Aiming at the fact that large-scale penetration of wind power will to some extent weaken the small signal stability of power systems, in this paper, the dynamic model of a doubly fed induction generator (DFIG) is established firstly, to analyze the impact of wind generation on power oscillation damping. Then, based on the conventional maximum power point tracking control of variable speed wind turbine, a supplementary control scheme is proposed to increase the damping of power system. To achieve best performance, parameters of the damping control are tuned by using a genetic algorithm. Results of eigenvalue analysis and simulations demonstrate the effectiveness of supplementary damping control with fixed wind speed. At last, due to the problem that fluctuation of output power of wind generators would cause the unstable performance of the DFIG damping controller above, a new algorithm that adapts to the wind variation is added to the supplementary damping control scheme. Results of the simulation show that an improved damping control scheme can stably enhance system damping under various wind speeds and has higher practical value.

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

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In this paper, the optimal demand response strategy of a commercial building-based virtual power plant with real-world implementation in heavily urbanised area is studied. Instead of modelling the decision-making process as an optimisation problem, a reinforcement learning method is used to seek the optimal strategy, which could update its performance with minimal manpower manipulation. Specifically, the data collection from several commercial buildings, including hotel, shopping mall and office, in Huangpu district, Shanghai city is analysed to deploy the demand response program. Compared with the conventional demand response strategy based on optimisation, the learnt strategy does not rely on the forecasting information as input and could adapt to the changing demand response incentive automatically. It may not produce the best result every time, but can guarantee the benefit in a non-deterministic way in long-term operation. The real-world deployment of the Huangpu virtual power plant involving hardware and software platform is also introduced, as well as its future development projection.

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