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A smart home power management system is critical for stand-alone home-photovoltaic (HPV) with battery energy storage. Existing approaches often focus on maximizing power extraction from PV systems without considering real-time power adjustments or battery state of charge (SoC), which can lead to over-current or over-voltage issues that damage the battery. To address these limitations, this paper proposes a home power management algorithm that dynamically adjusts the power flow between the PV system, home loads, and the battery based on real-time system power measurements and battery SoC. This dynamic adjustment ensures an uninterrupted power supply to the home loads while maintaining battery safety. The proposed home manager algorithm features multiple operating modes: Maximum power point (MPP) charging, partial charging, fast charging, float charging, and partial discharging. Each mode is integrated with its dedicated model predictive controller (MPC) to achieve its specific control objective. Finally, through a semi-experimental simulation using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335, testing under various irradiance levels shows that the home manager achieves significant improvement over conventional approaches. For instance, over the MPP mode it achieve a power efficiency of 99.36% with a 2.05% SoC increase, while fast charging mode resulted in 87.44% efficiency with an 11.2% SoC increase. However, float charging mode maintained an 87.27% efficiency with a 2.6% SoC increase, and partial discharging led to a 1.3% SoC decrease. The overall battery efficiency reached 96.45%, demonstrating the algorithm’s ability to optimize power flow, ensure a reliable energy supply, and maintain battery safety under varying weather conditions.

This paper presents a novel station manager algorithm for grid-connected PV-EV charging stations, designed to address key challenges in current systems. Existing charging stations often encounter issues such as unstable PV power generation and dependence on grid stability, which can interrupt the EV charging process during grid faults. Additionally, PV arrays are typically designed to extract maximum power, leading to over-current or over-voltage situations that compromise the safety of the charging infrastructure and the EV. Furthermore, these systems often require multiple power electronics converters, increasing complexity and costs while reducing overall system efficiency. To overcome these limitations, the proposed algorithm dynamically switches between on-grid and off-grid modes based on real-time weather conditions, grid availability, and the state of charge of the battery electric vehicle (BEV). This approach maximizes PV power utilization, minimizes grid dependency, and enhances BEV charging performance while prioritizing EV safety and ensuring an uninterrupted power supply. It also provides flexibility in BEV power sizing, optimizing the use of power electronics converters to reduce costs and complexity. Two distinct operating modes, adaptive charging and fast charging, are introduced, each integrated with dedicated model predictive controllers (MPC) to achieve specific control objectives. Semi-experimental simulations using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335 demonstrate that the station manager significantly improves performance over conventional methods under various irradiance levels. Moreover, numerical results demonstrate the superiority of the proposed MPC-based approach over traditional controllers such as Proportional-Integral-Derivative (PID) and Sliding Mode Control (SMC) in different charging modes. For instance, the MPC controller achieved an Integral Absolute Error (IAE) of 11.45%, lower than that of the PID controller, and 4.3% lower than the SMC controller.

This article presents the complete design of a nonlinear control system for multiple stationary bikes connected to a mechanical energy conversion system equipped with squirrel cage induction generators (SIGs) linked to AC/DC converters. The primary objective of this study is to investigate the feasibility of powering multiple treadmills using the DC bus via DC/AC converters. The novelty of our approach lies in the integration of a new strategy of control system to achieve multiple control objectives within a gym microgrid environment, and an energy management algorithm to ensure the energy flow between intermittent generation and the considered, somewhat random, demand. The system is composed of several subsystems: i) stationary bikes connected to the DC bus via inverters acting as intermittent power sources; ii) a Li-ion battery-based energy storage system interfaced through a Buck-Boost converter; iii) electromechanical loads, including treadmills, powered by DC/AC converters, in addition to other DC loads. The main control objectives are as follows: a) each stationary bike extracts energy from the DC network by regulating the torque applied by the athlete, ensuring that the torque follows the reference torque; b) the treadmill’s speed must follow the reference generated based on the athlete’s condition; c) ensuring the protection of the energy storage system by monitoring its current and voltage; d) all mentioned objectives are achieved while maintaining the DC bus voltage at a reference value. To achieve these objectives, a nonlinear control approach based on Lyapunov theory is used to design the controllers. A formal analysis is conducted to assess the performance of the studied system. The system’s performance is demonstrated using the MATLAB/Simulink environment. Numerous simulations demonstrate that every control objective is achieved. Specifically, the system maintained the DC bus voltage at 500 V with a maximum deviation of 1 V, and the treadmill speed followed the reference within a margin of 0.1 m/s.

