7 research outputs found
A New Power Sharing Method for Improving Power Management in DC Microgrid with Power Electronic Interfaced Distributed Generations
No full textANN-Based Real-Time Optimal Voltage Control In Islanded AC Microgrids
Full text linkThe purpose of this paper is to explore an innovative primary control strategy for a voltage source inverter (VSI). This strategy involves integrating an Artificial Neural Network (ANN) into the proportional resonant (PR) regulator within the inner loop of the primary control system. The primary objective is to regulate the output voltage and minimize deviations under various operating conditions, thereby enhancing the inverter's overall performance. The ANN, employed as a predictive analytic method, facilitates real-time tuning of the PR controller parameters. In addition, the outer level of primary control incorporates a droop control loop to distribute power among distributed generators (DG). This proposed approach reduces the converter control system's reliance on specific operating conditions and seamlessly accommodates varying loading conditions. To ensure adaptability and stability in various operating conditions, real-time simulations are implemented using OPAL-RT (OP4510), and the results demonstrate the efficacy of the proposed control strategy
AC Microgrid Modeling and Adaptive Control Using Biomimetic Valence Learning:An AI-Based Approach
Full text linkAC microgrids play a crucial role in integrating distributed energy resources and facilitating localized power management in contemporary power networks. Nevertheless, conventional droop control methods in these microgrids have constraints in guaranteeing precise power distribution, stability of voltage/frequency, and flexibility in response to changing operating conditions. This study introduces an approach, with adaptive droop control using Biomimetic Valence Learning (BVLAC). Inspired by the emotional and rational decision-making processes within the brain, BVLAC dynamically adjusts droop coefficients, optimizing power sharing and transient response in microgrid operation. Simulations were conducted using SIMULINK/MATLAB and the results showcase the superiority of the proposed BVLAC approach in achieving precise power-sharing, maintaining voltage and frequency stability, and improving the control performance of microgrids, under varying load conditions. This work advances the field of microgrid control by offering a robust, AI-inspired solution for the challenges faced by conventional droop control techniques.</p
Robust Control of Voltage Source Converters:A Tube-Based Model Predictive Approach
Full text linkFinite control set-model predictive control (FCS-MPC) operates based on the assumption that optimal control responses executed on a prediction model match closely an actual system. Thus, model uncertainties and external disturbances affect the controller’s performance. To weaken the impact of uncertainties, this brief proposes a tube-based FCS-MPC to control the output voltage of a voltage source converter (VSC). The proposed controller is composed of two FCS-MPCs. The first one generates an initial control action and a prediction of the output voltage based on a system, including no uncertainties. This control action and the output voltage are then sent to the second FCS-MPC, together with the output voltage of the uncertainty-involved system, to choose the best switching states of the VSC. Simulation and experimental validations demonstrate the proposed method’s effectiveness and robustness compared to a conventional FCS-MPC
Intelligent Primary Control of Voltage Source Converters in AC Microgrids
Full text linkThis paper proposes an intelligent primary control strategy for voltage source converter (VSC)-based ac microgrid (MG). This is implemented by using a proportional resonant (PR) regulator adopted in the inner level of primary control of VSCs. An approach based on brain emotional learning (BEL) is proposed to provide an online and adaptive tuning of control coefficients of the PR regulator. The proposed BEL approach is fully model-free, indicating that the coefficients are regulated without previous knowledge of the system model and parameters. The outer level of primary control employs a droop control loop to regulate power-sharing among different distributed generators. Unlike the conventional control methods with constant coefficients, which are typically designed for a specified operating condition, the proposed approach avoids the dependency of the converter control system on the operating conditions and accommodates varying loading conditions. A sensitivity analysis is also performed to investigate the effects of PR coefficients on the system stability. Moreover, a Mesh analysis is carried out to examine the stability of dominant frequency modes of the whole AC-MG using the proposed control scheme. Simulations are provided to demonstrate the performance of the proposed control scheme
Decentralized Reinforcement Learning for Adaptive Power Sharing in Hybrid DC Microgrids
No full text This paper proposes a decentralized voltage control strategy for islanded DC microgrids that replaces conventional droop control with a reinforcement learning (RL)-based approach. Using a Deep Deterministic Policy Gradient (DDPG) agent, the controller learns to generate real-time voltage references based solely on local measurements, eliminating the need for inter-unit communication. Compared to droop control, the proposed method reduces power sharing error from +30% to +8% and halves bus voltage deviation under high line impedance scenarios. The framework adapts to dynamic load and network conditions, offering a scalable and resilient control solution for next-generation microgrids.</p
Adaptive Damping Control to Enhance Small-Signal Stability of DC Microgrids:Intelligent control to Enhance Stability of Microgrids
Full text linkThis article proposes an adaptive active control approach for damping the low-frequency oscillations in a dc microgrid (DC-MG). The DC-MG is comprised of hybrid power sources (HPSs) formed by a parallel set of supercapacitor modules and photovoltaic systems. The HPS controller includes a multiloop voltage controller for adjusting the DC-MG voltage and a virtual impedance loop for damping current oscillations. The virtual impedance loop is augmented to the inner loop of the voltage controller. An adaptive tuning strategy is developed to adjust the damping coefficient of the virtual impedance loop optimally. In the tuning process, a small-signal analysis is used to determine an initial adjustment for the damping coefficient. Subsequently, an approach based on intelligent neural network is intended to provide accurate online correction of the damping coefficient, which passes the dependence of the converter control system on the operating point conditions and accommodates different operation conditions. A sensitivity analysis is also conducted to investigate the effects of the system parameters on the HPS stability. Moreover, a mesh analysis is carried out to examine the stability of low-frequency modes of the whole DC-MG using the proposed control scheme. Case studies are conducted to demonstrate the performance of the proposed control strategy, and the analysis results are verified by hardware-in-the-loop (HIL) setup using OPAL-RT (OP5600) and dSPACE (DS1202) simulators
