Voltage and frequency stability is one of the major concerns with the continuously increasing penetration of offshore wind farms (OWFs) integrated into onshore grid via VSC-MTDC system, and it is a challenge to coordinate its low voltage ride-through (LVRT) and frequency regulation (FR) control when a local fault or large disturbance occurs in onshore grid. To address this challenge, a coordinated voltage-frequency support (CVFS) method for the VSC-MTDC integrated OWFs system is proposed in three parts: 1) an adaptive maximum power point tracking (MPPT) strategy is developed for wind turbines (WTs) to satisfy the power demands for LVRT and FR; 2) an active and reactive power quantification strategy, which firstly considers the static load characteristics of nodes nearest the grid side VSC (GSVSC) stations, is proposed for GSVSC stations to support LVRT and FR respectively; 3) an adaptive power allocation strategy, which considers the capacity constraint and the coordination factor with deviation and rate of change of voltage and frequency of different GSVSC stations, is conducted to achieve differential power distribution between LVRT and FR. Extensive test results have validated the effectiveness and performance of the proposed CVFS method.

With the continuously increasing penetration of networked microgrids (MGs) on the local utility grid (UG), MGs face the challenge to avoid increasing system fault currents during low-voltage ride-through (LVRT). To solve this challenge, an active fault current limitation (AFCL) method is proposed with three parts: 1) a novel phase angle adjustment (PAA) strategy is conducted to relieve the impact of MGs output fault current on system fault current; 2) the current injection (CI) strategy for LVRT is formulated to fit the function of PAA; 3) a novel converter current generation (CCG) strategy is developed to achieve a better voltage support ability by considering network impedance characteristics. The proposed AFCL method is applied to the back-to-back converter, as a connection interface between MGs and UG. Extensive tests and pertinent results have verified the improvements of proposed AFCL method with better LVRT performance, while the networked MGs output fault current does not increase the amplitude of system fault current.

The present inaccurate fault current calculation, due to the incomplete consideration of whole process operation stages of fault ride-through (FRT) of DFIG-based wind farms (WFs), has a distinct impact on the safe operation of local power grid. To tackle this challenge, a fault current unified calculation (FCUC) method is proposed to achieve high-accuracy and high-speed mathematical analytical calculations of DFIG’s output fault current during FRT. The proposed FCUC method mainly includes: 1) Whole process unified state space model, which contains transition stage, crowbar protection stage, demagnetization stage, reactive current compensation stage, and dc-chopper protection stage, is established to thoroughly consider FRT’s different internal protection circuits and control strategies; 2) Stator output fault current is conducted by the unified state space model based on the stator residual voltage and the stator flux linkage; 3) Grid side converter (GSC) output fault current, which considers both injection mode and absorption mode, is developed based on the grid residual voltage and the unified state space model; 4) Fault current contribution mechanism, which is aggregated from both stator and grid side converter, is revealed based on Laplace transform from time-frequency-time domain, and is first expressed as an unified fault current expression. Extensive simulation/experimental/calculation results under different voltage sags have validated the correctness and effectiveness of the proposed FCUC method.

The existing load forecasting method based on the per‐unit curve static decoupling (PCSD) would easily lead to the deviation and translation of forecasting results. To tackle this challenge, a per‐unit curve rotated decoupling (PCRD) method is proposed for day‐ahead load forecasting with convolutional neural network and temporal convolutional network framework. The PCRD method decomposes the load into three parts: the rotated per‐unit load curve, the 0 AM load, and the daily average load. The shape feature of the load curve is extracted by CNN, the temporal features of the 0 AM load and daily average load are extracted by TCN. The rotation operation is to rotate the per‐unit load curve at the midpoint of the curve until the first load point is aligned to the same point, in order to improve the similarity of per‐unit load curves and to alleviate the deflection of forecasting results. The 0 AM load can verify the accuracy of the daily average load, which alleviates the translation of forecasting results. Several experimental results show that the proposed method has higher accuracy and stability than the existing PCSD method. After repeated experiments on multiple data sets, the generalization ability of the model is also verified.

The lack of relevant protection schemes and standards brings significant challenges to the promotion of direct current microgrid (DCMG) technologies. The existing line fault diagnosis methods usually require additional measuring devices to obtain fault signals. Although reducing the number of sensors can lower the system costs, it also increases the difficulty of diagnosis. Pole-to-pole (PP) and positive pole-to-ground (PPG) fault classification under unknown fault resistance, and negative-to-ground (NPG) fault detection under unobvious positive pole current characteristics are two major difficulties. To solve the above issues, a fuzzy granulation interval (FGI) theory-based line fault diagnosis method is proposed in this paper. Only locally measured bus-side voltage and positive pole current signals shared with the converter controller inputs are required. Firstly, the fault characteristics and diagnosis difficulties are analyzed. Then a new NPG fault index interval range (IR) is designed to extract the abnormal post-fault voltage fluctuations. Moreover, the PP/PPG fault classification strategy is designed according to the interval characteristics and capacitor discharge process. Finally, the performance of the proposed method has been evaluated through MATLAB/Simulink simulations and hardware-in-the-loop (HIL) tests. The results demonstrate that the proposed method can accurately discriminate different line faults within 1.5 ms under different fault conditions.