Deep Reinforcement Learning-Assisted Universal Active Filter for Power Quality Enhancement in a SyRG-Based Standalone Renewable Energy System with Hybrid Energy Storage
Jul 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
Abstract
The rapid deployment of standalone renewable energy systems has increased the need for intelligent power quality
enhancement techniques capable of operating under highly dynamic and nonlinear conditions. Conventional proportionalintegral (PI) controllers and recently developed Artificial Neural Network (ANN)-based controllers improve voltage regulation
and harmonic mitigation; however, their performance depends on prior training and fixed learning structures. This paper
proposes a Deep Reinforcement Learning (DRL)-assisted Universal Active Filter (UAF) for a Synchronous Reluctance
Generator (SyRG)-based standalone renewable energy system integrated with a photovoltaic array and hybrid battery energy
storage system. Unlike conventional controllers, the proposed DRL controller continuously learns the optimal switching policy
from real-time operating conditions without requiring repeated controller tuning. The controller coordinates the operation of the
series and shunt voltage source converters to suppress harmonic currents, compensate reactive power, stabilize the DC-link
voltage, and maintain sinusoidal load voltage under varying renewable generation and nonlinear load conditions.
MATLAB/Simulink simulations demonstrate significant improvements in dynamic response, voltage regulation, power factor
correction, and harmonic suppression compared with conventional PI with ANN controller. The proposed intelligent controller
effectively minimizes Total Harmonic Distortion (THD), improves converter efficiency, enhances system robustness against
disturbances, and satisfies IEEE-519 power quality standards, making it suitable for next-generation standalone renewable
microgrids.
The rapid integration of renewable energy resources into modern distribution networks has significantly improved the
sustainability of electrical power systems. However, the intermittent characteristics of photovoltaic (PV) and wind energy
conversion systems (WECS), together with the increasing penetration of nonlinear loads, have introduced serious power quality
challenges, including harmonic distortion, voltage fluctuations, reactive power imbalance, and poor dynamic stability. This
paper proposes an intelligent hybrid microgrid incorporating a Photovoltaic (PV) system, Wind Energy Conversion System
(WECS), Battery Energy Storage System (BESS), and Unified Power Quality Conditioner (UPQC) controlled by an Adaptive
Neuro-Fuzzy Inference System (ANFIS). The proposed ANFIS controller replaces the conventional proportional-integral (PI)
controller to achieve faster dynamic response, adaptive reference current generation, and superior DC-link voltage regulation
under varying operating conditions. The coordinated operation of PV, WECS, and BESS ensures continuous energy availability
while minimizing dependence on the utility grid. The UPQC effectively compensates voltage disturbances, mitigates harmonic
currents, and maintains near-unity power factor, thereby improving the overall power quality of the hybrid microgrid.
MATLAB/Simulink simulations demonstrate that the proposed ANFIS-based strategy significantly reduces total harmonic
distortion (THD), enhances transient performance, improves renewable energy utilization, and increases system reliability
compared with the conventional PI-controlled configuration, making it an effective solution for future smart grid applications
D. Sangeetha, Kadingu Shivashankar· International Journal for Re...· 0 citations
Convergence and multi-run statistical analysis further confirm the robustness, stability, and reproducibility of the trained policy, demonstrating the effectiveness of DRL as an intelligent and scalable solution for next-generation microgrid PQ control.
