Flexible Direct Current Transmission System (FDCTS) starts from the desert renewable energy base, and delivers power to load center usually. Under the background of large-capacity renewable power transmission through FDCTS, the modular multilevel converter (MMC) in the receiving end grid faces increasing stability challenges. In particular, impedance coupling between the receiving end modular multilevel converter and the AC grid may trigger wideband oscillations, thereby threatening secure and stable system operation. To address this issue, a harmonic-state-space-based frequency-domain impedance model is established for the receiving-end MMC. The impedance characteristics and stability margins under different main-circuit and controller parameters are then evaluated, and an impedance reshaping strategy is adopted for typical vulnerable operating conditions. This study provides theoretical support and engineering reference for the stability analysis and control design of receiving-end converter stations in large-scale hybrid HVDC transmission systems.
In grid-connected wind power systems, the wind energy generator (WEG) must operate across a wide speed range to align turbine and generator characteristics. This paper proposes a low-cost, practical, battery-assisted slip-power recovery scheme for a grid-connected wound-rotor induction generator (WRIG), in which the rotor slip power is recovered through a three-phase diode bridge and transferred to a battery through a duty-ratio-controlled boost DC–DC converter. A two-dimensional lookup-table-based control strategy using wind speed and battery voltage is developed to determine the required duty ratio of the chopper. In the proposed system, variable-speed operation is achieved by regulating the rotor chopper duty ratio, thereby optimizing turbine–generator matching and maximizing the power delivered to the grid. The proposed slip power recovery approach is experimentally investigated using a 3-kW wound-rotor induction machine and a 200-W MOSFET-based boost chopper connected to a battery bank. The battery serves simultaneously as a slip-power recovery sink and an energy-storage element, while its voltage and wind speed are used to determine the optimum chopper duty ratio. The complete system is simulated using MATLAB SIMULINK over the wind-speed range 7–25 m/s. Generator slip is regulated to align the wind turbine and generator characteristics under changing wind conditions. System performance is evaluated using wind profiles from selected Indian sites. The results show improved wind energy harnessing, effective slip power recovery, increased system efficiency, and reliable operation across a wide range of wind speeds. The proposed control is particularly effective for medium-to-high wind speeds (13–25 m/s), whereas rotor short-circuit operation remains preferable in the 7–12 m/s range. Weibull-based field-performance assessment at five South Indian sites predicts an annual energy gain ranging from 0.65
This paper proposes an integrated event-triggered optimization framework for resilient reconfiguration and dispatch in TSO–DSO coordinated systems under extreme weather events. A disaster-aware stochastic model incorporates spatiotemporal hazard fields, component fragility curves, and correlated failure scenarios. The transmission-level problem co-optimizes topology switching and generator re-dispatch; each distribution-level problem optimizes feeder reconfiguration, distributed energy resource (DER) dispatch, battery storage scheduling, and load shedding, coupled through boundary power exchange. A two-level decomposition—outer TSO–DSO coordination iterations and inner accelerated Benders decomposition with adaptive scenario bundling—solves the large-scale mixed-integer program within operational time limits. Case studies on IEEE 118-bus and IEEE 30-bus coupled transmission–distribution systems under hurricane and ice storm scenarios demonstrate 38.7 × speedup over direct CPLEX with a 0.42
Abnormal data in wind power curves often reflect failures or certain extreme operating conditions of wind turbines, which are of great significance for the operation and maintenance of wind farms. To obtain complete and reliable wind speed data, an Adaptive Multi-Density Quality Connection Clustering Method (AMDMCM) and a multi-factor spatiotemporal reconstruction method considering spatiotemporal short-term correlations are proposed. In terms of data cleaning, the proposed AMDMCM is based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) architecture. Compared to state-of-the-art adaptive clustering methods such as Multi-Density Based Spatial Clustering of Applications with Noise (MDBSCAN), AMDMCM overcomes the reliance of traditional algorithms on absolute spatial density by introducing a nearest-neighbor-guided isolation similarity to capture the local topological features of the data. Furthermore, it utilizes an adaptive k-Nearest Neighbors Mass(K-M)metric to facilitate optimal boundary searching, enabling the autonomous determination of the ideal Epsilon (Eps) and Minimum Points (MinPts) parameters. while ensuring the model’s robustness, it reduces the impact of subjective parameter settings. In terms of data reconstruction, a Spatiotemporal Convolutional Generative Adversarial Network (STGAN) is proposed, which incorporates environmental factors as auxiliary generation features. By utilizing a Temporal Convolutional Network (TCN), the network thoroughly exploits the auxiliary generative information of spatiotemporal correlations. This approach is then applied to interpolate the cleaned data, ensuring data continuity and accuracy. The experimental results show that this method significantly improves the cleaning efficiency of abnormal wind power data through experimental verification on the measured data from four wind farms in Guangdong Province.
It is essential to increase the resilience of power distribution systems to deal with natural disasters and ensure a continuous supply of critical loads. Microgrids equipped with energy storage systems provide a promising means of enhancing distribution system resilience. This paper proposes the integration of hydrogen energy storage systems (HESSs) with photovoltaic-based microgrids to improve the resilience of distribution systems. Improving resilience is pursued by maximizing the supplied loads and maximising restored load while minimising the number of electrically isolated microgrids. The optimal steady-state feasible configuration of the network is determined by using the graph method, considering the fault location and the network tie-lines. To evaluate the performance of hydrogen storage and compare it with batteries under similar weather conditions and damage levels, an IEEE 33-bus network with three microgrids is used. The results show that HESSs, when supplied by an adequate pre-stored hydrogen inventory, are less sensitive to short-term reductions in PV generation, can improve the resilience of distribution networks more effectively than batteries. In the case of severe damage events, HESSs can supply up to 28
