Driven by the recent advances and applications of smart-grid technologies, our electric power grid is undergoing radical modernization. Microgrid (MG) plays an important role in the course of modernization by providing a flexible way to integrate distributed renewable energy resources (RES) into the power grid. However, distributed RES, such as solar and wind, can be highly intermittent and stochastic. These uncertain resources combined with load demand result in random variations in both the supply and the demand sides, which make it difficult to effectively operate a MG. Focusing on this problem, this paper proposed a novel energy management approach for real-time scheduling of an MG considering the uncertainty of the load demand, renewable energy, and electricity price. Unlike the conventional model-based approaches requiring a predictor to estimate the uncertainty, the proposed solution is learning-based and does not require an explicit model of the uncertainty. Specifically, the MG energy management is modeled as a Markov Decision Process (MDP) with an objective of minimizing the daily operating cost. A deep reinforcement learning (DRL) approach is developed to solve the MDP. In the DRL approach, a deep feedforward neural network is designed to approximate the optimal action-value function, and the deep Q-network (DQN) algorithm is used to train the neural network. The proposed approach takes the state of the MG as inputs, and outputs directly the real-time generation schedules. Finally, using real power-grid data from the California Independent System Operator (CAISO), case studies are carried out to demonstrate the effectiveness of the proposed approach.

Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.

The proliferation of distributed renewable energy resources (RESs) poses major challenges to the operation of microgrids due to uncertainty. Traditional online scheduling approaches relying on accurate forecasts become difficult to implement due to the increase of uncertain RESs. Although several data-driven methods have been proposed recently to overcome the challenge, they generally suffer from a scalability issue due to the limited ability to optimize high-dimensional continuous control variables. To address these issues, we propose a data-driven online scheduling method for microgrid energy optimization based on continuous-control deep reinforcement learning (DRL). We formulate the online scheduling problem as a Markov decision process (MDP). The objective is to minimize the operating cost of the microgrid considering the uncertainty of RESs generation, load demand, and electricity prices. To learn the optimal scheduling strategy, a Gated Recurrent Unit (GRU)-based network is designed to extract temporal features of uncertainty and generate the optimal scheduling decisions in an end-to-end manner. To optimize the policy with high-dimensional and continuous actions, proximal policy optimization (PPO) is employed to train the neural network-based policy in a data-driven fashion. The proposed method does not require any forecasting information on the uncertainty or a prior knowledge of the physical model of the microgrid. Simulation results using realistic power system data of California Independent System Operator (CAISO) demonstrate the effectiveness of the proposed method.

This paper presents a novel simulation approach to evaluate new ancillary service controls in the context of large-scale wind power integration. We compare different types of turbine inertial control methods with the proposed modifications to cope with realistic wind conditions in the field. The simulation procedure is started with the software-based simulation, in which we employ the high-fidelity wind turbine simulator FAST that models a real 3-bladed Controls Advanced Research Turbine (CART3). The advantages of using FAST is that it can provide convincing simulation results and address the interactions between turbine electrical and mechanical systems. The developed controller model is then rapidly prototyped for the real-time simulation with the hardware-in-the-loop (HIL) scheme. The CART3 will respond to a virtual frequency event triggered in the emulated electric grid modelled in a digital real-time simulator (DRTS). The introduced simulation platform streamlines the procedure of designing turbine auxiliary controls, and these simulations results give insights on the turbine controls and their impacts on the interconnected power system, as well as the effects on turbine mechanical components. The results indicate that inertial controls tend to reduce the out-of-plane mechanical loadings in region 2, while such loadings are dominated by pitch actions in region 3.

Recently, programing by demonstration (PbD) becomes a white-hot research area with increasing demands in the robot manipulation area, especially in industrial applications. However, the key challenges of PbD, to date, has been the recognition of demonstrator's fine hand motions and the uncertainty of manipulation environments, which limit the efficiency and accuracy of PbD. In this study, inspired by humanoid manipulation, a novel PbD approach using the implicit muscular task planning strategy is presented to extract features from the arms' giant movement and the hands' fine motions during the demonstrator's operation. Furthermore, we integrate a deep reinforcement learning control method that further improves the manipulations' adaptive ability in the unknown or changeable environments. The experimental results show that our proposed approach can deal with relative complex assembly task from single demonstration with more than 67% success rate under about 4.2 mm fit tolerance.