Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Utilization of a Time Domain Simulator in the Technical and Economic Analysis of a Wind Turbine Electric Drive Train

The amount of installed wind power has been growing exponentially during the past ten years. As wind turbines have become a significant source of electrical energy, the interactions between the turbines and the electric power network need to be studied more thoroughly than before. Especially, the behavior of the turbines in fault situations is of prime importance; simply disconnecting all wind turbines from the network during a voltage drop is no longer acceptable, since this would contribute to a total network collapse. These requirements have been a contributor to the increased role of simulations in the study and design of the electric drive train of a wind turbine. When planning a wind power investment, the selection of the site and the turbine are crucial for the economic feasibility of the installation. Economic feasibility, on the other hand, is the factor that determines whether or not investment in wind power will continue, contributing to green electricity production and reduction of emissions. In the selection of the installation site and the turbine (siting and site matching), the properties of the electric drive train of the planned turbine have so far been generally not been taken into account. Additionally, although the loss minimization of some of the individual components of the drive train has been studied, the drive train as a whole has received less attention. Furthermore, as a wind turbine will typically operate at a power level lower than the nominal most of the time, efficiency analysis in the nominal operating point is not sufficient. This doctoral dissertation attempts to combine the two aforementioned areas of interest by studying the applicability of time domain simulations in the analysis of the economicfeasibility of a wind turbine. The utilization of a general-purpose time domain simulator, otherwise applied to the study of network interactions and control systems, in the economic analysis of the wind energy conversion system is studied. The main benefits of the simulation-based method over traditional methods based on analytic calculation of losses include the ability to reuse and recombine existing models, the ability to analyze interactions between the components and subsystems in the electric drive train (something which is impossible when considering different subsystems as independent blocks, as is commonly done in theanalytical calculation of efficiencies), the ability to analyze in a rather straightforward manner the effect of selections other than physical components, for example control algorithms, and the ability to verify assumptions of the effects of a particular design change on the efficiency of the whole system. Based on the work, it can be concluded that differences between two configurations can be seen in the economic performance with only minor modifications to the simulation models used in the network interaction and control method study. This eliminates the need ofdeveloping analytic expressions for losses and enables the study of the system as a whole instead of modeling it as series connection of independent blocks with no lossinterdependencies. Three example cases (site matching, component selection, control principle selection) are provided to illustrate the usage of the approach and analyze its performance.

Forecasting financial weather - can we foresee market sentiment? Quantitative analysis of nodal prices in the New Zealand Electricity Spot Market

Time series of hourly electricity spot prices have peculiar properties. Electricity is by its nature difficult to store and has to be available on demand. There are many reasons for wanting to understand correlations in price movements, e.g. risk management purposes. The entire analysis carried out in this thesis has been applied to the New Zealand nodal electricity prices: offer prices (from 29 May 2002 to 31 March 2009) and final prices (from 1 January 1999 to 31 March 2009). In this paper, such natural factors as location of the node and generation type in the node that effects the correlation between nodal prices have been reviewed. It was noticed that the geographical factor affects the correlation between nodes more than others. Therefore, the visualisation of correlated nodes was done. However, for the offer prices the clear separation of correlated and not correlated nodes was not obtained. Finally, it was concluded that location factor most strongly affects correlation of electricity nodal prices; problems in visualisation probably associated with power losses when the power is transmitted over long distance.

Design and Drive-Cycle Based Analysis of Direct-Driven Permanent Magnet Synchronous Machine for a Small Urban Use Electric Vehicle

A direct-driven permanent magnet synchronous machine for a small urban use electric vehicle is presented. The measured performance of the machine at the test bench as well as the performance over the modified New European Drive Cycle will be given. The effect of optimal current components, maximizing the efficiency and taking into account the iron loss, is compared with the simple id=0 – control. The machine currents and losses during the drive cycle are calculated and compared with each other.