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Large-scale wind farms (WFs) generally consist of hundreds of wind turbines (WTs), and the WFs simulation model construction would be complex and even impossible if we develop each individual WT in detail. Therefore, the WFs equivalent simulation model with required accuracy is essential to be developed to explore the WFs operation characteristics. This article proposes an equivalent method for large-scale WFs using incremental clustering and key parameters optimization. Firstly, to acquire more comprehensive and distinguishable representations of WTs operation characteristics, the time series of WT active power, reactive power, voltage and current are selected as the multi-view clustering indicator (CI). Then, considering the computer memory pressures encountered by traditional clustering algorithms in dealing with large-scale WFs, a novel clustering algorithm namely multi-view incremental transfer fuzzy C-means (MVIT-FCM) is proposed, and this algorithm can process the WTs clustering problems without requiring to consider the scale of the WFs. Finally, to further increase the equivalent accuracy of the WFs equivalent simulation model, key parameters in the equivalent model are found using Sobol' criterion and then optimized using the designed Q-learning based non-dominated sorting genetic algorithm II (NSGA-II). To verify the effectiveness of the proposed method, the modified WFs system in China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent accuracy of the proposed model is higher when comparing with other models. Also, the proposed model has the advantage of processing the WFs equivalent problems with any scales.

In this paper, the performance characteristics analysis and an improved arm current control of modular multilevel converter (MMC) with asymmetric arm parameters are presented. The performance characteristics analysis of MMC is conducted based on the average switching function current model, which demonstrates that fundamental and double frequency ripples exist in the arm circulating current under asymmetric arm parameter conditions. Besides, there will be dc and double frequency components in the ac side current, while fundamental and double frequency ripples also appear in the dc side current. According to the analysis results, the arm current control based on an improved PIR (Proportional Integral-Resonant) controller via the FAC (feedback adjustment control) method is proposed to suppress the asymmetric components with significantly enhanced dynamic response. Theoretical analysis shows that by introducing the integration and feedback loops of the FAC method to the conventional PIR controller, the inertia and damping effects are emerged in the system, which can help to enhance the system dynamic response during transient and achieve better suppression performance of the asymmetric components. Simulation cases verify the correctness of the theoretical analysis and the validity of the proposed control.

The hybrid AC/DC microgrid with different types of distributed generations (DGs) and load demands is considered to be the preferred microgrid mode in the future. For hybrid AC/DC microgrid, autonomous and bidirectional power flow between AC and DC subgrids with superior dynamic performance is the fundamental objective to realise proportional power sharing and robust operation. In this study, an improved active power control strategy of the bidirectional AC/DC main converter (BMC) based on virtual synchronisation machine (VSM) for inertia improvement of the AC bus frequency and DC bus voltage of the hybrid microgrid is proposed. The VSM control is derived through the virtual inertia equation and the virtual capacitance equation, which can manage the AC frequency and DC voltage simultaneously. Then, the small signal and large signal characteristics analysis of the BMC are conducted to analyse the VSM control feature with different control parameters. On the basis of the small signal analysis, a proportional power sharing principle to select the control parameters is given to make DGs share the power commensurate with their power ratings. Finally, simulation cases are carried out to show the validity and efficiency of the proposed control strategy.

The inherent stochastic nature of wind power poses significant challenges to the safe and economic operation of power systems, and an accurate model of wind power forecast error can give operators great help in dealing with the negative impact of wind power uncertainty on the power system. To obtain accurate forecast error intervals, a wind power forecast error (WPFE) model based on dynamic Copula theory is proposed in this paper. By combining Autoregressive integrated moving average (ARIMA) method and generalized autoregressive conditional heteroscedasticity (GARCH) model, the marginal distribution of WPFE model with the ability to describe its time-varying feature is achieved. In this proposed model, 4 dynamic Copula functions are estimated and evaluated by the combination method of Inference Functions for Margins (IFM) and Akaike Information Criterion (AIC), and the best-fitted one is selected to be applied in a set of synchronous data of wind power and its forecast. The result shows that compared with the conventional method, the proposed method can provide more accurate forecast error intervals. This advantage can benefit the scheduling of the power system with high wind power penetration rate, which is also verified by a stochastic unit commitment case in modified IEEE 118-Bus system.