The operation and control of the modern power system has become complex and difficult due to the incessant penetration of nonconventional energy sources integrated to the power grid and the structural variation of power system with continuing escalation of power demand in recent years. This entails the implementation of intelligent control strategy for satisfactory operation of the power system. Hence, a fractional order fuzzy proportional integral derivative (FOFPID) controller is suggested in this article for automatic generation control of two unequal area interconnected power system with diverse generating units such as thermal, hydro, diesel and wind power plants. The dynamic performance of the system is investigated by using proportional integral derivative (PID), fractional order PID (FOPID), fuzzy PID (FPID) and fractional order fuzzy PID (FOFPID) controllers separately. The parameters of these controllers
This paper presents a single-phase grid-connected photovoltaic power system with the capability of maximum power point tracking (MPPT) of photovoltaic (PV) array and reactive power compensation. The non linear characteristics of the proposed modified p-q theory have been improved with the help of maximum power point tracking (MPPT) controller. MPPT controller helps to feed the inverter with maximum power from the solar grid, for switching pattern generation hysteresis controller is used. The simulation results developed in MATLAB/Simulink software, verify the performance and advantages of the proposed system used to integrate the single phase grid to photovoltaic for enhancing the power quality. Also the proposed scheme has been verified in laboratory setup for its real time implementation.
Nowadays image compression has become a necessity due to a large volume of images. For efficient use of storage space and data transmission, it becomes essential to compress the image. In this paper, we propose a dictionary based image compression framework via sparse representation, with the construction of a trained over-complete dictionary. The overcomplete dictionary is trained using the intra-prediction residuals obtained from different images and is applied for sparse representation. In this method, the current image block is first predicted from its spatially neighboring blocks, and then the prediction residuals are encoded via sparse representation. Sparse approximation algorithm and the trained overcomplete dictionary are applied for sparse representation of prediction residuals. The detail coefficients obtained from sparse representation are used for encoding. Experimental result shows that the proposed method yields both improved coding efficiency and image quality as compared to some state-of-the-art image compression methods.
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