With the wide application of distributed DC microgrids and the increasing of DC loads, the distributed control method has become one of the research hotspots. However, the distributed control method based on distributed communication network usually involves the problem of communication delay, which may lead to the failure of the controller in the practical application and even affect the stability of the system. This paper introduces a novel distributed secondary control algorithm without droop control, which can be used to realize voltage recovery and load sharing simultaneously. Based on this, two stability criterions under different conditions are derived for the DC microgrid system considering communication time delay, which are delay-dependent stability criterion under constant delay and time-varying delay-dependent stability criteria by using Linear Matrix Inequality (LMI). These stability criterions are helpful to guide the selection of control parameters. Simulation results verify the effectiveness and feasibility of the proposed control strategy, and show the correctness of the delay-independent stability criterion and delay-dependent stability criterion.

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 construction and gradual installation of turbines on wind farms has been hindered by the high cost of the energy production. An effective way to minimize energy costs is via the optimal design of wind turbines and their layout, but relevant and synthetic studies are lacking. This paper proposes a method to minimize the energy cost of offshore wind farms by simultaneously optimizing the rated wind speed, the rotor radius of wind turbines and their layout. Firstly, a new, mixed mathematical formulation of the energy cost is presented, considering the Weibull distribution for wind, the characterizing parameters of wind turbines and the distance between two turbines. Secondly, to obtain the minimum energy cost, a composite optimization algorithm was developed, which consists of an iterative method and an improved particle swarm optimization algorithm. The former was used to search the minimal energy costs that relate to the design parameters of a single wind turbine, while the latter was adopted for optimizing the layout of the wind turbines iteratively. Finally, the proposed method was applied to three case studies with variable wind speed and constant wind direction. Results of the case studies show that the reduced energy cost after optimization has a range of 0–0.001 $/kWh, which confirms the effectiveness of the proposed approach. Meanwhile, the layout of the wind turbines after optimization tends to locate the two wind turbines with the biggest spacing in the wind direction, which justifies the utilization of layout optimization. Furthermore, exploring the optimally designed parameters of wind turbines revealed that the wind farms with a high mean wind speed can have a wider range of turbine capacity than those with a low wind speed, which offers more freedom for the designers when constructing offshore wind farms at wind sites with rich wind resources.

In the study of power electronics topology, the existing graph structure description methods cannot comprehensively manifest properties of topologies, leading to difficulties in their equivalence judgment. To address this issue, this article proposes a novel power electronics topology description method based on the fusion of graph and number theory. First, prime number weights are assigned to represent physical properties, such as components and connections. Then, the fundamental theorem of arithmetic and bijection functions are used to map the topology into a unique, concise, and reversible graph form with physical properties. Therefore, power electronics topology equivalence judgment can be conducted straightforwardly based on graph isomorphism. The proposed method effectively simplifies physical topology descriptions and related equivalence judgment. Case studies demonstrate the proposed method's effectiveness.

As onshore wind energy has depleted, the utilization of offshore wind energy has gradually played an important role in globally meeting growing green energy demands. However, the cost of energy (COE) for offshore wind energy is very high compared to the onshore one. To minimize the COE, implementing optimal design of offshore turbines is an effective way, but the relevant studies are lacking. This study proposes a method to minimize the COE of offshore wind turbines, in which two design parameters, including the rated wind speed and rotor radius are optimally designed. Through this study, the relation among the COE and the two design parameters is explored. To this end, based on the power-coefficient power curve model, the annual energy production (AEP) model is designed as a function of the rated wind speed and the Weibull distribution parameters. On the other hand, the detailed cost model of offshore turbines developed by the National Renewable Energy Laboratory is formulated as a function of the rated wind speed and the rotor radius. Then, the COE is formulated as the ratio of the total cost and the AEP. Following that, an iterative method is proposed to search the minimal COE which corresponds to the optimal rated wind speed and rotor radius. Finally, the proposed method has been applied to the wind classes of USA, and some useful findings have been obtained.