Data-Driven Distributed Predictive Control for Voltage Regulation and Current Sharing in DC Microgrids With Communication Constraints.

The use of nonideal communication networks makes communication constraints become a topical issue in the research of dc microgrids. How to design a distributed secondary control scheme for voltage recovery and accurate current sharing in islanded dc microgrids subject to communication constraints is of interest in this article. In order to restore the bus voltage to the rated value, a nonlinear element is first introduced into the primary control layer. Then, the closed-loop system of primary control is modeled as a data-driven time-varying linear system. Based on the established model, considering communication constraints, a distributed secondary predictive control strategy is developed to achieve accurate current sharing. While actively compensating for network delays and packet losses, the proposed method renders mathematical physical models unnecessary for the traditional predictive control, and simultaneously completes the multitask in dc microgrids. Finally, several case studies are conducted on a hardware microgrid experimental platform, which not only verifies the effectiveness of the designed data-driven predictive control strategy but also tests microgrid properties such as the plug-and-play ability.

Online Virtual Reality Networked Control Laboratory Applied in Control Engineering Education.

Online laboratories play an important role in engineering education. This work discusses a WebVR-based virtual laboratory system. The user enters the simulated laboratory environment through a virtual reality (VR) device and interacts with the experimental equipment, similar to hands-on experiments in a physical laboratory. In addition, the proposed system allows users to design their own control algorithms and observe the effects of different control parameters to enhance their understanding of the experiment. To illustrate the features of the proposed virtual laboratory, an example is provided in this paper, which is an experiment on a double inverted pendulum system. The experimental results show that the proposed system allows users to conduct experiments in an immersive and interactive manner and provides users with a complete experimental process from principal design to experimental operation. A solution is also provided to change any virtual laboratory into a WebVR-based virtual laboratory for education and training.

Mathematically Improved XGBoost Algorithm for Truck Hoisting Detection in Container Unloading.

Truck hoisting detection constitutes a key focus in port security, for which no optimal resolution has been identified. To address the issues of high costs, susceptibility to weather conditions, and low accuracy in conventional methods for truck hoisting detection, a non-intrusive detection approach is proposed in this paper. The proposed approach utilizes a mathematical model and an extreme gradient boosting (XGBoost) model. Electrical signals, including voltage and current, collected by Hall sensors are processed by the mathematical model, which augments their physical information. Subsequently, the dataset filtered by the mathematical model is used to train the XGBoost model, enabling the XGBoost model to effectively identify abnormal hoists. Improvements were observed in the performance of the XGBoost model as utilized in this paper. Finally, experiments were conducted at several stations. The overall false positive rate did not exceed 0.7% and no false negatives occurred in the experiments. The experimental results demonstrated the excellent performance of the proposed approach, which can reduce the costs and improve the accuracy of detection in container hoisting.

Consensus-based secondary control for DC microgrids with communication delays via a networked predictive control strategy.

In today's cyber-physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids' communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber-physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems' stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy.

Digital twin based monitoring and control for DC-DC converters.

The monitoring and control of DC-DC converters have become key issues since DC-DC converters are gradually playing increasingly crucial roles in power electronics applications such as electric vehicles and renewable energy systems. As an emerging and transforming technology, the digital twin, which is a dynamic virtual replica of a physical system, can potentially provide solutions for the monitoring and control of DC-DC converters. This work discusses the design and implementation of the digital twin DC-DC converter in detail. The key features of the physical and twin systems are outlined, and the control architecture is provided. To verify the effectiveness of the proposed digital twin method, four possible cases that may occur during the practical control scenarios of DC-DC converter applications are discussed. Simulations and experimental verification are conducted, showing that the digital twin can dynamically track the physical DC-DC converter, detect the failure of the physical controller and replace it in real time.

Priori-guided and data-driven hybrid model for wind power forecasting.

