Future power networks are certain to have high penetrations of renewable distributed generation such as photovoltaics (PV). At times of high PV generation and low customer demand (e.g., summer), network voltage is likely to rise beyond limits mandated by grid codes resulting in a curtailment of PV generation, unless appropriate control means are used. This leads to a reduction in energy yield and consequently reduces the economic viability of PV systems. This work focuses on scenario-based impact assessments underpinned by a net prosumer load forecasting framework as part of power system planning to aid sustainable energy policymaking. Based on use-case scenarios, the efficacy of smart grid solutions demand side management (DSM) and Active Voltage Control in maximizing PV energy yield and therefore revenue returns for prosumers and avoided costs for distribution networks between a developed country (the UK) and developing country (India) is analyzed. The results showed that while DSM could be a preferred means because of its potential for deployment via holistic demand response schemes for India and similar developing nations, technically the combination of the weaker low voltage network with significantly higher solar resource meant that it is not effective in preventing PV energy curtailment. K E Y W O R D Sactive voltage control, demand side management, distributed generation, energy yield curtailment INTRODUCTIONThe decarbonisation of the energy network has created higher demand for electricity over oil and coal. Some of the electrical power network assets such as transformers and switchgear assets were installed as early as the 1950s and are still in use today. 1 For example, the UK's National Infrastructure Delivery Plan 2016-2021 identifies that "much of the existing infrastructure which has served us well is now old" and that "major investment is required to accommodate new generation and replace aging assets". However, there is also a greater focus now on lowering the cost of deliveringThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

With a large number of Local Binary Patterns (LBP) variants being currently used today, the significant and importance of visual descriptors in computer vision applications are prominent. This paper presents a novel visual descriptor, i.e., SIM-LBP. It employs a new matrix technique called the Symmetric Inline Matrix generator method, which acts as a new variant of LBP. The key feature that separates our variant from existing counterparts is that our variant is very efficient in extracting facial expression features like eyes, eye brows, nose and mouth in a wide range of lighting conditions. For testing our model, we applied SIM-LBP on the JAFFE dataset to convert all the images to its corresponding SIM-LBP transformed variant. These transformed images are then used to train a Convolution Neural Network (CNN) based deep learning model for facial expressions recognition (FER). Several performance evaluation metrics, i.e., recognition accuracy rate, precision, recall, and F1-score, were used to test mode efficiency in comparison with those using the traditional LBP descriptor and other LBP variants. Our model outperformed in all four matrices with the proposed SIM-LBP transformation on the input images against those of baseline methods. In comparison analysis with the other state-of-the-art methods, it shows the usefulness of the proposed SIM-LBP model. Our proposed SIM-LBP variant transformation can also be applied on facial images to identify a person’s mental states and predict mood variations.

The fault diagnosis in power transformers is carried out using Dissolved Gas Analysis (DGA). Although DGA does provide key information for fault detection, the method is inherently complex. Several methods have been developed for DGA, but still possess challenges in accurately detecting the fault. A method has been developed to generate synthetic data using Monte-Carlo simulation. The generated synthetic data is feed into DGA excel tool to investigate the accuracy of fault detection. The synthetic data can be used to further enhance the DGA tool, improve its accuracy and investigate the inclusive faults. A model has been proposed for the integration of synthetic data generator with DGA tool for machine learning and to obtain an automated and improved DGA tool for fault diagnoses in power transformers.

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