Smart driving : a new approach to meeting driver needs

The use of machine learning algorithms in different automated applications is increasing rapidly. The effectiveness of algorithms performances helps the user to operate their machine accurately and on time. Road sign classification is a very common type of problem for an automated driving support system. In this research, road speeding measure and sign identification is conducted using four popular machine learning algorithms to develop a smart driving system. This system informs forward-looking decision making and the initiation of suitable actions to prevent any future disastrous events. The robustness of the classification algorithms is examined for classification accuracy through 10-fold cross validation and confusion matrix. Experimental results proofs that the accuracy of Support Vector Machine (SVM) and Neural Network (NN) is almost 100 % and it is very promising compared to the earlier research performance. However, in terms of computational complexity NN is a slower classifier. Therefore, the experimental results suggest that SVM can make an effective interpretation and point out the ability of design of a new intelligent speed control system

Integration of renewable energy resources into the distribution network : a review on required power quality

Power quality is the critical element of modern power system network where more and more distributed energy resources (DER) can be found. Distributed generation, generates electricity from many small DER particularly from renewable sources. Distributed generator (DG) within the network from renewable energy resources (RER) like solar and wind, bring significant challenges to maintain acceptable power quality (PQ) at the consumer end. This paper investigates PQ issues associated with RER. It reviews existing PQ standards for distribution network (DN) and also summarized the experiences of several Distributed Network Service Provider (DNSP) while integrating DGs into the grid. It was found that few PQ parameter ranges varies in different standards due to lack of harmonization and that may hinder to accept bulk renewable energy into the grid

Estimation of energy storage and its feasibility analysis

Storage significantly adds flexibility in Renewable Energy (RE) and improves energy management. This chapter explains the estimation procedures of required storage with grid connected RE to support for a residential load. It was considered that storage integrated RE will support all the steady state load and grid will support transient high loads. This will maximize the use of RE. Proper sized RE resources with proper sized storage is essential for best utilization of RE in a cost effective way. This chapter also explains the feasibility analysis of storage by comparing the economical and environmental indexes

Rule-based classification approach for railway wagon health monitoring

Modern machine learning techniques have encouraged interest in the development of vehicle health monitoring systems that ensure secure and reliable operations of rail vehicles. In an earlier study, an energy-efficient data acquisition method was investigated to develop a monitoring system for railway applications using modern machine learning techniques, more specific classification algorithms. A suitable classifier was proposed for railway monitoring based on relative weighted performance metrics. To improve the performance of the existing approach, a rule-based learning method using statistical analysis has been proposed in this paper to select a unique classifier for the same application. This selected algorithm works more efficiently and improves the overall performance of the railway monitoring systems. This study has been conducted using six classifiers, namely REPTree, J48, Decision Stump, IBK, PART and OneR, with twenty-five datasets. The Waikato Environment for Knowledge Analysis (WEKA) learning tool has been used in this study to develop the prediction models

Predicting vertical acceleration of railway wagons using regression algorithms

The performance of rail vehicles running on railway tracks is governed by the dynamic behaviors of railway bogies, particularly in cases of lateral instability and track irregularities. To ensure reliable, safe, and secure operation of railway systems, it is desirable to adopt intelligent monitoring systems for railway wagons. In this paper, a forecasting model is developed to investigate the vertical-acceleration behavior of railway wagons that are attached to a moving locomotive using modern machine-learning techniques. Both front- and rear-body vertical-acceleration conditions are predicted using popular regression algorithms. Different types of models can be built using a uniform platform to evaluate their performance. The estimation techniques' performance has been measured using a set of attributes' correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE), and computational complexity for each of the algorithms. Statistical hypothesis analysis is applied to determine the most suitable regression algorithm for this application. Finally, spectral analysis of the front- and rear-body vertical condition is produced from the predicted data using the fast Fourier transform (FFT) and is used to generate precautionary signals and system status that can be used by a locomotive driver for necessary actions

Application of machine learning techniques for railway health monitoring

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions

Significance of storage on solar photovoltaic system : a residential load case study in Australia

Energy storage is an essential part in effective utilization of Renewable Energy (RE). Most RE sources cannot provide constant energy supply and introduce a potential unbalance in generation and demand, especially in off-peak periods when RE generates more energy and in peak period when load demand rises too high. Storage allows intermittent sources like solar Photovoltaic (PV) to address timely load demand and adds flexibility in load management. This paper analyses the significance of storage for residential load considering solar PV as RE generator. The significance of storage was evaluated in off-grid or stand alone and grid connected configurations. Moreover it outlined the significance of storage in terms of environment and economics by comparing the Renewable Fraction (RF), Greenhouse Gas (GHG) emission, Cost of Energy (COE) and Net Present Cost (NPC). Investigation showed that storage has positive influences on both (off-grid and grid connected) configurations by improving PV utilization. It was found that in grid connected configuration storage reduced 46.47% of GHG emission, reduced COE, NPC and improved RF compared to the system without storage

Significance of storage on solar photovoltaic system : a residential load case study in Australia

Signing of History book "The Community's College: A History of Johnson County Community College (1969-1999)" by Charles C. Bishop in Carlsen Center on April 3rd, 200

Potential challenges : integrating renewable energy with the smart grid

This is the published version

Prospects of solar energy in Australia

Today, more than 80% of energy is produced from fossil fuels that pollute the air and surrounding environments each and every day, creating global warming. Therefore it is time to think about alternative sources of energy to build a climate friendly environment. In contrast to fossil fuels, renewable energy offers alternative sources of energy which are in general pollution free, unlimited, and environmentally sustainable. This paper presents a feasibility study undertaken to investigate the prospects of solar energy for the climate similar to Australia so as to further investigate the impacts of renewable energy sources in existing and future smart power systems. The monthly average global solar radiation has been collected for twenty-one locations in Australia from the National Aeronautics and Space Administration (NASA). Hybrid Optimisation Model for Electric Renewable (HOMER), and Renewable-energy and Energy-efficient Technologies (RETScreen) computer tools were used to perform comparative analysis of solar energy with diesel and hybrid systems. Initially, total net present cost (NPC), cost of energy (COE) and the renewable fraction (RF) were measured as performances metrics to compare the performances of different systems. For better optimisation, the model has been refined with a sensitivity analysis which explores performance variations due to solar irradiation and electricity prices. Finally, a statistical analysis was conducted to select the best potential places in Australia that produce maximum solar energy