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To boost the performance of the air-cooling battery thermal management system, this study designed a novel vortex adjustment structure for the conventional air-cooling battery pack used in electric vehicles. T-shape vortex generating columns were proposed to be added between the battery cells in the battery pack. This structure could effectively change the aerodynamic patterns and thermodynamic properties of the battery pack, including turbulent eddy frequency, turbulent kinetic energy, and average Reynolds number, etc. The modified aerodynamic patterns and thermodynamic properties increased the heat transfer coefficient with little increase in energy consumption and almost no additional cost. Different designs were also evaluated and optimized under different working conditions. The results showed that the cooling performance of the Design 1 improved at both low and high air flow rates. At a small flow rate of 11.88 L/s, the Tmax and ΔT of Design 1 are 0.85 K and 0.49 K lower than the conventional design with an increase in pressure drop of 0.78 Pa. At a relative high flow rate of 47.52 L/s, the Tmax and ΔT of the Design 1 are also 0.46 K and 0.13 K lower than the conventional design with a slight increase in pressure drop of 17.88 Pa. These results demonstrated that the proposed vortex generating design can improve the cooling performance of the battery pack, which provides a guideline for the design and optimization of the high-performance air-cooling battery thermal management systems in electric vehicles. [ABSTRACT FROM AUTHOR]

This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach--the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output. Index Terms--Adaptive neuro-fuzzy inference systems (ANFIS), intelligent systems, very short-term forecasting, windpower.

In complex emergency situations, failed protection relays and circuit breakers (CBs) have to be identified in order to begin the restoration process of a power system. This paper proposes a novel neural-network approach to identify multiple failures of protection relays and/or CBs. The approach uses information received from protection systems in the form of alarms and is able to deal with incomplete and distorted data. All possible emergencies are simulated and analyzed separately for each section of a power system. Taking into consideration supervisory control and data-acquisition system malfunctions, the corrupted patterns are used to train neural networks. The preliminary classification of emergencies into two different classes is applied to improve the system's performance. The evaluation of results shows that the overall error rate does not exceed 5%. The developed system was tested on a real power system. Index Terms--Alarm systems, fault diagnosis, identification, neural networks, pattern recognition.

This paper presents a neural-fuzzy technology-based classifier for the recognition of power quality disturbances. The classifier adopts neural networks in the architecture of frequency sensitive competitive leaning and learning vector quantization (LVQ). With given size of codewords, the neural networks are trained to determine the optimal decision boundaries separating different categories of disturbances. To cope with the uncertainties in the involved pattern recognition, the neural network outputs, instead of being taken as the final classification, are used to activate the fuzzy-associative-memory (FAM) recalling for identifying the most possible type that the input waveform may belong to. Furthermore, the input waveforms are preprocessed by the wavelet transform for feature extraction so as to improve the classifier with respect to recognition accuracy and scheme simplicity. Each subband of the transform coefficients is then utilized to recognize the associated disturbances. Index Terms--Fuzzy associative memory (FAM), neural networks, pattern recognition, power quality disturbances, wavelet transform.

A knowledge based tutoring system is used to support the education of power engineering students. The aim of this project is to make teaching and learning more productive and efficient by employing modern technologies. It seeks to find new methods to teach large numbers of students with no increase in staff. The tutoring system is based on an expert system shell. It provides a functionally interacting set of theory and problems, and supports student progress through monitoring and assessment. This paper describes the development of the tutoring system for teaching electrical engineering subjects, and in particular, fault analysis in power systems. The expert system based software has been successfully used by power engineering students. They found this software easy to use and understand, and it has become an extra teaching tool. Keywords: Power Engineering Education, Tutoring System, Expert System Shell, Fault Analysis.

This paper presents an application of the steady state network equivalents and expert system for voltage and reactive power (VAR) control in large-scale power systems. A steady state network equivalencing technique is used to construct the 'three-tier' subsystem which is adequate to solve voltage violation problems. An expert system utilises the sensitivity tree method to select the optimal set of control actions to alleviate the voltage problem. The expert system was developed using the VP-EXPERT system shell. VP-EXPERT interacts with the power system analysis software providing analysis of the network sensitivity matrix and data for the knowledge base. Practical application of the developed expert system is demonstrated on the example of the Hydro-Electric Commission (HEC) power system of Tasmania, however the proposed approach is not limited by the system size. Keywords - network equivalent, expert system, voltage control, sensitivity matrix.

This paper presents an application of the steady state network equivalents and expert system for voltage and reactive power (VAR) control in large-scale power systems. A steady state network equivalencing technique is used to construct the 'three tier' subsystem which is adequate to solve voltage violation problems. An expert system utilises the sensitivity tree method to select the optimal set of control actions to alleviate the voltage problem. The expert system was developed using the VP-EXPERT system shell. VP-EXPERT interacts with the power system analysis software providing analysis of the network sensitivity matrix and data for the knowledge base. Practical application of the developed expert system is demonstrated on the example of the Hydro-Electric Commission (HEC) power system of Tasmania, however the proposed approach is not limited by the system size. Keywords: network equivalent, expert system, voltage control, sensitivity matrix.

This paper describes the application of an expert system for the evaluation of the short time thermal rating and temperature rise of overhead conductors. The expert system has been developed using a database and Leonardo expert system shell which is gaining popularity among commercial tools for developing expert system applications. The expert system has been found to compare well when evaluated against the site tests. A practical application is given to demonstrate the usefulness of the expert system developed. Keywords: Expert System, Thermal Rating, Safety Limit, Current Carrying Capacity