This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine gearbox. Different from traditional approaches where feature extraction and classification are separately designed and performed, this study aims to automatically learns effective fault features directly from raw vibration signals and meanwhile classifies the type of faults in a single learning framework, thus leading to an end-to-end learning based fault diagnosis system for wind turbine gearbox without additional signal processing techniques and diagnostic expertise. Considering multi-scale characteristics inherent in vibration signals of a gearbox, in this paper, a new multiscale convolutional neural network (MSCNN) framework is proposed to perform multi-scale feature extraction and classification simultaneously. A key advantage of the proposed MSCNN is the ability to learn complementary and rich fault pattern features at different time scales by incorporating multiscale learning into the CNN architecture, which greatly improves feature learning to enable better diagnosis performance. The proposed MSCNN framework is evaluated through experiments on a wind turbine test rig. The results demonstrate that the proposed MSCNN framework outperforms state-of-the-art methods and exhibits superior robustness against noise.

Driven by the recent advances in electric vehicle (EV) technologies, EVs become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user’s commuting behavior, and pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.

The smart grid is arguably one of the most complex cyber‐physical systems (CPS). Complex security challenges have been revealed in both the physical and the cyber parts of the smart grid, and an integrative analysis on the cyber‐physical (CP) security is emerging. This paper provides a comprehensive and systematic review of the critical attack threats and defence strategies in the smart grid. We start this survey with an overview of the smart grid security from the CP perspective, and then focuses on prominent CP attack schemes with significant impact on the smart grid operation and corresponding defense solutions. With an in‐depth review of the attacks and defences, we then discuss the opportunities and challenges along the smart grid CP security. We hope this paper raises awareness of the CP attack threats and defence strategies in complex CPS‐based infrastructures such as the smart grid and inspires research effort toward the development of secure and resilient CP infrastructures.

Electric vehicles (EVs) have been popularly adopted and deployed over the past few years because they are environment-friendly. When integrated into smart grids, EVs can operate as flexible loads or energy storage devices to participate in demand response (DR). By taking advantage of time-varying electricity prices in DR, the charging cost can be reduced by optimizing the charging/discharging schedules. However, since there exists randomness in the arrival and departure time of an EV and the electricity price, it is difficult to determine the optimal charging/discharging schedules to guarantee that the EV is fully charged upon departure. To address this issue, we formulate the EV charging/discharging scheduling problem as a constrained Markov Decision Process (CMDP). The aim is to find a constrained charging/discharging scheduling strategy to minimize the charging cost as well as guarantee the EV can be fully charged. To solve the CMDP, a model-free approach based on safe deep reinforcement learning (SDRL) is proposed. The proposed approach does not require any domain knowledge about the randomness. It directly learns to generate the constrained optimal charging/discharging schedules with a deep neural network (DNN). Unlike existing reinforcement learning (RL) or deep RL (DRL) paradigms, the proposed approach does not need to manually design a penalty term or tune a penalty coefficient. Numerical experiments with real-world electricity prices demonstrate the effectiveness of the proposed approach.

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H _∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

This article is concerned with the problem of fixed-time (FXT) and preassigned-time (PAT) synchronization for discontinuous dynamic networks by improving FXT stability and developing simple control schemes. First, some more relaxed conditions for FXT stability are established and several more accurate estimates for the settling time (ST) are obtained by means of some special functions. Based on the improved FXT stability, FXT synchronization for discontinuous networks is discussed by designing a simple controller without a linear feedback term. Besides, the PAT synchronization is also explored by developing several nontrivial control protocols with finite control gains, where the synchronized time can be prespecified according to actual needs and is irrelevant with any initial value and any parameter. Finally, the improved FXT stability and the synchronization for complex networks are confirmed by two numerical examples.