Percorrer por autor "Pires, A. J."
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- An Adaptive PID Speed Controller for an 8/6 Switched Reluctance MachinePublication . Rafael, Silviano; Santos, P. J.; Pires, A. J.This paper presents a classical controller with parameters adaptation capability, in an automatic way. This controller is based on a PID where a parameters adaptation algorithm is used and applied to the switched reluctance motor (SRM) speed control. This PID design do not require any kind of adjustment or calibration from the operator. The parameters adaptation algorithm implemented is based on one fuzzy system with a Takagi-Sugeno inference mechanism with some simplifications. These simplifications had the goal to select the parameters adaptation algorithm contributing for a fast controller response. The developed adaptive PID algorithm was modelled and simulated.
- Analysis of wind energy production offshore in a scenario of extreme droughPublication . Santos, P. J.; Moreira, Sandrina Berthault; Pires, A. J.; Lobato, Pedro
- DC Microgrids: Benefits, Architectures, Perspectives and ChallengesPublication . Pires, Vítor Fernão; Pires, A. J.; Cordeiro, ArmandoOne of the major paradigm shifts that will be predictably observed in the energy mix is related to distribution networks. Until now, this type of electrical grid was characterized by an AC transmission. However, a new concept is emerging, as the electrical distribution networks characterized by DC transmission are beginning to be considered as a promising solution due to technological advances. In fact, we are now witnessing a proliferation of DC equipment associated with renewable energy sources, storage systems and loads. Thus, such equipment is beginning to be considered in different contexts. In this way, taking into consideration the requirement for the fast integration of this equipment into the existing electrical network, DC networks have started to become important. On the other hand, the importance of the development of these DC networks is not only due to the fact that the amount of DC equipment is becoming huge. When compared with the classical AC transmission systems, the DC networks are considered more efficient and reliable, not having any issues regarding the reactive power and frequency control and synchronization. Although much research work has been conducted, several technical aspects have not yet been defined as standard. This uncertainty is still an obstacle to a faster transition to this type of network. There are also other aspects that still need to be a focus of study and research in order to allow this technology to become a day-to-day solution. Finally, there are also many applications in which this kind of DC microgrid can be used, but they have still not been addressed. Thus, all these aspects are considered important challenges that need to be tackled. In this context, this paper presents an overview of the existing and possible solutions for this type of microgrid, as well as the challenges that need to be faced now.
- Fault detection and diagnosis technique for a SRM drive based on a multilevel converter using a machine learning approachPublication . Amaral, Tito G.; Pires, Vítor; Foito, Daniel José Medronho; Pires, A. J.; Martins, J. F.SRM drives based on multilevel converters is now a solution well accepted due to their interesting features like extended voltage range and capability to fault tolerance. However, one aspect that is fundamental to ensure fault tolerance or preventive maintenance is the fault detection and diagnosis of failures in power semiconductors. In this way, in this paper it is presented a new diagnostic method for the failure of those semiconductors in asymmetric neutral point clamped converters. The proposed method will be based on the development of specific patterns that are associated to each semiconductor and fault type. The procedures presented here are based on the image identification of the currents patterns in the multilevel converter that allow the identification of distinct fault type. The pattern recognition system uses visual-based efficient invariants features for continuous monitoring of multilevel converter The proposed method will be verified through several tests in which were used a simulation tool and an experimental prototype.
- Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCAPublication . Amaral, Tito G.; Pires, Vitor Fernão; Pires, A. J.Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.
- A Fault Diagnosis Scheme Based on the Normalized Indexes of the Images eccentricity for a Multilevel Converter of a Switched Reluctance Motor DrivePublication . Amaral, Tito G.; Pires, Vitor; Foito, Daniel José Medronho; Cordeiro, Armando; Rocha, José Inácio Pinto Rosado; Pires, A. J.; Martins, J. F.This paper addresses the fault detection and diagnosis of a fault in the switches of the Switched Reluctance Machine (SRM) power electronic converter. Due to the advantages of using multilevel converters with these machines, a fault detection and diagnosis algorithm is proposed for this converter. The topology under consideration is the asymmetric Neutral Point Clamped (ANPC), and the algorithm was developed to detect open and short circuit faults. The proposed algorithm is based on an approach that discriminates eccentricity of the images formed by the converter voltages. This discrimination is realized through the development of normalized indexes based on the entropy theory. Besides the different fault type the algorithm is also able to detect the transistor under fault. The possibility to implement the proposed approach will be verified through simulation tests.
