A. Bahgat

Maximum Collectable Solar Energy by Different Solar Tracking Systems

The output energy from any solar energy system depends on the solar energy input to that system. Using different ways to track the solar energy system to follow the sun can increase solar energy input according to the type of the tracker. A practical study was carried out on different solar tracking systems. The layout of these systems are a fixed system facing south and tilted 40 degrees, a vertical-axis tracker, a 6 degrees tilted-axis tracker, and a two-axis tracker. All the trackers are microprocessor controlled systems, and all systems have photovoltaic arrays for electric energy production. The evaluation of the different systems is based on a complete year of measurements for solar radiation input to the systems and the electric power output from them. The study also includes the effect of some operating parameters on the tracker operation. These studies showed that the collected solar energy as well as the electrical output energy of the tracking solar system are more than that of the stationary s...The output energy from any solar energy system depends on the solar energy input to that system. Using different ways to track the solar energy system to follow the sun can increase solar energy input according to the type of the tracker. A practical study was carried out on different solar tracking systems. The layout of these systems are a fixed system facing south and tilted 40 degrees, a vertical-axis tracker, a 6 degrees tilted-axis tracker, and a two-axis tracker. All the trackers are microprocessor controlled systems, and all systems have photovoltaic arrays for electric energy production. The evaluation of the different systems is based on a complete year of measurements for solar radiation input to the systems and the electric power output from them. The study also includes the effect of some operating parameters on the tracker operation. These studies showed that the collected solar energy as well as the electrical output energy of the tracking solar system are more than that of the stationary system. These gains are higher in the case of the two-axis tracker and decrease gradually from the vertical-axis tracker to the tilted-axis tracker.

Using Ant Colony Optimization algorithm for solving project management problems

Network analysis provides an effective practical system for planning and controlling large projects in construction and many other fields. Ant Colony System is a recent approach used for solving path minimization problems. This paper presents the use of Ant Colony Optimization (ACO) system for solving and calculating both deterministic and probabilistic CPM/PERT networks. The proposed method is investigated for a selected case study in construction management. The results demonstrate that - compared to conventional methods - ACO can produce good optimal and suboptimal solutions.

Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks

This paper presents an application of the neural networks for identification of the maximum power (MP) and the normal operating power (NOP) of a photovoltaic (PV) module. Two neural networks are developed; the first is the maximum power neural network (MPNN) and the second is the normal operating power neural network (NOPNN). The two neural networks receive the solar radiation and the PV module surface temperature as inputs, and estimate the MP and the NOP of a PV module as outputs. The training process for the two neural networks used a series of input/output data pairs. The training inputs are the solar radiation and the PV module surface temperature, while the outputs are the PV module MP for the MPNN and the PV module NOP for the NOPNN. The results showed that, the proposed neural networks introduced a good accurate prediction for the PV module MP and NOP compared with the measured values.

Computation of the solar energy captured by different solar tracking systems

There are many ways to maximize solar energy input to solar collectors. A theoretical study was carried out for different solar tracking systems. This study was based on calculation of solar radiation energy collected by different systems using measured global and diffuse radiation on a horizontal surface and measured beam radiation. The layouts of these systems are a fixed system facing south and tilted 40 degrees a vertical-axis tracker, a 6 degrees tilted-axis tracker, and a two-axis tracker. The theoretical results were compared with the practical solar energy measured from the installed systems in the test field. The annual precision values obtained from the present study are 5.36%, 9.07%, 7.92%, and 5.98%, respectively. The analysis also showed that this precision value changed from month to month according to the accuracy of the measurements, nature of the solar radiation, and weather conditions.There are many ways to maximize solar energy input to solar collectors. A theoretical study was carried out for different solar tracking systems. This study was based on calculation of solar radiation energy collected by different systems using measured global and diffuse radiation on a horizontal surface and measured beam radiation. The layouts of these systems are a fixed system facing south and tilted 40 degrees a vertical-axis tracker, a 6 degrees tilted-axis tracker, and a two-axis tracker. The theoretical results were compared with the practical solar energy measured from the installed systems in the test field. The annual precision values obtained from the present study are 5.36%, 9.07%, 7.92%, and 5.98%, respectively. The analysis also showed that this precision value changed from month to month according to the accuracy of the measurements, nature of the solar radiation, and weather conditions.

