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Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user’s information need so as to improve the search results. Previous PRF methods mainly select expansion terms with high occurrence frequency in top-retrieved documents or with high semantic similarity with the original query. However, existing PRF methods hardly try to understand the content of documents, which is very important in performing effective query expansion to reveal the user’s information need. In this paper, we propose a QA-based framework for PRF called QA4PRF to utilize contextual information in documents. In such a framework, we formulate PRF as a QA task, where the query and each top-retrieved document play the roles of question and context in the corresponding QA system, while the objective is to find some proper terms to expand the original query by utilizing contextual information, which are similar answers in QA task. Besides, an attention-based pointer network is built on understanding the content of top-retrieved documents and selecting the terms to represent the original query better. We also show that incorporating the traditional supervised learning methods, such as LambdaRank, to integrate PRF information will further improve the performance of QA4PRF. Extensive experiments on three real-world datasets demonstrate that QA4PRF significantly outperforms the state-of-the-art methods.

Besides well-known phase-locked loops, frequency-locked loops (FLLs) are applicable to grid synchronization for grid-tied power converters. The models, principles, and algorithm structures of existing notch filter-based single-phase FLLs are often described with different theories in previous studies. Consequently, a theoretically unified understanding and further advancements of such type of FLLs are restricted. This paper reinvestigates notch filter-based single-phase FLLs and develops a generalized design framework for such types of FLLs. The model derivations of the FLLs are made uniformly via the Popov's model reference adaptive control theory, in order to achieve the theoretically unified understanding. Three major contributions are received in this paper. First, a family of FLLs is derived, and existing seemingly different FLL designs are accordingly developed into a family of homologous FLLs. Second, the performance differences between the FLLs in the whole family are evaluated and compared quantitatively from the algorithm structure point of view, in terms of stability, convergence speed, and harmonic suppression capability. Finally, an enhanced single-phase FLL (named the 1φ-EFLL) is recommended by the family of the homologous FLLs. The designs and the performance evaluations are verified by both simulations and experiments.

Wind farms (WFs) controlled with conventional vector control (VC) algorithms cannot be directly integrated to the power grid through line commutated rectifier (LCR)-based high voltage direct current (HVDC) transmission due to the lack of voltage support at its sending-end bus. This paper proposes a novel coordinated control scheme for WFs with LCC-HVDC integration. The scheme comprises two key sub-control loops, referred to as the reactive power-based frequency (Q-f) control loop and the active power-based voltage (P-V) control loop, respectively. The Q-f control, applied to the voltage sources inverters in the WFs, maintains the system frequency and compensates the reactive power for the LCR of HVDC, whereas the P-V control, applied to the LCR, maintains the sending-end bus voltage and achieves the active power balance of the system. Phase-plane analysis and small-signal analysis are performed to evaluate the stability of the system and facilitate the controller parameter design. Simulations performed on PSCAD/EMTDC verify the proposed control scheme.