Impact of IoT Fault Detection in Urban Microgrids: An Empirical Field Study

Thesis Abstract

This study investigates the role of Internet of Things (IoT)?based fault detection systems in enhancing reliability, resilience, and economic efficiency of urban microgrids under real?world operating conditions. The problem addressed is the limited empirical understanding of how IoT fault detection influences operational metrics such as service uptime, energy theft identification, fault isolation speed, and maintenance scheduling in densely populated urban settings with high penetration of distributed energy resources. The aim is to evaluate the effectiveness of IoT fault detection in reducing unplanned outages and improving fault diagnosis accuracy, while identifying implementation challenges in urban microgrids. Specific objectives include (1) quantifying changes in system reliability metrics pre? and post?IoT deployment, (2) evaluating the accuracy and timeliness of fault detection algorithms under varying load and generation patterns, (3) assessing the impact on maintenance planning efficiency and capital expenditure, (4) exploring user and operator perceptions of IoT visibility and decision support, and (5) formulating a pragmatic implementation framework tailored to city microgrids. The study adopts a mixed?methods design anchored in empirical field data from a metropolitan microgrid pilot consisting of 12 distributed energy resources, 6 feeders, and 4 interties, with IoT fault detection deployed across 80 intelligent sensors and 32 edge gateways. The population comprises grid operators, field technicians, and remotely monitored assets within the pilot network. A purposive stratified sampling approach selects 15 operators and 20 technicians for qualitative inquiry, while a quantitative strand analyzes operational data from 24 months prior to and 24 months during IoT deployment. Data collection instruments include (i) sensor?level telemetry logs (voltage, current, fault flags, and islanding events), (ii) maintenance records and failure incident reports, (iii) operator interview protocols focusing on decision support and system trust, and (iv) a structured survey measuring perceived reliability, response time, and workload. Validity and reliability procedures encompass triangulation across telemetry data, maintenance records, and interview transcripts; instrument validity is enhanced through expert panel review and pilot testing in a neighboring urban microgrid. Quantitative analysis employs interrupted time series (ITS) to detect shifts in reliability metrics, multivariate regression to identify factors influencing fault detection performance, and survival analysis to compare mean time to fault isolation before and after IoT adoption. Classification and clustering techniques (K?means, hierarchical clustering) are used to categorize fault patterns and optimize maintenance scheduling. On the qualitative side, thematic analysis of interview data is conducted with coding validated through intercoder reliability checks (Cohen’s kappa ? 0.8). Theoretical grounding integrates the Fault Diagnosis Theory and Technology Acceptance Model (TAM) to explain algorithmic effectiveness and user adoption dynamics, respectively, with a conceptual framework illustrating interactions among IoT sensing fidelity, communication latency, operator decision support, and grid resilience. Anticipated findings indicate that IoT fault detection will significantly reduce mean time to isolation (MTTI) by 28–42%, decrease unplanned outage duration by 15–25%, and improve fault?diagnosis accuracy by 12–18 percentage points, particularly for transient faults and equipment aging scenarios. The study expects improved maintenance efficiency evidenced by a 10–20% reduction in reactive maintenance costs and more optimized preventive schedules. Qualitative insights are expected to reveal increased operator trust in automated alerts but highlight concerns regarding cybersecurity, data governance, and interface design. The study contributes to knowledge by providing robust empirical evidence of IoT fault detection efficacy in urban microgrids, bridging a gap between theoretical models and practical deployment, and offering a framework for scalable implementation that aligns with city resilience objectives. Practical implications include guidance on sensor density, edge processing configurations, data fusion strategies, and decision?support workflows aligned with regulatory and market contexts. The main conclusion posits that IoT fault detection can measurably enhance urban microgrid reliability and operational efficiency when integrated with appropriate data governance, cybersecurity measures, and user?centric analytics. Recommendations emphasize standardized data protocols, cybersecurity hardening, staged rollouts with continuous performance monitoring, and capacity building for operators to interpret IoT?generated insights, alongside policy considerations to foster investment in resilient urban energy infrastructures. Further research is suggested to explore long?term cost–benefit analyses, cross?city comparative studies, and the integration of machine learning models for predictive maintenance within IoT fault detection ecosystems.