Peijie Lin

The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with $6\times3$ PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.

Magnetic tracking approach (MTA) is mainly based on the observation of the magnetic field produced by a magnetic tracking target. A permanent magnet is usually employed as the tracking target, and magnetic dipole model is used to estimate the magnetic field. In this study, we introduce an intuitive method to solve the magnetic tracking problem based on graph optimization. A graph is constructed to formulate the tracking problem, whose nodes correspond to the poses of the permanent magnet at different time and whose edges represent constraints derived by sensor measurements. The accuracy and robustness of MTA play a vital role in biomedical and industrial applications. However, a potential abnormal edge will appear in the graph when a magnetic sensor is disturbed by an interference source like a small magnet, which will deteriorate the localization accuracy of MTA. To improve its robustness, Huber cost function is used to reduce the weight of the abnormal edge. Finally, experiments were carried out to verify the performance of the proposed approach with and without magnetic field interference. Comparing the localization errors with the MTA based on the standard Levenberg-Marquardt algorithm, the results illustrate that the proposed approach could achieve superior localization accuracy and anti-interference ability.

Improving the accuracy of very-short-term (VST) photovoltaic (PV) power generation prediction can effectively enhance the quality of operational scheduling of PV power plants, and provide a reference for PV maintenance and emergency response. In this paper, the effects of different meteorological factors on PV power generation as well as the degree of impact at different time periods are analyzed. Secondly, according to the characteristics of radiation coordinate, a simple radiation classification coordinate (RCC) method is proposed to classify and select similar time periods. Based on the characteristics of PV power time-series, the selected similar time period dataset (include power output and multivariate meteorological factors data) is reconstructed as the training dataset. Then, the long short-term memory (LSTM) recurrent neural network is applied as the learning network of the proposed model. The proposed model is tested on two independent PV systems from the Desert Knowledge Australia Solar Centre (DKASC) PV data. The proposed model achieving mean absolute percentage error of 2.74–7.25%, and according to four error metrics, the results show that the robustness and accuracy of the RCC-LSTM model are better than the other four comparison models.

Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.

Accurate fault diagnosis of photovoltaic (PV) array is important for effective operation of PV systems. The back propagation neural network (BPNN) based classifier model has wide application in fault diagnosis for PV array. Due to insufficient accuracy obtained by using single BPNN, this paper proposes a novel fault diagnosis scheme based on BP-Adaboost strong classifier. Firstly, several indicators constitute an effective feature vector which is applied to build several BPNN based weak classifier models. Secondly, Adaboost algorithm is adopted to build a strong classifier by combining those weak classifiers into the final output with certain weights. Four operation conditions including normal condition, short circuit fault, partial shade fault, open circuit fault can be accurately identified by the proposed method. Dataset from a 1.8 kW grid-connected PV system with 6 × 3 PV array are applied to experimentally test the performance of the developed method.

With the rapid development of social economy, vigorously developing solar energy has become a powerful means to solve energy and environmental problems. However, the instability of weather condition makes the output of PV power have strong randomness, fluctuations and intermittence. Thus accurate photovoltaic (PV) power forecast eliminates the negative impacts of the grid connection of PV power generation systems, which are very meaningful for effectively integrating the PV power systems into the grid. The paper presents a Simulate Anneal and Genetic Algorithm (SAGA), fuzzy c-means clustering (FCM) and least square support vector machine (LSSVM) (SAGA-FCM-LSSVM) model-based power short-term forecasting of PV power plants approach. The experimental effect of the proposed prediction method is verified by employing large datasets from the Desert Knowledge Australia Solar Center (DKASC) website. In this work, the FCM clustering algorithm is adopted to cluster the historical power datasets, and the LSSVM technique maps the multivariate meteorological factors and power data nonlinear relationship. The SAGA method is applied to improve the initial clustering centers of the FCM clustering algorithm to obtain a higher prediction performance. The prediction result of the method in this paper is contrasted with back propagation neural network (BPNN) and LSSVM models, and reveals excellent effect in improving the accuracy of prediction.

It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs – a motif grammar – located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model. We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar. Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.