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The vulnerability of electric potential generation from water resources has been evidenced due to uncertainties of climatic origin and energy planning. Changes in rainfall regimes are multifactorial phenomena difficult to model. Rainfall is the main natural way of restoring the reservoir levels of hydroelectric power plants. The reduction in rainfall levels may increase in the coming years, resulting in a probable change of the energy matrix configuration of the state of Minas Gerais (MG). This could reduce the share of renewable sources increasing dependence on the importance of energy from fossil fuels, which are the immediate primary sources of energy available. Prior examinations have portrayed the depletion of hydroelectricity in Brazil, or its macro-regions, however this work tries to supply a more focused look at the effects on the electricity and social sectors of the state of Minas Gerais. The study developed in this paper aims to investigate the hydrological potential alteration impacts and consequences on the energy matrix of the state, the greenhouse gas emissions and the social reflexes of these changes due to scenarios motivated by climate change. The investigation intends to show the importance of the water-energy-emissions nexus for energy planning and decision-making, in order to evaluate the best options for dealing with adverse and potentially destabilizing conditions. The results of the analyses show that while hydropower demand was 5.24 Mtoe in 2015, in 2030 the supply of hydropower will be 5.81 Mtoe and 4.72 Mtoe, for the conservative and severe scenarios, respectively. In addition, the effect of climate change may require an increase in the share of non-renewable sources in the energy matrix, which could increase CO 2 e emissions from 4.2% to 7.4% by 2030, compared to the levels in 2016. • Reduction in rainfall levels will change the energy matrix configuration of MG state. • Population growth will boost demand for energy. • It is shown the importance of the water-energy-emissions nexus for energy planning and decision-making. • The energy matrix may considerably increase the CO 2 e emissions by 2030, compared with 2016 levels in the MG state. [ABSTRACT FROM AUTHOR]

The energy matrix is a useful tool for understanding the trends of production and consumption in a country and for evaluating long-term energy policies. This paper presents a behavioral simulation model of the evolution of Chile’s energy matrix. The model is developed and calibrated by considering a dynamic substitution of energy sources. Chile’s stated policy is to become less dependent on imported fossil fuels, to increase security of supply and improve the environment. The simulation experiments show that the energy matrix is very robust with regards to external fuel prices and changes in investment cost. Thus, in order to be able to observe changes in the composition of the energy matrix, there is a need for strong intervention by the regulator, or the government, to provide incentives for renewables. [ABSTRACT FROM AUTHOR]

• Proposed an intelligent approach for transmission line fault analysis based on capsule network. • An unsupervised model is developed which requires no manual labeling of the data. • The performance against line parameter variation and high impedance faults is assessed. • Noises are considered to justify the robustness of the proposed approach. • The performance is compared with the baseline capsule network and existing approaches. Detection and classification of transmission line (TL) faults are key factors for the fault root cause analysis and rapid restoration of the power network. Deep learning can extract representative features automatically from big data, thus becoming an important breakthrough in fault analysis. Existing deep learning-based fault diagnosis models rely on a large number of data obtained from a variety of fault conditions to be generalized. Indeed, in the TL domain, it is difficult to define and, therefore to collect the fault incepted features of all possible fault scenarios. This paper proposes an unsupervised framework for fault detection and classification (FDC) of TL based on a capsule network (CN). Instead of using the baseline CN, an extension to this with a sparse filtering technique is adopted in this work. The capsule network with sparse filtering (CNSF) voluntarily learns the expensive fault features and significantly improves the model performance without involving a large number of data. The proposed scheme receives 1 / 2 cycle post-fault three-phase signals and encodes them into a single image that is defined as the input for the proposed CNSF model. The effectiveness of the proposed CNSF model is corroborated by four different TL topology confirming the model's adaptability to a topology change in response to intended control action or the switching actions due to cascading faults. Further assessment of the model's performance against noise, high impedance faults (HIF), and line parameter variations are also carried out in order to confirm the high reliability of the proposed model. In addition, a rigorous comparative study is conducted to guarantee the state-of-the-art performance of the proposed model. [ABSTRACT FROM AUTHOR]