There are many methods that have been studied by earlier researchers in order to detect the acoustic properties of Partial Discharge (PD) emitted by PD sources. One of the methods known as ultrasonic sensing on medium voltage Cross-Linked Polyethylene (XLPE) cable was adopted to detect partial discharges on commercial applications, usually by using Acoustic Emission (AE) sensors. This paper presents the processes of designing a PD sensor to detect the acoustic properties of the partial discharges on medium voltage XLPE cable. This PD sensor method works by detecting the partial discharges occurred at the joint of the cable which can act as an early warning device to help minimize the repair and maintenance costs of degrading cable. Result of the experiment shows the complete design of the prototype device, the device after fabrication and the functionality of the device. This design of the prototype can be beneficial for future uses in designing cost efficient and smaller sized PD detection devices. By positioning the sensor in horizontal position directly to the source of PD on the cable, the sensor will be able to detect acoustic properties of PD, by picking up the frequencies beyond 40 kHz. By varying the voltage applied values, a design of experiment (DOE) is carried out accordingly. Result of the experiment shows that the prototype device is functioning as expected, and hence this finding will be very useful to the consumers of power industries as the sensor device can serve as an alternative device to the commercialized PD sensing devices which are bulky and expensive.
Hydrofluoroether (HFE) impurities detection is an issue related to detecting chemical contamination within a high volume manufacturing (HVM) chiller caused by a rapid emulated environmental attack. The aftereffects of the cycle emulated attack may eventually create a micro-crack in the heat-exchanger. This event eventually causes the contamination of HFE due to multiple chemical interactions. This study proposes a new classification methodology to detect HFE chemical impurities using induction by a 532nm laser. The purpose of the laser induction is to leverage its laser speckle contrast attributes and amplify its detection. One of the reasons for choosing the 532nm laser spectrum is its highest quantum efficiency. Once amplification of detection is achieved, the detector tends to be in a highly sensitive mode due to low marginality to differentiate between two different conditions. This mode is prone to be stochastic. Thus, a new form of architecture known as Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network (DL-LSC-ESNN) is proposed. The architecture utilizes speckle contrast domain conversion, dimensional additivity of the receptive neuron of evolving spiking neural network (ESNN), followed by the strength of Convolution Neural Network (CNN) feature extraction (FE) capability. Ultimately, the evolving and adaptive ability of ESNN is assimilated and integrated seamlessly. CNN acted to extract only an important spike train, and the result is its essences of important spikes or spike feature maps. The spike feature maps are then fed into the ESNN neuron repository, which either assimilates or creates a new neuron repository. The proposed methods show significant improvement in the accuracy of detection against multiple baseline state of the art CNN architecture and ultimately demonstrated its capability to detect real-time contamination of HFE thus improved the detection rate significantly for the HVM environment.
High voltage assets play a vital role in providing uninterrupted power to the consumers and any slight problems experienced by the assets may cause losses in millions of dollars to businesses. Therefore it is of utmost importance to monitor the health of high voltage assets. This research presents the development process of a Partial Discharge (PD) device that is able to detect PD acoustic waves for monitoring high voltage assets purposes. Medium voltage Cross-Linked Polyethylene (XLPE) cable was used which was introduced with spherical void defects at the joints of the cable that functioned to produce PD acoustic waves. Outcome of the development processes provides the finished design of the PD sensing device, known as Partial Discharge Detection (PDD) device. The functionality of the PDD device was also assessed through controlled experimentations, and they proved to be successful. Pure PD waveform captured by the ultrasonic sensor was similar when compared to a HFCT sensor’s pure PD waveform. The PDD device is a small and affordable, and is opened to various improvements such as integrating Artificial Intelligence (AI) unto the device, and one day may replace most existing bulky and expensive PD sensing devices that are readily available in the market.
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