CONTROL AND MANAGEMENT OF A SOLAR-WIND HYBRID SYSTEM FOR POWER QUALITY IMPROVEMENT

The main aim of this paper is to present a modified Maximum Power Point Tracking (MPPT) control strategy of a solar-wind hybrid power system, which allows producing a maximum of energy while enhancing the produced power quality by reducing its fluctuation rate. Indeed, the Photovoltaic System (PVS) is based on a PV field of panels connected to the grid through a DC-DC and DCAC PWM converters and the Wind Energy Conversion System (WECS) is based on a stator field oriented Doubly Fed Induction Generator (DFIG) which its rotor is connected to the network via a back to back AC-DC-AC PWM converter. The proposed control strategy ensures a conventional Maximum Power Point Tracking (MPPT) for the WECS. Furthermore, it guarantees a smooth power injected in the grid by applying a modified MPPT technique applied to the PV system. This strategy uses a part of the PVS available power to compensate the WECS power fluctuations due to wind gusts and generates simultaneously the maximum of smoothed power from the residual part. The simulations results obtained in the case of the proposed control strategy have been compared to those of a conventional MPPT technique and of a Guaranteed Minimum Available Power (GMAP) control strategy. It is obvious that the proposed modified MPPT keeps a good compromise between the quantity and the quality of the total hybrid system produced power

Modeling and Controller Design of Non Ideal Z-Source Converter

Abstract. This paper deals with the study of the non-ideal ZSource inverter where the inductors and capacitors resistance are taken into account, while the main aim of the Z-source inverter for performing voltage buck and boost capabilities using a unique impedance network between the power source and converter circuit is guarded. In this paper an average model for the ZSource inverter using state space averaging is proposed. Then, the behaviors of the studied system is analyzed based on the stability of the presented model, this analyze leads to the design of the suitable controller to be used to ensure the stability of the presented model. Finally the average model analyze is verified by simulation results

Multiphase Z-source inverter using maximum constant boost control

This paper deals with the impedance source (Z-source) multiphase inverter, where the maximum constant boost control method is studied and analyzed in the general case of number of phases. On the other side the impact of the modulation index and the number of phases on the duty cycle shoot-through and on the gain of the output voltage ranges is presented. To validate advantages of the Z-source multiphase inverter, the proposed topology and the maximum constant boost control are implemented in simulation and in real time experimentation with Z-source five phase inverter. The output voltage is applied to two parallel loads, a five phase resistive load and a five phase induction machine

Application of the model predictive control and the SVPWM techniques on five-phase inverter

The multi-phase inverters have attracted recently much attention among the researchers, practitioners and industries due to their many advantages compared to their conventional count parts of single and three-phase inverters, which have been used widely in almost the domestic, commercial and industrial application. The present paper presents an invitation on the application of the space vector pulse width modulation (SVPWM) control technique and the model predictive control (MPC) technique on a five-phase inverter powering a star connected five-phase load. Firstly, the principle of each control technique has been presented in details, then two simulation experiments have been performed on five-phase two-level inverter feeding a balanced star five-phase RL load using the SVPWM control technique and the MPC control technique separately, where a load change is considered in this two tests. The main aim from this study is to discover the main pros and cons of each control technique and their applicability in ensuring the control of five-phase inverter under different operation constraints or conditions. The obtained results for the both cases are presented and discussed based on the main key factors such as the harmonics content, the THD, the dynamic response to the load variation, the dynamic behaviours at transient situation and the computing requirement. It can be said that the present comparative study has allowed shedding the light on the main features and requirements of the applicability of the both control techniques with multi-phase inverters, which are depending on the requirement of the related application and the merits of each control technique.This work was financially supported by the Applied Automation and Industrial Diagnostics Laboratory (LAADI), Ziane Achour University of Djelfa in Algeria under the Doctoral Habilitation approved by the decision of the Ministry of High Education and Scientific Research (MESRS)decision number 333 of 12th July 2015.Scopu

Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems

The main aim of the present paper is the implementation of a fault detection strategy to ensure the fault detection in a gas turbine which is presenting a complex system. This strategy is based on an adaptive hybrid neuro fuzzy inference technique which combines the advantages of both techniques of neuron networks and fuzzy logic, where, the objective is to maintain the desired performance of the studied gas turbine system in the presence of faults. On the other side, the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior. The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions, furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System (ANFIS) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques, can be a promising technique to be applied in several industrial application for faults detection

Availability phase estimation in gas turbine based on prognostic system modeling

The present paper deals mainly with the improvement of the degradation indicators of a gas turbine. Therefore, to achieve this purpose a prognostic approach is used in order to provide an adequate diagnostic function of the studied gas turbine. In this context, this paper proposes a degradation modeling of the studied gas turbine system in order to increase its safety and to ensure accurate future decision making process that allow to enhance the operating state of this industrial equipment. Indeed, the prognostic system proposed in this work takes into account the eventual vibration impacts over all phases of the life cycle process of the studied system to provide a diagnostic function with the required availability at with lowest maintenance cost

Detection of vibrations defects in gas transportation plant based on input / output data analysis: gas turbine investigations

In oil and gas industrial production and transportation plants, gas turbines are considered to be the major pieces of equipment exposed to several unstable phenomena presenting a serious danger to their proper operation and to their exploitation. The main objective of this work is to improve the competitiveness performance of his type of equipment by analyses and control of the dynamic behaviors and to develop a fault monitoring system for the equipment exposed and subject to certain eventual anomalies related to the main components, namely the shaft and the rotors. This study will allow the detection and localization of vibration phenomena in the studied gas turbine based on the input / output data

Evaluation of the Shunt Active Power Filter apparent power ratio using particle swarm optimization

The main objective of this present paper is the study of the Shunt Active Power Filter (APF) compensations capability for different perturbations in AC power system such as current unbalance, phase shift current and undesired harmonics generated by nonlinear load and/or by the power system voltage. This capability is determined by the maximum rate of the apparent power that can be delivered. This study is based on the definition of the effective apparent power as defined in IEEE 1459-2000 which was proved to be the suitable amount to be concerned in the design process of different devices

Current/Voltage Ripple Minimization of DC/DC Interface System for Renewable Energies

The main aim of the proposed paper is a study of the DC/DC buck-boost converter for interfacing with Renewable Energy systems, while overcoming the problem of voltage/current ripples. This goal can be achieved by the reduction or the elimination of low order harmonics from the output voltage spectrum. To fulfill this requirement; the heuristic evolutionary algorithm of particle swarm optimization (PSO) is used, where the Selective Harmonics Elimination (SHE) method is applied based on the minimization of an objective function. This objective function is presenting the switching angles used for the DC-DC converter which lead to the elimination of output voltage ripple and keeping its average equal to the desired value. Simulation applications are used to study the capabilities of the optimization method and the objective function is used to achieve the main goal of the present work, which is minimization of voltage/current ripples