Stability analysis of PV generators with consideration of P&O-based power control

Photovoltaic (PV) generators have continuously increased in recent years, whose power is usually controlled through the perturbation and observation (P&O) method. In essence, the P&O method is nonlinear and discontinuous. Hence, the conventional small-signal stability analysis is not suitable anymore when the influence of the P&O-based power control is considered. Focusing on this problem, this paper adopts the nonlinear describing function (DF) method to conduct the accurate stability analysis of PV generators with consideration of P&O-based power control. The detailed procedures about the DF method are introduced, and then the related influence factors like perturbation size, filters, and so on are analyzed quantitatively. Furthermore, the comparison with the conventional stability analysis methods is made, which suggests that the DF method can effectively enhance the accuracy of the stability analysis. All the conclusions are verified by the real-time hardware-in-loop tests

The ultra-short-term photovoltaic power prediction based on multi-exposure high-resolution total sky images using deep learning

With the increasing installed capacity of renewable energy sources such as photovoltaic (PV) generation in the power system, ultra-short-term PV power prediction is more important for the stability. However, the prediction accuracy in the traditional methods cannot be guaranteed. Based on multi-exposure high-resolution total sky images (TSIs), this paper presents an advanced ultra-short-term PV prediction method using deep learning. To fully utilize the high-resolution images, an overlapping sliding window cutting and concatenating strategy are described to capture the global and local features of an image. The multi-exposure images are fused to provide more details about edge information and brightness. To better extract the image features and sequential features for PV prediction, a convolutional long–short-term memory model (CLSTM) with a multi-head self-attention mechanism is presented. The experiments use real-world datasets in the Zero Carbon Emission laboratory at Zhejiang University. The simulation results show that the proposed model can utilize TSIs to achieve the desired accuracy consistently. Under different weather conditions, the prediction accuracy of this model is improved by 49.1%–66% compared with that of other models

Distributed Coordination Control Based on State-of-Charge for Bidirectional Power Converters in a Hybrid AC/DC Microgrid

This paper proposes a distributed coordination control for multiple bidirectional power converters (BPCs) in a hybrid AC/DC microgrid with consideration of state-of-charge (SOC) of storages. The researched hybrid AC/DC microgrid is composed of both AC and DC subgrids connected by multiple parallel BPCs. In the literature, the storages of a hybrid microgrid are considered to allocate in only the AC subgrid or DC subgrid, which reduces the reliability of the whole system, especially during the islanded mode. Besides, the SOC management has not been considered in BPCs’ operating strategy. This paper considers a hybrid microgrid topology which has energy storages in both AC side and DC side. This ensures the reliability while increasing the complexity of the control strategy at the same time. Further, a distributed coordination control method for multiple BPCs based on SOC was proposed to enhance the reliability of hybrid microgrid. Finally, the performance of the proposed control methods was verified by real-time hardware-in-loop (HIL) tests