Ahmed M. Kassem

Single-diode Model Based Photovoltaic Module: Analysis and Comparison Approach

Abstract—This article presents a thorough comparative analysis study for various kinds of single-diode model based photovoltaic power source. The main target is to explore the effect of increasing the embedded degree of complexity of the single-diode model on the simulated behavior of a particular photovoltaic module by comparing the dynamic performance of such photovoltaic models with the experimental data from the manufacturers data sheet under varying atmospheric conditions. The relative errors between each photovoltaic single-diode model output and the experimentally validated data are computed at three indicative points, namely open-circuit voltage, short-circuit current, and maximum power point. The result of this comparison leads to determining the best photovoltaic single diode module—the one that is more suitable than the others in the context of single diode model—to be applied in different power system applications irrespective of changing environmental conditions over different periods of the day. The comparison study in this article concludes by determining the relevant single-diode model at low, medium, and high temperatures and irradiance levels as well as at standard test conditions.

Performance improvements of a permanent magnet synchronous machine via functional model predictive control

This paper investigates the application of the model predictive control (MPC) approach to control the speed of a permanent magnet synchronous motor (PMSM) drive system. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. In order to validate the effectiveness of the proposed FMPC scheme, the performance of the proposed controller is compared with a classical PI controller through simulation studies. Obtained results show that accurate tracking performance of the PMSM has been achieved.

Functional Predictive Control for Voltage Stability Improvements of Autonomous Hybrid Wind–Diesel Power System

Abstract This study investigates the application of the model predictive control technique for voltage stability of an isolated hybrid wind–diesel power system based on reactive power control. The proposed generation system mainly consists of a synchronous generator for a diesel-generator system and an induction generator for a wind energy conversion system. A static VAR compensator is used to stabilize load voltage through compensating reactive power. Two control paths are used to stabilize load bus voltage based on model predictive control. The first control path is used to adjust the total reactive power of the system by controlling the static VAR compensator firing angle. The second is proposed to control the excitation voltage of the synchronous generator. Model predictive control is used to determine t optimal control actions, including system constraints. To mitigate calculation effort and reduce numerical problems, especially in a large prediction horizon, an exponentially weighted functional model predictive control (F-model predictive control) is applied. The proposed controller was tested through step change in load reactive power plus step increase in input wind power. The performance of the proposed system with the proposed controller was compared with classical model predictive control; moreover, this scheme is tested against parameter variations.

Stand Alone Wind Energy Conversion System Based on Functional Predictive Control

This paper investigates the application of the model predictive control (MPC) approach to control the voltage and frequency of a stand alone wind generation system. This scheme consists of a wind turbine which drives an induction generator feeding an isolated load. A static Var compensator (SVC) is connected at the induction generator terminals to regulate the load voltage. The rotor speed, and thereby the load frequency are controlled via adjusting the mechanical power input using the blade pitch-angle control. The MPC is used to calculate the optimal control actions including system constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional model predictive control (FMPC) is employed. Digital simulations have been carried out in order to validate the effectiveness of the proposed scheme. The proposed controller has been tested through step changes in the wind speed and the load impedance. Simulation resu...

Firefly Optimization Algorithm for the Reactive Power Control of an Isolated Wind-Diesel System

Abstract This paper investigates the application of firefly optimization algorithm to design an optimal control for voltage stability of a stand-alone hybrid renewable generation unit based on reactive power control. The studied renewable generation unit mainly consists of a permanent magnet induction generator driven by wind turbine and a synchronous generator driven by diesel engine. A STATCOM is used to stabilize the terminal load bus voltage via compensating of reactive power. The main control objective aims to stabilize the terminal load voltage against any disturbances in load reactive power and/or input wind power by adjusting the total system reactive power. This is accomplished by controlling STATCOM phase angle and hence to control the load bus voltage and also by controlling the excitation voltage of the synchronous generator. The proposed renewable energy power system based on the proposed optimal controller has been tested through step change in input wind power and load reactive power. The system performance based on the proposed control is compared with model predictive control, a robust H∞ control, and a classical PI control.