Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique.

This paper presents an efficient Low-Power Viterbi Decoder Design using T-algorithm. It implements the viterbi decoder using T-algorithm for decoding a bit-stream encoded by a corresponding forward error correction convolutional encoding system. A lot of digital communication systems incorporated a viterbi decoder for decoding convolutionally encoded data. The viterbi decoder is able to correct errors in received data caused by channel noise. We proposed an architecture implementing a Viterbi Decoder with Talgorithm deployed with threshold generator unit and purge unit to reduce the number of states which reduce power consumption. We propose modified architecture for the survivor Metric Unit to reduce the memory Access power during the trace back operation. The proposed viterbi decoder is carried out for rate-1/2 with a standard constraint length 7. The Synthesis results will be done using cadence RTL Encounter Tool. For ASIC synthesis, we use TSMC 45-nm CMOS Process. The architecture which reduces the complexity and power Consumption by as much as 70% without effecting the decoding speed.

The objective of this paper is to show the characteristics of smart meters enabling to monitor and analyze the low-voltage (LV) network. This is achieved by developing use cases, where power quality and outage data are transferred from smart meters through distribution network to the control center. To visualize the monitoring process of LV network, the use cases are mapped into smart grid architecture model. The paper proposes a solution to analyze the LV network interruption and power quality problems (over-voltage, under-voltage, voltage sags, and swells). Thus, this paper provides a smart platform for monitoring LV network.

Partial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.

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