• Energy Research

This paper considers the three-phase grid-connected inverter (GCI) as a MIMO system, and presents some new perspectives on the stability of GCI. It is found that with traditional Nyquist criterion (NC)-based method which ignores the off-diagonal components of impedance or admittance matrix, the stability results of impedance and admittance models are different. To make up this disadvantage, a simple stability analysis method based on system feedback ratio matrix (SFRM) is proposed. Instead of ignoring the couplings of impedance or admittance, the SFRM in the proposed method can be equivalent to diagonal matrix by adding a perturbation matrix into the system model, so identical results of stability analysis under impedance and admittance models are attained. Furthermore, to ensure the correctness of stability result, the boundary conditions of the proposed method are also provided. Theoretical analysis, simulation and experimental results show that compared with the state of art methods, the proposed SFRM-based method is of low computational complexity and high accuracy, and is more suitable for engineering application.

With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment’s results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system.

As the impacts of environmental change become more severe, reliable and sustainable power generation and efficient aerodynamic power collection of onshore and offshore wind turbine systems present some of the associated key issues to address. Therefore, this review article aims to present current advances and challenges in the aerodynamic power extraction of wind turbines, associated supporting technologies in pitch, yaw, and torque control systems, and their advantages and implications in the renewable energy industry under environmental challenges. To do this, first, mathematical modeling of the environmental characteristics of the wind turbine system is presented. Next, the latest technological advances consider the environmental challenges presented in the literature, and merits and drawbacks are discussed. In addition, pioneering research works and state-of-the-art methodologies are categorized and evaluated according to pitch, yaw, and torque control objectives. Finally, simulation results are presented to demonstrate the impact of environmental issues, improvement claims, findings, and trade-offs of techniques found in the literature on super-large wind turbine systems. Thus, this study is expected to lay the groundwork for future intensive efforts to better understand the performance of large-scale wind turbine systems in addressing environmental issues.

The offshore wind power sector has witnessed exponential growth over the past decade, with large-scale offshore wind farms grappling with the challenge of elevated construction and maintenance expenses. Given that the collector system constitutes a substantial part of the investment cost in wind farms, the design and optimization of this system are pivotal to enhancing the economic viability of offshore wind farms. A thorough examination of collector system design and optimization methodologies is essential to elucidate the critical aspects of collector system design and to assess the comparative merits and drawbacks of various optimization techniques, thereby facilitating the development of collector systems that offer superior economic performance and heightened reliability. This paper conducts a review of the evolving trends in collector system research, with a particular emphasis on topology optimization models and algorithms. It juxtaposes the economic and reliability aspects of collector systems with varying topologies and voltage levels. Building on this foundation, the paper delves into the optimization objectives and variables within optimization models. Furthermore, it provides a comprehensive overview and synthesis of AI-driven optimization algorithms employed to address the optimization challenges inherent in offshore wind farm collector systems. The paper concludes by summarizing the existing research limitations pertaining to offshore wind farm collector systems and proposes innovative directions for future investigative endeavors. The overarching goal of this paper is to enhance the comprehension of offshore wind farm collector system design and optimization through a systematic analysis, thereby fostering the continued advancement of offshore wind power technology.

Operation optimization for large-scale offshore wind farms can cause the fatigue loads of single wind turbines to exceed their limits. This study aims to improve the economic profit of offshore wind farms by conducting multi-objective optimization via decoupled group operations of turbines. To do this, a large-scale wind farm is firstly divided into several decoupled subsets through the parallel depth-first search (PDFS) and hyperlink-induced topic search (HITS) algorithms based on the wake-based direction graph. Next, three optimization objectives are considered, including total output power, total fatigue load, and fatigue load dispatch on a single wind turbine (WT) in a wind farm. And then, the combined Monte Carlo and beetle swarm optimization (CMC-BSO) algorithms are applied to solve the multi-objective non-convex optimization problem based on the decentralized communication network topology. Finally, the simulation results demonstrate that the proposed method balances the total power output, fatigue load, and single fatigue loads with fast convergence.

This paper proposes a virtual impedance-based bandwidth control method for multi-parallel harmonic-compensation grid-connected inverters (HCGIs). Firstly, the influence of the resonance points caused by the interaction of multiple HCGIs on the control bandwidth is analyzed, and the analysis result shows that the control bandwidth becomes narrow due to the appearance of a new resonance point. Then, to increase the control bandwidth of multi-parallel HCGIs, six different types of virtual impedance circuits are constructed and compared, and the bandwidth control method based on virtual impedance by capacitor voltage feedback is proposed. Following that, the relationship between feedback coefficient and bandwidth is established, the design approach of parameters for the proposed method is presented. Finally, the proposed method is confirmed by the simulation and experimental tests. The simulation and experimental results show that the proposed control method can effectively shift resonance frequencies right to solve the issue of control bandwidth reduction in multi-parallel HCGIs systems, while without affecting the low-frequency harmonic current compensation.

This study presents an advanced model predictive control (MPC) using multistep prediction model for the electrical motor-based yaw system of an industrial wind turbine. The proposed method introduces a finite control set under constraints for the demanded yaw rate, predicts the multistep yaw error using the control set element and the prediction wind directions, and employs an exhaustive search method to search the control output candidate giving the minimal value of the objective function. As the objective function is designed for a joint power and actuator usage optimization, the weighting factor in the objective function is optimally selected using the Pareto curve-based tuning method. Finally, the proposed method is demonstrated by simulation tests using Bladed software and the achievable performance is investigated among six MPC controllers with different prediction steps. Investigation results show that, along with the extended prediction horizon, the power production is improved while maintaining the same yaw actuator usage. Meanwhile, the MPC-based yaw controllers have certain effects on the fatigue loads of the tower and yaw bearing, while their effects on the blade root and hub are small. Besides, it is observed that the potential performance achieved by the MPC controllers is obviously affected by the wind conditions.

The power system network grows yearly with a large number of nonlinear power generation systems. In this scenario, accurate modeling, control, and monitoring of interface systems and energy conversion systems are critical to the reliability and performance of the overall power system. In this trend, the permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) equipped with a full-rated converter significantly contribute to the development of new and renewable energy generation. The various components and control systems involved in operating these systems introduce higher complexity, uncertainty, and highly nonlinear control challenges. To deal with this, state estimation remains an ideal and reliable procedure in the relevant control of the entire WTS. In essence, state estimation can be useful in control procedures, such as low-voltage ride-through operation, active power regulation, stator fault diagnosis, maximum power point tracking, and sensor faults, as it reduces the effects of noise and reveals all hidden variables. However, many advanced studies on state estimation of PMSG-based WTS deal with real-time information of operating variables through filters and observers, analysis, and summary of these strategies are still lacking. Therefore, this article aims to present a review of state-of-the-art estimation methods that facilitate advances in wind energy technology, recent power generation trends, and challenges in nonlinear modeling. This review article enables readers to understand the current trends in state estimation methods and related issues of designing control, filtering, and state observers. Finally, the conclusion of the review demonstrates the direction of future research.