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It is the 21st century and scientists say that by the end of this century, resources will be replenished and the only way the future generations can access energy is through renewable resources— those which are inexhaustible. One such source is sunlight, which has a guaranteed stay in the long run. The energy thus given is termed as solar energy. In the present paper it is tried to solve the issue of limited resources and their adverse effects. Since the power generated from solar energy systems is highly variable, due to its dependence on meteorological conditions, an efficient method of usage of this fluctuating but precious energy source has to come in picture. This requires the scope of reliable forecast information as the development of predictive control algorithms for efficient energy management and monitoring for residential grid connected photovoltaic systems. The paper has given an overview of different applications and models for solar irradiance and photovoltaic power prediction, including time series models based on live measured data from rooftop solar power plant located at 17.5203° N, 78.3674° E. For experimentation, data collected over four years from the solar power plant was used in order to the train machine and understand the characteristics of the solar power plant and gives the predicted energy as the result. The use of Deep Learning is done where LSTM is used for the training and keras and tensorflow are used for obtaining the result. The mean square error thus obtained is 0.015.

The use of Hybrid Electric Vehicles (HEVs) across the world is growing enormously every day. The single-phase bi-directional convertors are presented in this study for HEVs on-board charging(OBC). In HEVs, we use power electronics converters for the converting and inverting operations. Artificial Neural Network(ANN) is presented in this study for simple operation and high optimization approaches. ANN control technique regulates the system's THD and enhances charging system optimization, enables two-way power delivery that is from the grid to vehicle and the vehicle to grid. An ANN based current controller model that achieves fast-dynamic reaction and that improves grid current harmonic characteristics is proposed in this study. The system's THD is reduced by the ANN controller being suggested. The results prove the validity and feasibility of design and control technique of the proposed integrated charging system.

<span>Sustained growth in the demand with unprecedented investments in the transmission infrastructure resulted in narrow operational margins for power system operators across the globe. As a result, power networks are operating near to stability limits. This has demanded the electrical utilities to explore new avenues for control and protection of wide area systems. Present supervisory control and data acquisition/energy management systems (SCADA/EMS) can only facilitate steady state model of the network, whereas synchrophasor measurements with GPS time stamp from wide area can provide dynamic view of power grid that enables supervision, and protection of power network and allow the operator to take necessary control/remedial measures in the new regime of grid operations. Construction of phasor measurement unit (PMU) that provide synchrophasors for the assessment of system state is widely accepted as an essential component for the successful execution of wide area monitoring system (WAMS) applications. Commercial PMUs comes with many constraints such as cost, proprietary hardware designs and software. All these constraints have limited the deployment of PMUs at high voltage transmission systems alone. This paper addresses the issues by developing a cost-effective PMU with open-source hardware, which can be easily modified as per the requirements of the applications. The proposed device is tested with IEEE standards.</span>

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