Search Results (53)

This study aims to reduce reverse power and improve frequency regulation performance in hydropower systems. To achieve this objective, a refined hydropower plant (HPP) simulation model is developed and coupled with a battery energy storage system (BESS), implementing an Integrated Adaptive Virtual Droop Control (IAVDC) strategy. The refined HPP model achieves a simulation accuracy of 98.5%, representing a 26.2% improvement over conventional simplified models. With the BESS integrated under the IAVDC strategy, reverse power is completely eliminated, and frequency regulation time is substantially shortened. The results demonstrate that the joint HPP-BESS frequency regulation effectively mitigates the adverse impact of water hammer, while the proposed IAVDC strategy enhances system responsiveness and reduces frequency recovery time, thereby improving the quality of primary frequency control. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

Distributed photovoltaic grid-connected converters adopting droop control can provide dual support for voltage and frequency in the distribution system. However, under fault conditions, droop-controlled inverters will face the problem of transient synchronization instability, and their transient characteristics are significantly affected by fault conditions, control parameter configurations, and other factors. Nevertheless, at present, the transient operation boundaries of droop inverters, considering key sensitive parameters, are unclear, and the transient stability control mechanism is lacking, which poses a threat to the safe and stable operation of distributed photovoltaic systems. To this end, this paper fully considers the influences of control parameters and fault severity and conducts a multidimensional quantitative characterization of the transient stability boundaries of droop-controlled inverters. Furthermore, a stability enhancement control structure for droop-controlled inverters, based on power angle deviation feedforward, is proposed, and an adaptive configuration method for feedforward coefficients is put forward to ensure the safe and stable operation of droop inverters at different fault sag depths. Finally, the accuracy of the theoretical analysis and the proposed control structure is verified through simulations and experiments. Full article

This paper introduces the Exponent Droop Function (EDF), a nonlinear grid-forming (GFM) control paradigm that enhances the flexibility and performance of droop-based control in microgrids. Unlike conventional droop mechanisms, the EDF establishes a generalized framework that unifies multiple nonlinear droop relations, enabling adaptive shaping of droop characteristics through the adjustment of a single tuning parameter. This capability effectively mitigates the inherent limitations of traditional droop, particularly frequency degradation, while ensuring flexible power-sharing and improved dynamic performance. The proposed approach is rigorously validated through (i) detailed system modeling and small-signal stability analysis of EDF-controlled microgrids under variable load and droop conditions, (ii) dynamic assessments of distributed generators (DGs) supported by frequency-domain analysis, and (iii) extensive time-domain simulations encompassing seven representative operating scenarios. Comparative studies against state-of-the-art GFM controllers demonstrate that EDF achieves superior transient and steady-state performance with minimal control complexity, highlighting its potential as a practical and efficient next-generation GFM control strategy for microgrids. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

The interlinking converter, an important device in a hybrid AC-DC microgrid, undertakes the task of power distribution between the AC sub-microgrid and DC sub-microgrid. To address the limitations of traditional bidirectional droop control in islanding mode, particularly the lack of consideration for regulation priority between AC frequency and DC voltage, this paper proposes an adaptive bidirectional droop control strategy. By introducing an adaptive weight coefficient based on normalized AC frequency and DC voltage, the strategy prioritizes regulating larger deviations in AC frequency or DC voltage. Interlinking converter action thresholds are set to avoid unnecessary frequent starts and stops. Finally, a hybrid AC-DC microgrid system in islanding mode is established in the Matlab/Simulink R2020a simulation platform to verify the effectiveness of the proposed control strategy. Full article

In islanded microgrids with high-proportion renewable energy, the disconnection from the main grid leads to the characteristics of low inertia, weak damping, and high impedance ratio, which exacerbate the safety risks of frequency and voltage. To balance the requirements of system operation economy and frequency–voltage safety, a coordinated optimization scheduling method for frequency and voltage in islanded microgrids considering the active support of battery energy storage (BES) is proposed. First, to prevent the state of charge (SOC) of BES from exceeding the frequency regulation range due to rapid frequency adjustment, a BES frequency regulation strategy with an adaptive virtual droop control coefficient is adopted. The frequency regulation capability of BES is evaluated based on the capacity constraints of grid-connected converters, and a joint frequency and voltage regulation strategy for BES is proposed. Second, an average system frequency model and an alternating current power flow model for islanded microgrids are established. The influence of steady-state voltage fluctuations on active power frequency regulation is analyzed, and dynamic frequency safety constraints and node voltage safety constraints are constructed and incorporated into the optimization scheduling model. An optimization scheduling method for islanded microgrids that balances system operation costs and frequency–voltage safety is proposed. Finally, the IEEE 33-node system in islanded mode is used as a simulation case. Through comparative analysis of different optimization strategies, the effectiveness of the proposed method is verified. Full article

