Smart grids have been gradually replacing the traditional power grids since the last decade. Such transformation is linked to adding a large number of smart meters and other sources of information extraction units. This provides various opportunities associated with the collected big data. Hence, the triumph of the smart grid energy paradigm depends on the factor of big data analytics. This includes the effective acquisition, transmission, processing, visualization, interpretation, and utilization of big data. The paper provides deep insights into various big data technologies and discusses big data analytics in the context of the smart grid. The paper also presents the challenges and opportunities brought by the advent of machine learning and big data from smart grids.

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Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications

Multiscale Gaussian process regression-based generalized likelihood ratio test for fault detection in water distribution networks

Fault detection and diagnosis in power transformers: a comprehensive review and classification of publications and methods

Analog physical reduced scale model of GICs in power transformers. The instrumentation employed to carry out this study consists of a single-phase reduced scale transformer, a controllable current source, a resistive load and a data acquisition system. The work establishes not only that it is possible to model the behavior of magnetic variables and to extrapolate the results to large full size power transformers, but also provides insight into GICs generation and their effects on power transformers.

http://repositorio.ineel.mx/jspui/bitstream/123456789/68/1/pdf4.pdf

Core saturation effects of geomagnetic induced currents in power transformers

Various unwanted phenomena that are taken place in the transformer may occasionally mal-operate selected fault classification based protective schemes. Hence, it is necessary to discriminate internal fault from external abnormal conditions for unit protection of power transformer. This study presents a new hierarchical ensemble extreme learning machine (HE-ELM) based classifier technique to identify faults in & out of transformer. The component extreme learning machine (ELM) is structured hierarchically to improve its fault data classification accuracy. The developed algorithm is evaluated by simulating multiple disorders on 100 MVA, 132/220 kV transformer with the help of PSCAD software. DWT is used to extract features from acquired current signals from transformer. The feature vector formed after extraction process is fed to the HE-ELM algorithm for data classification. The fault discrimination accuracy of HE-ELM technique is 99.91%. This shows its effectiveness with respect to other classifier techniques. Moreover, the developed algorithm is successfully tested on hardware prototype in laboratory environment under various inrush and fault conditions using Cortex M4 microcontroller (STM32F407) with maximum identification time of 27 ms. The proposed HE-ELM technique is compared with existing support vector machine, probabilistic neural network and ELM techniques for identical fault data. Results demonstrate that HE-ELM outperforms than existing schemes in cross-domain recognition task.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-smt.2019.0102

Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique

Identification of faults within power transformers is the means of ensuring unit transformer protection. Existing relay maloperates during abnormalities such as magnetising inrush, CT saturation and high resistance internal fault condition. Therefore, it is essential to categorise the internal fault and external abnormality/fault in case of transformer protection. This study presents a new scheme, based on relevance vector machine (RVM) as a fault classifier. The developed algorithm is assessed by simulating various disorders on 345 MVA, 400/220 kV transformer in PSCAD/EMTDC software and also on prototype model with 2 kVA, 230/110 V multi-tapping transformer. One cycle post fault current signals are captured from primary and secondary to form feature vectors. These feature vectors are used as an input to RVM for classification of various test cases. Wide variation in system parameters and fault conditions are considered for test data generation and validation. The proposed scheme is compared with the support vector machine (SVM) and probabilistic neural network (PNN)-based techniques. The proposed scheme successfully discriminates various types of internal faults and external abnormalities in power transformer within a short time. The fault classification accuracy obtained by proposed RVM technique is more than 99% in comparison to SVM and PNN-based schemes.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-epa.2017.0562

Design and development of fault classification algorithm based on relevance vector machine for power transformer

Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions

[2_TD$DIFF]Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer

Due to deregulation of the power system, grid reliability becomes more complicated. Even, day by day small-scale and renewable power generation is inserted in a distribution system effectively due to people awareness. A power transformer is one of the most important equipment in the grid to reliably and efficiently transmit power to the consumers. Asset management and protection are the best concepts for enlargement of transformer lifespan as well as to increase grid reliability. This article reflects on electrical and other parameter based power transformer asset management. Voltage, current, and power based inrush detection and Power Differential Protection (PDP) are applied to protect the transformer. Various data such as load history, losses and temperature will be monitored in real time to enhance the working capability of the transformer. The proposed method adopts various electrical and nonelectrical parameters for condition monitoring. The proposed scheme is successfully tested on SkV A laboratory transformer using Arm CORTEX-M4 processor. A fitness function estimated from the collected data in the processor will reflect the condition of the transformer. The hardware result confirms the effectiveness of the scheme for monitoring and protection of transformer.

Dharmesh Patel

Papers

Design and development of fault classification algorithm based on relevance vector machine for power transformer

Identification of internal fault against external abnormalities in power transformer using hierarchical ensemble extreme learning machine technique

Efficient CNN-XGBoost technique for classification of power transformer internal faults against various abnormal conditions

[2_TD$DIFF]Total Harmonic Distortion (THD) based discrimination of normal, inrush and fault conditions in power transformer

Real-Time Monitoring & Protection of Power Transformer to Enhance Smart Grid Reliability