This work presents a new and useful method to dimension wind turbines and control systems and to optimize their mechanical design. This method allows determining the principal curves for characterizing a small capacity wind turbine designed with a Permanent Magnet Synchronous Generator (PMSG). For the wind turbine characterization it was considered the losses in the process of energy transformation in the wind rotor, electric generator and in the bridge rectifier. The equivalent electric model of the synchronous generator was used to determine the electric parameter performance. The work of the wind rotor was considered in its maximum power curve and the PMSG performance in the linear region of its magnetization curve. This leads to develop a new methodology for the complete wind turbine characterization from the nominal parameters of the wind rotor and the electric generator. This method also allows obtaining the power curves and the parameters of voltage, current and efficiency around the wind speed domain and angular speed in the wind rotor. The method was tested for small-capacity wind turbine (1 kW and 10 kW) performances and the numerical and experimental results are described.

The design of autonomous systems for the rural electrification is a complex task due to the diversity of variables involved in such processes. The absence of programs and methods that carry out this task in a clear and precise manner constitutes a barrier to the dissemination of these systems, although some tools have been developed that present other possible limitations. The exclusion of the environmental dimension in the design and evaluation process of hybrid systems means that the true benefits are not evaluated in terms of quality and quantity. In an attempt to overcome such deficiencies, this work presents a new method of design; approached from the multi-objective optimization of systems. The multi-objective optimization by means of enumerative search implemented by the Hybrid Optimization Model for Electric Renewable program is used to generate a set of solutions optimized economically by the value of the net present cost (NPC). The analysis of greenhouse gas emissions (in tCO₂-eq.) in the life cycle of each one of the system components is carried out and a set of solutions with the values of the two objective functions is generated, namely NPC and NAE_SLC (net avoided emissions in the system life cycle). The method is applied to a case study in a Cuban rural community. The compromise solution obtained by means of the proposed algorithm includes a wind turbine (WT) of 25.4 and 8 kW of photovoltaic panels, while that of the HOGA includes a WT of 76 and 21 kW of photovoltaic panels. Both commitment solutions consider hydrogen storage instead of storage in batteries, as a better option for the energy storage. Copyright © 2011 John Wiley & Sons, Ltd.

This paper presents the design, simulation, and experimental evaluation of a low-cost, fixed-step MPPT algorithm based on the incremental conductance technique for operation in a low-power photovoltaic (PV) system with a full-bridge DC-AC converter. The performance of the MPPT algorithm was improved by selecting an appropriate fixed perturbation step size and frequency, ensuring efficient power tracking. The implementation was further optimized by restructuring the conventional algorithm and adapting the DC-AC converter control parameters, which enhanced overall performance and optimized coupling for AC loads. The simulation was performed in Simulink/Matlab with a 560 Wp PV system and a resistive load, under variable irradiation conditions. The perturbation step size was set to 1%, and the perturbation frequency ranged between 2 Hz and 15 Hz, with the converter output at 60 Hz. Experimentally, it was validated at an irradiance of 1000 W/m² and an ambient temperature of 45 °C. The algorithm achieved simulation efficiencies of up to 98.93% and an average experimental efficiency of 96.76%. The response time improved by 86% with a perturbation frequency of 15 Hz. This developed MPPT algorithm demonstrates its reliability, accuracy, and feasibility for implementation.