To address the challenge of virtual synchronous generator (VSG) control technology in simultaneously achieving transient oscillation suppression and steady-state accuracy, as well as the poor anti-interference capability of fixed damping parameters under scenarios such as sudden changes in the short-circuit ratio (SCR), this paper proposes a transient damping optimization VSG control strategy based on a dissipative Hamiltonian neural network (DHNN) adaptive mechanism. Without affecting the original droop characteristics and rotational inertia, a transient damping feedback (TDF) branch is introduced to provide an additional damping ratio for the system to suppress low-frequency oscillations. The TDF control directly acts on the rotor motion equation through active-power low-frequency component feedback, featuring a simple structure without requiring complex computations. A small-signal model was established to quantitatively analyze the oscillation suppression mechanism. Furthermore, the dissipative Hamiltonian neural network (DHNN) was employed to dynamically optimize TDF parameters, ensuring a robust system performance under disturbances. The simulation and experimental results ultimately validated the effectiveness of the proposed control strategy. Full article

High penetration of green energy sources presents substantial challenges to grid stability and resilience, primarily due to inherent voltage and frequency variability, which worsens during critical events. This study proposes an integrated framework for stability and resilience enhancement in renewable-dense power grids by designing optimized universal droop controllers (UDCs) tailored for grid-forming operations under high-impact contingencies. The UDC incorporates fault localization functionality via grid-forming inverters embedded with phasor measuring capabilities (phase voltage magnitude and angle) to facilitate real-time fault detection and response, thus augmenting operational reliability. Leveraging integrated solution environments, the developed framework employs numerical optimization routines for resource allocation, load prioritization, economic dispatch of distributed energy resources (DERs), and adaptive network reconfiguration under constrained conditions and during critical events that may necessitate decentralized network configurations in the wake of main grid failures. Validation conducted on the IEEE 123-node distribution network indicates that the optimized UDC framework achieves superior voltage and frequency regulation compared to conventional droop-based methods, ensuring optimal resource distribution and sustained load support. Full article

In distributed microgrid systems, inverters serve as the core components when distributed generation (DG) modules are integrated into the grid. Traditional inverters typically employ droop control; however, they lack damping and inertia mechanisms. Consequently, fluctuations in the grid frequency and voltage occur when system loads change, leading to a suboptimal power distribution. To address these limitations, this paper introduces an adaptive strategy into conventional droop control. Based on an adaptive algorithm, the real and reactive power are dynamically computed. Through coordinate transformation, decoupled control, and adaptive frequency compensation, the inverter’s output frequency and voltage are effectively regulated. By adjusting the reference current in a dual-loop control scheme, the active and reactive power distribution is optimized. Additionally, an improved adaptive algorithm is developed to compute the inverter’s AC frequency compensation, enabling the self-adaptive adjustment of the PI controller’s output. This facilitates frequency compensation in droop control, ensuring that the inverter’s output current and voltage remain synchronized with the grid phase, thereby enhancing grid stability during connection. Finally, the feasibility of the proposed algorithm is validated through Simulink simulations. Full article

The increasing integration of renewable energy sources has posed significant challenges to grid frequency stability. To maximize the advantages of energy storage in primary frequency regulation, this paper proposes a comprehensive control strategy for a hybrid energy storage system (HESS) based on supercapacitor battery. Firstly, considering the characteristics of the HESS and different control strategies, the battery responds to virtual droop control to reduce frequency deviation, while the supercapacitor responds to inertia control to suppress frequency drops and facilitate frequency recovery. Simultaneously, a reasonable dynamic dead zone is configured to prevent frequent actions of the battery and thermal unit while allowing flexible adjustments according to the load condition. Thirdly, an algebraic S-curve-based adaptive droop coefficient incorporating SOC is proposed, while the inertia coefficient additionally considers load type, enhancing adaptability. Furthermore, to better maintain the battery’s SOC, an improved adaptive recovery strategy within the battery dead zone is proposed, considering both SOC recovery requirements and system frequency deviation constraints. Finally, a simulation validation was conducted in MATLAB/Simulink. Compared to the conventional strategy, the proposed control strategy reduces the frequency drop rate by 17.43% under step disturbance. Under compound disturbances, the RMS of frequency deviation decreases by 13.34%, and the RMS of battery SOC decreases by 68.61%. The economic benefit of this strategy is 3.212 times that of the single energy storage scheme. The results indicate that the proposed strategy effectively alleviates sudden frequency disturbances, suppresses frequency fluctuations, and reduces battery output while maintaining the SOC of both the supercapacitor and the battery, thereby extending the battery lifespan and improving economic performance. Full article

Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to the lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive droop control to provide compensation during transient conditions, thereby improving the power delivery capability. In this context, fuzzy logic-based adaptive droop control is a state-of-the-art technique that was developed based on empirical knowledge of the system. However, this way of designing the droop coefficient values without considering the mathematical knowledge of the system leads to instability during transient conditions. This problem further aggravates when dominant inductive load changes occur in the system. To address this limitation, this paper proposes an improved fuzzy logic-based adaptive droop control method. In the proposed methodology, the values of droop coefficients that are assigned for different membership functions are designed based on the stability analysis of the microgrid. In this analysis, the feasible range of active power–frequency droop values that could avoid instability during large inductive load changes is identified. Accordingly, the infeasible values are avoided in the design of the fuzzy controller. The performance of the proposed and the conventional fuzzy logic methods is verified through simulation in MATLAB/Simulink. From the results, it is identified that the proposed method has improved the power delivery capability of the microgrid by 14% compared to the conventional method. Full article

The rapid proliferation of EVs has ushered in a transformative era for the power industry, characterized by increased demand volatility and grid frequency instability. In response to these challenges, this paper introduces a novel approach that combines fuzzy logic with adaptive inertia control to improve the frequency stability of grids amidst large-scale electric vehicle (EV) integration. The proposed methodology not only adapts to varying charging scenarios but also strikes a balance between steady-state and dynamic performance considerations. This research establishes a solid theoretical foundation for the inertia-adaptive virtual synchronous generator (VSG) concept and introduces a pioneering fuzzy inertia-based VSG methodology. Additionally, it incorporates adaptive output scaling factors to enhance the robustness and adaptability of the control strategy. These contributions offer valuable insights into the evolving landscape of adaptive VSG strategies and provide a pragmatic solution to the pressing challenges arising from the integration of large-scale EVs, ultimately fostering the resilience and sustainability of contemporary power systems. Finally, simulation results illustrate that the new proposed fuzzy adaptive inertia-based VSG method is effective and has superior advantages over the traditional VSG and droop control strategies. Specifically, the proposed method reduces the maximum frequency change by 25% during load transitions, with a peak variation of 0.15 Hz compared to 0.2 Hz for the traditional VSG. Full article

As the penetration of renewable energy sources (RESs) in power systems continues to increase, their volatility and unpredictability have exacerbated the burden of frequency regulation (FR) on conventional generator units (CGUs). Therefore, to reduce frequency deviations caused by comprehensive disturbances and improve system frequency stability, this paper proposes an integrated strategy for hybrid energy storage systems (HESSs) to participate in primary frequency regulation (PFR) of the regional power grid. Once the power grid frequency exceeds the deadband (DB) of the HESS, the high-frequency signs of the power grid frequency are managed by the battery energy storage system (BESS) through a division strategy, while the remaining parts are allocated to pumped hydroelectric energy storage (PHES). By incorporating positive and negative virtual inertia control and adaptive droop control, the BESS effectively maintains its state of charge (SOC), reduces the steady-state frequency deviation of the system, and provides rapid frequency support. When the system frequency lies within the DB of the HESS, an SOC self-recovery strategy restores the BESS SOC to an ideal range, further enhancing its long-term frequency regulation (FR) capability. Finally, a regional power grid FR model is established in the RT-1000 real-time simulation system. Simulation validation is conducted under three scenarios: step disturbances, short-term continuous disturbances, and long-term RES disturbances. The results show that the proposed integrated strategy for HESS participation in PFR not only significantly improves system frequency stability but also enhances the FR capability of the BESS. Full article

The system inertia insufficiency brought on by a high percentage of wind power access to a power grid can be effectively resolved by wind-storage collaborative participation in primary frequency regulation (PFR). However, the impact of energy storage participation in system-frequency regulation is significantly influenced by its state of charge (SOC). In this paper, considering the SOC of energy storage (ES) and the stochastic characteristics of wind turbine (WT) output, the control strategy of wind-storage collaborative participation in the PFR of a system is proposed. Firstly, a WT adaptive inertia control and a model of storage droop control were constructed. Additionally, to prevent the problem of secondary frequency drop brought on by a separate rotational kinetic energy control, a wind-storage collaborative frequency-regulation control scheme was constructed. Secondly, considering changes in wind speed and the SOC of ES, an improved dynamic droop-control strategy for ES is proposed. This strategy was combined with the adaptive inertia control of the WT to establish the PFR of the WT collaborative participation system. Lastly, a simulation example of a two-region, four-machine system was used to validate the efficacy of the frequency-control strategy presented in this paper. The results show that a significant percentage of WTs connected to a power grid can effectively have their frequency-response ability improved by wind-storage collaborative control. Full article