The high voltage power transformer is the critical element of the power system, which requires continuous monitoring to prevent sudden catastrophic failures and to ensure an uninterrupted power supply. The most common failures in the transformer are due to partial discharge (PD) in electrical insulations which are the results of the insulation degradation over time. Different approaches have been proposed to monitor, detect, and locate the partial discharge in power transformers. This paper reviews and evaluates the current state-of-the-art methods for PD detection and localization techniques, and methodologies in power transformers. Detailed comparisons of PD detection techniques have been identified and discussed in this paper. The drawbacks and challenges of different partial discharge measurement techniques have been elaborated. Finally, brief reviews of PD denoising signals, feature extraction of PD signals, and classification of partial discharge sources have been addressed.

Power utilities are struggling to reduce power failure incidents in substations and their components to operate more reliably and economically [1]. Many power failures are produced directly or indirectly because of the insulation system of utility components [2], [3]. The selection of the insulation should ensure power plant operational continuity along with completely resolving or significantly limiting the actual power system's failures [4]. Gas insulated substations (GIS) have the best insulation performance which ensures achieving minimum failure incidents, although at high installation cost. The most common insulating gas used in GIS is Sulfur hexafluoride (SF 6 ) gas, which is widely used as an effective electrical insulation as well as an arc-quenching medium [5]. Basic GIS and gas insulated transmission lines (GITL or GIL) consist of a conductor supported by solid insulators inside an enclosure filled with SF 6 gas or its mixture [6].

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 theopportunities of havingassociated with the collected big data.Now, Hence, the triumph of the smart grid utilizationenergy paradigm depends on the factor of smart gridbig data analytics. which wouldThis includes the effective acquisition, transmission, processing,and effective visualization, interpretation, and utilization of big dataof the data that would bring benefits and increase efficiency of the power systems. The paper provides deep insights aboutinto various big data technologies and discusses the literature onbig data analytics in the context of the smart grid. Also, itThe paper also presents the challenges and opportunities , tools and emerging technologiesbrought by the advent ofthe internet of things, machine learning and big data from smart gridsand cloud platforms.

The microgrid (MG) can be considered as a small-scale power grid that consists of distributed energy resources (DERs), loads, and controllers. One of the major advantages of the MG is that it can operate in grid-connected or islanded modes that can generate, distribute, and regulate the power flow to local consumers. The increasing interest in smoothly integrating renewable energy into the power grid is one of the main reasons for the development of the MG concept. In addition, most renewable energy systems provide the network with the needed power, which can be directly used or stored through energy storage systems, which enhances the overall performance of the MG. Developing a proper control and management system for MGs is not an easy task. There are many challenges to ensuring a reliable system and efficient operation. Recently, industry authorities have begun many efforts in the field of MG implementation and controls. This article provides a complete description of the main features and challenges of ac MGs and describes the hierarchical control architecture as an effective solution. A detailed discussion of the control levels is presented as well.

Different aggregation levels of the electric grid's big data can be helpful to develop highly accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Whilst different models are proposed for STLF, they are based on small historical datasets and are not scalable to process large amounts of big data as energy consumption data grow exponentially in large electric distribution networks. This paper proposes a novel hybrid clustering-based deep learning approach for STLF at the distribution transformers' level with enhanced scalability. It investigates the gain in training time and the performance in terms of accuracy when clustering-based deep learning modeling is employed for STLF. A k-Medoid based algorithm is employed for clustering whereas the forecasting models are generated for different clusters of load profiles. The clustering of the distribution transformers is based on the similarity in energy consumption profile. This approach reduces the training time since it minimizes the number of models required for many distribution transformers. The developed deep neural network consists of six layers and employs Adam optimization using the TensorFlow framework. The STLF is a day-ahead hourly horizon forecasting. The accuracy of the proposed modeling is tested on a 1,000-transformer substation subset of the Spanish distribution electrical network data containing more than 24 million load records. The results reveal that the proposed model has superior performance when compared to the state-of-the-art STLF methodologies. The proposed approach delivers an improvement of around 44% in training time while maintaining accuracy using single-core processing as compared to non-clustering models.