Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

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Mixed models in cerebral ischemia study

The data modeling from longitudinal studies stands out in the current scientific scenario, especially in the areas of health and biological sciences, which induces a correlation between measurements for the same observed unit. Thus, the modeling of the intra-individual dependency is required through the choice of a covariance structure that is able to receive and accommodate the sample variability. However, the lack of methodology for correlated data analysis may result in an increased occurrence of type I or type II errors and underestimate/overestimate the standard errors of the model estimates. In the present study, a Gaussian mixed model was adopted for the variable response latency of an experiment investigating the memory deficits in animals subjected to cerebral ischemia when treated with fish oil (FO). The model parameters estimation was based on maximum likelihood methods. Based on the restricted likelihood ratio test and information criteria, the autoregressive covariance matrix was adopted for errors. The diagnostic analyses for the model were satisfactory, since basic assumptions and results obtained corroborate with biological evidence; that is, the effectiveness of the FO treatment to alleviate the cognitive effects caused by cerebral ischemia was found.

Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam

In hydroelectric plants, the responsibility for the operation of the reservoirs typically lies with the national system operator, who controls the level of the reservoirs based on a stochastic problem for the economy of the potential energy available in the reservoir. However, in an emergency, the responsibility for the operation and control of the reservoir becomes the plant’s management. To have a faster decision-making process, it is important to have a forecast of water affluence in relation to the turbine capacity and use of the spillway. With the objective of evaluating the forecast increase in the level of the reservoir of a hydroelectric plant, this paper compares the use of the bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble learning models to analyze this problem. The case study is based on data from a 690 MW hydroelectric plant, which has a 94 km reservoir and a 185 m high dam. The random subspace and stacking models had the best results for lower error, with a low time required for convergence in relation to the other models. The ensemble models resulted in greater accuracy for the assessed problem than long short-term memory

Wavelet group method of data handling for fault prediction in electrical power insulators

Electric power is increasingly being used in the globalized day-to-day and keeping the electric power system running is necessary. Insulators are important components of the electric power system. In case of failure in these components, there may be disconnections and, consequently, no electricity. Contaminated insulators can develop irreversible failures if they are not inspected. One equipment used for the inspection of the electric power system is the ultrasound, which generates an audible noise based on a time series that is used to identify possible failures. the time series forecast can be used for possible prediction of the development of failure. In this paper, a hybrid method that uses Wavelet Energy Coefficient (WEC) for feature extraction and Group Method of Data Handling (GMDH) for time series prediction is proposed, being defined as Wavelet GMDH. For comparison and validation of the proposed method, a benchmark is made with well-established algorithms such as Long Short-Term Memory (LSTM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). For a fairer analysis, these algorithms are also evaluated based on the same data extraction with WEC. the proposed method proved to have good accuracy comparing with LSTM and ANFIS, and is much faster than the compared methods.Coordination for the Improvement of Higher Education Personnel (CAPES)National Council of Scientific and Technologic Development of Brazil -(CNPq) [307958/2019-1-PQ, 307966/2019-4-PQ, GS2404659/2016-0-Univ, GS2405101/2016-3-Univ]PRONEX 'Fundacao Araucaria'Fundacao Araucaria [042/2018]info:eu-repo/semantics/publishedVersio