Browsing by Author "Silva, William Rodrigues"
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- Building of smart plugs to energy efficiency in the residence load managementPublication . Silva, William Rodrigues; Brito, Thadeu; Gambôa, Luis; Lima, JoséIt is known that electrical energy consumption is higher during the day than at night.This is a challenge to balance the consumption levels because when the consumption is high at night, it does not have energy production to supply and the tariff usage is cheaper. Aspiring to avoid the users consuming too much electrical energy and work on this usage control during the night, the present work aims to develop smart plug modules that could self-manage power in residence utilizing the minimum of grid energy. In this sense, the modules may use the overproduction of energy coming from generator systems (such as photovoltaic panels), eliminating the necessity of battery usage. Sometimes, the power supply could provide different values of current, consequently, the use of this electric energy needs to adapt according to the production. Therefore, the final objective is to build an intelligent electrical management system that works on energy efficiency.
- Building of smart plugs to energy efficiency in the residential load managementPublication . Silva, William Rodrigues; Lima, José; Brito, Thadeu; Lopes, Luis C.G.Giving a background of the importance of Energy Efficiency and Renewable Sources, the present work aims to develop a low-cost smart plug module that could self-manage loads automatically in a residence. The system uses renewable energy by controlling current based on zero-crossing synchronism and switching using a semiconductor TRIAC. On this, the power could be customized according to the device requirements, being controlled remotely and being able to communicate with a supervisory using the Wi-Fi connection and MQTT publish/subscriber protocol to change data. Thus, it also discusses alternatives to use this surplus energy in the photovoltaic system context, giving highlighting resistive loads which can deal better with the high current and voltage variation due to switching. After test different microncontrollers and reach a final version, it has been made a basic supervisory, especially for tests that contemplate the sending and receiving data.
- Cascade MIMO P-PID controllers applied in an over-actuated quadrotor Tilt-RotorPublication . Santos, Murillo F. dos; Honório, Leonardo de Mello; Silva, Mathaus F. da; Silva, William Rodrigues; Lima, José; Mercorelli, Paolo; Carmo, Marlon José doTo map the Virtual Control Actions (VCAs) into Real Control Actions (RCAs), over-actuated systems typically require nonlinear control allocation methods. On embedded robotic platforms, computational efforts are not always available. With this in mind, this work presents the design of a Quadrotor Tilt-Rotor (QTR) through a new concept of control allocation with uncoupled RCAs, where a nonlinear system is divided into partially dependent and linear subsystems with fast and robust convergence. The RCAs are divided into smaller and linearized sets and solved sequentially. Then, the cascade Multipe-Input-Multipe-Output (MIMO) Proportional (P)- Proportional, Integral and Derivative (PID) controllers tuning were presented with saturation constants and successive loop closure technique, where some open-field environment tests were conducted to validate the respective tuning. In the end, it showed to be reliable, robust, efficient, and applicable when VCAs are overlapped between the subsystems.
- Development of surplus power generation forecast for use by residential loadsPublication . Dias, Paloma Greiciana de Souza; Brito, Thadeu; Silva, William Rodrigues; Pereira, Ana I.; Lopes, Luis C.G.; Santos, Murillo F. dos; Costa, Paulo Gomes da; Lima, JoséEnergy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy.