In this paper, we develop a deep learning model using back propagation neural network (BPNN) that helps to obtain maximum power point. This deep learning model aims to maximise the output power from the solar grids when the panels are connected with the boost converter under different variable load conditions. BPNN-DL enables the prediction of reference voltage at different weather conditions for severing the various output power that ensures maximum power with stable output voltage. The proposed BPNN-DL is tested under different conditions to estimate the robustness of the modules under internal/external interferences. The results of the simulation show that the proposed method achieves maximum output power from each panel compared with existing methods in terms of regression analysis on training, testing, and validation.

The nonlinear nature of the system behavior causes several disturbances due to which the stability of the system gets affected. Controlled operation of power systems is very critical and most important factoris to achieve stable power system. For this reason there is a need of an adaptive control technique. PID controllers are widely used due to their simplicity and ease of re-tuning on-line. Several tuning methods such as Pattern Search (PS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are being used for the optimum setting of PID controller parameters so as to improve the system stability. In this paper the performance of PID controller tuned by different techniques is compared so as to design optimum controller.

The following paper shows us the study and comparison of various trends of controllers applied for stabilization of an AVR system. Now a day's mainly PID controllers are used in conjunction with an AVR but this does not ensure robust stability of the system. Thus the H-infinity and Hybrid controllers come in to picture. As H-infinity controllers require complex calculations. MATLAB turned out to be a useful tool. We have applied PSO optimization technique to get the best controller designed. This paper shows us the comparison of AVR output for various controllers employed with AVR system.

In mobile network, nodes are normally placed in some locations after travelling with various speeds to another location. Packets were broadcast to some location receiver node, but they are moved to another location, due to that node is not able to receive those packets. Attacker node present in routing path should accept those packets, and it acts as original node. Communication privacy is reduced for mobile network. It improves the communication overhead and end to end delay. So, the proposed Enhanced Packet Acceptance for Target Position Alteration (EPATP) technique exactly monitors the target node position, depending on the position to assign the relay node for packet forwarding from sender to target node. Multiaccepter Assigning Algorithm is designed, and if any target node should not receive those packets, it provides another chance for packet receiving by next target node, and it assigns multiple target node for accuracte communication. It reduces communication overhead and end to end delay.