Fault Detection & Classification in UPFC Integrated Transmission Line Using DWT

Fault detection and classification in UPFC (Unified Power Flow Controller) integrated transmission line using single terminal based DWT (Discrete Wavelet Transform) is proposed. The current is extracted from the sending end bus and processed through wavelet transform to evaluate the spectral energy (SE) using db4 mother wavelet. Three level decomposition is framed to extract the fundamental frequency component from non-stationary signal, considering sampling frequency of 2kHz system. The fundamental frequency component of respective phase currents are used to compute SE at sending end. The SE of individual phase current is the key factor for deciding the fault pattern detection and classification. The advantage of using this it requires less cost and protect entire transmission line with minimal fault detection time. The various types of fault (L-G, L-L, L-L-G, L-L-L) are simulated by considering the parameter like fault resistance, source impedance, fault inception angle, multi-location fault, reverse power flow and UPFC system parameter. The scheme works reliable and efficient to detect and classify the fault within a cycle of sample period 40 or a cycle of time period 20ms compared to other conventional relaying scheme

Modification of the vertically generalized production model for the turbid waters of Ariake Bay, Southwestern Japan

The vertically generalized production model (VGPM), which was designed for open ocean waters (Behrenfeld and Falkowski, 1997a; henceforth BF), was evaluated using in situ measurements of primary productivity (PP) in the characteristically turbid coastal waters of Ariake Bay, southwestern Japan, to develop a regionally modified version of the model. The euphotic depth (Zeu)-integrated PP (IPP) calculated from the VGPM using in situ chlorophyll a (Chl a) and sea surface temperature (SST) was significantly overestimated (by factors of 2–3), but 52% of the observed variability was explained. The weak correlation could have partially resulted from overestimations by the sub-models embedded in the original VGPM model for estimation of Zeu (Morel and Berthon, 1989; henceforth MB) and the optimal Chl a-normalized PP (). The sub-model estimates of and Zeu with in situ and Zeu showed significant improvement, accounting for 84% of the variability and causing less overestimation. Zeu was the most important parameter influencing the modeled IPP variation in Ariake Bay. Previous research suggested that the Zeu model, which was based on surface Chl a, overestimated in situ Zeu by a factor of 2–3, resulting in weak correlation between the modeled and in situ IPP. The Zeu sub-model was not accurate in the present study area because it was basically developed for clear (case 1) waters. A better estimation of Zeu could be obtained from the in situ remote sensing reflectance (Rrs) using a quasi-analytical algorithm (QAA) in this turbid water ecosystem. Among the parameters of PP models, is conventionally considered the most important. However, in this study was of secondary importance because the contribution of to the variation in modeled IPP was less than the contribution of Zeu. The modeled and in situ were weakly correlated with 50% of the data points that overestimated the in situ values. The estimation of Chl a was improved by optimizing the Chl a algorithm with in situ Rrs data. Incorporating the QAA-based Zeu and the optimized Chl a and constant (median) value led to improved performance of the VGPM for the study area. Thus, even though the VGPM is a global open ocean model, when coupled with turbid water algorithms for Zeu and Chl a and constant (median) , it provided realistic estimates of IPP in the turbid water ecosystem of Ariake Bay

Kongsfjorden Ecosystem - a Nitrogen Sink during the Arctic Summer.

Kongsfjorden nitrogen budget is dominated by exchanges of nitrate with the sea. The fjord is a nitrogen and carbon sink during summer. The C:N molar ratio of the Source-Sink term is 7.3, which is close to the expected Redfield ratio (6.6). Negative nutrient and carbon sink indicate autotrophic metabolism.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech