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The great expansion in offshore power plants is raising the concern regarding the cumulative effect of the electromagnetic field emissions caused by submarine power cables. In this sense, owners are required to predict these emissions during the permitting and consenting process of new power plants. This is a challenging task, especially in the case of HVAC three-core armored cables due to their complex geometry. Customarily, 2D approaches based on the finite element method (FEM) have been employed for evaluating the magnetic field emissions caused by these cables. However, inaccurate results are obtained since the phase conductors and armor twisting is omitted. This work develops, for the first time in the literature, an in-depth analysis of the magnetic field caused by this type of cable through an ultra-shortened 3D-FEM model, which is also faced to experimental measurements taken on an actual 132 kV, 800 mm2 three-core armored cable. Relevant conclusions are derived regarding the impact of the cable design on the magnetic field emissions, including material properties, as well as single and double-layer armors, presenting the proposed model not only as a valuable tool for predicting purposes, but also for optimizing cable design in terms of magnetic field emissions.

Nowadays, governments and electricity companies are making efforts to increase the integration of renewable energy sources into grids and microgrids, thus reducing the carbon footprint and increasing social welfare. Therefore, one of the purposes of the microgrid is to distribute and exploit more zero emission sources. In this work, a Stochastic Unit Commitment of a hybrid and isolated microgrid is developed. The microgrid supplies power to satisfy the demand response by managing a photovoltaic plant, a wind turbine, a microturbine, a diesel generator and a battery storage system. The optimization problem aims to reduce the operating cost of the microgrid and is divided into three stages. In the first stage, the uncertainties of the wind and photovoltaic powers are modeled through Markov processes, and the demand power is predicted using an ARMA model. In the second stage, the stochastic unit commitment is solved by considering the system constraints, the renewable power production, and the predicted demand. In the last stage, the real-time operation of the microgrid is modeled, and the error in the demand forecast is calculated. At this point, the second optimization problem is solved to decide which generators must supply the demand variation to minimize the total cost. The results indicate that the stochastic models accurately simulate the production of renewable energy, which strongly influences the total cost paid by the microgrid. Wind production has a daily impact on total cost, whereas photovoltaic production has a smoother impact, shown in terms of general trend. A comparison study is also considered to emphasize the importance of correctly modeling the uncertainties of renewable power production in this context.

The stability of power systems is very sensitive to voltage or current variations caused by the discontinuous supply of renewable power feeders. Moreover, the impact of these anomalies varies depending on the sensitivity/resilience of customer and transmission system equipment to those deviations. From any of these points of view, an instantaneous characterization of power quality (PQ) aspects becomes an important task. For this purpose, a wavelet-based power quality indices (PQIs) are introduced in this paper. An instantaneous disturbance index (ITD(t)) and a Global Disturbance Ratio index (GDR) are defined to integrally reflect the PQ level in Power Distribution Networks (PDN) under steady-state and/or transient conditions. With only these two indices it is possible to quantify the effects of non-stationary disturbances with high resolution and precision. These PQIs offer an advantage over other similar because of the suitable choice of mother wavelet function that permits to minimize leakage errors between wavelet levels. The wavelet-based algorithms which give rise to these PQIs can be implemented in smart sensors and used for monitoring purposes in PDN. The applicability of the proposed indices is validated by using a real-time experimental platform. In this emulated power system, signals are generated and real-time data are analyzed by a specifically designed software. The effectiveness of this method of detection and identification of disturbances has been proven by comparing the proposed PQIs with classical indices. The results confirm that the proposed method efficiently extracts the characteristics of each component from the multi-event test signals and thus clearly indicates the combined effect of these events through an accurate estimation of the PQIs.

The economic impact associated with power quality (PQ) problems in electrical systems is increasing, so PQ improvement research becomes a key task. In this paper, a Stockwell transform (ST)-based hybrid machine learning approach was used for the recognition and classification of power quality disturbances (PQDs). The ST of the PQDs was used to extract significant waveform features which constitute the input vectors for different machine learning approaches, including the K-nearest neighbors’ algorithm (K-NN), decision tree (DT), and support vector machine (SVM) used for classifying the PQDs. The procedure was optimized by using the genetic algorithm (GA) and the competitive swarm optimization algorithm (CSO). To test the proposed methodology, synthetic PQD waveforms were generated. Typical single disturbances for the voltage signal, as well as complex disturbances resulting from possible combinations of them, were considered. Furthermore, different levels of white Gaussian noise were added to the PQD waveforms while maintaining the desired accuracy level of the proposed classification methods. Finally, all the hybrid classification proposals were evaluated and the best one was compared with some others present in the literature. The proposed ST-based CSO-SVM method provides good results in terms of classification accuracy and noise immunity.

