Jul 2026· International Seminar on Intelligent Technology and Its Applications· pp. 334-339· 0 citations· 14 references
Abstract
The rapid growth of electric vehicles and energy storage systems requires efficient two-way power conversion systems, such as bidirectional VSIs operating in Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. Unfortunately, conventional Hysteresis Current Control (HCC) methods lead to unstable switching frequencies, degrading the system's power quality and performance. This study proposes a single-phase two-way Voltage Source Inverter (VSI) with a full bridge topology controlled by an Artificial Neural Network (ANN) based Adaptive Hysteresis Current Control (AHCC) scheme. ANN is used to define hysteresis bands adaptively to keep the switching frequency stable. The system consists of a two-way VSI connected to the grid and a two-way DC-DC converter that connects the battery via DC-Link. The simulation results showed that the proposed method produced a narrower instantaneous frequency switching range of 5.263 kHz-25 kHz compared to Fixed-HCC of 11,111kHz-50 kHz, so that AHCC-ANN produced an average frequency switching value of 13.37 kHz, close to the desired frequency switching of 15 kHz, while Fixed-HCC was 20,25 kHz. On the other hand, the% of THD for AHCC-ANN (2.72%) is lower than that for Fixed-HCC (3.2%). On the other hand, the DC-link voltage can also be maintained at 400 V during charging and discharging. These results show that AHCC with ANN can stabilize switching frequencies and DC-link voltages and support effective bidirectional power flow.
Experimental results demonstrate that the proposed ANN-based adaptive DC-link voltage control algorithm achieves lower total harmonic distortion (THD) than PHAPFs employing a constant Vdc_ref and exhibits better harmonic suppression performance despite the processing load and filtering delays.
With the widespread adoption of voltage-source converters for grid-connected new energy power generation, the transient stability of VSCs under high penetration of new energy has become a key issue in the construction of modern power systems. Grid-following (GFL) and grid-forming (GFM) controls each have their applicable scenarios and inherent limitations. Traditional hybrid synchronous control uses fixed parameters, making it difficult to adapt to complex and variable grid conditions. Therefore, this paper proposes a control method suitable for an adaptive hybrid synchronous architecture with grid-following and grid-forming dual characteristics, employing a model predictive control strategy to achieve dynamic adjustment of GFL and GFM control ratios in both the synchronization loop and the voltage loop. A simulation model was built in PSCAD/EMTDC to verify the effectiveness and adaptability of the proposed control method under various transient conditions, such as load changes and voltage sags. The simulation results indicate that the method can effectively suppress system frequency and voltage fluctuations, enhance the transient stability of VSCs, and provide a feasible control reference for practical engineering applications.
Guiyuan Li, F. Peng, Yinsheng Su et al.· Electronics· 0 citations
This paper presents a robust control strategy for grid-connected photovoltaic (PV) systems, combining the Super-Twisting Algorithm (STA) and Integral Sliding Mode Control (ISMC) to enhance performance. The proposed ap-proach addresses both Maximum Power Point Tracking (MPPT) and Voltage Source Inverter (VSI) control. An STA-based Artificial Neural Network (ANN) controller regulates the DC/DC Boost converter to optimize power ex-traction. The ANN generates a reference Maximum Power Point (MPP) volt-age, which is then compared with the measured voltage. The STA utilizes this error to generate the converter's Pulse Width Modulation (PWM) signal. On the AC side, dual STA-ISMC-based Voltage-Oriented Controllers (VOCs) generate control vectors for space vector modulation (SVM), significantly re-ducing root mean square error (RMSE) compared to conventional control methods and ensuring precise DC bus voltage regulation. The effectiveness of these control strategies has been rigorously evaluated using the MATLAB/Simulink environment under various operating conditions, including fluctuating PV output power and load profiles. The results demonstrate the su-periority of the proposed techniques in enhancing power quality and achieving optimal efficiency across a wide range of operating scenarios.