- Fault-Tolerant SRM Drive with a Diagnosis Method Based on the Entropy Feature ApproachPublication . Pires, Vitor Fernão; Amaral, Tito G.; Cordeiro, Armando; Foito, Daniel José Medronho; Pires, A. J.; Martins, João F.The power electronic converter design is essential for the operation of the switched reluctance motor (SRM). Thus, a fault-tolerant power converter is fundamental to ensure high reliability and extend the drive operation. To achieve fault tolerance, fault detection and diagnosis methods are critical in order to identify, as soon as possible, the failure mode of the drive. To provide such capability, it is proposed in this paper a new fault-tolerant power converter scheme combined with a fault detection method regarding the most common power semiconductors failures in SRM drives. The fast and reliable proposed diagnosis method is based on the entropy theory. Based on this theory, normalized indexes (diagnostic variables) are created, which are independent from the load and speed of the motor. Through this method, it is possible to identify the faulty leg, as well as the type of power semiconductor fault. To test and evaluate the proposed solution several laboratory experiments were carried out using a 2 kW four-phase 8 / 6 SRM.
- Field-Based Model for Switched Reluctance Generators in Direct Drive Wind Energy ConvertersPublication . Lobato, Pedro; Dente, J. A.; Pires, A. J.This work is motivated by the application of Switched Reluctance Generator (SRG) to direct-drive wind turbines and other low speed renewable energy systems. Direct drive energy converters take important benefits from the elimination of the gearbox, which has traditionally been used to interface a slowly prime mover shaft with the generator shaft. Moreover, the actual trend of exploiting the offshore wind resources makes robustness and reliability vital to the economic operation of wind turbines in that specific environment. The proposed field-based model with a triangular approach of the characteristics of flux-linkage makes it easy to incorporate magnetic saturation by introducing some constraints. With a specific aid of finite element analysis, the triangular approach of magnetic characteristics is used to make the comparison of SRG topologies a less time-consuming task. This work underlines dimensional and similarity arguments to extend previous discussions about SRG modeling into a more general context.
- Field-based models for low speed switched reluctance machine designsPublication . Lobato, Pedro; Dente, J. A.; Martins, J.F.; Pires, A. J.This paper presents a design assistance methodology of low speed Switched Reluctance Machines (SRM) using field-based models. The magnetic properties of the iron, the number of rotor poles, and the number of poles per phase, all play a significant role in the machine design. The proposed comparison procedure uses field-based models along with scale models, based on similarity laws, to compare SRM designs. The field-based models are here applied in dimensional analysis of regular and non-regular topologies distinguished by different characteristics of electric and magnetic circuits. As an added value for this methodology, similarity laws take into account physical phenomena like thermal changes and magnetic saturation. Hypotheses introduced in the methodology formulation were verified by finite element analysis. This work is motivated by the application of SRM to direct drive wind converters and other low speed renewable energy systems. As an application example of this methodology, a non-regular topology with short flux-paths was compared with a regular prototype, 3-phase, 12/16, SRM, designed for a direct drive wind turbine: a gain of power per unit of mass is achieved with the former one.
- Load forecasting, the importance of the probability “tails” in the definition of the input vectorPublication . Santos, P. J.; Rafael, Silviano; Pires, A. J.The load forecast is part of the global management of the electrical networks, namely at the transport and distribution levels. This type of methodologies allows to the system operator, to establish and take some important decisions concerning to the mix production and network management, with the minimum of discretionarity. The load forecast in particularly the peak load forecast, represents an important economic improvement in the global electrical systems. Also in certain circumstances, allow reducing the contribution of the non-renewable units, in the daily mixing production. The regressive methodologies specially the artificial neural networks, are normally used in this type of approaches, with satisfactory results. In this paper is proposed a careful analysis in order to define the best-input vector in order to feed the regressive methodology. It was establish careful analyses of the load consumption series. It makes use of a procedural sequence for the pre-processing phase that allows capturing certain predominant relations among certain different sets of available data, providing a more solid basis to decisions regarding the composition of the input vector to ANN. The methodological approach is discussed and a real life case study is used for illustrating the defined steps, the ANN and the quality level of the results.