This paper proposed a frequency support control scheme for large scale wind farm(WF) by considering distributed layout of wind turbines(WT) and wake effects inside the WF. The frequency support of WF is formulated as optimization problems. A novel clustering based optimization method is proposed to coordinate all the WTs so that the whole WF can work at the optimal operating states. In large scale WF, number of WTs is numerous. With the proposed method, the WTs are firstly clustered into groups according to their wind profiles. By dispatching the same control commands to the WTs belonging to the same group, each group can be further seen as one single WT. As a result, the number of the WTs and control variables reduces significantly and hence the optimization problems are highly simplified. A case study is carried out to verify the effectiveness of the proposed method.

Large scale wind farms (WFs) are gradually required to participate in power system frequency regulation especially in a weak grid. Corresponding ancillary frequency control functionalities and the contribution of the WF to system frequency stability are well studied. However, the impact of WF ancillary frequency control on wind turbine generators (WTGs) is rarely considered. It is found in this paper that the ancillary frequency control deteriorates drive-train torsional oscillation of the WTG, which restricts application of the ancillary control. To overcome this problem, this paper proposes an adaptive damping control scheme based on reactive power modulation of the WTG to suppress drive-train torsional oscillation, which can assist the WF to meet the grid code requirements. Superior to the traditional active power modulation method, the proposed adaptive control scheme can achieve satisfactory damping improvement with less control effort. Furthermore, the adaptive scheme with a torsional frequency updating mechanism can effectively counteract torsional damping deterioration caused by parameter uncertainties, operating condition or control mode variations. Feasibility and effectiveness of the proposed damping control are validated by simulations and real-time experiments.

Equivalent inertia of the power system is reducing with the increasing wind power penetration. To alleviate this issue, more and more countries have put forward grid codes which require wind farms (WFs) to provide inertial response as traditional synchronous generation does. It is found in this paper that the synthetic inertial control of the WF in a weak grid deteriorates drive-train torsional oscillation of wind turbine generators (WTGs). Furthermore, the WF emulating inertial response is involved in power system oscillations and induces a new critical low frequency oscillation. The above oscillations threaten small-signal stability of the WF and the power system, which restricts the WF to fulfill grid code requirements. To overcome this problem, this paper proposes a two-level damping control scheme consisting of a WTG level torsional oscillation damping control and a WF level low frequency oscillation adaptive damping control to enhance damping characteristic of the WF. Simulation results show that the two-level damping control can simultaneously counteract these concerned multiple oscillations under various operating conditions, which facilitates the WF to meet grid code requirements and improves dynamic stability of the WF and the power system.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.

This paper aims to explore a viable solution for a doubly-fed induction generator (DFIG)-based wind farm to meet the reactive support requirement of the low voltage ride-through (LVRT) grid code with safe grid-connected operation during asymmetrical grid faults. First, the control scheme for the DFIG-based wind energy conversion system (WECS) is designed. Then, the controllability issue is analyzed by means of an optimal method, and the derived controllable regions indicate that the DFIG-based WECS can only remain controllable under mild asymmetrical fault situations. Afterwards, the static synchronous compensator (STATCOM) is introduced as extra equipment to ensure that the DFIG-based wind farm remains controllable under severe asymmetrical fault situations. For this purpose, a voltage compensation control scheme and a corresponding capacity matching method for the STATCOM are proposed. The simulation results verify that, with the proposed coordinated control between the DFIG-based wind farm and the STATCOM, the required positive-sequence reactive current can be supplied to support the power grid. The oscillations on the electromagnetic torque and direct current (DC)-link voltage of the DFIG-based WECS can also be eliminated. Therefore, the control scheme can be helpful to improve the reliability of both the wind farm and the power system during grid faults.

The open circuit voltage (OCV) of lithium-ion batteries is widely used in battery modeling, state estimation, and management. However, OCV is a function of state of charge (SOC) and battery temperature (T_bat) and is very hard to estimate in terms of time efficiency and accuracy. This is because two problems arise in normal operations: (1) T_bat changes with the current (I), which makes it very hard to obtain the data required to estimate OCV—terminal voltage (U) data of different I under the same T_bat; (2) the difference between U and OCV is a complex nonlinear function of I and is very difficult to accurately calculate. Therefore, existing methods have to design special experiments to avoid these problems, which are very time consuming. The proposed method consists of a designed test and a data processing algorithm. The test is mainly constant current tests (CCTs) of large I, which is time-efficient in obtaining data. The algorithm solves the two problems and estimates OCV accurately using the test data. Experimental results and analyses showed that experimental time was reduced and estimation accuracy was adequate.