Capítulos (Ingeniería Eléctrica)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11353

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Mostrando 1 - 9 de 9

Development of a surveillance system for maintenance and diagnosis of buses based on can-bus data transmitted wirelessly
(Taylor and Francis, 2022-01) Jiménez-Espadafor Aguilar, Francisco José; Vozmediano Torres, Juan Manuel; Universidad de Sevilla. Departamento de Ingeniería Energética; Universidad de Sevilla. Departamento de Ingeniería Telemática; Universidad de Sevilla. TEP137: Máquinas y Motores Térmicos; Universidad de Sevilla. TIC154: Departamento de Ingeniería Telemática
From the point of view of vehicle maintenance, one of the most important systems of urban buses is the cooling system. These vehicles run typically more than 80,000 km per year, and the radiator of the system gets fouled due to dust and dirt of the cooling air, which produces an increase in water temperature. This situation forces to stop the vehicle and perform washing of the radiator. This study is focused on the development of a model of the cooling system of urban buses based on an artificial neural network (ANN), which is used for system diagnosis and engine surveillance. Data are gathered from the CAN-bus system of every bus, which have allowed the development of a dynamic ANN that fits the cooling dynamics.
Digital factory for small- and medium-sized advanced transport companies
(Taylor and Francis, 2022-01) Torres García, Miguel; Quirosa, Gonzalo; Universidad de Sevilla. Departamento de Ingeniería Energética; Universidad de Sevilla. TEP137: Máquinas y Motores Térmicos
The project develops the concept and implementation of Industry 4.0 for small- and medium-sized companies, which is currently lacking in the industrial sector. The aim is to obtain a methodology or procedure to facilitate the conversion of medium-sized industrial manufacturing companies into “digital factory” working models, in accordance with Industry 4.0.
Partitioning-Clustering Techniques Applied to the Electricity Price Time Series
(2007) Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal; Riquelme Santos, Jesús Manuel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract useful information from the electricity price time series. To be precise, two clustering techniques, K-means and Expectation Maximization, have been utilized for the analysis of the prices curve, demonstrating that the application of these techniques is effective so to split the whole year into different groups of days, according to their prices conduct. Later, this information will be used to predict the price in the short time period. The prices exhibited a remarkable resemblance among days embedded in a same season and can be split into two major kind of clusters: working days and festivities.
Time-Series Prediction: Application to the Short-Term Electric Energy Demand
(2003) Troncoso Lora, Alicia; Riquelme Santos, Jesús Manuel; Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and minimum forecasting errors.
Application of Evolutionary Computation Techniques to the Optimal Short-Term Scheduling of the Electrical Energy Production
(2003) Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal; Martínez Ramos, José Luis; Riquelme Santos, Jesús Manuel; Gómez Expósito, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
In this paper, an evolutionary technique applied to the optimal short-term scheduling (24 hours) of the electric energy production is presented. The equations that define the problem lead to a nonlinear mixed-integer programming problem with a high number of real and integer variables. Consequently, the resolution of the problem based on combinatorial methods is rather complex. The required heuristics, introduced to assure the feasibility of the constraints, are analyzed, along with a brief description of the proposed genetic algorithm. Finally, results from realistic cases based on the Spanish power system are reported, revealing the good performance of the proposed algorithm, taking into account the complexity and dimension of the problem.
Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production
(2003) Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal; Martínez Ramos, José Luis; Riquelme Santos, Jesús Manuel; Gómez Expósito, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.
Electricity Market Price Forecasting: Neural Networks versus Weighted-Distance k Nearest Neighbours
(2002) Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal; Riquelme Santos, Jesús Manuel; Martínez Ramos, José Luis; Gómez Expósito, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
In today’s deregulated markets, forecasting energy prices is becoming more and more important. In the short term, expected price profiles help market participants to determine their bidding strategies. Consequently, accuracy in forecasting hourly prices is crucial for generation companies (GENCOs) to reduce the risk of over/underestimating the revenue obtained by selling energy. This paper presents and compares two techniques to deal with energy price forecasting time series: an Artificial Neural Network (ANN) and a combined k Nearest Neighbours (kNN) and Genetic algorithm (GA). First, a customized recurrent Multi-layer Perceptron is developed and applied to the 24-hour energy price forecasting problem, and the expected errors are quantified. Second, a k nearest neighbours algorithm is proposed using a Weighted-Euclidean distance. The weights are estimated by using a genetic algorithm. The performance of both methods on electricity market energy price forecasting is compared.
A Comparison of Two Techniques for Next- Day Electricity Price Forecasting
(2002) Troncoso Lora, Alicia; Riquelme Santos, Jesús Manuel; Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their optimal bidding strategies and maximize their benefits. Therefore, if generation companies have a good accuracy in forecasting hourly prices they can reduce the risk of over/underestimating the income obtained by selling energy. This paper presents and compares two energy price forecasting tools for day-ahead electricity market: a k Weighted Nearest Neighbours (kWNN) the weights being estimated by a genetic algorithm and a Dynamic Regression (DR). Results from realistic cases based on Spanish electricity market energy price forecasting are reported.
Overload screening of transmission systems using neural networks
(1998) Riquelme Santos, José Cristóbal; Gómez Expósito, Antonio; Martínez Ramos, José Luis; Peças Lopes, J.A.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. Departamento de Ingeniería Eléctrica
The process of determining whether a power system is in a secure or insecure state is a crucial task which must be addressed on-line in any Energy Management System. In this paper, an Artificial Neural Network, capable of accurately identifying the set of harmful contingencies, is presented, along with several results obtained from a real-size power network. The proposed approach makes use of classical numerical techniques to compensate the ANN'S inputs so that it can deal with topological changes in the power system.

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