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Dam monitoring usually involves environmental variables (e.g., the water level and temperature) and effect variables (deformation, cracking, seepage, etc.).The associated monitoring data can reflect the trends in these variables over time and are important information for managers to understand the operational status of a dam. Therefore, research on monitoring data analysis methods is very important for monitoring dam safety. Dam monitoring data analysis methods can be divided into monitoring model, monitoring index, and abnormal value detection methods. A monitoring model takes environment variables as independent variables and effect variables as dependent variables. By studying the interactions among variables, the trends of effect variables can be learned for monitoring and prediction. A monitoring index is established to denote warning or extreme value considering the previous changes in effect variables to determine whether future changes are safe.Abnormal value detection is also an important method of finding abnormal changes in the dam state. This paper summarizes the principles, research progress, deficiencies, and development trends of these three types of monitoring data analysis methods. This review promotes research in the field of dam safety monitoring.

Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies. This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models. Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied. Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes. An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect. The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models. The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.

A decentralised control method that deals with current sharing issues in dc microgrids (MGs) is proposed in this study. The proposed method is formulated in terms of 'modified global indicator' concept, which was originally proposed to improve reactive power sharing in ac MGs. In this work, the 'modified global indicator' concept is extended to coordinate dc MGs, which aims to preserve the main features offered by decentralised control methods such as no need of communication links, central controller or knowledge of the microgrid topology and parameters. This global indicator is inserted between current and voltage variables by adopting a virtual capacitor, which directly produces an output current sharing performance that is less relied on mismatches of the multi-bus network. Meanwhile, a voltage stabiliser is complementary developed to maintain output voltage magnitude at steady state through a shunt virtual resistance. The operation under multiple dc-buses is also included in order to enhance the applicability of the proposed controller. A detailed mathematical model including the effect of network mismatches is derived for analysis of the stability of the proposed controller. The feasibility and effectiveness of the proposed control strategy are validated by simulation and experimental results.

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