The increasing household loads make series arc faults more complex, which are difficult to be detected by traditional circuit breakers and lead to the frequent occurrence of residential fire accidents. In this paper, a comprehensive approach of complex load recognition and series arc detection is proposed on the basis of principal component analysis and support vector machine (PCA-SVM) combination model. Several typical loads were selected and analyzed, especially nonlinear and complex loads like power electronics load and multi-state load. Three time-domain parameters, maximum slip difference (MSD), zero current period (ZCP) and maximum Euclidean distance (MED), and nine frequency-domain harmonics information are collected to complex waveforms. To decrease the computation cost and further to enhance the response velocity, all the time-domain and frequency-domain information were blended and dimensionally reduced to three parameters by principal component analysis (PCA). Prior to the series arc detection, load recognition is trained and completed with the artificial intelligence (AI) algorithm. At last, the comprehensive model of load recognition and series arc detection is achieved based on a support vector machine (SVM). The accuracy of load recognition and series arc detection reaches 99.1% and 99.3%, respectively, demonstrating the excellent performances of the intelligent approach to diagnose the series arcing activities in modern household applications.

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High-frequency transformers are the core components of power electronic transformers (PET), whose insulation is deeply threatened by high voltage (HV) and high frequency (HF). The partial discharge (PD) test is an effective method to assess an electrical insulation system. A PD measurement platform applying different frequencies was set up in this manuscript. PD signals were acquired with a high-frequency current transducer (HFCT). For improving the signal-to-noise (SNR) ratio of PD pulses, empirical mode decomposition (EMD) was used to increase the SNR by 4 dB. PD characteristic parameters such as partial discharge inception voltage (PDIV) and PD phase, number, and magnitude were all analyzed as frequency dependent. High frequency led to high PDIV and a smaller discharge phase region. PD number and magnitude were first up and then down as the frequency increased. As a result, a suitable frequency for evaluating the insulation of high-frequency transformers is proposed at 8 kHz according to this work.

Trigger-action (TA) programming is a programming paradigm that allows end-users to automate and connect IoT devices and online services using if-trigger-then-action rules. Early studies have demonstrated this paradigms usability, but more recent work has also highlighted complexities that arise in realistic scenarios. To facilitate end-users in TA programming, we propose AutoTAR, a context-aware conversational recommendation technique for recommending TA rules. AutoTAR leverages a TA knowledge graph to encode semantic features and abstract functionalities of rules, and then takes a two-phase method to recommend TA rules to end-users: during the context-aware recommendation phase, it elicits user preferences from programming context and recommends the top-N rules using a mixed content and collaborative technique; during the conversational recommendation phase, it justifies recommendations by iteratively raising questions and collecting feedback from end-users. We evaluate AutoTAR on Mturk and real data collected from the IFTTT community. The results show that our method outperforms state-of-the-arts significantly — its context-aware recommendation outperforms RecRules by 26% on R@5 and 21% on NDCG@5; its conversational recommendation outperforms LARecommender (a conversational recommender with the LA model) by 67.64% on accuracy. In addition, AutoTAR is effective in solving three problems frequently occurring in TA rule recommendations, i.e., the cold-start problem, the repeat-consumption problem, and the incomplete-intent problem.