M. Benzaouia, Ahmed Bentaleb, A. M. Mabwe et al.· EPJ Web of Conferences· 0 citations
In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based duty-cycle correction method is proposed for a non-isolated interleaved bidirectional DC–DC converter used in a hybrid photovoltaic (PV)–battery system. The ANFIS controller was developed using training data generated from an optimized conventional fuzzy-logic controller operating in both buck and boost modes. The main objective of the proposed control strategy is to produce a smoother duty-cycle response, improve transient behavior, and maintain better output-voltage stability than the conventional fuzzy-logic approach. The converter and control system were modeled and tested in MATLAB/Simulink under constant-voltage and constant-current operating conditions. The simulation results indicate that the proposed ANFIS-based controller improves the converter's dynamic response and provides smoother duty-cycle adjustment. In buck mode, the output-voltage ripple is reduced from 0.1181 V to 0.1051 V, indicating a modest improvement. A more significant improvement is observed in boost mode, where the voltage ripple decreases from 2.963 V to 1.348 V. The results indicate that the proposed ANFIS controller works effectively in boost mode, particularly given its greater sensitivity to duty-cycle changes, switching dynamics, and transient disturbances.
Yunifa Miftachul Arif, Saodah Omar, A. Ahmed et al.· Engineering, Technology &...· 0 citations
This paper presents the development of a multifunctional grid-connected hybrid energy system (HES) integrating solar photovoltaic (PV) modules, a wind energy conversion system (WECS), and battery energy storage. To address the inherent drawbacks of conventional maximum power point tracking (MPPT) algorithms such as delayed response and suboptimal tracking under fast-varying environmental conditions a Mamdani type fuzzy logic-based MPPT controller is proposed. This controller adaptively modulates the duty cycles of individual DC–DC boost converters, enabling optimal power extraction from both PV and wind sources under dynamic operating conditions. The hybrid energy coordination is implemented without requiring linearized system models, thereby increasing control robustness against parameter variations and environmental uncertainties. Grid interfacing is accomplished using a phase-locked loop (PLL) to ensure synchronization in frequency and phase, while an LCL filter is employed at the point of common coupling (PCC) to attenuate high-frequency switching harmonics, thereby improving power quality in accordance with grid codes. The proposed methodology enhances system dynamic response, stabilizes power output, and facilitates seamless energy sharing among multiple sources. Time-domain simulations conducted in MATLAB/Simulink validate the effectiveness of the proposed control scheme, demonstrating improved system stability, reduced harmonic content, and efficient energy regulation. The novelty of this work lies in the adaptive fuzzy logic-driven coordination of hybrid renewable sources in real time, enabling superior performance, high efficiency, and compliance with grid power quality standards in a hybrid energy system environment.
Keywords: Fuzzy logic; Hybrid energy; Solar; Wind; Battery storage system.
Shaik Nagulu, T. Kumar, J. Balaji· International Journal of Tec...· 0 citations
Traditional grid-forming converter (GFC) control faces fundamental challenges in maintaining DC bus stability during rapid power transients, primarily due to the limited dynamic response capability of source-side energy storage devices. To address this, this paper proposes a hybrid control strategy integrating long short-term memory (LSTM) networks with a joint GFC and storage converter (SC) control scheme. The LSTM detects short-term voltage trends from historical DC bus data to generate a feedforward compensation signal, while the joint SC-GFC control dynamically incorporates the GFC’s inertial power demand into the SC’s power reference. Hardware-in-the-loop experiments show that, compared to traditional independent control under the same step transient conditions, the proposed method can reduce power overshoot by approximately 79.2%. The LSTM-enhanced joint control maintains stable power flow and significantly suppresses low-frequency oscillations, validating the necessity of data-driven trend prediction for achieving superior inertial support in practical constrained environments. This work provides a communication-free, practical solution for enhancing GFC performance.
Yu Qi, Dabin Mi, Tao Ma et al.· Electronics· 0 citations
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