To overcome the high uncertainty and randomness of wind and enable the grid to optimize advance preparation, a priori-guided and data-driven hybrid method is proposed to provide accurate and reasonable wind power forecasting results. Fuzzy C-Means (FCM) clustering algorithm is used first to recognize the characteristics of the weather in different regions. Then, for the purpose of making full use of both priori information and collected measured data, a three-stage hierarchical framework is designed. First, via fuzzy inference and dimension reduction of Numerical Weather Prediction (NWP), more applicable wind speed information is obtained. Second, the accessible wind power generation patterns are served as a guide for mining the actual power curve. Third, the forecasted power is derived through the recorded data and the predictable wind conditions via data-driven model. This forecasting framework ingeniously introduces a gateway that can import priori knowledge to steer the iterative learning, thus possessing both adaptive learning ability and Volterra polynomial representation, and can present forecasted outcomes with robustness, accuracy and interpretability. Finally, a real-world dataset of a wind farm as well as an open source dataset are used to verify the performance of the proposed forecasting method. Results of the ablation analyses and comparative experiments demonstrate that the introduction of domain knowledge improves the forecasting performance.

Toward an international platform: A web-based multi-language system for remote and virtual laboratories using react framework.

Universality is crucial for systems to provide services to users with different backgrounds. A good language system can remove language barriers to help to improve system universality. To improve user experience and provide new features to users, the Networked Control System Laboratory (NCSLab) has been redeveloped based on React. To achieve toward an international platform, this paper introduces a web-based multi-language system for remote and virtual laboratories based on React. The language system is implemented based on an open source tool named react-intl-universal. The architecture and modular design concept of the language system are investigated and the design and implementation are explored in detail from the perspective of the front-end and back-end separation scheme. The proposed language system has been integrated into the new React-based NCSLab system, which is scalable and can improve universality to serve not only domestic users in China, but also international users from all over the world.

Security tracking control for discrete-time stochastic systems subject to cyber attacks.

This paper is concerned with the security tracking problem under the quadratic cost criterion for a class of discrete-time stochastic linear networked control systems (NCSs) exposed to cyber attacks, covering false data injection attacks as well as a class of DoS attacks. Taking into account factors such as network congestion and the defensive role of intrusion detection systems, successful attack events are modeled as a Bernoulli random sequence. To describe the transient trajectory of an NCS under the impact of a random attack, a probabilistic definition of secure trackability is taken. Therefore, an observer-based dynamic output feedback controller is designed in order to achieve the specified probabilistic secure trackability. Specifically, the probabilistic safety output tracking problem is transformed into an input-to-state stability problem in the probabilistic sense for closed-loop systems with some new sufficient conditions, provided that an augmented incremental model is utilized Then, the controller parameters and the upper bound of the quadratic cost function are determined by solving matrix inequalities, while easy-solution forms of the matrix inequalities to be solved are presented by the Schur complementary lemma. Both simulation studies and practical experiments demonstrate the effectiveness of the proposed control scheme.

Interactive and Visualized Online Experimentation System for Engineering Education and Research.

Experimentation is crucial in engineering education. This work explores visualized experiments in online laboratories for teaching and learning and also research. Interactive and visualizing features, including theory-guided algorithm implementation, web-based algorithm design, customizable monitoring interface, and three-dimensional (3-D) virtual test rigs are discussed. To illustrate the features and functionalities of the proposed laboratories, three examples, including the first-order system exploration using a circuit-based system with electrical elements, web-based control algorithm design for virtual and remote experimentation, are provided. Using user-designed control algorithms, not only can simulations be conducted, but real-time experiments can also be conducted once the designed control algorithms have been compiled into executable control algorithms. The proposed online laboratory also provides a customizable monitoring interface, with which users can customize their user interface using provided widgets such as the textbox, chart, 3-D, and camera widget. Teachers can use the system for online demonstration in the classroom, students for after-class experimentation, and researchers to verify control strategies.