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Hydroelectric and thermal generation provides a safe and regular operation in front of load shape. However, changes in the expansion policy that includes wind and solar power plants modify the path of transmission power flow and load response requirements. In order to accommodate these new renewable sources, long-term systemic approach is complex and should be based on hydrothermal optimization and load shape allocation. Recent studies show that these resources are not sufficient for the systemic needs. Energy storage can be an alternative to the regularization of renewable sources. The problem is to scale this equipment in a long-term scenario in a suitable way so as to charge a fair price for the service. At this point, operational reserve is fundamental, but often overlooked in hydrothermal expansion models. Current expansion planning models cannot adequately meet the new system demands, but they are critical for determining long-term prices, so they must be modernized and replaced gradually to avoid further problems. The paper proposes a transition approach from the current modeling through the inclusion of a new step in the expansion planning process, called the operational reserve analysis, which seeks to meet the load–generation balance criteria and to scale, if necessary, the use of additional storage devices. It is also proposed a power flow analysis to find optimal location in transmission grid, in addition to evaluating different storage devices needs and how transmission network expansion with multiple AC and DC that provides greater benefits to interconnected system. To validate proposed algorithm application, a case study was carried out for IEEE 14 bus database, and a real application was carried out for the Brazilian system, considering up-to-date planning tools and hourly allocation in load curves. This case demonstrates that the method is suitable as transition for new planning tools. [ABSTRACT FROM AUTHOR]

Dissolved gas analysis of insulating oil in refrigerated power transformer oil is a widespread technique for detecting incipient faults. However, this technique involves safety procedures for the collection of oil samples, laboratory response times and, in some cases, removing the transformer from operation. Removing a transformer from operation in certain situations can become very costly as there are production environments that must run uninterrupted so that stoppages such as this represent financial loss. Infrared thermography is a non-destructive temperature measurement technique commonly used to detect anomalies and predict possible faults without disrupting system operation. This paper presents studies based on the use of infrared temperature measurement to detect incipient faults in transformers through dissolved gas analysis of the insulating oil. This study’s methodology uses intelligent systems to analyse transformer face temperatures and detect incipient faults. The results obtained in this work present 86 and 83% of classification correctness using artificial neural networks and fuzzy logic, respectively. [ABSTRACT FROM AUTHOR]

Non-technical losses (NTL) have been a growing problem over the years, causing significant financial losses for electric utilities. Among the methods for detecting this type of loss, those based on Artificial Intelligence (AI) have been the most popular. Many works use the electricity consumption profile as an input for AI models, which may not be sufficient to develop a model that achieves a high detection rate for various types of energy fraud that may occur. In this paper, using actual electricity consumption data, additional statistical and temporal features based on these data are used to improve the detection rate of various types of NTL. Furthermore, a model that combines both the electricity consumption data and these features is developed, achieving a better detection rate for all types of fraud considered. [ABSTRACT FROM AUTHOR]

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series. [ABSTRACT FROM AUTHOR]

Time series pattern discovery is of great importance in a large variety of environmental and engineering applications, from supporting predictive models to helping to understand hidden underlying processes. This work develops a multiresolution time series method for extracting patterns in weather records, particular temperature data. The topic is important, as, given a warming climate, morbidity andmortality are expected to rise as heatwave frequency and intensity increase. By analysing summer temperature quantiles at different levels of coarseness, it was found that compounding models can contain a complete description of severe weather events. This new multiresolution quantile approach is developed as an extension of the symbolic aggregate approximation of the temperature time series in which quantiles are computed at every stretch of the piecewise partition. The process is iterated at different scales of the partition, and it was found to be a very useful approach for finding patterns related to both heatwave periods and intensities. The method is successfully tested using real weather records from Brazil (Recife) and the UK (London), and it was found that in both locations heatwave intensity and frequency are increasing at a substantial rate. In addition, it was found that the rate of increase in intensity of the heatwaves is far outstripping the rate of increase in mean summer temperature: by a factor of 2 in Recife and a factor of 6 in London. The approach will be of use to those looking at the impact of future climates on civil engineering, water resources, energy use, agriculture and health care, or those looking for sustained extreme events in any time series. [ABSTRACT FROM AUTHOR]