In light of the ongoing decline in photovoltaic (PV) generation costs and its growing competitiveness with retail electricity prices, accurately predicting PV performance is increasingly important. While manufacturers have typically rated PV modules at standard test conditions (STCs), their ratings are now being enhanced by reporting module data at low irradiance conditions (LICs) and nominal operating cell temperature (NOCT). Recently, an enhanced PV model was proposed Angulo et al., 2024, capable of reproducing the behavior of a PV module across a wide range of atmospheric conditions. Although the superiority of this model is thoroughly discussed in Angulo et al., 2024, the identification of its characterizing parameters from ratings provided by manufacturers is not addressed. This paper proposes a parameter identification methodology relying on STC, LIC, and NOCT ratings. The problem at hand involves solving a complex system of eleven nonlinear equations, and is approached by progressively reducing the search space and generating adjustment functions. The methodology is tested in an automated fashion over the entire California Energy Commission PV database, which currently contains 17 710 modules, achieving a convergence rate of 99.8%. The quality of the identified model is assessed by comparing energy predictions against experimental measurements, including state-of-the-art models available in the literature. Results indicate that the identified model reduces prediction errors by about 9% compared to the best competitive model.

As the cost of photovoltaic (PV) power generation declines and becomes competitive in the electricity business, there is an increasing need to accurately predict the performance of this technology under a wide range of operating conditions. The performance of a PV module may be captured via its current–voltage (I–V) characteristic. The single-diode model is an adequate approximation of this characteristic when the parameters are determined for the atmospheric conditions at which the curve was measured. However, capturing the dependency of these parameters so that the model can reproduceI–Vcharacteristics for a wide range of atmospheric conditions is a challenging task. The objective of this article is to develop such model. To accomplish this task, a large-scale data repository consisting of climatic and operational measurements is used to train an artificial neural network (NN) that captures the behavior of each parameter. The trained NN is then utilized to recreateI–Vcurves for a broad spectrum of environmental conditions. The analysis of the parameter behavior and the curves predicted by the NN model allows the identification of an improved PV model by searching through a kernel of functions. As the results show, the proposed model outperforms current functional models available in the literature, by reducing the error in power estimation by about 6% when measured for a wide operating range.

This article presents the mathematical formulations of two Overmodulation Strategies (OSs) for two-level inverters. The OSs aim to minimize the Equivalent Total Harmonic Distortion (THDe) or the Equivalent Weighted THD (WTHDe) of the αβ reference modulation indexes accomplishing the desired fundamental frequency magnitude. Straightforward analytical forms using just the first π/6 radians were built to approximate the solutions given by numerical optimization. The proposed OSs were compared to the state-of-the-art OSs, verifying their optimal performance in the whole overmodulation range. Experiments with an RL load allow observing the inverter commutation’s effect when implementing the proposed OSs for different switching frequencies.

This work proposes a novel control strategy for torque–ripple minimization of a switched reluctance machine drive. The strategy is composed of two stages: (i) an outer flux-linkage reference generation layer delivers optimized patterns obtained offline via mixed-integer quadratic programming (MIQP); (ii) an inner flux-linkage control loop that tracks references of the outer layer by implementing an optimal switching sequence model predictive control algorithm (OSS-MPC). Simulation results show that the proposed strategy can produce lower torque–ripple and better flux tracking performance than state–of–the–art control techniques based on torque sharing function (TSF) and finite control set model predictive control algorithm (FCS-MPC)

This paper introduces a mathematical formulation to deal with the control problem of injecting balanced grid currents when the available power from each phase of a grid- tied photovoltaic (PV) Cascaded H-Bridge (CHB) inverter is severely asymmetrical. The optimal solution, i.e., the Optimal Common-Mode Voltage (OCMV), is obtained utilizing optimal control techniques. This OCMV has an analytical expression, which uses the available power, the power factor, the grid voltage, and the maximum dc voltage. Feasible regions are analytically derived, for which the total available power at the inverter side is transferred into the grid. A parametric analysis of the OCMV implementation over these regions considering a grid-tied PV seven-level CHB inverter is presented. Even though severely unbalanced powers are considered, the proposed OCMV injection method embedded in a standard Voltage-Oriented Control (VOC) scheme shows symmetrical and low distorted grid currents.

This paper presents a model predictive control (MPC) strategy for the control and modulation of power converters. The method uses optimal pulse patterns (OPP) combined with a multistep finite–control–set (MFCS). It is well known that OPP can properly address the trade–off between the currents harmonic distortion and converter switching frequency. On the other hand, MFCS has already proved to be viable in real– time using commercial platforms such as field–programmable gate arrays. The paper leverages these features and proposes a framework where OPPs are used to generate current references tracked by an MFCS controller. A key attribute of the approach is the ability to generate these references online, storing the harmonic components of the OPP instead of its switching angles. A three–level neutral point clamped (3L–NPC) converter is presented as a case study. Results indicate a performance similar to that of OPP modulation alone but with a fast dynamic response, comparable to state–of–the–art MPCs.

This article presents a stochastic dynamic programming model for a condition-based maintenance application. The approach seeks to determine the most opportune moment to inspect and execute preventive maintenance over each component of a nonredundant system, where the number of inspections to be performed simultaneously during each period is limited due to resource constraints. The model minimizes the total maintenance cost per unit of time, considering failure, maintenance, and inspection costs. Unlike most related literature, the model proposed herein allows nonperiodic inspections; it does not require to predefine a maintenance threshold and does not necessarily connect inspections to maintenance actions. Also, the criticality of each component is not static through time, or defined beforehand, but dynamically determined according to the available resources and the risk of failure. A numerical example illustrates the performance of the proposed model in comparison to three traditional maintenance models, namely corrective maintenance, age-based maintenance, and condition-based maintenance with periodic inspections. Results suggest that the proposed model yields the best solution among the studied policies and is more efficient, with a significant reduction of 90% in inspection resources.