There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Partial discharge classifications: Review of recent progress

As one of the most important directions in the field of computer vision, facial emotion recognition plays an important role in people's daily work and life. Human emotion recognition based on facial expressions is of great significance in the application of intelligent human-computer interaction. However, in the current research on facial emotion recognition, there are some problems such as poor generalization ability of network model and low robustness of recognition system. In this content, we propose a method of feature extraction using the deep residual network ResNet-50, which combines convolutional neural network for facial emotion recognition. Through the experimental simulation of the specified data set, it can be proved that this model is superior to the current mainstream facial emotion recognition models in the performance of facial emotion detection.

Facial expression recognition via ResNet-50

Condition monitoring of an operating apparatus is essential for lifespan assessment and maintenance planning in a power system. Electrical insulation is a critical aspect to be monitored, since it is susceptible to failure under high electrical stress. To avoid unexpected breakdowns, the level of partial discharge (PD) activity should be continuously monitored because PD occurrence can accelerate the aging process of insulation in high voltage equipment and result in catastrophic failure if the associated defects are not treated at an early stage. For on-site PD detection, the ultra-high frequency (UHF) method was employed in the field and showed its effectiveness as a detection technique. The main advantage of the UHF method is its immunity to external electromagnetic interference with a high signal-to-noise ratio, which is necessary for on-site monitoring. Considering the detection process, sensors play a critical role in capturing signals from PD sources and transmitting them onto the measurement system. In this paper, UHF sensors applied in PD detection were comprehensively reviewed. In particular, for power transformers, the effects of the physical structure on UHF signals and practical applications of UHF sensors including PD localization techniques were discussed. The aim of this review was to present state-of-the-art UHF sensors in PD detection and facilitate future improvements in the UHF method.

Application of UHF Sensors in Power System Equipment for Partial Discharge Detection: A Review

In a deregulated power system, Demand Side Management (DSM) plays a vital role in handling the uncertain renewable power generation and load. The flat load-profile can be obtained using the Demand Response (DR) techniques with the storage elements and proper switching. The increasing penetration of Renewable Energy Sources (RES) and Electric Vehicles (EVs) supports the DR measures which facilitate both the utility and the consumer. The objective of DSM is to minimize the peak demand, electricity cost and emission rate by the effective utilization of storage with RES. This review article mainly focuses on the layers of microgrid, different techniques involved in DSM, mathematical models of DSM, latest optimization techniques and application of storage devices such as battery energy storage system and EVs in DSM. The state of the art of this article lies on the critical analysis related to datascience, advanced metering infrastructure and blockchain technologies which are the uniqueness of this article. The key issues and approaches are examined critically with the existing works to show how DSM implementation can be effectively done in the microgrids to reduce the electricity cost. This article helps the researchers to identify the research gap by gaining knowledge on the implementation of DSM in the microgrid and the factors affecting the DSM implementation. Few recommendations are discussed to provide future directions for researchers who started working in the DSM implementation. 

Demand side management in microgrid: A critical review of key issues and recent trends

The present work represents a methodology to detect the location of single as well as multiple Partial Discharge (PD) sources by optical method and to investigate the performance of optical sensors for this purpose. An experimental setup has been arranged in the laboratory for generation of PDs, optical sensing and analysis of the recorded signals obtained from multiple optical sensors. The analysis results prove the effectiveness of the methodology using optical sensors to find whether PD is occurring at single location or multiple locations. For identification of PD locations pattern recognition technique has been utilized by considering the received optical energy as a feature. For feature selection and classification two techniques have been evaluated, viz. Gaussian Mixture Model (GMM) and Support Vector Machine (SVM), and both have shown promising performance. SVM in regression mode was used for identification of unknown PD location/locations. In this case average accuracy obtained was 92.6% when PD is occurring at one location and 80.1% when PD is occurring at two locations. The obtained results indicate that, the proposed methodology can be used to locate partial discharges in high voltage equipment where the optical signals due to discharges find a path to get radiated towards the outer surface.

A methodology for identification and localization of partial discharge sources using optical sensors

This article presents a new convolutional neural network (CNN) topology using wavelet kernels to detect and discriminate single or multiple partial discharge (PD) locations in high voltage power apparatus with increased accuracy. The method is tested on an electrical equipment model with acoustic PD sensors. A cubical tank has been emulated in the laboratory representing the equipment under test and partial discharge sources have been placed at different positions along with the required data acquisition hardware. The present scheme eliminates the requirement of separate algorithms for feature extraction and classification of the acquired PD signals. Wavelet kernels of the CNN play a crucial role in feature learning, and the proposed CNN architecture as a whole, can classify the features in a supervised manner. The performance of the proposed scheme is compared with other existing methods using the same data set . It is found that an overall accuracy of 97.64% is achieved by the proposed method, outperforming other existing methods by a significant margin of at least 5% in terms of accuracy. The developed module is a generic one and can be adapted for different high voltage electrical apparatus with similar topological structures; hence, it can be used in various ways in power industry.

Wavelet Kernel-Based Convolutional Neural Network for Localization of Partial Discharge Sources Within a Power Apparatus

An approach based on rough set theory for identification of single and multiple partial discharge source

In this paper, a scheme has been developed for detecting individual, as well as multiple partial discharge (PD) sources within a transformer tank through acoustic sensors. For that purpose, in the laboratory, a transformer tank has been emulated inside which, provisions are made to place artificially created partial discharge sources at different locations. An experimental setup has been prepared to record the acoustic PD signal through multiple acoustic sensors placed at the outside surfaces of the walls. Presented results show the signature of the acquired data pattern for both individual and multiple discharges. Acoustic PD detection method experiences difficulty in locating the PD source accurately due to interference or attenuation of signals from the environmental noise. It is seen that Cross-wavelet transform (XWT) has an ability to nullify the effect of random noises present in the acquired acoustic PD signal. Thus, cross-wavelet transform based feature extraction with ensemble binary support vector machine based classifier is used for locating the partial discharge sources within the tank. These methodologies have been chosen as they suit well to discriminate between the PD sources at different locations inside the tank. Results indicate that the average classification accuracy is 92.8%. The contribution of this research work is in discriminating individual as well as multiple partial discharge sources which produce acoustic emission simultaneously. Experimental results indicate that the proposed methodology can be used to localize partial discharge sources in high voltage apparatus where the acoustic signals due to partial discharges find a path to propagate towards the outer surface.

Subrata Biswas

Papers

A methodology for identification and localization of partial discharge sources using optical sensors

Wavelet Kernel-Based Convolutional Neural Network for Localization of Partial Discharge Sources Within a Power Apparatus

An approach based on rough set theory for identification of single and multiple partial discharge source

Cross-spectrum analysis based methodology for discrimination and localization of partial discharge sources using acoustic sensors

Evidence of self-affine fluctuations of compound multiplicity distributions in nucleus-nucleus interactions at4.5AGeV