Pratibha V. Hurkadli, G. A. Kumar, T. Manjunath· Advances in Data Science and...· 0 citations
The increasing integration of renewable energy sources into microgrids has intensified the need for intelligent energy management strategies capable of addressing the intermittency of solar and wind generation while ensuring reliable and cost-effective operation. Although Rule-Based Control (RBC) methods are straightforward to implement, their limited adaptability often leads to suboptimal utilisation of Hybrid Energy Storage Systems (HESS). This study develops and evaluates a Deep Reinforcement Learning (DRL)-based energy management system employing a Deep Q-Network (DQN) to coordinate battery–supercapacitor operation within a renewable microgrid. A Gymnasium-compatible simulation environment was constructed using a publicly available time-series dataset comprising renewable generation, load demand, electricity prices, battery state of charge (SoC), and supercapacitor SoC. Feature engineering, incorporating sinusoidal temporal representations and Min-Max normalisation, was applied to enhance learning stability and capture cyclical demand and generation patterns. The DQN agent was trained over 50 episodes and benchmarked against a conventional RBC strategy under identical operating conditions. Training performance demonstrated progressive policy improvement, with cumulative rewards increasing from approximately -1200 to -400, indicating enhanced decision-making capability during learning. The learned controller exhibited adaptive energy scheduling through dynamic utilisation of the supercapacitor and selective grid interaction in response to varying operating conditions, whereas the RBC followed a deterministic control strategy with limited flexibility. However, comparative evaluation revealed that the DQN did not consistently outperform the RBC in cumulative economic performance, suggesting the need for further refinement of the reward function, training process, and hyperparameter configuration. Nevertheless, the proposed framework demonstrates the feasibility of applying deep reinforcement learning to coordinated battery–supercapacitor energy management and highlights its potential to enhance operational flexibility and intelligent resource utilisation in renewable microgrids. The study contributes a dataset-driven reinforcement learning framework that provides a foundation for future research on advanced AI-based energy management systems and the integration of more sophisticated reinforcement learning algorithms for resilient and sustainable microgrid operation.
Daniel Owusu· American Journal of Neural N...· 0 citations
This paper introduces a shunt active power filter (SAPF) with machine learning that is used to provide power quality (PQ) improvement to a PV-Battery hybrid inverter in a grid-connected system. A predictive compensation strategy based on a long short-term memory (LSTM) is used to produce adaptive reference current to reduce harmonic distortion and supply reactive power. The proposed approach takes advantage of time dependence of disturbance profiles, unlike traditional fixed-gain PI-based controllers typical of active filters, which provide proactive compensation to nonstationary conditions generated by renewable variability and nonlinear loads. An in-depth MATLAB/Simulink analysis integrating the hybrid patterns of irradiance with intermittent dips at cloud fronts was used in testing the PQ performance. The suggested controller resulted in a decrease of the current total harmonic distortion (THD) to 28.6% (without compensation) and 7.8% (PI-controlled SAPF), and an increase in power factor to unity. The momentary analyses indicated lower overshoot and quicker convergence in PV and load disturbances. Computational profiling showed an average inference latency of 38 μs, meeting real-time control requirements on embedded ARM-dsp designs. These findings suggest that predictive control with machine learning support can be used to supplement the traditional SAPF capabilities and improve auxiliary PQ services in renewable-based distribution networks.
Nagaraja Bodravara, G. Ezhilarasan· Advances in Data Science and...· 0 citations
The proposed DT-DRL framework establishes a closed-loop cyber–physical architecture in which continuously synchronized Digital Twin states are directly incorporated into a Proximal Policy Optimization (PPO)-based decision-making process to jointly minimize operating cost, voltage deviation, and battery degradation while satisfying network operational constraints.
Minh Phong Le· International journal of res...· 0 citations
The increasing penetration of renewable energy sources has increased the need for advanced control strategies capable of maintaining stability under low-inertia, converter-dominated operating conditions. In grid-connected wind energy conversion systems (WECSs), constant power loads (CPLs) exhibit negative incremental impedance characteristics that can amplify DC-link oscillations and complicate the coordination between the electrical and mechanical subsystems. The main contribution of this work is a Soft Actor–Critic (SAC) reinforcement learning algorithm that tunes the outer proportional-integral gains of the machine-side DC-voltage-squared control loop together with the active damping gain, allowing online adaptation of the controller according to the operating condition and disturbance level, thereby improving energy system sustainability. The proposed control framework includes a two-mass shaft model, virtual inertia control, and DC-link load uncertainty in the form of both resistive loads and CPLs. The system is modeled and evaluated using MATLAB/Simulink, and its performance is compared with that of a conventional fixed-gain controller under AC load disturbances and wind speed variations. It has been found that for a 25% load disturbance, the maximum DC-link voltage deviation is reduced by 1.2% under resistive loading and 6.5% under CPL operation. For a 1 m/s reduction in wind speed, the corresponding reductions are 0.8% and 0.9%, respectively. The proposed controller also provides smoother output power and improved damping of the rotor speed and system frequency responses.
Islam A. Zenhom, M. Marei, Ahmed M. I. Mohamad· Sustainability· 0 citations
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