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A novel hybrid entropy-clustering approach for optimal placement of pressure sensors for leakage detection in water distribution systems under uncertainty

机译：一种新型混合熵聚类方法，用于在不确定度下水分配系统泄漏检测压力传感器的最佳放置

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摘要

This study presents a novel hybrid entropy-clustering framework for placing pressure sensors in water distribution systems (WDS) to detect leakage. Leakages are simulated at all potential nodes of WDS, and then potential pressure sensors (PPS) in WDS are classified using a K-means clustering algorithm. Transinformation entropy for each potential pair of PPS was also computed, which in turn helped to reduce redundant information. PPS locations were subsequently optimized using a multi-objective optimization model. Furthermore, to capture the sensitivity of sensors' layout in WDS to sensor error, a fuzzy-based analysis is integrated with a multi-objective optimization model. Finally, the best compromise solution of PPS placement in each category was selected using an ELECTRE multi-criteria decision making model. Reducing redundant information of pressure sensors based on information theory and choosing the best possible solution based on the ELECTRE model are the main novelties of this study. Results of C-Town WDS attest to the proposed framework' efficiency.
机译：本研究提出了一种新的混合熵聚类框架，用于将压力传感器放置在分配系统（WDS）中的压力传感器来检测泄漏。在WDS的所有潜在节点上模拟泄漏，然后使用K-means聚类算法对WDS中的潜在压力传感器（PPS）进行分类。还计算了每个潜在的PP的转换熵，这反过来有助于减少冗余信息。随后使用多目标优化模型进行PPS位置。此外，为了将传感器的布局捕获到传感器误差的传感器的敏感性，基于模糊的分析与多目标优化模型集成。最后，使用电型多标准决策模型选择每个类别中PPS放置的最佳妥协解决方案。基于信息理论，减少压力传感器的冗余信息，基于电模型选择最佳的解决方案是本研究的主要新奇。 C-Town WDS的结果证明了拟议的框架效率。

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