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Parametric identification of the single diode model of a photovoltaic generator is a key element in simulation and diagnosis. Parameters’ values are often determined by using experimental data the modules manufacturers provide in the data sheets. In outdoor applications, the parametric identification is instead performed by starting from the curren...
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... sixth section proposes a discussion about the results presented in the paper and closes with the conclusions. Figure 1 shows the SDM circuit: it includes the photoinduced current generator I ph , which models the photovoltaic effect; a diode D modeling the P-N junction; and the resistances R s and R h representing the ohmic losses and the recombination losses respectively. Thus, the following five parameters appear in the model: ...
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... minimum RMSE value achieved is 0.0659. In Figure 10, the I-V curve generated by SDM using the best sub-interval, which is shown in the third column of Table 7, is depicted: the contraction of the initial interval set with respect to the Figure 8 is evident. ...
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... set of intervals is similar to the one obtained by using all the experimental values; R s is the only one showing an improved contraction. In Figure 12, the red bars correspond to SDM evaluated by IA for the solution presented in Table 9. As in the previous case, large ranges result from the union of the feasible sub-intervals at the final dividing level where the algorithm terminated. ...
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... analysis of the RMSEs for all the feasible sub-intervals at the division level 5 gives a narrower range. The best sub-interval is the same as that achieved in the previous example, and is shown in Table 7 and Figure 10. The best sub-interval set is [P] 5,2272 , and the minimum RMSE value is 0.0776. ...
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... best sub-interval set is [P] 5,2272 , and the minimum RMSE value is 0.0776. Figure 12. I-V curve: experimental vs. SDM using D&C algorithm with a reduced set of experimental data ...
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... third remark concerns the size of interval current [I], as it is shown in Figures 8, 10 and 12. In the SDM solution shown in Figure 8 and Table 6, the relative width of the interval parameters' solution (wid m [P]), is calculated by wid [x,x] mid [x,x] . ...
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Citations
... Interval arithmetic also plays a significant role in enhancing the power of higher performance computing, such as GPUs [2]. Furthermore, in the era of data science and artificial intelligence, many scholars and practitioners from various backgrounds have incorporated the interval analysis concepts into their existing models or methods in order to investigate uncertainty propagation in specific data or systems [3][4][5]. These applications have led to increased attention and more rigorous studies on interval methods over the recent years. ...
This paper describes the extended method of solving real polynomial zeros problems using the single-step method, namely, the interval trio midpoint symmetric single-step (ITMSS) method, which updates the midpoint at each forward-backward-forward step. The proposed algorithm will constantly update the value of the midpoint of each interval of the previous roots before entering the preceding steps; hence, it always generate intervals that decrease toward the polynomial zeros. Theoretically, the proposed method possesses a superior rate of convergence at 16, while the existing methods are known to have, at most, 9. To validate its efficiency, we perform numerical experiments on 52 polynomials, and the results are presented, using performance profiles. The numerical results indicate that the proposed method surpasses the other three methods by fine-tuning the midpoint, which reduces the final interval width upon convergence with fewer iterations.
... Normalization of input data is an important method in many applications involving numerical data. When such input data contain impreciseness they are normally represented by intervals, which can be operated by various available interval arithmetics-for example, [1][2][3]. There is no reference relating normalization and interval arithmetics. ...
The impreciseness of numeric input data can be expressed by intervals. On the other hand, the normalization of numeric data is a usual process in many applications. How do we match the normalization with impreciseness on numeric data? A straightforward answer is that it is enough to apply a correct interval arithmetic, since the normalized exact value will be enclosed in the resulting “normalized” interval. This paper shows that this approach is not enough since the resulting “normalized” interval can be even wider than the input intervals. So, we propose a pair of axioms that must be satisfied by an interval arithmetic in order to be applied in the normalization of intervals. We show how some known interval arithmetics behave with respect to these axioms. The paper ends with a discussion about the current paradigm of interval computations.
