The integration of renewable distributed generation into distribution systems has been studied comprehensively, due to the potential benefits, such as the reduction of energy losses and mitigation of the environmental impacts resulting from power generation. The problem of minimizing energy losses in distribution systems and the methods used for optimal integration of the renewable distributed generation have been the subject of recent studies. The present study proposes an analytical method which addresses the problem of sizing the nominal power of photovoltaic generation, connected to the nodes of a radial distribution feeder. The goal of this method is to minimize the total energy losses during the daily insolation period, with an optimization constraint consisting in the energy flow in the slack bus, conditioned to the energetic independence of the feeder. The sizing is achieved from the photovoltaic generation capacity and load factors, calculated in time intervals defined in the typical production curve of a photovoltaic unit connected to the distribution system. The analytical method has its foundations on Lagrange multipliers and relies on the Gauss-Jacobi method to make the resulting equation system solution feasible. This optimization method was evaluated on the IEEE 37-bus test system, from which the scenarios of generation integration were considered. The obtained results display the optimal sizing as well as the energy losses related to additional power and the location of the photovoltaic generation in distributed generation integration scenarios.

A new topology was recently developed to drive generators, aiming to avoid power electronic devices directly connected to the grid, and making possible the hybridization of the wind power with other sources. The system is composed by an induction machine with rotor in squirrel cage, and a rotating armature endowed with a three-phase winding that may be fed by a secondary source. The previous purpose was to convert a variable velocity imposed by the wind turbine to the armature in a constant velocity to be developed by the cage rotor, driving a shaft of synchronous generator. This article proposes the use of an induction generator instead of a synchronous one in order to explore the maximum available wind energy (MPPT). The simulation results show that the proposed topology is viable and supports both variations in wind speed and disturbances in power grid.

The major objective of the investigation reported in this article is to demonstrate the feasibility of controlling a Doubly Fed Induction Generator actuating directly on the rotor voltage produced by the Rotor Side Converter, as its reference value may be determined analytically, after definition of the control objective. Two usual objectives are here considered: maximum power extraction from wind (MPPT) and stator reactive power equal to zero. This last objective defines the reference slip to be considered in the formulation of developed power that, jointly with the reactive power equation, forms the system to calculate the rotor reference voltages. The process is completed by specifying the desired dynamical response. Thus, the angular velocity of the rotor should quickly reach its reference value, which requires maximal power acceleration at the beginning, but respects the restriction that no overshoot should be allowed. This is achieved by means of a constrained optimization process solved in real time. Following recent trends, only measurements obtained from stator (voltages and currents) sensors are used. This way, angular velocity and rotor currents are estimated in real time. An algorithm for inductance estimation is also included, which prevents deviations of nominal values that could lead to false reference voltages.

The power quality analysis is an essential issue in the integration of distributed energy resources to the grid. Recent standards regulate the harmonics disturbances due to the increasing penetration of intermittent energy sources interconnected with the grid employing power converters. This paper aims to analyze the power quality of an interconnected wind turbine system based on a Squirrel Cage Induction Generator (SCIG) driven by an Electromagnetic Frequency Regulator (EFR). The steady state of the EFR harmonic model is developed in the stationary frame based on the conventional induction generator modeling, which allows the study of the harmonic disturbances in the electrical and mechanical variables due to the PWM inverter of the EFR’s armature voltage. There is no electrical connection between the EFR and SCIG, and the results show that the inherent system inertia contributes to the mitigation of the harmonic content at the grid side generated by the switching. In addition to the steady-state results, the Total Rated Distortion (TRD), which considers the harmonics and interharmonics components, was computed and presented a good performance compared to the IEEE 1547 standard and real data extracted of a single Doubly Fed Induction Generator (DFIG). Finally, the harmonic performance of the proposed system was evaluated considering the impact of the equivalent Thevenin impedance of the grid at the Point of Common Coupling (PCC).

Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in power systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances $providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbances occurrences in the network. This paper presents a methodology based on the discrete wavelet transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks