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Power hardware in the loop (PHIL) is a modern experimental technique that allows the emulation of a full-scale converter (FSC) with the combination of a scaled-down converter (SDC), a power amplifier, and a real-time simulator, thus enabling the study of real-time interactions of power electronics with large power systems. However, assembling an accurate scaled-down replica of an FSC with off-the-shelf laboratory SDCs is practically impossible due to a mismatch in per unit losses, as well as in the impedance of the $L/LC/LCL$ filter. Consequently, the scaled-up power flow capability of SDCs differs from that of FSCs, restricting emulation to smaller regions of the four quadrants than those corresponding to the FSCs' nominal active and reactive capacity. These PHIL test beds cannot be used to emulate FSCs demanding bidirectional active and reactive power flow. Any scaling method on SDCs, emulating the entire operation of FSCs, demands underutilization of SDCs, reducing the advantages of PHIL tests. This article, therefore, proposes a physics-informed scaling method that exploits power capability curves to emulate FSCs in all four quadrants of operation. This method is independent of SDC topology, filter type, and interfacing methods. A visual identification of semiconductor device constraints bounding the emulation is also presented, utilizing the physics of converter control. A theoretical analysis of the proposed method is presented, followed by validation with MATLAB simulations and experimental tests using a 50-kVA SDC.

Power hardware-in-the-loop (PHIL) is an experimental technique that uses power amplifiers and real-time simulators for studying the dynamics of power electronic converters and electrical grids. Power hardware-in-the-loop (PHIL) tests provide the means for functional validation of advanced control algorithms without the burden of building high-power prototypes during early technology readiness levels. However, replicating the behavior of high-power systems with laboratory scaled-down converters (SDCs) can be complex. Inaccurate scaling of the SDCs coupled with an exclusive focus on instantaneous voltages and currents at the fundamental frequency can lead to PHIL results that are only partially relatable to the high-power systems under study. Test beds that fail to represent switching frequency harmonics cannot be used for studying harmonic penetration or loss characterization of large-scale converters. To tackle this issue, this article proposes a harmonic-invariant scaling method that exploits the volt-ampere rating of preexisting laboratory SDCs for more accurately replicating harmonic phenomena in a PHIL test bench. First, a theoretical analysis of the proposed method is presented and, subsequently, the method is validated with MATLAB simulations and experimental tests.

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems. [ABSTRACT FROM AUTHOR]

Toxicological effect of pulp and papermill effuent in the fish, Oreochromis mossambicus was investigated in the present study. Pulp and papermill effluent discharged from Hindustan News Print LTD, Peruva, Kottayam, Kerala were collected and adult fish of size 15±2 g were exposed to a sublethal concentrations (1/5th and 1/10th LC50 value) of the effluent for a period of 10, 20 and 30 days. Pathological conditions such as fatty degeneration (FD), pyknosis (P), haemosiderosis (HS), degenerated renal tubule (DRT) and Vacular hypertrophy of tubular epithelium (VHT). Fatty degeneration (FD) was observed in both concentrations of 10, 20 and 30 days of exposure. Pyknosis (P) was started from 1/5th concentration of 10 days exposed fishes. In experimental fishes haemosiderosis (HS) was found in 1/5th concentration of 20 days and both concentrations of 30 days. Degenerated renal tubule (DRT) was observed in both concentrations of 30 days of papermill effluent exposed fishes. Vacular hypertrophy of tubular epithelium (VHT) and haemosiderosis (HS) was found in 1/5th concentration of 30 days effluent exposed fishes. The present study revealed that the histopathological conditions affect the physiological functions such as metabolic processes and excretion. [ABSTRACT FROM AUTHOR]