Voltage source converter-based high-voltage direct current (VSC-HVDC) transmission systems are becoming increasingly popular for long-distance power transmission due to their numerous advantages. However, the fault ride-through (FRT) capabil-ity of VSC-HVDC systems during AC faults remains a key challenge that needs to be addressed to ensure their uninterrupted operation under challenging grid conditions. This paper presents and analyzes a novel nonlinear control scheme based on a robust sliding mode control approach to improve the FRT capability of VSC-HVDC systems. The proposed control scheme based on the modification of the converter’s active power to make it more sensitive to any changes in voltage magnitude at the points of common coupling (PCCs) on the AC side. This helps to maintain the DC voltage sta-bility and prevent the converter from tripping during AC faults. The performance of the proposed control scheme is evaluated using a series of ex-perimental investigations using a digital signal processor (DSP) within Processor-in-the-Loop (PIL) quasi-real-time simulation. The PIL simulations closely examine the system’s response to AC faults with varying remaining voltages and durations. The results demonstrate that the proposed control scheme can effectively improve the FRT capability of VSC-HVDC systems and ensure their stable operation during AC faults.

This paper presents a study on the application of nonlinear control and optimal power management techniques in a grid-connected wind energy conversion system with battery storage. The objective is to efficiently supply power to a load while considering performance constraints. The energy system under investigation comprises a wind turbine, battery, AC-grid, and load. Power is delivered to the load through various converters and a DC-link. However, the intermittent nature of wind energy and frequent load fluctuations can negatively impact the battery's lifetime and charging performance. To address these challenges, a nonlinear controller and a novel energy management system are designed. The energy management system generates multiple energy flow scenarios to achieve equilibrium in energy distribution between the load and energy sources. This equilibrium aims to minimize overall system costs, ensure DC-grid stability, and enhance power quality. Two distinct operation modes are identified: i) MPPT mode, which utilizes an optimizer to maximize power extraction, and ii) adaptive power point tracking (APPT) mode, which considers demand-driven production and the state of charge (SOC) of the storage system. The convergence of the closed-loop control is formally analyzed, and simulation results confirm the improved performance of the proposed multi-criteria design for energy management.

This research work addresses the challenge of controlling a wind energy conversion system (WECS) connected to the grid, which incorporates a battery energy storage system and operates under varying real wind speed profiles. The system comprises a Doubly-Fed Induction Generator (DFIG) connected to the grid through an AC/DC/AC converter, along with a Li-ion battery pack storage unit linked to the Back-to-Back converter DC via a DC/DC converter. The study highlights that internal parametric variations and the non-linear behavior of the wind turbine negatively affect the energy quality produced, as well as the battery charging performance and its overall lifespan. To overcome these issues, the research proposes a robust control strategy based on Integral action Sliding Mode Control (ISMC) to independently manage the powers of the WECS, particularly the DFIG, to enhance dynamic performance. Furthermore, effective battery charging and discharging controllers are crucial for efficiently distributing the generated power to both the grid and the storage unit, ensuring proper energy extraction and battery charging/discharging. Depending on the battery’s State of Charge (SOC), the available extracted energy, and the power injected into the grid, two current regulation modes are employed: buck charging mode and boost discharging mode. A storage system energy management algorithm is then utilized to determine the appropriate charging mode, ensuring battery safety. To validate the system’s performance, a 1.5 MW wind power conversion system is tested using Matlab/Simulink simulations. The results demonstrate the robustness and efficiency of the proposed control approach in enhancing the overall system’s performance.

This paper addresses the problem of output feedback control for high voltage direct current (HVDC) systems based on voltage source converters (VSC). Specifically, the system under study consists of two power grids and two VSC stations connected via a long HVDC cable. The primary goal of this paper is to propose a different approach for the online estimation of the grid voltage placed at a great distance from the control unit which is considered generally known, moreover the proposed technique aims to online estimation of the grid pulsation and the HVDC cable states, generally considered available. A nonlinear observer is intended to address these concerns. Only the line current measurements are used to estimate all state variables. Furthermore, utilizing the estimation offered by the proposed observer, an output feedback control strategy is formulated, using the backstepping design technique, to meet the three control objectives: 1) tight output voltage regulation of the HVDC cable; 2) ensuring a perfect power quality at both sides of the HVDC system; 3) regulation of the active power at the inverter station. The high effectiveness of the proposed output feedback controller’s performance is highlighted via a semi-experimental platform that includes a Processor In the Loop (PIL) on the embedded board eZdsp. Extra numerical simulations show that The proposed output feedback performance enjoys additional robustness properties and meets all control objectives.

This research paper addresses the issue of enhancing the operational availability of NPC three-level converter-based high-voltage direct current (HVDC) transport systems during alternating current (AC) grid fault conditions. During short-circuit faults in power transmission lines, voltage sags can occur, causing fluctuations in the DC link voltage of converter systems. These voltage sags have the potential to induce a reversed power flow and trip the VSC-HVDC transmission system. The objective of this paper is to develop a nonlinear control technique that investigates the fault ride-through (FRT) capability of VSC-HVDC transmission system characteristics during voltage sag events. To achieve this, we conduct semi-experimental investigations using Processor-in-the-Loop (PIL) simulations and analyze the results. Symmetrical and asymmetrical voltage sag events with different remaining voltages are applied to an AC grid, and their effects are observed for varying durations. The proposed nonlinear control technique aims to mitigate the impact of voltage sags on the operational availability of HVDC transport systems. By analyzing the semi-experimental results, we aim to gain insights into the FRT capability of the VSC-HVDC transmission system.