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With the development of smart grids, appliance‐level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non‐intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage–current (V–I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V–I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large‐scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state‐of‐art methods. [ABSTRACT FROM AUTHOR]

To avoid dramatic economic losses and unrecoverable failures in offshore wind farms, it is necessary to accurately detect faults at the early stage of doubly-fed induction generators. The new extended Park's vector method is proposed in the study. The amplitude of the twice fundamental frequency component of the square of the Park's vector module is selected as the new fault feature to realise the early fault detection. The relationship between the extended Park's vector method and negative sequence current of the stator is also analysed in the study. The Fryze–Buchholz–Dpenbrock algorithm and the non-linear least squares method are applied to estimate the mathematic relationship among unbalanced stator voltage, load change and the negative sequence current of the stator. The experiments were conducted and the results showed that the proposed method is robust to the effects of the unbalanced stator voltage and load change. Therefore, the proposed method can accurately detect faults at the early stage with high sensitivity and reliability. [ABSTRACT FROM AUTHOR]

To avoid dramatic economic losses and unrecoverable failures in offshore wind farms, it is necessary to accurately detect faults at the early stage of doubly‐fed induction generators. The new extended Park's vector method is proposed in the study. The amplitude of the twice fundamental frequency component of the square of the Park's vector module is selected as the new fault feature to realise the early fault detection. The relationship between the extended Park's vector method and negative sequence current of the stator is also analysed in the study. The Fryze–Buchholz–Dpenbrock algorithm and the non‐linear least squares method are applied to estimate the mathematic relationship among unbalanced stator voltage, load change and the negative sequence current of the stator. The experiments were conducted and the results showed that the proposed method is robust to the effects of the unbalanced stator voltage and load change. Therefore, the proposed method can accurately detect faults at the early stage with high sensitivity and reliability. [ABSTRACT FROM AUTHOR]