ABSTRACT
Agrophotovoltaics (APV) is the coexistence of solar photovoltaics (PV) and agriculture on the same piece of land. Although the concept of APV is known for the last two decades, its actual penetration in society is inconsiderable. The objective of the current article is to discuss the various APV systems explored in the past and to highlight the futuristic APVs. Furthermore, this study presents the review of the available experimental work on the performance and environmental and techno-economic aspects of the APV systems. The key features, crop selection criteria, feasible crops for Indian climatic conditions, and the future research directions of APV systems have been summarized. Furthermore, apart from the known techno-economic benefits of APV, a premise on its another utility for the thermal management of the solar PV modules by crops' natural transpiration cooling has been presented in this study. A theoretical study demonstrates the gain in the electrical output of the solar PV plant as compared with the conventional PV installation. The theoretical study has been carried out considering the meteorological data of Nagpur (21.1458° N, 79.0882° E). The estimation has been carried out using Nominal Operating Cell Temperature (NOCT) model, NREL irradiance database-NSRDB, and System Advisor Model (SAM). An experimental study has been conducted on APV systems with a 2-kW solar PV plant and tomato crops to investigate its actual performance. The study shows an increment of 17.96% in the daily energy generation as compared with the conventional solar PV power plant.
Subject(s)
Solar Energy , Agriculture , Sunlight , Temperature , Models, Theoretical , Crops, AgriculturalABSTRACT
In this paper, we propose a particle-filter-based technique for the analysis of a reconstructed interference field. The particle filter and its variants are well proven as tracking filters in non-Gaussian and nonlinear situations. We propose to apply the particle filter for direct estimation of phase and its derivatives from digital holographic interferometric fringes via a signal-tracking approach on a Taylor series expanded state model and a polar-to-Cartesian-conversion-based measurement model. Computation of sample weights through non-Gaussian likelihood forms the major contribution of the proposed particle-filter-based approach compared to the existing unscented-Kalman-filter-based approach. It is observed that the proposed approach is highly robust to noise and outperforms the state-of-the-art especially at very low signal-to-noise ratios (i.e., especially in the range of -5 to 20 dB). The proposed approach, to the best of our knowledge, is the only method available for phase estimation from severely noisy fringe patterns even when the underlying phase pattern is rapidly varying and has a larger dynamic range. Simulation results and experimental data demonstrate the fact that the proposed approach is a better choice for direct phase estimation.
ABSTRACT
In this research work, we introduce a novel approach for phase estimation from noisy reconstructed interference fields in digital holographic interferometry using an unscented Kalman filter. Unlike conventionally used unwrapping algorithms and piecewise polynomial approximation approaches, this paper proposes, for the first time to the best of our knowledge, a signal tracking approach for phase estimation. The state space model derived in this approach is inspired from the Taylor series expansion of the phase function as the process model, and polar to Cartesian conversion as the measurement model. We have characterized our approach by simulations and validated the performance on experimental data (holograms) recorded under various practical conditions. Our study reveals that the proposed approach, when compared with various phase estimation methods available in the literature, outperforms at lower SNR values (i.e., especially in the range 0-20 dB). It is demonstrated with experimental data as well that the proposed approach is a better choice for estimating rapidly varying phase with high dynamic range and noise.
