Enhancing Grid Visibility in Resource-Limited Settings Using Sensor Networks and Data Technologies: A Case Study from Ghana

In many developing countries, including Ghana, energy distribution systems face challenges related to power reliability, monitoring, and fault detection (Adom, Forkuo, & Osei, 2020), (Owusu, Nyarko, & Koomson, 2019). This paper proposes a lightweight, low-cost wireless sensor network architecture for real-time energy monitoring and data collection in decentralized grid environments (Gungor, Lu, & Hancke, 2010), (Elgargouri, Abdelsadek, & Wahab, 2021). The system uses sensor nodes deployed at critical grid points to capture voltage, current, and power quality data, which is transmitted via a low-power wide-area network (LoRaWAN) to a centralized server for processing (Elgargouri, Abdelsadek, & Wahab, 2021), (Li, Xu, & Zhao, 2015). The backend supports data storage, visualization, and anomaly detection using basic machine learning techniques (Molderink et al., 2010), (Hossain, Fotouhi, & Hasan, 2018). The framework is designed for scalability, ease of deployment, and suitability for rural or under-resourced communities (Elgargouri, Abdelsadek, & Wahab, 2021), (Owusu, Nyarko, & Koomson, 2019). We test the system in a simulated microgrid environment based on data from selected communities in Ghana. Preliminary results show the system’s effectiveness in identifying abnormal power patterns and providing timely alerts. This work demonstrates how accessible sensor networks and basic data analytics can contribute to the development of smarter, more reliable energy infrastructure in resource-limited settings (Gungor, Lu, & Hancke, 2010), (Elgargouri, Abdelsadek, & Wahab, 2021).