Robust adaptive fuzzy logic power system stabilizer

This paper introduces a robust adaptive fuzzy controller as a power system stabilizer (RFPSS) used to damp inter-area modes of oscillation following disturbances in power systems. In contrast to the IEEE standard multi-band power system stabilizer (MB-PSS), robust adaptive fuzzy-based stabilizers are more efficient because they cope with oscillations at different operating points. The proposed controller adopts a dynamic inversion approach. Since feedback linearization is practically imperfect, components that ensure robust and adaptive performance are included in the control law to compensate for modelling errors and achieve acceptable tracking errors. Two fuzzy systems are implemented. The first system models the nominal values of the systems nonlinearities. The second system is an adaptive one that compensates for modelling errors. A feedback linearization-based control law is implemented using the identified model. The gains of the controller are tuned via a particle swarm optimization routine to ensure system stability and minimum sum of the squares of the speed deviations. A bench-mark problem of a 4-machine 2-area power system is used to demonstrate the performance of the proposed controller and to show its superiority over other conventional stabilizers used in the literature.

Parameter identification of induction motor using modified Particle Swarm Optimization algorithm

This paper presents a new technique for induction motor parameter identification. The proposed technique is based on a simple startup test using a standard V/F inverter. The recorded startup currents are compared to that obtained by simulation of an induction motor model. A Modified PSO optimization is used to find out the best model parameter that minimizes the sum square error between the measured and the simulated currents. The performance of the modified PSO is compared with other optimization methods including line search, conventional PSO and genetic algorithms. Simulation results demonstrate the ability of the proposed technique to capture the true values of the machine parameters and the superiority of the results obtained using the modified PSO over other optimization techniques.

Design of a hierarchical fuzzy logic PSS for a multi-machine power system

The performance of fuzzy-logic power system stabilizer (FPSS), which is tuned automatically as the operating conditions of power system change, is investigated by applying it to a multi-machine power system. FPSS is developed using speed deviation and the derivative of speed deviation as the controller inputs variables. Two scaling parameters are introduced to tune the FPSS. These scaling parameters are the output of another fuzzy-logic system (FLS), which gets its inputs from the operating condition of the power system. The proposed scheme is referred to as the self tuning fuzzy power system stabilizer (TFPSS). This mechanism of tuning the FPSS makes it adaptive to changes in the operating condition. The response of the system with three power system stabilizers (PSSs), namely CPSS, FPSS and self tuned FPSS, are compared. It is shown that the tuned FPSS is superior to both CPSS and fixed-parameter FPSS. The effect of the defuzzification methods on the on the control signal response is also shown in this paper.

Adaptive input-output linearization of induction motors with magnetic saturation

The problem of controlling induction motor with magnetic saturation is addressed from an input-output feedback linearization perspective. The induction motor /spl pi/-model is considered. An adaptive input-output feedback linearizing controller is developed under the assumption of measurable states but unknown rotor resistance and load torque, which are both online estimated by the controller. Simulation results are provided for illustration.

Stator fault estimation in induction motors using particle swarm optimization

The use of induction motors is extensive in industry. The working conditions of these motors make them subject to many faults. These faults must be detected in an early stage before they lead to catastrophic failures. This paper presents a scheme for detecting inter-turn faults in the stator windings of induction motors and estimating the fault severity. Detection of incipient inter-turn faults prevents further insulation failure. The proposed algorithm monitors the spectral content of stator currents to detect the fault. After the fault is detected and identified, a particle swarm approach is used to estimate the fault severity. The swarm estimator update is based on the error between the measured data and a complete model of the faulty motor. An experimental setup is used to validate the developed scheme and to implement an online fault detector.

Robust output feedback power system stabilizer design: an LMI approach

Design of output feedback power system stabilizers (PSSs) that guarantee robust pole clustering and robust performance for a wide range of loading conditions is described in this paper. The objectives considered are clustering the closed loop poles in a prescribed region in the s-plane while minimizing an Hinfin performance criterion for the uncertain system. The main difficulty in PSS design is that power systems encounter continuous variations in the load patterns and consequently the nominal model design does not guarantee satisfactory performance at other operating conditions. To cope with such variations, a systematic approach is proposed to present the plant uncertainty in the form of a polytopic model. Based on this model, the synthesis of output feedback PSS leads to a bilinear matrix inequality (BMI) optimization problem. A fast LMI-based procedure for computing an initially feasible controller is presented and an iterative LMI algorithm is suggested to solve the BMI optimization problem. Simulation results on a single machine infinite-bus nonlinear model illustrate the validity of the proposed design procedure.