Dirt Loss Estimator for Photovoltaic Modules Using Model Predictive Control

The central problem tackled in this article is the susceptibility of the solar modules to dirt that culminates in losses in energy generation or even physical damage. In this context, a solution is presented to enable the estimates of dirt losses in photovoltaic generation units. The proposed solution is based on the mathematical modeling of the solar cells and predictive modeling concepts. A device was designed and developed to acquire data from the photovoltaic unit; process them based on a predictive model, and send loss estimates in the generation unit to a web server to help in decision-making support. The results demonstrated the real applicability of the system to estimate losses due to dirt or electrical mismatches in photovoltaic plants.

A New Class of Estimators Based on a General Relative Loss Function

Motivated by the relative loss estimator of the median, we propose a new class of estimators for linear quantile models using a general relative loss function defined by the Box–Cox transformation function. The proposed method is very flexible. It includes a traditional quantile regression and median regression under the relative loss as special cases. Compared to the traditional linear quantile estimator, the proposed estimator has smaller variance and hence is more efficient in making statistical inferences. We show that, in theory, the proposed estimator is consistent and asymptotically normal under appropriate conditions. Extensive simulation studies were conducted, demonstrating good performance of the proposed method. An application of the proposed method in a prostate cancer study is provided.

Non-parametric efficient causal mediation with intermediate confounders

Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational study of the properties of the interventional (in)direct effect estimands based on the efficient influence function in the non-parametric statistical model. We use the efficient influence function to develop two asymptotically optimal, non-parametric estimators that leverage data-adaptive regression for the estimation of nuisance parameters: a one-step estimator and a targeted minimum loss estimator. We further present results establishing the conditions under which these estimators are consistent, multiply robust, n1/2-consistent and efficient. We illustrate the finite-sample performance of the estimators and corroborate our theoretical results in a simulation study. We also demonstrate the use of the estimators in our motivating application to elucidate the mechanisms behind the unintended harmful effects that a housing intervention had on adolescent girls' risk behavior.

Model-Based Quasi-Sliding Mode Control with Loss Estimation Applied to DC–DC Power Converters

This paper presents the experimental implementation of a buck converter with quasi-sliding mode control combined with a loss estimator function. An online loss estimator is developed to estimate, in real time, the parasitic resistances of the converter and variations of the resistance in the load. The estimated loss resistance and the resistance of the load are embedded, in real time, into the model equations of the controller using Zero Average Dynamics and Fixed Point Induction Control techniques (ZAD-FPIC) to improve the control robustness to resistive parameter variations. Details of the experimental setup are presented to show developed electrical and electronic circuits, and experimental techniques are described to ensure the successful digital implementation of closed-loop control of the buck power converter. The proper shielding of electrical wiring in power electronics allows improvement to the quality of the measures by removing noise induced by electromagnetic interference. A trigger signal is used to implement the Pulse-Width Modulation (PWM) with centered pulse and to synchronize the sampling of analogical signals from the buck converter. Such synchronization allows the use of a lower sampling frequency and ensures the measurements at the right instant in time. Experimental results are in good agreement with numerical simulations, showing the effectiveness of the control approach.