A major problem in the photovoltaic (PV) system is to determine the maximum power point (MPP) and to overcome the limitations of environmental change. To resolve the limitation of different techniques with high convergence rate and less fluctuations, a hybrid model of fractional open circuit voltage is proposed. For partial shading, incremental conductance is used. The proposed technique is extremely useful, provides high efficiency, and takes less time to achieve the MPP. The tenacity of the proposed method has been checked using MATLAB/Simulink, which clearly shows that the proposed technique has high efficiency compared to other MPP tracking methods.

The transformer is one of the most discussed and important components of electrical power systems because of its reliability, durability and energy conversion capability. It is also useful in load sharing, which reduces system burden, but is also responsible for a sufficient number of losses, as it is used in different types of electric appliances that require voltage conversion. The no-load losses of transformers have gained much attention from research perspective because of its operating cost throughout its lifetime. Many studies were carried out to achieve the highest possible efficiency, decreasing certain losses by using different methods and materials. However, the local market in Pakistan is far behind in the field of efficient core material manufacturing of transformers, which is why consumers are unable to obtain efficient electric appliances. Due to these loss-making appliances, the overall residential load increases and the consumers are charged with heavy electricity bills. This proposed study discusses core losses, different core comparisons, T/F efficiency and advancement in the core material. To accomplish a core comparison, two locally available core materials are used to fabricate two different T/F, and some tests such as open-circuit and short-circuit tests are performed to discover their losses, thermal degradation, and output efficiencies.

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Load forecasting of a micro-grid system has become a challenging task due to its high volatile nature and uncertainty. Residential energy consumption is one of the most talked-about and confusing topics among different electricity loads in terms of future information and is mainly affected by irregular human activity and changing weather conditions. Therefore, techniques and algorithms are needed to reduce energy consumption and enhance the smartness of the system. Load forecasting of an optimized residential system using a machine learning (ML) algorithm is proposed for an islanded green residential system. The load profile of residential electricity consumption is developed by real-time data collected. Photovoltaic (PV) and wind energy (WE) units are considered renewable energy sources in batteries to entertain the residential loads in the proposed prototype. An efficient energy management system (EMS) is introduced to create a balance between power generation and consumption with the help of intelligent appliances under a controlled framework and to overcome peak time consumption. Prediction of load and proper energy utilization are presented to ensure the stability and durability of the system. For efficient micro-grid energy management, the residential load is forecasted using a ML algorithm named non-linear autoregressive exogenous (NARX) neural network (NN) with a minute mean absolute percentage square error of 0.226% which is far less than that of previous work performed in different forecasting scenarios. As a result, an efficient model is designed for a standalone DC micro-grid.