System Investigation for Hybrid Electric Vehicle

A hybrid electric vehicle is a fast-growing concept in the field of vehicle industry. Nowadays two global problems make manufactures to develop such systems. These problems are: the growing cost of a fuel and environmental pollution. Also development of controlled electric drive with high control accuracy and reliability allows improving of vehicle drive characteristics. The objective of this Diploma Thesis is to investigate the possibilities of electrical drive application for new principle of parallel hybrid vehicle system. Electric motor calculations, selection of most suitable control system and other calculations are needed. This work is not final work for such topic. Further investigation with more precise calculations, modeling, measurements and cost calculations are needed to answer the question if such system is efficient.

Hardcopy quality parameters to ensure structures detection at digital mammography

Objective To develop procedures to ensure consistency of printing quality of digital images, by means of hardcopy quantitative analysis based on a standard image. Materials and Methods Characteristics of mammography DI-ML and general purpose DI-HL films were studied through the QC-Test utilizing different processing techniques in a FujiFilm®-DryPix4000 printer. A software was developed for sensitometric evaluation, generating a digital image including a gray scale and a bar pattern to evaluate contrast and spatial resolution. Results Mammography films showed maximum optical density of 4.11 and general purpose films, 3.22. The digital image was developed with a 33-step wedge scale and a high-contrast bar pattern (1 to 30 lp/cm) for spatial resolution evaluation. Conclusion Mammographic films presented higher values for maximum optical density and contrast resolution as compared with general purpose films. The utilized digital processing technique could only change the image pixels matrix values and did not affect the printing standard. The proposed digital image standard allows greater control of the relationship between pixels values and optical density obtained in the analysis of films quality and printing systems.

Predicting overall service quality: a structural equation modelling approach

In this article, the results of a modified SERVQUAL questionnaire (Parasuraman et al., 1991) are reported. The modifications consisted in substituting questionnaire items particularly suited to a specific service (banking) and context (county of Girona, Spain) for the original rather general and abstract items. These modifications led to more interpretable factors which accounted for a higher percentage of item variance. The data were submitted to various structural equation models which made it possible to conclude that the questionnaire contains items with a high measurement quality with respect to five identified dimensions of service quality which differ from those specified by Parasuraman et al. And are specific to the banking service. The two dimensions relating to the behaviour of employees have the greatest predictive power on overall quality and satisfaction ratings, which enables managers to use a low-cost reduced version of the questionnaire to monitor quality on a regular basis. It was also found that satisfaction and overall quality were perfectly correlated thus showing that customers do not perceive these concepts as being distinct

The effects of real time control of welding parameters on weld quality in plasma arc keyhole welding

Joints intended for welding frequently show variations in geometry and position, for which it is unfortunately not possible to apply a single set of operating parameters to ensure constant quality. The cause of this difficulty lies in a number of factors, including inaccurate joint preparation and joint fit up, tack welds, as well as thermal distortion of the workpiece. In plasma arc keyhole welding of butt joints, deviations in the gap width may cause weld defects such as an incomplete weld bead, excessive penetration and burn through. Manual adjustment of welding parameters to compensate for variations in the gap width is very difficult, and unsatisfactory weld quality is often obtained. In this study a control system for plasma arc keyhole welding has been developed and used to study the effects of the real time control of welding parameters on gap tolerance during welding of austenitic stainless steel AISI 304L. The welding tests demonstrated the beneficial effect of real time control on weld quality. Compared with welding using constant parameters, the maximum tolerable gap width with an acceptable weld quality was 47% higher when using the real time controlled parameters for a plate thickness of 5 mm. In addition, burn through occurred with significantly larger gap widths when parameters were controlled in real time. Increased gap tolerance enables joints to be prepared and fit up less accurately, saving time and preparation costs for welding. In addition to the control system, a novel technique for back face monitoring is described in this study. The test results showed that the technique could be successfully applied for penetration monitoring when welding non magnetic materials. The results also imply that it is possible to measure the dimensions of the plasma efflux or weld root, and use this information in a feedback control system and, thus, maintain the required weld quality.

