Energy storage (ES) has become increasingly important in modern power system, whereas no single type of ES element can satisfy all diverse demands simultaneously. This study proposes a hybrid energy storage system (HESS) based on superconducting magnetic energy storage (SMES) and battery because of their complementary characteristics for the grid integration of wind power generations (WPG). This study investigates the mathematical model and the topology of the proposed HESS, which is equipped with a grid-side DC/AC converter, a battery buck/boost converter and a SMES DC chopper. The advanced control strategies comprised of device level and system level are designed. The control strategy for the converters which can be considered as device level is briefly discussed. The significant contribution of this study is proposing a novel system-level control strategy for reasonable and effective power allocation between SMES and battery. According to the control objectives, a fuzzy logic controller optimised with genetic algorithm is adopted. The detailed controller designs are described, meanwhile system stability and HESS operation performance are evaluated. MATLAB simulations are presented to demonstrate the effectiveness of the proposed strategies.

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When photovoltaic, wind, energy storage batteries, and other new forms of energy are connected to the grid, power electronic converters are needed, and there are a lot of nonlinear devices in the grid. The characteristics of sustainable energy generation determine the variability and intermittency, which will produce harmonic components. Active power filters (APF) are commonly used in industry for harmonic compensation, so it is of great significance to control APF quickly and effectively. The multi-objective, single-factor, multistep finite control set model predictive control (FCS-MPC) of an APF proposed in this paper is suitable for a multi-objective, multi-level converter control. This method is applied to the three-level APF structure, which changes the traditional three-level FCS-MPC control method. The traditional three-level FCS-MPC includes four control objectives, stable control of the DC-side voltage, power grid harmonic currents generated under non-linear loads, and balance of the capacitor voltage on the DC side when switching frequency. This method uses the redundant switching state of the three-level structure to achieve the voltage balance of the two capacitors on the DC side, which reduces the difficulty of target optimisation caused by the selection of weight factors. Based on the multi-step prediction, power feedback control is added on the DC side to increase the DC side’s reaction speed, eliminate the influence of uncertainty, and realise better dynamic performance. According to the simulation results, we can observe that the proposed method has good followability, can compensate for the harmonics of the power grid, reduces the harmonic content to less than 5%, and can balance the DC-side capacitor voltage.

Model predictive control (MPC) methods are widely used in the power electronic control field, including finite control set model predictive control (FCS-MPC) and continuous control set model predictive control (CCS-MPC). The degree of parameter uncertainty influence on the two methods is the key to evaluate the feasibility of the two methods in power electronic application. This paper proposes a research method to analyze FCS-MPC and CCS-MPC’s influence on the current prediction error of three-phase active power filter (APF) under parameter uncertainty. It compares the performance of the two model predictive control methods under parameters uncertainty. In each sampling period of the prediction algorithm, different prediction error conditions will be produced when FCS-MPC cycles the candidate vectors. Different pulse width modulation (PWM) results will be produced when CCS-MPC solves the quadratic programming (QP) problem. This paper presents the simulation results and discusses the influence of inaccurate modeling of load resistance and inductance parameters on the control performance of the two MPC algorithms, the influence of reference value and state value on prediction error is also compared. The prediction error caused by resistance mismatch is lower than that caused by inductance mismatch, more errors are caused by underestimating inductance values than by overestimating inductance values. The CCS-MPC has a better control effect and dynamic performance in parameter mismatch, and the influence of parameter mismatch is relatively tiny.

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