When the panels in a photovoltaic (PV) array are subjected to a nonuniform irradiance level, some bypass diodes may turn on, and the overall power production might be affected significantly. An increase of the power delivered by the whole array might be obtained by means of its electrical reconfiguration, that is, the change of the series-parallel connection among the panels of which it is made up. The computation of the electrical connection among the panels that ensures the maximum delivered power is a combinatorial problem requiring powerful optimization methods. This paper is devoted to the formulation of an optimization procedure for determining the best electrical configuration among the panels. The proposed algorithm requires simple mathematical calculations, and it uses a vectorized structure; thus, it is suitable to be implemented in any embedded system for the purpose of a real-time PV array reconfiguration. The algorithm is first explained by using a pilot example, and afterward, its performance is shown by applying it to a real domestic PV field. The results show that the optimization algorithm computes an optimized configuration with a low computational burden.

This paper presents a communications test-rig based on a customized Intelligent Electronic Device (IED) for its use in a laboratory-scale microgrid. The laboratory setup for this work uses the following three types of subsystems: distributed energy resources DERs (solar, wind), energy storage (batteries) and loads (active, reactive, linear and nonlinear). A Raspberry Pi computer, using the Linux operating system, allows programming the algorithms needed by the communications platform. The protocol employed for data exchange, between the different subsystems and the central supervisor module, is the IEC 61850 standard. Three experimental test cases serve to show the proposed IEDs performance. The first test transfers data between a solar source and a client station. The second test transfers data, in distributed mode, between four nodes associated to different generation sources. The last test shows a secondary control using two experimental inverters operating in parallel connection. The IEDs communicate the compensating values for regulating frequency and voltage in the system composed of a master and one or more slave inverters.

The DC-link capacitor is one of the components that are more prone to faults in energy-distributed systems based on voltage source inverters. A predictive maintenance approach should allow to foresee the risk of an unexpected system shutdown. In this study, a two-stage diagnostic approach that is aimed at determining the health status of the DC-link capacitor in a single-phase grid-connected PV system was proposed. The equivalent series resistance (ESR) and the capacitance (C) values were used as indicators in the estimation of the degradation stage. Electrochemical impedance spectroscopy (EIS) was used to estimate the impedance curve of the DC-link capacitor, and a multi-fitting algorithm allowed us to determine the ESR and C parameters. A comparison between the estimated values C and ESR and the nominal values was used to quantify the fault severity. It was demonstrated that the EIS allowed the determination of the capacitor impedance regardless of the actual operating conditions of the photovoltaic generator, such as during irradiance changes and with the maximum power point algorithm turned off. By using the capacitor simplified model and a multi-fitting algorithm, the C and ESR values were estimated with an error that was lower than 1%. An analysis of the hardware required to implement the proposed approach in real applications by achieving the desired accuracy was also proposed.

This paper introduces the design and development of control system courses in an undergraduate program by using Project-Based Learning (PBL) when the curriculum uses Traditional Education. The paper presents a complete outline of control courses that takes into account the problem definition, project duration, support resources and student evaluation. In this approach, students are organized into teams to develop a project seeking to find the solution to a control problem. The approach is devoted to placing a professional challenge into the course, so students learn the topics while they solve the problem, and develop the transversal skills needed to face the new challenges of control. The approach uses the peer-assessment and self-assessment activities to evaluate abilities, knowledge, and observe the development of transversal skills. The impact of the proposed approach is evaluated by using a survey and observing the student performance. Aspects about the survey design and survey results are presented analyzing the contribution of the proposed approach to develop skills like teamwork, self-learning, problem solving and communication abilities.

Photovoltaic panels can be affected by partial shading, which causes some shaded cells to consume the energy generated by other cells of the panel. That is, shaded cells stop operating in the first quadrant and start operating in the second quadrant, with negative voltage at their terminals, causing power losses and other negative effects in the cells. The Bishop model is an accurate representation of the cells behavior at the second quadrant, but estimating its parameters is not a trivial task. Therefore, this paper presents a procedure to estimate the parameters of the Bishop model by using the Chu–Beasley optimization technique. The effectiveness of this procedure was evaluated using different accuracy measures such as RMSE and MAPE, obtaining values lower than 0.5%. In addition, the results of this study demonstrate that it is essential to estimate all the parameters of the Bishop model, illustrate the variation in the parameters according to the cell technology and show the strong influence of the shunt resistance into the behavior at the second quadrant.

Hybrid energy storage systems (HESSs) are essential for adopting sustainable energy sources. HESSs combine complementary storage technologies, such as batteries and supercapacitors, to optimize efficiency, grid stability, and demand management. This work proposes a semi-active HESS formed by a battery connected to the DC bus and a supercapacitor managed by a Sepic/Zeta converter, which has the aim of avoiding high-frequency variations in the battery current on any operation condition. The converter control structure is formed by an LQG controller, an optimal state observer, and an adaptive strategy to ensure the correct controller operation in any condition: step-up, step-down, and unitary gain. This adaptive LQG controller consists of two control loops, an internal current loop and an external voltage loop, which use only two sensors. Compared with classical PI and LQG controllers, the adaptive LQG solution exhibits a better performance in all operation modes, up to 68% better than the LQG controller and up to 84% better than the PI controller. Therefore, the control strategy proposed for this HESS provides a fast-tracking of DC-bus current, driving the high-frequency component to the supercapacitor and the low-frequency component to the battery. Thus, fast changes in the battery power are avoided, reducing the degradation. Finally, the system adaptability to changes up to 67% in the operation range are experimentally tested, and the implementation of the control system using commercial hardware is verified.