Majlesi Journal of Energy Management
Volume:8 Issue: 1, Mar 2019

Hybrid renewable energy sources (HRES) similar to PV, wind and diesel generator are the most charming configurations worn for various applications and most probably for the self-contained systems to generate power. While considering different sources Energy management control will be essential. An energy conservation control system is a computer assisted tool commonly used to monitor, measure and control the generation and transmission system performance. In this article surveillance of photovoltaic, diesel and wind with battery storage is introduced. The energy stability of favoured scheme is done by the Artificial Neural Network (ANN). In this approach Multi-level FFN (Feed Forward Network) which is the form of artificial neural network is used for governing process of the hybrid renewable energy source. The Levenberg Marquardt algorithm is an interconnection of perceptron’s in which the data and calculation will flow in an accurate direction from input to output. It is quite simple and easy to resolve the different operating procedures of the hybrid system on the basis of the time varying conditions. The process is implemented under MATLAB R2016a then the attained results are displayed and the comparison results are carried out with fuzzy logic controller and displays the feasibility of the suggested method.

The main objective of this research is to design a fuzzy controller for a DC-DC converter of a flyback-bosst combination type. For this purpose, an introduction to the BIFRED converter is first provided and its working locations are checked. Typically, selecting and designing switching power supplies is considered to be important advantages, including the extremely high voltage output range, small size, low cost, and simplification. Of course, it should be noted that achieving all these benefits is not possible at the same time. A DC-DCboost converter integrated with flyback, renowned BIFREDconverted, is often with the advantages of the above. The high performance of this converter is caused by the fact that any energy saving energy in this converter changes its state from other elements. As the converter name suggests, the BIFRED converter is a combination of BOOST and FLY BACK converters. Then, based on the analysis, the space state averaged model is used for this converter. Finally, to demonstrate the correctness of the controller's performance, the simulation results are presented in two voltage values of 20 and 40 voltages.

In this article, we present a comparative study between three-level neural space vector pulse width modulation (3LNSVPWM) and two-level neural space vector pulse width modulation (2L-NSVPWM) strategy in neuro-second order sliding mode control (NSOSMC) of stator active and stator reactive power command of a doubly fed induction generator (DFIG) for wind turbine systems (WTSs). Two commands schemes using NSOSMC-3L-NSVPWM and NSOSMC-2L-NSVPWM are proposed and compared. The validity of the proposed commands schemes is verified by simulation tests of a DFIG. The rotor current, active power and reactive power is determined and compared in the above techniques. The obtained results showed that the proposed NSOSMC with 3L-NSVPWM strategy have stator active and reactive power with low powers ripples and low rotor current harmonic distortion than 2L-NSVPWM strategy.

This article presents a second order sliding mode control (SOSMC) with adaptive network-based fuzzy inference system (ANFIS) controller for the doubly fed induction generator (DFIG) using seven-level neural space vector pulse width modulation (NSVPWM) strategy. The proposed control combines the advantages of the robustness of SOSMC technique and easy implementation of ANFIS controller. However, the ANFIS controller is the combination of artificial neural networks (ANNs) and fuzzy logic controller (FLC). The stability of the ANFIS-SOSMC technique is proved using Lyapunov theorem. Finally, the SOSMC control with ANFIS controller is used to regulate the active and reactive power of a DFIG supplied by the seven-level NSVPWM technique and confirms the validity of the proposed approach. Results of simulations containing tests of robustness and tracking tests are presented.

In an electrical distribution unit transformer plays an important role. All transformers are subjected to various tests at the manufacturers test laboratory before being sent to the site of erection. The most important of the guaranteed parameters for a transformer that influence system economy are the no- load ( also called as magnetic losses) and the load losses( also called as copper losses). These losses occur at low to extremely low power factors as it is an inductive circuit, hence devices used in extension of instrument range, the devices used for actual measurement of parameters and the output impedance of the source circuit forms the most important components of such low power factor measurements, in particular for power transformers.

Intelligent building engineering design is enhanced with a new generation of buildings that have all facilities, including electrical, mechanical objects and programmable controllers which are controlled by computer networks and personal computers. Today, solar energy is usually used in smart home technology. Thus causing it to smart home energy management tries to overcome the non-linear characteristics of the photovoltaic modules by using control Perturbation and observation technique and improve the efficiency of power produced by photovoltaic systems