Partial discharge (PD) activity is a key indicator of the cable insulation health in medium-voltage networks. Many of these cables are used as transmission media, because power line communication (PLC) modems are installed at their ends. This paper proposes two techniques that allow broadband PLC receivers to detect PDs at the same time that the PLC signal is being demodulated. The proposed techniques are based on how PDs alter both, null, and zero-bit loaded carriers, commonly used in orthogonal frequency division multiplexing modems. Using the proposed techniques, a simple firmware modification is sufficient to add this new functionality to the already installed modems. This paper is mainly concerned with theoretical aspects of the proposed techniques, and no experimental validation has been done. Nevertheless, simulation results show that the proposed techniques can be used to detect different types of PDs. Four cases studies are analyzed: short- and long-distance PDs, and low- and high-energy PDs. Finally, the impact of the inter-carrier interference on the detection capability is studied, and some recommendations are made to mitigate its effects.

Transformer inrush current can be efficiently mitigated by controlled switching. For energization of three-phase transformers, several strategies have been developed that depend on whether the residual flux can be measured and whether independent-pole-operated or three-pole-operated circuit-breakers are used. In this paper, a comparative analysis of these strategies has been carried out. Residual flux uncertainty and dispersion of the characteristics of the circuit-breaker can significantly affect the performance of the controlled switching. These issues have been taken into account in the analysis of each strategy since they can influence the performance in different ways. The study has resulted in the selection of the most appropriate strategy for each case.

El proyecto se enmarca en una colaboración entre cuatro grupos de trabajo: la asociación Autismo Sevilla, un Centro de Educación Infantil y Primaria de la ciudad de Sevilla, la Escuela de Ingeniería Informática de la Universidad de Sevilla, y la Facultad de Psicología de la misma Universidad, estas últimas implicadas en el proyecto Sinergia de trabajos de fin de grado. Objetivos: 1. Diseñar una aplicación informática, basada en la estructura básica del método de Ventoso (2003), que sea adecuada a las características y capacidades de estos usuarios y que ofrezca un primer nivel de enseñanza de la lectura. Por tanto se centra en el método global de palabras completas y la asociación de estas a una imagen. 2. Realizar un posterior pilotaje del prototipo diseñado en una pequeña muestra de niños afectados con TEA, analizando la adecuación de la aplicación.

In this paper, a generalization of the concept of electrical power for periodic current and voltage waveforms based on a new generalized complex geometric algebra (GCGA) is proposed. This powerful tool permits, in n-sinusoidal/nonlinear situations, representing and calculating the voltage, current, and apparent power in a single-port electrical network in terms of multivectors. The new expressions result in a novel representation of the apparent power, similar to the Steinmetz's phasor model, based on complex numbers, but limited to the purely sinusoidal case. The multivectorial approach presented is based on the frequency-domain decomposition of the apparent power into three components: the real part and the imaginary part of the complex-scalar associated to active and reactive power respectively, and distortion power, associated to the complex-bivector. A geometrical interpretation of the multivectorial components of apparent power is discussed. Numerical examples illustrate the clear advantages of the suggested approach.

A virtual instrument, named Power Quality Meter, is presented for (a) measuring power consumption and harmonics in three-phase systems, under non-sinusoidal and imbalance conditions (b) detecting, classifying and organizes power disturbance events. Measurement of the power consumption follows the formulation proposed by the members of the IEEE Working Group on Nonsinusoidal Situations (1996). So, definitions are based on the analysis of functions in the frequency domain, separating the fundamental terms from the harmonic terms of the Fourier series. The virtual instrument has been developed too for monitoring and measuring power disturbances, which are automatically classified and organized in a database while they are being recorded. Software tools use the database structure to present summaries of power disturbances and locate an event by severity or time of occurrence. Records of actual measurements are included to demonstrate the versatility of the instrument.