This paper proposes the chaotic Arctic puffin optimization (CAPO) algorithm to solve the optimal power flow (OPF) problem in hybrid power systems. The systems incorporate various renewable energy conversion systems (RECSs), including wind, solar, and plug‐in electric vehicles (PEVs), thereby reducing power generation costs as well as the resulting environmental emissions. In this regard, the proposed CAPO method introduces chaos into the solution space to improve the diversity of candidate solutions. It also enhances the efficiency of the search process by balancing the exploration and exploitation processes in the search space and accelerates convergence. Importantly, introducing chaos in the search agents reduces the likelihood of premature convergence by minimizing the chances of the algorithm getting trapped in the local optima. The CAPO algorithm is implemented in MATLAB to optimize the multidimensional, nonlinear OPF problem. Simulation results demonstrate that integration of RECS significantly reduces the fuel cost along with the level of environmental emissions. Furthermore, the results show better performance of the CAPO algorithm in terms of convergence speed and robustness compared to the conventional Arctic puffin optimization (APO) algorithm. The effectiveness of the proposed algorithm is validated on standard systems such as IEEE‐30, 57, and 118 bus systems.
The distinct dynamic characteristics of virtual synchronous generators (VSGs) and synchronous generators (SGs) make the transient behavior of their parallel operation difficult to characterize using a single analytical approach. This paper investigates the transient stability of an SG–VSG parallel microgrid through a cross‐validated framework integrating phase‐trajectory analysis, an energy‐based direct method, and detailed electromagnetic transient (EMT) simulation. First, we develop an equivalent model of the parallel system and characterize its transient instability mechanisms under fault scenarios using the equal‐area criterion (EAC), providing qualitative insights into stability trends. Subsequently, we derive the system’s nonlinear state‐space equations and perform an analysis to evaluate stability boundaries under variations of critical parameters and current‐limiting conditions, thereby quantifying the mechanisms of parametric influence. Furthermore, by constructing the transient energy function via Takagi–Sugeno (TS) fuzzy modeling combined with linear matrix inequalities (LMIs), we comparatively evaluate the geometric properties of attraction domains under diverse parameter configurations. Finally, the EMT simulations verify the consistency between theoretical predictions and dynamic responses, with additional sensitivity assessments under representative operating and network variations and supplementary verification under an asymmetric single‐phase voltage sag. The results further quantify the effects of key parameters and current‐limiting control, as well as the stability differences between SG–VSG and VSG–VSG parallel configurations.
To address the source‐load mismatch and photovoltaic (PV) curtailment caused by the large‐scale integration of distributed PV into distribution networks, this paper proposes an optimal control strategy for PV accommodation considering the coordination of a centralized energy storage system (CESS) and demand response. To achieve coordinated regulation on both the supply and demand sides, a multiobjective optimization model is established. It minimizes the comprehensive operating cost, including electricity purchasing, PV curtailment, CESS operation and maintenance, and the peak‐to‐valley load difference. On this basis, a time‐of‐use pricing mechanism is introduced to establish a piecewise linear load transfer rate model, which quantifies users’ dynamic electricity consumption behavior and optimizes the allocation of peak and valley loads. The proposed model is solved using particle swarm optimization (PSO). Case study results show that, compared with the scenario considering only CESS, the coordinated strategy reduces PV curtailment by 12.34%, electricity purchasing by 10.0%, and the total operating cost by 8.37%, demonstrating good performance in improving PV accommodation and the economic efficiency of the distribution network.
To overcome the issues of mode mixing, low identification accuracy, and poor noise robustness in parameters identification of subsynchronous oscillation (SSO) in doubly fed induction generator (DFIG), an improved parameters identification method combining multivariate variational mode decomposition (MVMD) and total least squares estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) is proposed. Initially, the grid-connected current signal of DFIG is decomposed using MVMD. During the decomposition process, the northern goshawk optimization (NGO) algorithm is employed to select the number of modes K and penalty factor α. Correlation analysis is then performed to identify the dominant modes from the decomposed components. The dominant modes are resampled to construct the Hankel matrix, followed by singular value decomposition (SVD). The signal subspace order b is optimized based on singular value entropy (SVE), and TLS-ESPRIT is subsequently employed to identify the parameters of the SSO components. Time-domain simulations have been conducted using a composite signal and a 4-machine 2-area system incorporating a DFIG-based wind farm, and the proposed method has also been validated using measured SSO data from the Guyuan wind farm in Hebei Province. The results show that, compared with the comparative decomposition methods, the proposed method improves data processing efficiency by more than 60%. For the composite signal, the minimum identification accuracies of amplitude, frequency, phase, and damping factor have increased by 8.50%, 9.04%, 8.56%, and 8.50%, respectively, compared with the corresponding conventional methods. The identification results obtained at different noise levels closely match the theoretical values, and the minimum identification accuracy remains above 96% even under 10 dB noise interference. These results demonstrate the accuracy and superiority of the proposed method for SSO parameters identification.
In order to limit the output-voltage harmonics without sacrificing the conversion efficiency and the dynamic response, three-phase solar multilevel inverters need to be equipped with a good switching control mechanism. The use of conventional PWM control might lead to higher harmonic distortion and switching losses, especially when the operational circumstances are fluctuating. In this work, we suggest that to counter these problems the switching control of the solar multilevel inverters should be adaptive deep learning–based switching control strategy based on using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. The ultimate purpose will be, using the knowledge of the output voltage and the associated voltage error, to make switching decisions that are optimized while minimizing the harmonic distortion and enhancing the conversion efficiency, at the same time. The LSTM- and GRU-based controllers which have been suggested in this paper have been designed and tested in MATHEMATICA software using PWM datasets created for 400-V DC-link voltage and 50-Hz fundamental frequency. Total harmonic distortion, conversion efficiency, dynamic response, and switching activity were the performance indices of these two proposed controllers against a conventional PWM control that was implemented in the same simulation environment. In the experiment, for THD and conversion efficiency, the conventional PWM controller reached values of 8.805% and 91.04%, respectively, whereas LSTM controller was able to keep harmonic distortion around 2.405%, while the efficiency was up to 95.13%. On the other hand, the GRU controller was reported to be the best performers among simulated versions, showing very low THD at 1.484% and efficiency of 97.70% along with very quick time for achieving the required result and the least switching activity. According to results, the application of a deep learning switching control based upon recurrence of signals leads to a quite considerable enhancement in both harmonics reduction and conversion performance compared to the use of PWM control. Among all the examined topographies, GRU offers the best compromise between suppression levels of harmonics, energy usage, calculation difficulty, and capability in dynamic environment. The present work has been constrained by the use of only MATLAB-based validation. Consequently, it is highly recommended to validate the performance of the controllers through experimental DSP/FPGA implementation and evaluation in conditions such as realistic irradiance, temperature, partial shading, and grid disturbances where real-time robustness of these control methods can be proven.
