Sensor Placement Optimization for Power Grid Condition Monitoring Based on a Backup Coverage Model: A Case Study of Guangzhou

The increasing complexity and vulnerability of urban power grids necessitate advanced monitoring systems to ensure operational reliability and resilience. The optimal placement of sensors is a critical yet challenging task that directly impacts the effectiveness and cost of such systems. This study addresses the need for a sensor deployment strategy that not only maximizes coverage but also guarantees monitoring redundancy for critical assets. We propose a novel optimization framework based on the Backup Coverage Sensor Location Problem (BCSLP). First, a multi-dimensional risk assessment, integrating infrastructure proximity and population density, was conducted using the Entropy Weight Method (EWM) to objectively determine the monitoring priority for each power tower in Guangzhou, China. Subsequently, the BCSLP model was formulated to optimize the trade-off between primary coverage (breadth) and backup coverage (resilience). The model was solved using both the Gurobi exact solver for a representative district and a bespoke improved Genetic Algorithm (GA) to ensure scalability. The case study in Guangzhou’s Haizhu District revealed that extreme strategies focusing solely on either breadth or resilience were suboptimal. We adopt a balanced, resilience-biased strategy (ω=0.4) that supports robust monitoring of critical towers while maintaining broad network coverage. The proposed risk-informed BCSLP framework provides a scientifically robust and scalable tool for designing resilient sensor networks for power grids, offering valuable decision support for enhancing urban infrastructure security in smart cities.