On the impact of rigorous approaches on the quality of development

Software systems are expanding and becoming increasingly present in everyday activities. The constantly evolving society demands that they deliver more functionality, are easy to use and work as expected. All these challenges increase the size and complexity of a system. People may not be aware of a presence of a software system, until it malfunctions or even fails to perform. The concept of being able to depend on the software is particularly significant when it comes to the critical systems. At this point quality of a system is regarded as an essential issue, since any deficiencies may lead to considerable money loss or life endangerment. Traditional development methods may not ensure a sufficiently high level of quality. Formal methods, on the other hand, allow us to achieve a high level of rigour and can be applied to develop a complete system or only a critical part of it. Such techniques, applied during system development starting at early design stages, increase the likelihood of obtaining a system that works as required. However, formal methods are sometimes considered difficult to utilise in traditional developments. Therefore, it is important to make them more accessible and reduce the gap between the formal and traditional development methods. This thesis explores the usability of rigorous approaches by giving an insight into formal designs with the use of graphical notation. The understandability of formal modelling is increased due to a compact representation of the development and related design decisions. The central objective of the thesis is to investigate the impact that rigorous approaches have on quality of developments. This means that it is necessary to establish certain techniques for evaluation of rigorous developments. Since we are studying various development settings and methods, specific measurement plans and a set of metrics need to be created for each setting. Our goal is to provide methods for collecting data and record evidence of the applicability of rigorous approaches. This would support the organisations in making decisions about integration of formal methods into their development processes. It is important to control the software development, especially in its initial stages. Therefore, we focus on the specification and modelling phases, as well as related artefacts, e.g. models. These have significant influence on the quality of a final system. Since application of formal methods may increase the complexity of a system, it may impact its maintainability, and thus quality. Our goal is to leverage quality of a system via metrics and measurements, as well as generic refinement patterns, which are applied to a model and a specification. We argue that they can facilitate the process of creating software systems, by e.g. controlling complexity and providing the modelling guidelines. Moreover, we find them as additional mechanisms for quality control and improvement, also for rigorous approaches. The main contribution of this thesis is to provide the metrics and measurements that help in assessing the impact of rigorous approaches on developments. We establish the techniques for the evaluation of certain aspects of quality, which are based on structural, syntactical and process related characteristics of an early-stage development artefacts, i.e. specifications and models. The presented approaches are applied to various case studies. The results of the investigation are juxtaposed with the perception of domain experts. It is our aspiration to promote measurements as an indispensable part of quality control process and a strategy towards the quality improvement.

Tabular cusum control charts of chemical variables applied to the control of surface water quality

The aim of this study was to evaluate the application possibility of tabular CUSUM control charts in the quality control of chemical variables in surface water. It was performed bibliographic and field research to collect water samples from 2003 to 2009, totaling 30 samples, some monthly and others semi-annual in order to observe the variables that regulate water quality. It was found that these charts may be applied to control the quality of river water; showing to be effective in the perception of changes during the process, especially for small samples (n=1) which there is no repetition as in this research. It was also concluded that the Mandurim River does not presents significant levels of pollution.

Critical infrastructure protection against cyber threats

A postgraduate seminar series with a title Critical Infrastructure Protection against Cyber Threats held at the Department of Military Technology of the National Defence University in the fall of 2013 and 2014. This book is a collection of some of talks that were presented in the seminar. The papers address origin of critical infrastructure protection, wargaming cyberwar in critical infrastructure defence, cyber-target categorization, supervisory control and data acquisition systems vulnerabilities, electric power as critical infrastructure, improving situational awareness of critical infrastructure and trust based situation awareness in high security cloud environment. This set of papers tries to give some insight to current issues of the network-centric critical infrastructure protection. The seminar has always made a publication of the papers but this has been an internal publication of the Finnish Defence Forces and has not hindered publication of the papers in international conferences. Publication of these papers in peer reviewed conferences has indeed been always the goal of the seminar, since it teaches writing conference level papers. We still hope that an internal publication in the department series is useful to the Finnish Defence Forces by offering an easy access to these papers.

Households’ willingness to engage in demand response in the Finnish retail electricity market: an empirical study

If electricity users adjusted their consumption patterns according to time-variable electricity prices or other signals about the state of the power system, generation and network assets could be used more efficiently, and matching intermittent renewable power generation with electricity demand would be facilitated. This kind of adjustment of electricity consumption, or demand response, may be based on consumers’ decisions to shift or reduce electricity use in response to time-variable electricity prices or on the remote control of consumers’ electric appliances. However, while demand response is suggested as a solution to many issues in power systems, actual experiences from demand response programs with residential customers are mainly limited to short pilots with a small number of voluntary participants, and information about what kinds of changes consumers are willing and able to make and what motivates these changes is scarce. This doctoral dissertation contributes to the knowledge about what kinds of factors impact on residential consumers’ willingness and ability to take part in demand response. Saving opportunities calculated with actual price data from the Finnish retail electricity market are compared with the occurred supplier switching to generate a first estimate about how large savings could trigger action also in the case of demand response. Residential consumers’ motives to participate in demand response are also studied by a web-based survey with 2103 responses. Further, experiences of households with electricity consumption monitoring systems are discussed to increase knowledge about consumers’ interest in getting more information on their electricity use and adjusting their behavior based on it. Impacts of information on willingness to participate in demand response programs are also approached by a survey for experts of their willingness to engage in demand response activities. Residential customers seem ready to allow remote control of electric appliances that does not require changes in their everyday routines. Based on residents’ own activity, the electricity consuming activities that are considered shiftable are very limited. In both cases, the savings in electricity costs required to allow remote control or to engage in demand response activities are relatively high. Nonmonetary incentives appeal to fewer households.

