Modelling and Parameter Identification Using Reduced I-V Data

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A linear method to extract diode model parameters of solar panels from a single I–V curve

The I-V characteristic curve is very important for solar cells/modules being a direct indicator of performance. But the reverse derivation of the diode model parameters from the I-V curve is a big challenge due to the strong nonlinear relationship between the model parameters. It seems impossible to solve such a nonlinear problem accurately using linear identification methods, which is proved wrong in this paper. By changing the viewpoint from conventional static curve fitting to dynamic system identification, the integral-based linear least square identification method is proposed to extract all diode model parameters simultaneously from a single I-V curve. No iterative searching or approximation is required in the proposed method. Examples illustrating the accuracy and effectiveness of the proposed method, as compared to the existing approaches, are presented in this paper. The possibility of real-time monitoring of model parameters versus environmental factors (irradiance and/or temperatures) is also discussed

Very short term irradiance forecasting using the lasso

We find an application of the lasso (least absolute shrinkage and selection operator) in sub-5-min solar irradiance forecasting using a monitoring network. Lasso is a variable shrinkage and selection method for linear regression. In addition to the sum of squares error minimization, it considers the sum of ℓ1-norms of the regression coefficients as penalty. This bias–variance trade-off very often leads to better predictions.<p></p> One second irradiance time series data are collected using a dense monitoring network in Oahu, Hawaii. As clouds propagate over the network, highly correlated lagged time series can be observed among station pairs. Lasso is used to automatically shrink and select the most appropriate lagged time series for regression. Since only lagged time series are used as predictors, the regression provides true out-of-sample forecasts. It is found that the proposed model outperforms univariate time series models and ordinary least squares regression significantly, especially when training data are few and predictors are many. Very short-term irradiance forecasting is useful in managing the variability within a central PV power plant.<p></p&gt

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

Statistical modeling, parameter estimation and measurement planning for PV degradation

Photovoltaics (PV) degradation is a key consideration during PV performance evaluation. Accurately predicting power delivery over the course of lifetime of PV is vital to manufacturers and system owners. With many systems exceeding 20 years of operation worldwide, degradation rates have been reported abundantly in the recent years. PV degradation is a complex function of a variety of factors, including but not limited to climate, manufacturer, technology and installation skill. As a result, it is difficult to determine degradation rate by analytical modeling; it has to be measured. As one set of degradation measurements based on a single sample cannot represent the population nor be used to estimate the true degradation of a particular PV technology, repeated measures through multiple samples are essential. In this chapter, linear mixed effects model (LMM) is introduced to analyze longitudinal degradation data. The framework herein introduced aims to address three issues: 1) how to model the difference in degradation observed in PV modules/systems of a same technology that are installed at a shared location; 2) how to estimate the degradation rate and quantiles based on the data; and 3) how to effectively and efficiently plan degradation measurements

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation. In this work, we combine these models with knowledge based heuristic time series decomposition methods to improve the forecasting accuracy and computational efficiency.<p></p> In particular, three decomposition methods are proposed. The first method implements an additive seasonal-trend decomposition as a preprocessing technique prior to ETS. This can reduce the state space thus improve the computational efficiency. The second method decomposes the GHI (global horizontal irradiance) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; and their corresponding results are recombined via the closure equation to obtain the GHI forecasts. In the third method, the time series of the cloud cover index is considered. ETS is applied to the cloud cover time series to obtain the cloud cover forecast thus the forecast GHI through polynomial regressions. The results show that the third method performs the best among three methods and all proposed methods outperform the persistence models.<p></p&gt

Non-Contact Measurement of POA Irradiance and Cell Temperature for PV Systems

This paper presents a non-contact measurement of irradiance on plane of array (POA) and cell temperature for PV systems. The idea is motivated from the diode model of PV, where POA irradiance and cell temperature are proportional to the photocurrent and modified ideality factor, respectively. Based on the recent progress of diode model identification, the photocurrent and modified ideality factor can be linearly determined from I-V characteristics, which makes it feasible to develop a non-contact measurement approach for POA irradiance and cell temperature, i.e., both of them will be derived completely from the diode mode parameter identification without the need of any sensors. The calibration of the proportional factors is done from the indoor module flash test and then applied to outdoor module testbed to show the accuracy and effectiveness of the proposed method

A linear identification of diode models from single I-V characteristics of PV panels

This paper presents a novel approach on diode model parameters identification from the I-V characteristics of PV panels. Other than the prevailing methodology of solving a group of nonlinear equations from a few points on the I-V curve, the proposed one views the diode model as the equivalent output of a dynamic system. From this new viewpoint, diode model parameters are linked to the transfer function (after Laplace transform) of the same dynamic system whose parameters are then identified by a simple integral-based linear square. Indoor flash test shows the accuracy and effectiveness of the proposed method, and outdoor module testing shows its ability of online monitoring and diagnostics. Comparisons to the methods of Lambert W function and evolution algorithms are also included

Adapting An Existing Example-Based Machine Translation (EBMT) System For New Language Pairs Based On An Optimized Bilingual Knowledge Bank (BKB).

Sourcing for large amount of text and translating them are some of the challenges in building an Example-Based Machine Translation (EBMT) system. These big amounts of translated texts are annotated into the S-SSTC format to cover an extensive vocabulary and sentence structures. However, the Bilingual Knowledge Bank (BKB), which is a collection of the S-SSTCs, will normally contain redundancy. Hence, the idea of an optimized BKB is born. An optimized BKB (redundancy reduced; is smaller in size but is as equally extensive in term of its sentence structure coverage compared to an un-optimized BKB. Therefore, an optimized BKB enhances the performance of the EBMT. In this paper, we introduce the idea of an optimized BKB and propose it to be re-used to effectively construct new BKBs in order to adapt an existing EBMT for new language pairs

Herd demography, sexual segregation and the effects of forest management on Bornean banteng Bos javanicus lowi in Sabah, Malaystian Borneo

Between 1973 and 2010, 39.5% of Sabah’s (Malaysian Borneo) natural forest cover was lost to deforestation and conversion to agriculture, therefore the remaining population of endangered Bornean banteng Bos javanicus lowi is being driven towards extinction. The Bornean banteng’s herd demography, sexual segregation and the effects of forest management were investigated at 393 camera locations in 6 forest reserves using generalised estimating equations (GEE) fitted via a generalised linear model (GLM). A total of 43344 camera trap nights and 832 independent banteng events were captured at 93 locations. The identification of 183 bantengs included 22 herds (>1 individual) and 12 solitary bulls, with a herd size range of 2 to 21. Significantly larger herds were observed in forest with <8 yr of post-logging regeneration (PLR), whereas herds were smaller in forest with <3, 4 and 16 yr of PLR. Within these forests, herds were significantly larger along logging roads than in open sites and on forest trails. Herds were significantly larger in upland compared to lowland dipterocarp forest, but significantly smaller when closer to the forest border. Bachelor herds were observed as frequently as mixed-sex herds, and there was a significantly higher capture frequency of female herds in the dry season, supporting the theory of sexual segregation. Frequency of calf births was highest in March and September, and calf captures peaked in June and July. This study contributes to a better understanding of banteng ecology, and will assist in the production of effective management strategies aimed at providing suitable habitat for re-population and enabling banteng population persistence