PV Parameter Identification using Reduced I-V Data

In this paper, possibility and accuracy of using reduced I-V data in PV parameter identification are discussed. Based on the linear identification method proposed in [1], six I-V points are used instead of the whole I-V curve to identify the PV parameters. The maximum power point (MPP) is then estimated using the identified I-V and P-V characteristics. Validation is done by using different sets of six points on the I-V curve. Experiment results show that the accurate curve fitting (with low RMSE and MPE) and good estimation of MPP can be achieved

PV panel modeling and identification

In this chapter, the modelling techniques of PV panels from I-V characteristics are discussed. At the beginning, a necessary review on the various methods are presented, where difficulties in mathematics, drawbacks in accuracy, and challenges in implementation are highlighted. Next, a novel approach based on linear system identification is demonstrated in detail. Other than the prevailing methods of using approximation (analytical methods), iterative searching (classical optimization), or soft computing (artificial intelligence), the proposed method regards the PV diode model as the equivalent output of a dynamic system, so the diode model parameters can be linked to the transfer function coefficients of the same dynamic system. In this way, the problem of solving PV model parameters is equivalently converted to system identification in control theory, which can be perfectly solved by a simple integral-based linear least square method. Graphical meanings of the proposed method are illustrated to help readers understand the underlying principles. As compared to other methods, the proposed one has the following benefits: 1) unique solution; 2) no iterative or global searching; 3) easy to implement (linear least square); 4) accuracy; 5) extendable to multi-diode models. The effectiveness of the proposed method has been verified by indoor and outdoor PV module testing results. In addition, possible applications of the proposed method are discussed like online PV monitoring and diagnostics, noncontact measurement of POA irradiance and cell temperature, fast model identification for satellite PV panels, and etc

Do partial and distributed tests enhance new learning?

Testing facilitates subsequent learning of new information, a phenomenon known as the forward testing effect. The effect is often investigated in multilist procedures, where studied lists are followed by a retrieval test, or a control task such as restudying, and learning is compared on the final list. In most studies of the effect, tests include all material from the preceding list. We report four experiments, three of which were preregistered, to determine whether tests that are partial (not including all studied items) and distributed (including retrieval of items from earlier lists) are effective in enhancing new learning. The results show that testing of all studied material is not necessary to produce beneficial effects on new learning or to reduce intrusions. The beneficial effects of testing were substantially mediated by reduced proactive interference. Importantly, there was minimal evidence that the forward learning benefits of partial and distributed tests are offset by a cost to untested items via retrieval-induced forgetting

Perovskite-polymer composite cross-linker approach for highly-stable and efficient perovskite solar cells.

Manipulation of grain boundaries in polycrystalline perovskite is an essential consideration for both the optoelectronic properties and environmental stability of solar cells as the solution-processing of perovskite films inevitably introduces many defects at grain boundaries. Though small molecule-based additives have proven to be effective defect passivating agents, their high volatility and diffusivity cannot render perovskite films robust enough against harsh environments. Here we suggest design rules for effective molecules by considering their molecular structure. From these, we introduce a strategy to form macromolecular intermediate phases using long chain polymers, which leads to the formation of a polymer-perovskite composite cross-linker. The cross-linker functions to bridge the perovskite grains, minimizing grain-to-grain electrical decoupling and yielding excellent environmental stability against moisture, light, and heat, which has not been attainable with small molecule defect passivating agents. Consequently, all photovoltaic parameters are significantly enhanced in the solar cells and the devices also show excellent stability

Evaluating the conceptual strategy change account of test-potentiated new learning in list recall

Prior testing potentiates new learning, an effect known as test-potentiated new learning (TPNL). Research using lists of related words has established that testing, by free recall, also increases semantic clustering of later recall output. It has been suggested that this is evidence that testing induces a strategy change in encoding and retrieval towards greater conceptual organisation. The current research evaluated whether this conceptual strategy change explains TPNL in three experiments. We found a) that a retrieval task that did not increase semantic clustering (list discrimination) consistently produced TPNL, and b) that factors (word-relatedness and list structure) that influenced the amount of semantic clustering had no effect on the magnitude of TPNL. These results suggest that conceptual strategy change is neither necessary nor sufficient for TPNL and is more likely to be an effect of testing, rather than a cause of TPNL

Extending representation formulas for boundary voltage perturbations of low volume fraction to very contrasted conductivity inhomogeneities

Imposing either Dirichlet or Neumann boundary conditions on the boundary of a smooth bounded domain $\Omega$, we study the perturbation incurred by the voltage potential when the conductivity is modified in a set of small measure. We consider $(\gamma _{n})_{n\,\in \,\mathbb{N}}$, a sequence of perturbed conductivity matrices differing from a smooth $\gamma _{0}$ background conductivity matrix on a measurable set well within the domain, and we assume $(\gamma _{n}-\gamma _{0})\gamma _{n}^{-1}(\gamma _{n}-\gamma _{0})\rightarrow 0$ in $L^{1}(\Omega )$. Adapting the limit measure, we show that the general representation formula introduced for bounded contrasts in a previous work from 2003 can be extended to unbounded sequences of matrix valued conductivities