Energy Management System for LVDC Island Networks

Recent developments in power electronics technology have made it possible to develop competitive and reliable low-voltage DC (LVDC) distribution networks. Further, islanded microgrids—isolated small-scale localized distribution networks— have been proposed to reliably supply power using distributed generations. However, islanded operations face many issues such as power quality, voltage regulation, network stability, and protection. In this thesis, an energy management system (EMS) that ensures efficient energy and power balancing and voltage regulation has been proposed for an LVDC island network utilizing solar panels for electricity production and lead-acid batteries for energy storage. The EMS uses the master/slave method with robust communication infrastructure to control the production, storage, and loads. The logical basis for the EMS operations has been established by proposing functionalities of the network components as well as by defining appropriate operation modes that encompass all situations. During loss-of-powersupply periods, load prioritizations and disconnections are employed to maintain the power supply to at least some loads. The proposed EMS ensures optimal energy balance in the network. A sizing method based on discrete-event simulations has also been proposed to obtain reliable capacities of the photovoltaic array and battery. In addition, an algorithm to determine the number of hours of electric power supply that can be guaranteed to the customers at any given location has been developed. The successful performances of all the proposed algorithms have been demonstrated by simulations.

Observations of Acoustic Emission in Power Semiconductors

Industrial, electrical power generation, and transportation systems, to name but a few, rely heavily on power electronics to control and convert electrical power. Each of these systems, when encountering an unexpected failure, can cause significant financial losses, or even an emergency. A condition monitoring system would help to alleviate these concerns, but for the time being, there is no generally accepted and widely adopted method for power electronics. Acoustic emission is used as a failure precursor in many applications, but it has not been studied in power electronics so far. In this doctoral dissertation, observations of acoustic emission in power semiconductor components are presented. The acoustic emissions are caused by the switching operation and failure of power transistors. Three types of acoustic emission are observed. Furthermore, aspects related to the measurement and detection of acoustic phenomena are discussed. These include sensor performance and mechanical construction of experimental setups. The results presented in this dissertation are the outset of a research program where it will be determined whether an acoustic-emission-based condition monitoring method can be developed.

Control of Bubbling Fluidized Bed Conditions

The objectives of this master’s thesis were to understand the importance of bubbling fluidized bed (BFB) conditions and to find out how digital image processing and acoustic emission technology can help in monitoring the bed quality. An acoustic emission (AE) measurement system and a bottom ash camera system were evaluated in acquiring information about the bed conditions. The theory part of the study describes the fundamentals of BFB boiler and evaluates the characteristics of bubbling bed. Causes and effects of bed material coarsening are explained. The ways and methods to monitor the behaviour of BFB are determined. The study introduces the operating principles of AE technology and digital image processing. The empirical part of the study describes an experimental arrangement and results of a case study at an industrial BFB boiler. Sand consumption of the boiler was reduced by optimization of bottom ash handling and sand feeding. Furthermore, data from the AE measurement system and the bottom ash camera system was collected. The feasibility of these two systems was evaluated. The particle size of bottom ash and the changes in particle size distribution were monitored during the test period. Neither of the systems evaluated was ready to serve in bed quality control accurately or fast enough. Particle size distributions according to the bottom ash camera did not correspond to the results of manual sieving. Comprehensive interpretation of the collected AE data requires much experience. Both technologies do have potential and with more research and development they may enable acquiring reliable and real-time information about the bed conditions. This information could help to maintain disturbance-free combustion process and to optimize bottom ash handling system.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Development of Total Quality Management System in Seafood (Freezing) Industry in Kerala

Quality related problems have become dominant in the seafood processing industry in Kerala. This has resulted in the rejection of seafood sent from India to many destinations. The latest being the total block listing of seafood companies from India from being exported to Europe and partial block listing by the US. The quality systems prevailed in the seafood industry in India were outdated and no longer in use in the developed world. According to EC Directive discussed above all the seafood factories exporting to European countries have to adopt HACCP. Based on this, EIA has now made HACCP system mandatory in all the seafood processing factories in India. This transformation from a traditional product based inspection system to a process control system requires thorough changes in the various stages of production and quality management. This study is conducted by the author with to study the status of the existing infrastructure and quality control system in the seafood industry in Kerala with reference to the recent developments in the quality concepts in international markets and study the drawbacks, if any, of the existing quality management systems in force in the seafood factories in Kerala for introducing the mandatory HACCP concept. To assess the possibilities of introducing Total Quality Management system in the seafood industry in Kerala in order to effectively adopt the HACCP concept. This is also aimed at improving the quality of the products and productivity of the industry by sustaining the world markets in the long run.

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

Modeling the Transport Sector: The Role of Existing Fuel Taxes in Climate Policy

Existing fuel taxes play a major role in determining the welfare effects of exempting the transportation sector from measures to control greenhouse gases. To study this phenomenon we modify the MIT Emissions Prediction and Policy Analysis (EPPA) model to disaggregate the household transportation sector. This improvement requires an extension of the GTAP data set that underlies the model. The revised and extended facility is then used to compare economic costs of cap-and-trade systems differentiated by sector, focusing on two regions: the USA where the fuel taxes are low, and Europe where the fuel taxes are high. We find that the interplay between carbon policies and pre-existing taxes leads to different results in these regions: in the USA exemption of transport from such a system would increase the welfare cost of achieving a national emissions target, while in Europe such exemptions will correct pre-existing distortions and reduce the cost.