With the widespread access of nonlinear loads, enterprises and consumers are faced with the problem of power quality disturbance (PQD) in the grid. The detection and classification of multiple power quality disturbances is considered to be a challenging task. In this paper, a novel hybrid method based on Stockwell Transform (ST) and Deep Learning is proposed to detect and classify multiple power quality disturbances (MPQDs). Compared with previous cases where only single or dual PQDs are considered, this paper fully considers the existence of MPQDs compounds in real situations and designs a more general and automated approach based on Deep Learning for automated feature selection and classification. Firstly, the S-transform is used to extract the features of the one-dimensional signal and take the modulus of the result to obtain the ST-matrix of the signal. In order to improve the anti-noise performance of the system, the parameters for generating the time-frequency domain contour are optimized, so that contour images with less noise can be drawn. Secondly, the above images are fed into the designed Convolutional Neural Network (CNN) for training. A total of 37 PQDs detection and classification tasks including single and multiple disturbances were completed. It is compared with other existing methods to demonstrate its robustness under noisy environments. Finally, an experimental platform for MPQDs was built to verify the proposed method, and the results demonstrated that the method can effectively detect and classify MPQDS.

A NiMn ₂ O ₄ NSs@rGO nanocomposite was successfully fabricated through a facile co-precipitation and thermal treatment process, which exhibits enhanced energy storage performance.

In industrial production processes, rotational speed is a key parameter for equipment condition monitoring and fault diagnosis. To achieve rotational speed measurement of rotational equipment under a condition of high temperature and heavy dust, this article proposes a digital approach using an electrostatic sensor. The proposed method utilizes a strip of a predetermined material stuck on the rotational shaft which will accumulate a charge because of the relative motion with the air. Then an electrostatic sensor mounted near the strip is employed to obtain the fluctuating signal related to the rotation of the charged strip. Via a signal conversion circuit, a square wave, the frequency of which equals that of the rotation shaft can be obtained. Having the square wave, the M/T method and T method are adopted to work out the rotational speed. Experiments were conducted on a laboratory-scale test rig to compare the proposed method with the auto-correlation method. The largest relative errors of the auto-correlation method with the sampling rate of 2 ksps, 5 ksps are 3.2% and 1.3%, respectively. The relative errors using digital approaches are both within ±4‰. The linearity of the digital approach combined with the M/T method or T method is also superior to that of the auto-correlation method. The performance of the standard deviations and response speed was also compared and analyzed to show the priority of the digital approach.

A distinctive hierarchical hybrid nanostructure which consists of the ultrathin and vertical NiMn₂O₄ nanosheets grown on reduced graphene oxide (NiMn₂O₄ NSs@rGO) is synthesised as a non-enzyme glucose sensor by a simple co-precipitation and hydrothermal procedure. The structures and morphologies of the NiMn₂O₄ NSs@rGO nanocomposites are investigated through X-ray diffraction (XRD), Field emission scanning electron microscopy (FESEM), Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). Cyclic voltammetry and amperometry measurement are applied to analyse the glucose sensing characteristics of NiMn₂O₄ NSs@rGO. The obtained NiMn₂O₄ NSs@rGO sensor exhibits two wide linear ranges of 0.001 mM-1 mM and 1 mM-3.5 mM with high sensitivity of 2615.2μAmM⁻¹ cm⁻² and 294.8μAmM⁻¹ cm⁻², which is much better than other Ni-based and Mn-based glucose sensors. In addition, the NiMn₂O₄ NSs@rGO nanocomposites have excellent anti-interference and stability. Moreover, the constructed sensor is viable in the analysis of real serum samples, which makes it become a medically dependable non-enzymatic glucose sensor with great possibility.