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Fernao Pires V.,Polytechnic Institute of Setubal | Fernao Pires V.,Center for Innovation in Electric and Energy Engineering | Martins J.F.,New University of Lisbon | Hao C.,China University of Mining and Technology
Solar Energy | Year: 2012

A fast and robust control strategy for a multilevel inverter in grid-connected photovoltaic system is presented. The multilevel inverter is based on a dual two-level inverter topology. There are two isolated PV generators that feeding each bridge inverter. The output of each inverter is connected to a three-phase transformer. The active and reactive powers flowing into the grid are controlled by a sliding mode algorithm. An alfa-beta space vector modulator is also used. The inverters DC voltages are also controller by a sliding mode controller. In this way, a fast and robust system controller is obtained. Several test results are presented in order to verify the effectiveness of the proposed system controller. © 2012 Elsevier Ltd. Source

Barros J.D.,University of Madeira | Silva J.F.A.,Center for Innovation in Electric and Energy Engineering | Jesus E.G.A.,University of Madeira
IEEE Transactions on Industrial Electronics | Year: 2013

The development of high-processing-capability microprocessors allows the implementation of new digital control methods for neutral-point-clamped (NPC) multilevel converter in power-electronic applications. This paper presents a new predictive digital control method for multilevel converters, called fast predictive. This method computes the optimal vector using the NPC three-phase multilevel dynamic model equations just once in each control cycle, while current predictive methods need 27 calculations. The closest vector to the optimal vector is found by minimizing the distance between each one of the 27 available vectors to the optimal vector. Space vector modulation could be also used. The obtained performance is similar to the predictive optimal control that uses the converter model to find all the 27 responses of the multilevel and then searches for the vector that minimizes control errors. Relative to predictive optimal control, the fast predictive improves digital processing speed by at least 150% in multilevel converters with 27 vectors. This speed improvement would allow multilevel converters with five or higher number of levels (125 instead of 27 vectors) to be controlled using the same sampling frequency of the three-level inverter. The fast-predictive controller is used in a multilevel rectifier with near-unity power factor to enforce the ac currents. Fast predictive control is also used in the rectifier dc voltage to reduce sensitivity of the dc voltage to dc load disturbances. The simulation and experimental results show that the fast-predictive controller is able to control the ac currents of a three-phase multilevel rectifier, achieving nearly 1.5% total harmonic distortion while balancing the capacitors' dc voltages. The use of predictive control to regulate the dc voltage shows an improvement of approximately 7% compared to a proportional-integral controller. © 2012 IEEE. Source

Catalao J.P.S.,University of Beira Interior | Catalao J.P.S.,Center for Innovation in Electric and Energy Engineering | Pousinho H.M.I.,University of Beira Interior | Mendes V.M.F.,Polytechnic Institute of Lisbon
Energy | Year: 2011

In this paper, a novel mixed-integer nonlinear approach is proposed to solve the short-term hydro scheduling problem in the day-ahead electricity market, considering not only head-dependency, but also start/stop of units, discontinuous operating regions and discharge ramping constraints. Results from a case study based on one of the main Portuguese cascaded hydro energy systems are presented, showing that the proposed mixed-integer nonlinear approach is proficient. Conclusions are duly drawn. © 2010 Elsevier Ltd. Source

Pousinho H.M.I.,University of Beira Interior | Mendes V.M.F.,Polytechnic Institute of Lisbon | Catalao J.P.S.,University of Beira Interior | Catalao J.P.S.,Center for Innovation in Electric and Energy Engineering
Energy Conversion and Management | Year: 2011

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. © 2010 Elsevier Ltd. All rights reserved. Source

Batista N.C.,University of Beira Interior | Melicio R.,University of Evora | Matias J.C.O.,University of Beira Interior | Catalao J.P.S.,University of Beira Interior | Catalao J.P.S.,Center for Innovation in Electric and Energy Engineering
Energy | Year: 2013

The actual electric grid was developed to offer electricity to the clients from centralized generation, so with large-scale distributed renewable generation there is an urgent need for a more flexible, reliable and smarter grid. The wireless technologies are becoming an important asset in the smart grid, particularly the ZigBee devices. These smart devices are gaining increased acceptance, not only for building and home automation, but also for energy management, efficiency optimization and metering services, being able to operate for long periods of time without maintenance needs. In this context, this paper provides new comprehensive field tests using open source tools with ZigBee technologies for monitoring photovoltaic and wind energy systems, and also for building and home energy management. Our experimental results demonstrate the proficiency of ZigBee devices applied in distributed renewable generation and smart metering systems. © 2012 Elsevier Ltd. Source

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