Implementation of a Novel Tabu Search Optimization Algorithm to Extract Parasitic Parameters of Solar Panel

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (RSh), series resistance (RS), photo current (IPh), diode current (Id), and diffusion constant (a1) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method's correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment.publishedVersio

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system makes the motor highly reliable, helping to avoid unsched-uled downtimes, which cause more revenue loss and disruption of production, thus making it as the extensively used industrial drive. This can be achieved only when the irregularities produced out of the fault circumstance are sensed at that instant itself and diagnosed as to what and where happened for suitable action by the protective equipment employed. This requires intelligent control with high performance scheme. Hence, Field Programmable Gate Array (FPGA) based Neuro-Genetic implementation with Back Propagation Neural Network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classifica-tion of neural network will provide the output within 2 µs although the clone procedure with mi-crocontroller requires 7 ms. This intelligent control with high performance technique is applied to the IMD fed by Voltage Source Inverter (VSI) to diagnose the fault external to the induction motor occurring in the VSI supply system. The proposed approach was simulated and experimentally validated.publishedVersio

A COMPARITIVE ANALYSIS OF MULTIPLIERS USING GDI TECHNIQUE

A bountiful of adders has been designed over the years in order to simplify the multiplication with various improvements. A comparison of Complementary Pass Transistor Logic and Shanno

FPGA Implementation of AI-Based Inverter IGBT Open Circuit Fault Diagnosis of Induction Motor Drives

In modern industrial manufacturing processes, induction motors are broadly utilized as industrial drives. Online condition monitoring and diagnosis of faults that occur inside and/or outside of the Induction Motor Drive (IMD) system makes the motor highly reliable, helping to avoid unsched-uled downtimes, which cause more revenue loss and disruption of production, thus making it as the extensively used industrial drive. This can be achieved only when the irregularities produced out of the fault circumstance are sensed at that instant itself and diagnosed as to what and where happened for suitable action by the protective equipment employed. This requires intelligent control with high performance scheme. Hence, Field Programmable Gate Array (FPGA) based Neuro-Genetic implementation with Back Propagation Neural Network (BPN) is suggested in this article to diagnose the fault more efficiently and almost instantly. It is reported that the classifica-tion of neural network will provide the output within 2 µs although the clone procedure with mi-crocontroller requires 7 ms. This intelligent control with high performance technique is applied to the IMD fed by Voltage Source Inverter (VSI) to diagnose the fault external to the induction motor occurring in the VSI supply system. The proposed approach was simulated and experimentally validated

Implementation of a Novel Tabu Search Optimization Algorithm to Extract Parasitic Parameters of Solar Panel

The aging of PV cells reduces their electrical performance i.e., the parasitic parameters are introduced in the solar panel. The shunt resistance (RSh), series resistance (RS), photo current (IPh), diode current (Id), and diffusion constant (a1) are known as parasitic or extraction parameters. Cracks and hotspots reduce the performance of PV cells and result in poor V–I characteristics. Certain tests are carried out over a long period of time to determine the quality of solar cells; for example, 1000 h of testing is comparable to 20 years of operation. The extraction of solar parameters is important for PV modules. The Tabu Search Optimization (TSO) algorithm is a robust meta-heuristic algorithm that was employed in this study for the extraction of parasitic parameters. Particle Swarm Optimization (PSO) and a Genetic lgorithm (GA), as well as other well-known optimization methods, were used to test the proposed method's correctness. The other approaches included the lightning search algorithm (LSA), gravitational search algorithm (GSA), and pattern search (PS). It can be concluded that the TSO approach extracts all six parameters in a reasonably short period of time. The work presented in this paper was developed and analyzed using a MATLAB-Simulink software environment