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1. Design of a Laboratory Scale Solar Microgrid Cyber-Physical System for Education

   https://doi.org/10.3390/electronics10131562  

   Guo, Liping; Kors, Jason (July 2021, Electronics)

   null (Ed.)

   Renewable energy sources such as solar and wind provide an effective solution for reducing dependency on conventional power generation and increasing the reliability and quality of power systems. Presented in this paper are design and implementation of a laboratory scale solar microgrid cyber-physical system (CPS) with wireless data monitoring as a teaching tool in the engineering technology curriculum. In the system, the solar panel, battery, charge controller, and loads form the physical layer, while the sensors, communication networks, supervisory control and data acquisition systems (SCADA) and control systems form the cyber layer. The physical layer was seamlessly integrated with the cyber layer consisting of control and communication. The objective was to create a robust CPS platform and to use the system to promote interest in and knowledge of renewable energy among university students. Experimental results showed that the maximum power point tracking (MPPT) charge controller provided the loads with power from the solar panel and used additional power to charge the rechargeable battery. Through the system, students learned and mastered key concepts and knowledge of multi-disciplinary areas including data sampling and acquisition, analog to digital conversion, solar power, battery charging, control, embedded systems and software programing. It is a valuable teaching resource for students to study renewable energy in CPS. 

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2. Frequency Response Improvement of PMSG Wind Turbines Using a Washout Filter

   https://doi.org/10.3390/en13184797  

   Shabestari, Parisa M.; Mehrizi-Sani, Ali (September 2020, Energies)

   null (Ed.)

   High integration of renewable energy resources, such as wind turbines, to the power grid decreases the power system inertia. To improve the frequency response of a low-inertia system, virtual inertia approach can be used. This letter proposes a control method to decrease the frequency transients and restore frequency to its nominal value. A wind turbine usually works based on maximum power point tracking (MPPT) curves to achieve the maximum power. In this letter, the proposed controller uses a non-MPPT method to leave power for frequency regulation during transients. Moreover, it uses a washout filter-based method to remove the steady-state error in the frequency. Simulation results in the PSCAD environment validate the improved performance of the proposed method during load changes by comparing it with the MPPT and non-MPPT methods. 

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3. Deep Neural Network Based Saturated Adaptive Control of Muscles in a Lower-Limb Hybrid Exoskeleton

   https://doi.org/10.1115/IMECE2023-112415  

   Ting, Jonathan A; Basyal, Sujata; Allen, Brendon C (October 2023, American Society of Mechanical Engineers)

   Hybrid exoskeletons are used to blend the rehabilitative efficacy and mitigate the shortcomings of functional electrical stimulation (FES) and exoskeleton-based rehabilitative solutions. This paper introduces a novel nonlinear controller that may potentially improve the rehabilitative efficiency of a lower limb hybrid exoskeleton by implementing four key features into the FES and exoskeleton controllers. First, the FES input to the user’s muscles is saturated based on user preference to ensure user comfort. Second, rather than discarding the excess control effort from the saturated FES input, it is redirected into the exoskeleton’s motor controller. Third, a safe deep neural network (DNN) is designed to estimate the unknown dynamics of the hybrid exoskeleton and the DNN is implemented in the FES controller to improve the control efficiency and tracking performance. Fourth, an adaptive update law is designed to estimate the unknown muscle control effectiveness to facilitate the implementation of the DNN. Lyapunov stability-based methods are used to generate real-time adaptive update laws that will train the adaptive estimate of the muscle effectiveness and the output layer weights of the DNN in real-time, ensure the effectiveness and safety of the controllers, and prove global asymptotic tracking of the desired trajectory. 

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4. Switching between MPPT and VSG Controls for Utility-Scale Photovoltaic Plants to Mitigate Power System Oscillations

   https://doi.org/10.1109/ECCE55643.2024.10861852  

   Ratnakumar, Rajan; Venayagamoorthy, Ganesh K (October 2024, IEEE)

   The decline of conventional synchronous generators in the modern power system is driven by the increasing demand for low-inertia/inertia-less renewable energy sources (RES), consequently leading to the growing integration of inverter-based resources (IBRs) into the power system. The incorporation of low-inertia/inertia-less IBRs makes the monitoring and damping of low-frequency electromechanical oscillations (EMOs) crucial. While Virtual Synchronous Generator (VSG) control introduces virtual inertia into the power system, it does not maximize energy capture from RES as effectively as maximum power point tracking (MPPT) does, as it should maintain a power reserve to provide the inertial support and damping. In this study, switching IBRs between MPPT and VSG controls based on an EMO index (EMOI) threshold is proposed to mitigate the emergence of EMO. The impact of the switching control of IBRs is illustrated for a modified two-area, four-machine power system with two large solar photovoltaic plants. Typical results are presented from a simulation on real-time digital simulator (RTDS) to show improved EMOI. 

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5. Tuning PID Controller for Quadrotor Using Particle Swarm Optimization

   https://doi.org/10.1109/UR61395.2024.10597517  

   Rodriguez, Eric X; Lu, Qi (June 2024, IEEE)

   Energy expenditure for quadrotor control has a likelihood of being costly given parameter-dependent controllers that are less than optimal. The cost can grow proportionally when applied to multiple quadrotors for tracking and collaborative navigation tasks. This research aims to establish a basic approach to tuning PID (Proportional-Integral-Derivative) parameters for a simulated quadrotor drone. A PID controller for autonomy provides a straightforward method for correcting robotic movement based on its current state. However, applying a PID system to a flight controller poses challenges with an inherently under-actuated system, which includes the likelihood of large overshoots and lengthy adjustment times. To address this, we utilize PSO (Particle Swarm Optimization) for optimizing PID parameters in a simulated quadrotor. The PSO is employed to find optimal PID values for thrust, yaw, and translational movement on x- and y-positions by identifying converging values across randomly created particles. We conducted a set of experiments and compared it to the default PID controller. The experiments demonstrate converging properties for particles that achieve minimal fitness scores, particularly in reducing overshoot. The results indicate that the optimized PID controller outperforms the default PID controller without optimization. Using optimized PID controllers can decrease the amount of positional error during flight and when adjusting position with collaborative navigation and collision avoidance algorithms. 

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