Development of Cloud Movement Prediction Method for Solar Photovoltaic System

Variability of power generation due to the prevalence of cloud cover over solar photovoltaics(PV)power plants is a challenge faced by grid operators and independent system operators(ISOs)in the integration of solar energy into the grid.Solar forecasts generated through ground⁃based sky imaging systems are useful for short⁃term cloud motion predictions.However,the cost of sky imaging systems currently available in industries is relatively high.Hence,a ground⁃based camera system utilizing a simple webcam is proposed in this study.The proposed method can produce predictions with high levels of accuracy.Forecasts were generated through video analysis using MATLAB for the computation of cloud motion predictions.The image processing involved in the implementation of the proposed system is based on the detection of cloud regions in the form of a cluster of white pixels within individual frames and tracking their motion through comparison of subsequent frames.This study describes the techniques and processes used in the development of the proposed method,along with the evaluation of performance through analysis of the results.The predictions were carried out over multiple time horizons.The time horizons selected include 5,10,15,20,25,and 30 s.The overall results computed showed promising accuracy levels above 94.60%,which makes it adequate for generating reliable forecasts.

Reliability Based Multi-Objective Thermodynamic Cycle Optimisation of Turbofan Engines Using Luus-Jaakola Algorithm

Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.