In this article, the challenges and a future vision of the cyber–physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyberattacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision-making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyberattacks in software and cybernetworks is described. Simulation and experimental results that show the impact of cyberattacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cybersecure power electronics systems. These opportunities include multiscale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. This letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc-dc and dc-ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. For comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics, file access records, can be forged. Furthermore, existing solutions mainly target cyber attacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single deep learning models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g. CPU, network, disk, etc. to identify cyber attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances.

Cyber attacks and malfunctions challenge the wide applications of Internet of Things (IoT). Since they are generally designed as embedded systems, typical auto-sustainable IoT devices usually have a limited capacity and a low processing power. Because of the limited computation resources, it is difficult to apply the traditional techniques designed for personal computers (PCs) or super computers, like traffic analyzers and anti virus software. In this paper, we propose to leverage statistical learning methods to characterize the device behavior and flag deviations as anomalies. Because the system statistics such as CPU usage cycles, disk usage, etc. can be obtained by IoT APIs (application program interface), the proposed framework is platform and device independent. Considering IoT applications, we train multiple machine learning models to evaluate their feasibility and suitability. For the target auto-sustainable IoT devices, which operate well-planned processes, the normal system performances can be modeled accurately. Based on time series analysis methods, such as local outlier factor (LOF), cumulative sum (CUSUM) and the proposed adaptive online thresholding (AOT), the anomalous behaviors can be effectively detected. Comparing their performances on detecting anomalies as well as the computation sources required, we conclude that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred.

Switched reluctance motors (SRMs) are promising traction machine candidates for electric vehicle powertrains and industrial driving systems due to their rugged construction and high efficiency. Precise speed and torque control of SRMs require real-time rotor position information from mechanical position sensors. With the advantages of lower costs and position sensor fault-tolerant capability, various position-sensorless control strategies have been developed for SRM drives in the past decades. This article presents a review of the state-of-the-art sensorless control schemes over a wide speed range. Low-speed methods based on high-frequency pulse voltage injection and high-speed methods based on fundamental excitation are summarized to give readers a systematic categorization. An in-depth comparison of major sensorless control algorithms in estimation accuracy, robustness, universality, and parameter sensitivity is presented. Finally, the existing challenges and future trends are discussed.