International Transactions on Electrical Energy Systems最新文献

In the context of integrating Renewable Energy Sources, Microgrid (MG) development is pivotal, particularly as a foundational technology for Smart-Grid evolution. Despite advancements in control techniques, challenges persist in ensuring system stability and accurate power sharing across diverse operational conditions and load types. The objective of this research is to control numerous paralleled inverters-based distributed generators (DGs) that contribute to power sharing in an island MG. The proposed methodology involves developing an innovative small-signal model for islanding MGs that incorporate virtual impedances. Subsequently, optimization algorithms based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are proposed and compared for designing the virtual impedances. These algorithms analyze all potential operating points, aiming to minimize reactive power mismatches while maximizing MG stability. The suggested objective function facilitates the simultaneous achievement of these objectives. The proposed approaches were tested using MATLAB-Simulink software, and the comparison of the results between conventional approach and the proposed optimal approaches shows significant improvement in terms of the dynamic response during load changes, such as a decrease in response time by up to 20%, a reduction in overshoot percentage by approximately 15%, and a settling time improvement of nearly 25%. These quantified improvements highlight the effectiveness of the GA and PSO methods in minimizing the reactive power-sharing error while optimizing MG performance and stability.

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions.

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.

Taking the Midpoint Injection type Bearingless Permanent Magnet Synchronous Motor (MPI-BL-PMSM) as an object, to solve its problems of large torque pulsation and insufficient suspension force when adopting Midpoint Suspension Current Unilateral Injection (MPSC-UI), a Midpoint Suspension Current Bilateral Injection (MPSC-BI) solution is proposed. Based on the half-winding structure of MPI-BL-PMSM, and from the electromechanical energy conversion principle, the torque model for MPSC-BI solution is established. On this basis, the torque model for MPSC-UI method was derived. The correctness of the established torque mathematical models based on half-winding structure was verified through the finite element method (FEM), and the “dual-frequency” electromagnetic coupling characteristics of suspension current on electromagnetic torque were compared and analyzed from the perspectives of theoretical model and FEM simulation. The results indicate that the MPSC-BI method can effectively suppress or avoid the torque pulsation coupled by suspension current and can obtain about 1-time increase of controllable suspension force; the advantages of MPSC-BI solution in dynamic torque decoupling characteristics are demonstrated, while the only downside is that the coupling effect of torque current on radial suspension force is slightly greater than that of the MPSC-UI method.

The output of photovoltaic (PV) systems is significantly impacted by the vagaries of ambient temperature, solar irradiance, and environmental fluctuations. To achieve the utmost attainable power from PV systems, it is desired to be efficient at the maximum power point in diverse weather climates. Maximum power point tracking (MPPT) is used to schedule a designated location from where the highest power can be harvested. In the context of solar photovoltaic systems connected with DC microgrid platforms, this study introduces a recently developed drone squadron optimization (DSO) scheme that tracks the global maximum power point under PSCS difficulties. Furthermore, an exhaustive comparative analysis has been presented among particle swarm optimization (PSO), cuckoo search algorithm (CUSA), and grey wolf optimization (GWO) under different operating environments to endorse the supremacy of the nominated technique. The suggested method performs noticeably faster than many other methods currently in use, and in addition to offering the highest power, it can also use bidirectional power flow regulation in both constant and variable air conditions. Lastly, an MPPT system interfaced with the DC microgrid based on DSO ensures a sustainable and reliable architecture to provide at load in low power generating situations.

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems.

Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller.