A novel clustering algorithm based on mathematical morphology for wind power generation prediction

Wind power has the characteristic of daily similarity. Furthermore, days with wind power variation trends reflect similar meteorological phenomena. Therefore, wind power prediction accuracy can be improved and computational complexity during model simulation reduced by choosing the historical days whose numerical weather prediction information is similar to that of the predicted day as training samples. This paper proposes a new prediction model based on a novel dilation and erosion (DE) clustering algorithm for wind power generation. In the proposed model, the days with similar numerical weather prediction (NWP) information to the predicted day are selected via the proposed DE clustering algorithm, which is based on the basic operations in mathematical morphology. And the proposed DE clustering algorithm can cluster automatically without supervision. Case study conducted using data from Yilan wind farm in northeast China indicate that the performance of the new generalized regression neural network (GRNN) prediction model based on the proposed DE clustering algorithm (DE clustering-GRNN) is better than that of the DPK-medoids clustering-GRNN, the K-means clustering-GRNN, and the AM-GRNN in terms of day-ahead wind power prediction. Further, the proposed DE clustering-GRNN model is adaptive

Seven Glycolysis-Related Genes Predict the Prognosis of Patients With Pancreatic Cancer

ObjectivesTo identify the key glycolysis-related genes (GRGs) in the occurrence and development of pancreatic ductal carcinoma (PDAC), and to construct a glycolysis-related gene model for predicting the prognosis of PDAC patients.MethodologyPancreatic ductal carcinoma (PDAC) data and that of normal individuals were downloaded from the TCGA database and Genotype-Tissue Expression database, respectively. GSEA analysis of glycolysis-related pathways was then performed on PDAC data to identify significantly enriched GRGs. The genes were combined with other patient’s clinical information and used to construct a glycolysis-related gene model using cox regression analysis. The model was further evaluated using data from the validation group. Mutations in the model genes were subsequently identified using the cBioPortal. In the same line, the expression levels of glycolysis related model genes in PDAC were analyzed and verified using immunohistochemical images. Model prediction for PDAC patients with different clinical characteristics was then done and the relationship between gene expression level, clinical stage and prognosis further discussed. Finally, a nomogram map of the predictive model was constructed to evaluate the prognosis of patients with PDAC.ResultsGSEA results of the training set revealed that genes in the training set were significantly related to glycolysis pathway and iconic glycolysis pathway. There were 108 differentially expressed GRGs. Among them, 29 GRGs were closely related to prognosis based on clinical survival time. Risk regression analysis further revealed that there were seven significantly expressed glycolysis related genes. The genes were subsequently used to construct a predictive model. The model had an AUC value of more than 0.85. It was also significantly correlated with survival time. Further expression analysis revealed that CDK1, DSC2, ERO1A, MET, PYGL, and SLC35A3 were highly expressed in PDAC and CHST12 was highly expressed in normal pancreatic tissues. These results were confirmed using immunohistochemistry images of normal and diseases cells. The model could effectively evaluate the prognosis of PDAC patients with different clinical characteristics.ConclusionThe constructed glycolysis-related gene model effectively predicts the occurrence and development of PDAC. As such, it can be used as a prognostic marker to diagnose patients with PDAC

Metabolic Characterization of Hyoscyamus niger Ornithine Decarboxylase

Ornithine decarboxylase (ODC) catalyzes ornithine decarboxylation to yield putrescine, a key precursor of polyamines, and tropane alkaloids (TAs). Here, to investigate in depth the role of ODC in polyamine/TA biosynthesis and to provide a candidate gene for engineering polyamine/TA production, the ODC gene (HnODC) was characterized from Hyoscyamus niger, a TA-producing plant. Our phylogenetic analysis revealed that HnODC was clustered with ODC enzymes of plants. Experimental work showed HnODC highly expressed in H. niger roots and induced by methyl jasmonate (MeJA). In the MeJA treatment, the production of both putrescine and N-methylputrescine were markedly promoted in roots, while contents of putrescine, spermidine, and spermine were all significantly increased in leaves. By contrast, MeJA did not significantly change the production of either hyoscyamine or scopolamine in H. niger plants. Building on these results, the 50-kDa His-tagged HnODC proteins were purified for enzymatic assays. When ornithine was fed to HnODC, the putrescine product was detected by HPLC, indicating HnODC catalyzed ornithine to form putrescine. Finally, we also investigated the enzymatic kinetics of HnODC. Its Km, Vmax, and Kcat values for ornithine were respectively 2.62 ± 0.11 mM, 1.87 ± 0.023 nmol min-1 μg-1 and 1.57 ± 0.015 s-1, at pH 8.0 and at 30°C. The HnODC enzyme displays a much higher catalytic efficiency than most reported plant ODCs, suggesting it may be an ideal candidate gene for engineering polyamine/TA biosynthesis

Physiological roles of fatty acyl desaturases and elongases in marine fish: Characterisation of cDNAs of fatty acyl delta6 desaturase and elovl5 elongase of cobia (Rachycentron canadum)

In the present paper, we investigated the expression of fatty acyl desaturase and elongase genes in a marine teleost, cobia, a species of great interest due to its considerable aquaculture potential. A cDNA was cloned that, when expressed in yeast, was shown to result in desaturation of 18:3n-3 and 18:2n-6, indicating that it coded for a Δ6 desaturase enzyme. Very low desaturation of 20:4n-3 and 20:3n-6 indicated only trace Δ5 activity. Another cloned cDNA enabled elongation of 18:4n-3, 18:3n-6, 20:5n-3 and 20:4n-6 in the yeast expression system, indicating that it had C18-20 and C20-22 elongase activity. Sequence comparison and phylogenetic analysis confirmed that it was homologous to human ELOVL5 elongase. However, the cobia Elovl5 elongase also had low activity toward C24 HUFA. The cobia Δ6 desaturase had a preference for 18:3n-3, but the elongase was generally equally active with both n-3 and n-6 substrates. Expression of both genes was 1-2 orders of magnitude greater in brain than other tissues suggesting an important role, possibly to ensure sufficient docosahexaenoic acid (DHA, 22:6n-3) synthesis in neural tissues through elongation and desaturation of eicosapentaenoic acid (EPA; 20:5n-3)

Modeling and Analysis of Caputo–Fabrizio Definition-Based Fractional-Order Boost Converter with Inductive Loads

This paper proposes a modeling and analysis method for a Caputo–Fabrizio (C-F) definition-based fractional-order Boost converter with fractional-order inductive loads. The proposed method analyzes the system characteristics of a fractional-order circuit with three state variables. Firstly, this paper constructs a large signal model of a fractional-order Boost converter by taking advantage of the state space averaging method, providing accurate analytical solutions for the quiescent operating point and the ripple parameters of the circuit with three state variables. Secondly, this paper constructs a small signal model of the C-F definition-based fractional-order Boost converter by small signal linearization, providing the transfer function of the fractional-order system with three state variables. Finally, this paper conducts circuit-oriented simulation experiments where the steady-state parameters and the transfer function of the circuit are obtained, and then the effect of the order of capacitor, induced inductor, and load inductor on the quiescent operating point and ripple parameters is analyzed. The experimental results show that the simulation results are consistent with those obtained by the proposed mathematical model and that the three fractional orders in the fractional model with three state variables have a significant impact on the DC component and steady-state characteristics of the fractional-order Boost converter. In conclusion, the proposed mathematical model can more comprehensively analyze the system characteristics of the C-F definition-based fractional-order Boost converter with fractional-order inductive loads, benefiting the circuit design of Boost converters

Photovoltaic Array Fault Diagnosis Based on Gaussian Kernel Fuzzy C-Means Clustering Algorithm

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays

Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid

We propose herein an extended power forecasting-based coordination dispatch method for PV power generation microgrid with plug-in EVs (PVEVM) to improve the local consumption of renewable energy in the microgrid by guiding electric vehicle (EV) orderly charging. In this method, we use a clustering algorithm and neural network to build a power forecasting model (PFM) based on real data which can effectively characterise the uncertainty of PV power generation and EV charging load. Based on the interaction between the energy control centre (ECC) of the PVEVM and the EV users, a one-leader multiple-follower Stackelberg game is formulated, and the Stackelberg equilibrium is determined by using a power forecasting-based genetic algorithm (GA). As a main contribution of this paper, the PV power generation and EV charging load output from the PFM are used to generate a better quality initial population of the GA to improve its performance. A case study using real data from the Aifeisheng PV power station in China and EV charging stations in the UK verifies the good performance of the proposed extended coordination dispatch algorithm

Input-Current and Load Voltage Sharing in Input-Parallel Output-Series Connected Boost Half Bridge DC-DC Converter Using Stable Control Scheme

This paper explores a configuration for boost half bridge DC-DC converters, connected in parallel at the input and series at the output, such that the converters share the input current and load voltage equally. A stable control scheme has been developed to achieve this objective. In order to verify the effectiveness and stability of control scheme, input-parallel output-series (IPOS) connected boost half bridge (BHB) DC-DC Converter has been simulated for the different load conditions such as for fixed load, half load and continuously varying load. The results are found to be satisfactory at each load condition and validate the proposed converter structure. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.418