Water distribution networks are highly vulnerable during natural disasters, where pipe damage and hydraulic instability can lead to hidden leakages, service disruption, and increased flood risk. Conventional IoT-based leakage detection systems perform well under stable conditions but deteriorate when exposed to disaster-induced noise, missing data, and irregular pressure–flow behaviour.
This paper presents a modular IoT-driven framework specifically designed for post-disaster environments. The framework augments benchmark leakage datasets with synthetic anomalies, such as pressure spikes, flow surges, sensor noise, and data outages, to emulate realistic flood- and earthquake-induced instability. A hybrid detection model combining rule-based logic with an unsupervised anomaly-detection module is then applied to extract and evaluate key hydraulic features.
Experimental results from simulated disaster scenarios demonstrate that the hybrid model achieves higher detection rates, reduced false-alarm occurrences, and improved temporal stability compared to threshold-only detection. These findings indicate the effectiveness of the proposed approach for rapid leakage identification and early flood-risk mitigation in disaster-affected water networks.
View Full Paper (PDF)This paper presents a comparative performance analysis of conventional coded orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) OFDM with index modulation (OFDM-IM) as candidate waveforms for future sixth-generation (6G) wireless communication systems. A Rayleigh fading channel model is considered, employing convolutional channel coding, M-QAM modulation (M ∈ {4, 16, 32, 64}), and minimum mean-square error (MMSE) equalization under identical bandwidth and transmit power constraints.
Unlike classical OFDM, OFDM-IM activates only a subset of subcarriers within each subblock, enabling additional information transmission through subcarrier indices while reducing average interference and spectral leakage. Simulation results demonstrate that OFDM-IM consistently outperforms conventional OFDM by achieving notable signal-to-noise ratio (SNR) gains at practical bit error rate (BER) levels, along with significantly improved energy efficiency and reduced out-of-band (OOB) emissions across all considered modulation orders.
These performance advantages highlight the strong potential of OFDM-IM as an energy-efficient and spectrally compact waveform for emerging 6G applications, including ultra-reliable low-latency communications, massive machine-type communications, and spectrum-constrained Internet-of-Things uplink scenarios.
View Full Paper (PDF)The increasing penetration of inverter-based resources (IBRs) has intensified the need for smart inverters capable of supporting power system stability in microgrids with high levels of distributed PV generation. This paper presents the design and hardware-in-the-loop (HIL) validation of a droop-controlled grid-forming (GFM) inverter for a 100kW photovoltaic (PV) system.
A high-fidelity model of the PV array, converter interface, and grid coupling was implemented in MATLAB/Simulink and executed in real time on an OPAL-RT OP5700 platform. The inverter was evaluated under islanded and grid-connected conditions through step-load disturbances, three-phase short-circuit faults, and low-voltage ride-through (LVRT) compliance testing.
Results show that the inverter maintains stable frequency and voltage during small disturbances, recovers within 0.5s after a three-phase short-circuit fault without load shedding, and satisfies the New Zealand Grid Code LVRT requirement without the use of FACTS devices. These findings demonstrate the feasibility of small-scale GFM inverters to contribute ancillary services and improve stability in renewable-dominated microgrids.
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