeder.

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Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems

Providing ride-hailing services with electric vehicles can help reduce greenhouse gas emissions and solve the last mile problem. This paper develops a reinforcement learning based algorithm to operate a community owned electric vehicle fleet, which provides ride-hailing services to local residents. The goals of operating the electric vehicle fleet are to minimize customer waiting time, electricity cost, and operational costs of the vehicles. A novel framework characterized by decentralized learning and centralized decision making is proposed to solve the electric vehicle fleet dispatch problem. The decentralized learning process allows the individual vehicles to share their operating experiences and deep neural network model for state-value function estimation, which mitigates the curse of dimensionality of state and action domains. The centralized decision making framework converts the vehicle fleet coordination problem into a linear assignment problem, which has polynomial time complexity. Numerical study results show that the proposed approach outperforms the benchmark algorithms in terms of societal cost reduction.

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Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning

https://par.nsf.gov/servlets/purl/10026381

Energy Efficient Building HVAC Control Algorithm with Real-time Occupancy Prediction

Dynamic distribution network reconfiguration (DNR) algorithms perform hourly status changes of remotely controllable switches to improve distribution system performance The problem is typically solved by physical model-based control algorithms, which not only rely on accurate network parameters but also lack scalability To address these limitations, this paper develops a data-driven batch-constrained reinforcement learning (RL) algorithm for the dynamic DNR problem The proposed RL algorithm learns the network reconfiguration control policy from a finite historical operational dataset without interacting with the distribution network The numerical study results on three distribution networks show that the proposed algorithm not only outperforms state-of-the-art RL algorithms but also improves the behavior control policy, which generated the historical operational data The proposed algorithm is also very scalable and can find a desirable network reconfiguration solution in real-time

Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

Jie Shi

Papers

Safe Off-Policy Deep Reinforcement Learning Algorithm for Volt-VAR Control in Power Distribution Systems

Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning

Energy Efficient Building HVAC Control Algorithm with Real-time Occupancy Prediction

Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration

Power System Event Identification Based on Deep Neural Network With Information Loading