A. A. Shojaei

Maximum power point tracking of partial shaded photovoltaic array using an evolutionary algorithm: a particle swarm optimization technique

Partial shading is one of the unavoidable complications in the field of solar power generation. Although the most common approach in increasing a photovoltaic (PV) arrays efficiency has always been to introduce a bypass diode to the said array, this poses another problem in the form of multi-peaks curves whenever the modules are partially shaded. To further complicate matters, most conventional Maximum Power Point Tracking methods develop errors under certain circumstances (for example, they detect the local Maximum Power Point (MPP) instead of the global MPP) and reduce the efficiency of PV systems even further. Presently, much research has been undertaken to improve upon them. This study aims to employ an evolutionary algorithm technique, also known as particle swarm optimization, in MPP detection.

Application of a nonlinear controller based on recurrent neural network for coordinated of PSS and ASMES

Flexible AC transmission system devices are used in power transmission networks in order to increase the maximum power transmission and stability. They as well, help to damp low frequency oscillations for both local and internal areas. On the other hand, the design of these devices with uncoordinated Power System Stabilizer (PSS) may degrade the performance of the power system. In addition, the power systems are vast, complex and nonlinear. Linear control strategies do not have a satisfactory performance for these systems, especially when some disturbances occur. In this study a nonlinear control strategy based on Recurrent Neural Network (RNN) is designed to control PSS and ASMES for a standard power system. The results demonstrate that the proposed coordinated control strategy based on RNN lead to improvement in stability of frequency and terminal voltage responses of the generator.