• Energy Research

AbstractThe importance of resolving stability concerns in weak AC grid‐connected doubly fed induction generator (DFIG) wind energy systems during low‐voltage ride‐through (LVRT) events cannot be ignored, given the increasing popularity of wind power‐based microgrids. Furthermore, the emergence of generation loss and postfault oscillation within a microgrid (MG) due to grid faults has also become a significant concern. The static synchronous compensator (STATCOM) under consideration in this study is tuned using particle swarm optimization (PSO), the artificial hummingbird algorithm (AHA), and a hybrid approach incorporating both PSO and AHA. Faults of both a symmetrical and an asymmetrical nature have occurred on the power grid side. The proposed hybrid PSO‐AHA‐tuned STATCOM strategy aims to improve LVRT, minimize power generation loss during faults, and reduce oscillations after a fault by controlling the flow of reactive power between point of common coupling (PCC) and MG. The MATLAB simulation environment was used to simulate the 16 MW MG test system. The performance of the PSO‐AHA‐tuned STATCOM was assessed by comparing results with those from conventional STATCOM, PSO, and AHA optimizer‐tuned STATCOM in four fault situations. A comparison of the results shows that the proposed strategy performed better than other approaches mentioned in this paper and achieved the desired objectives.

The optimal design of the proportional-integral (PI) controller using the walrus optimization algorithm (WOA) with the aim to enhance the dynamic performance of a grid-connected wave energy conversion (WEC) system when subjected to diverse operational conditions is presented in this paper. The proposed system under study consists of an oscillating water column (OWC) device coupled with a permanent magnet synchronous generator (PMSG) to supply power to the grid. Power electronics devices in the form of a generator-side converter (GSC) and a grid-side inverter (GSI) are used to couple the grid and WEC systems. The GSC is used to minimize generator losses and maximize generator real power through the control of d-axis and q-axis currents (id, and iq) of the PMSG. The GSI is used to control the point of common coupling (PCC) and DC-link voltages (VPCC,VDC), respectively. The PI controllers, used to minimize the error between the actual current and voltage values with their respective reference values, are optimally designed using the WOA. The fitness function of the optimization problem is based on the integral square error criterion (ISE). Presented in this paper is a model for the OWC-WEC system and a control strategy to maximize generated power, minimize generator losses, and keep the VDC, VPCC at required values, the usage of WOA to design the PI controllers, and the simulations of system results. The proposed WOA-based PI controller design’s effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. The proposed strategy shows an enhancement in the dynamic performance of OWC wave energy systems when compared to the other optimization algorithm-based PI controllers, as well as achieving the least value for ISE, which reached 0.172.

This research paper introduces the Generalized Continuous Mixed P-Norm Sub-Band Adaptive Filtering (GCMPNSAF) algorithm, designed for efficient online control of Superconducting Magnetic Energy Storage Devices (SMESDs) in Wind Energy Systems (WESs). The primary objective of this algorithm is for minimizing power ripples in WESs. The Wind Energy System (WES) under consideration is tied to the IEEE 39 bus system, with the Superconducting Magnetic Energy Storage Device (SMESD) integrated at the point of common coupling. The GCMPNSAF algorithm is applied to update or adapt proportional-integral (PI) controller gains of SMESD interface circuits. The proposed algorithm is an enhanced version of the CMPN by adding the sub-band filtering algorithm effect. It depends mainly on the actuating error signal, and it has a variable step size of the CMPN. The detailed modeling of the whole system is presented, including measured wind speed data, detailed switching techniques, a drive train model of the turbine, and real SMESD. The efficacy of the proposed SMESD has been validated through a comparative analysis with the Least Mean Square-SMESD approach, under conditions of varying and unpredictable wind speeds. The simulation results produced by the PSCAD software are used to evaluate the study's validity. The utilization of controlled SMESDs has the potential to significantly enhance the power quality of WESs.

The modeling accuracy of photovoltaic (PV) modules is essential nowadays due to the spread of PV power plant installation. Thus, precise PV modeling is vital as it ensures the optimal design, reliable performance prediction, and efficient energy management in solar power systems. The three-diode PV modeling is thus a suitable solution due to its precision and accuracy. However, it is complicated and includes nine unidentified parameters. The Puma optimization algorithm is presented in this paper for utilization in extraction and the optimization of nine unknown PV module parameters. The suggested methodology is applied to two commercial PV modules: the Kyocera KC200GT multi-crystalline and the Canadian PV monocrystalline modules CS6K280M. To ensure the superiority of the Puma algorithm, its results are compared with others resulting from more than four optimization algorithms, which include Artificial Electric Field Algorithm, Northern Goshawk Optimization, Grey Wolf Optimization, Coati Optimization Algorithm, and Particle Swarm Optimization algorithms. The Puma’s parameter extraction precision is further approved by the close agreement between the measured and estimated characteristics curves, extending its validation to variable temperature and irradiance level variations scenarios. The study establishes the Puma algorithm as a robust tool for parameter determination in the three-diode PV model. It opens new avenues for application in other complex optimization problems in renewable energy systems.

This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.

Offshore ship charging station (OSCS) projects can help to address the current demand for electric ships for ocean voyages to a large extent. The proper selection of the energy source for power generation is a key part of the OSCS project. To select the optimal renewable energy for OSCS with many difficulties such as the ambiguity of the decision-making environment, the differences in group assessment information, and the conflict and compensation between criteria, this paper proposed a fuzzy multi-criteria decision-making (MCDM) framework. First, a comprehensive criteria system was constructed. Second, the intuitionistic fuzzy set (IFS) was introduced to express experts' fuzzy cognition. Third, based on the quality of evaluation, a novel expert weighting method was proposed, and the generalized intuitionistic fuzzy weighted geometric interaction averaging (GIFWGIA) operator was used to aggregate the individual evaluations. Fourth, the criteria importance through intercriteria correlation (CRITIC) method, and the stepwise weight assessment ratio analysis II (SWARA II) method were introduced to determine the criteria weights. Fifth, considering criteria compensation, and individual and group ranks, the gained and lost dominance score (GLDS) method were used for ranking. Finally, to verify the applicability and reliability of the framework, a case study was conducted in Pingtan Island, Fujian Province. The results show that wind energy was the best alternative, followed by solar, wave and nuclear energy.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm2 single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

This study presents an improved, communication-free Fault Ride-Through (FRT) strategy for type-3 and type-4 wind turbine integrated modular multilevel converter-based high-voltage direct current (MMC-HVDC) systems in offshore wind power plants (OWPPs). The research aims to enhance the reliability and resilience of OWPPs by ensuring their connection with AC grids remains intact during and after faults. Simulation results conducted on a 580 kV, 850 MW MMC-HVDC system using PSCAD/EMTDC software v.4.6.2 demonstrate quick post-fault recovery operation and the ability to effectively manage DC link and capacitor voltages within safe limits. Furthermore, the circulating current (CC) and capacitor voltage ripple (CVR) remain within acceptable limits, ensuring safe and reliable operation. The study’s major conclusion is that the proposed FRT strategy effectively mitigates the adverse effects of short circuit faults, such as a rapid rise in DC-link voltage, on the performance of the MMC-HVDC system. By promptly suppressing DC-link overvoltage, the proposed FRT scheme prevents compromising the safe operation of various power electronics equipment. These findings highlight the significance of FRT capability in OWPPs and emphasize the practical applicability of the proposed strategy in enhancing the reliability of offshore wind power generation.