Short term complex hydro thermal scheduling using integrated PSO-IBF algorithm

In this article, an integrated evolutionary technique such as particle swarm optimization (PSO) algorithm and improved bacterial foraging algorithm (IBFA) have been developed to provide an optimum solution to the scheduling problem with complex thermal and hydro generating stations. PSO algorithm is framed based on the intelligent behavior of the fish school and a flock of birds and the optimal solution in the multidimensional search region is achieved by assigning a random velocity to each potential solution (called the particle). BFA is designed by following the prey-seeking (chemotactic) nature of E. coli bacteria. This technique is followed in an improved manner to get the convergence rate in dynamic for a hyperspace problem by implementing a chemotactic step in a linearly decreased way instead of the static one. The effectiveness of this integrated algorithm is evaluated by using it in a complex thermal and hydro generating system. In this testing system, multiple numbers of cascaded reservoirs in hydro plants have a time coupling effect and thermal power units have a valve point loading effect. The simulation results indicate its merits by comparing it with other meta-heuristic techniques related to the fuel cost required to generate the thermal power.

Relevance vector machine based fault classification in wind energy conversion system

This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility

Hysteresis-based Voltage and Current Control Techniques for Grid Connected Solar Photovoltaic Systems: Comparative Study

Solar PV system development and integration with existing grid is very fast in recent years all over the world, as they require limited maintenance, pollution free and simple structure. When observing the factors affecting the performance of the grid connected solar photovoltaic system, the inverter output voltage with harmonics add with the harmonics generated due to the non-linear loads, retain a bigger challenge to maintain power quality in the grid. To maintain grid power quality, better inverter control technique should be developed. This paper presents the two control techniques for grid-tied inverters. This study developed the hysteresis controller for the inverter. Hysteresis controller used in this work two way (i) Voltage control mode (ii) Current control mode. Matlab/Simulink model is developed for the proposed system. Further the study presents the comparative evaluation of the performance of both control techniques based on the percentage of total harmonic distortion (THD) with the limits specified by the standards such as IEEE 1547 and IEC 61727 and IEEE Std 519-201