Data Mining Techniques Assessment for Shutdowns Prediction of Electric Power Systems

The volume of historical information related to the behavior of electric power systems, has significantly increased the size of the energy companies databases, but without contributing to the improvement in the aspects of operation, maintenance and quality of service, except for any queries historical behavior of variables. Several studies in the literature have been proposed to investigate various aspects this problem. Some of these works address the problem of disconnections of occurrence prediction in power systems using mining techniques. However, due to the large volume of data, the search for the preparation of methodologies and data selection becomes important. In this work, a methodology using data mining techniques applied the synchronized phasor measurement time series to aid the prevention of shutdowns due to voltage deviations in electrical power systems is presented. Are tested some mining techniques on data obtained in a 230 kV transmission line linking the substations of Tucuruí, Altamira, Rurópolis, constituting the electrical system Tramo western Pará

A summary of artificial lift failure, remedies and run life improvements in conventional and unconventional wells.

Artificial lift (AL) systems are crucial for enhancing oil and gas production from reservoirs. However, the failure of these systems can lead to significant losses in production and revenue. This paper explores the different types of AL failures and the causes behind them. The article discusses the traditional methods of identifying and mitigating these failures and highlights the need for new designs and technologies to improve the run life of AL systems. Advances in AL system design and materials, as well as new methods for monitoring and predicting failures using data analytics and machine learning techniques, have been discussed. The findings of this work provide valuable insights for researchers and practitioners in the development of more reliable and efficient AL systems

A Decision Support System for Photovoltaic Potential Estimation

With knowledge on the photovoltaic potential of individual residential buildings, solar companies, energy service providers and electric utilities can identify suitable customers for new PV installations and directly address them in renewable energy rollout and maintenance campaigns. However, many currently used solutions for the simulation of energy generation require detailed information about houses (roof tilt, shading, etc.) that is usually not available at scale. On the other hand, the methodologies enabling extraction of such details require costly remote-sensing data from three-dimensional (3D) laser scanners or aerial images. To bridge this gap, we present a decision support system (DSS) that estimates the potential amount of electric energy that could be generated at a given location if a photovoltaic system would be installed. The DSS automatically generates insights about photovoltaic yields of individual roofs by analyzing freely available data sources, including the crowdsourced volunteered geospatial information systems OpenStreetMap and climate databases. The resulting estimates pose a valuable foundation for selecting the most prospective households (e.g., for personal visit and screening by an expert) and targeted solar panel kit offerings, ultimately leading to significant reduction of manual human efforts, and to cost-effective personalized renewables adoption

Evaluation procedure for blowing machine monitoring and predicting bearing SKFNU6322 failure by power spectral density

This work shows the results of the comparative study of characteristic frequencies in terms of Power Spectral Density (PSD) or RMS generated by a blower unit and the SKFNU322 bearing. Data is collected following ISO 10816, using Emonitor software and with speed values in RMS to avoid high and low frequency signal masking. Bearing failure is the main cause of operational shutdown in industrial sites. The difficulty of prediction is the type of breakage and the high number of variables involved. Monitoring and analysing all the vari- ables of the SKFNU322 bearing and those of machine operation for 15 years allowed to de- velop a new predictive maintenance protocol. This method makes it possible to reduce from 6 control points to one, and to determine which of the 42 variables is the most incidental in the correct operation, so equipment performance and efficiency is improved, contributing to increased economic profitability. The tests were carried out on a 500 kW unit of power and It was shown that the rotation of the equipment itself caused the most generating variable of vibrational energy