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Large-scale and rapid improvement in wastewater treatment is common practice in developing countries, yet this influence on nutrient regimes in receiving waterbodies is rarely examined at broad spatial and temporal scales. Here, we present a study linking decadal nutrient monitoring data in lakes with the corresponding estimates of five major anthropogenic nutrient discharges in their surrounding watersheds over time. Within a continuous monitoring dataset covering the period 2008 to 2017, we find that due to different rates of change in TN and TP concentrations, 24 of 46 lakes, mostly located in China’s populated regions, showed increasing TN/TP mass ratios; only 3 lakes showed a decrease. Quantitative relationships between in-lake nutrient concentrations (and their ratios) and anthropogenic nutrient discharges in the surrounding watersheds indicate that increase of lake TN/TP ratios is associated with the rapid improvement in municipal wastewater treatment. Due to the higher removal efficiency of TP compared with TN, TN/TP mass ratios in total municipal wastewater discharge have continued to increase from a median of 10.7 (95% confidence interval, 7.6 to 15.1) in 2008 to 17.7 (95% confidence interval, 13.2 to 27.2) in 2017. Improving municipal wastewater collection and treatment worldwide is an important target within the 17 sustainable development goals set by the United Nations. Given potential ecological impacts on biodiversity and ecosystem function of altered nutrient ratios in wastewater discharge, our results suggest that long-term strategies for domestic wastewater management should not merely focus on total reductions of nutrient discharges but also consider their stoichiometric balance.

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High-voltage direct current (HVDC) transmission line protection is becoming increasingly desirable with the expanding worldwide popularity of HVDC technologies in recent years. This paper proposes a transmission line backup protection scheme based on the integral of reactive power for HVDC systems. The directional characteristics of reactive power flow are theoretically analyzed for internal and external faults, and these characteristics are used to construct a directional protection scheme. The Hilbert transform is adopted to calculate the reactive power, which ensures a continuous output of calculation results and improves the reliability of the protection. A bipolar 12-pulse HVDC test system based on the CIGRE benchmark is modeled using PSCAD/EMTDC, and extensive simulations of various fault situations are conducted to test the effectiveness of the proposed scheme. The simulation results show that the proposed protection scheme correctly identifies internal and external faults and performs well with different fault distances and fault resistances. Furthermore, the proposed protection is insensitive to the sampling frequency, making it practical for future applications.Index Terms-directional protection, Hilbert transform, HVDC system, power system protection, reactive energy.

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High-Voltage Direct Current (HVDC) systems are being widely employed in various applications because of their numerous advantages such as bulk power transmission, efficient long-distance transmission, and flexible power-flow control. However, Line-Commutated Converter (LCC) based HVDC systems suffer from commutation failure which is a major drawback, leading to increased device stress and interruptions in transmitted power. This paper proposes a predictive control strategy, deploying a Commutation Failure Prevention Module (CFPM) to mitigate the commutation failures during AC system faults. The salient feature of the proposed strategy is that it has the ability to temporarily decrease the firing angle of thyristor valves depending on the fault intensity to ensure a sufficient commutation margin.In order to validate the performance of the proposed strategy several simulations have been conducted on CIGRE Benchmark HVDC model using PSCAD/EMTDC software. Additionally, practical performance and feasibility of the proposed strategy is evaluated through laboratory testing, using the real time Opal-RT hardware prototyping platform.Simulation and experimental results demonstrate that the proposed strategy can effectively inhibit the commutation failure or repetitive commutation failures under different fault types, fault impedances and fault initiation times.

Attribute independence has been taken as a major assumption in the limited research that has been conducted on similarity analysis for categorical data, especially unsupervised learning. However, in real-world data sources, attributes are more or less associated with each other in terms of certain coupling relationships. Accordingly, recent works on attribute dependency aggregation have introduced the co-occurrence of attribute values to explore attribute coupling, but they only present a local picture in analyzing categorical data similarity. This is inadequate for deep analysis, and the computational complexity grows exponentially when the data scale increases. This paper proposes an efficient data-driven similarity learning approach that generates a coupled attribute similarity measure for nominal objects with attribute couplings to capture a global picture of attribute similarity. It involves the frequency-based intra-coupled similarity within an attribute and the inter-coupled similarity upon value co-occurrences between attributes, as well as their integration on the object level. In particular, four measures are designed for the inter-coupled similarity to calculate the similarity between two categorical values by considering their relationships with other attributes in terms of power set, universal set, joint set, and intersection set. The theoretical analysis reveals the equivalent accuracy and superior efficiency of the measure based on the intersection set, particularly for large-scale data sets. Intensive experiments of data structure and clustering algorithms incorporating the coupled dissimilarity metric achieve a significant performance improvement on state-of-the-art measures and algorithms on 13 UCI data sets, which is confirmed by the statistical analysis. The experiment results show that the proposed coupled attribute similarity is generic, and can effectively and efficiently capture the intrinsic and global interactions within and between attributes for especially large-scale categorical data sets. In addition, two new coupled categorical clustering algorithms, i.e., CROCK and CLIMBO are proposed, and they both outperform the original ones in terms of clustering quality on UCI data sets and bibliographic data.