The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81% , 28.58% , 1.1431% , 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications. INDEX TERMSWind energy, Model predictive control, fuzzy logic, Microgrid NOMENCLATURE Support vector machines Back propagation Artificial intelligence Energy taken from wind turbine 1 Upstream turbine speed 2 Wind speed at turbine 3 Downstream turbine speed Wind velocity Rotor shaft area Wind Power Maximum wind power Swept area Densityof air V k *

<p>This paper presents the adaptive filtering based least mean square control<br />algorithm for distribution static compensator (DSTATCOM) in three-phase<br />grid tied system for linear/non-linear load, to solve the power quality<br />problems caused by solid-state equipment and devices. This is shown that the<br />active component weights obtained from the load currents in the LMS<br />adaptive filter are used to produce the reference currents and subsequently<br />produces the switching pulses for VSC of the compensator. The complete<br />circuit along with the adaptive technique and diode bridge rectifier type nonlinear load is simulated in Matlab/Simulink software. Initially the circuit was<br />simulated for a three phase linear inductive load. Later it was simulated for a<br />rectifier load connected at PCC with a disconnection of the load of any phase<br />for a short duration of time. It is concluded that the harmonics are found<br />within the limit. The harmonics and power results for both types of loads are<br />compared in a tabular form. Hence this three phase system with<br />DSTATCOM improves the power quality in the three-phase distribution<br />network therefore, serves to provide harmonics reduction, load balancing and<br />regulating the terminal voltage at the point of common coupling (PCC).</p>

This paper analyzes and demonstrates the performance of a solar photovoltaic (SPV)-fed permanent magnet DC (PMDC) motor under various operating conditions. In this configuration, a 5HP PMDC is coupled to a SPV system and a boost converter has been interfaced between them to regulate the DC output voltage acquired from the SPV system. The switching pulse to the converter has been provided by the maximum power point tracking (MPPT) controller (P&amp;O and INC) in order to acquire maximum and desired power across the DC link with varying irradiance. A battery energy storage system (BESS) is often used in association with this configuration caused by the non-linear nature of the SPV system and to overcome the volatility of the DC connection affected by environmental effects. For this purpose, a double loop PI controller is analyzed, and examined the DC link. Additionally, the operation of bidirectional DC-DC converter in buck and boost mode during battery charging and discharging is also performed. This operation ensures maintaining a constant and continuous power across the DC link to regulate the PMDC motor consistently. A comparison of results has also been presented for both incremental and conductance (INC) and P&amp;O controllers. The mathematical modeling of configuration has been performed in MATLAB/Simulink software. The results and key findings have been tabulated and even elaborated graphically.

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