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The trend todayHybrid solar-wind micro grid ofArtificial Intelligence (AI) decentralizedPredictive maintenance renewable energyRenewable energy hasFault diagnosis made hybrid solarEdge computing windRural electrification microgrids the flagship in remote and rural areas. However, maintaining their dependability is still challenging, withheld by the lack of telemetries accessibility and the availability of experts. This chapter proposes a new solution that incorporates edge computingEdge computing and artificial intelligence (AIArtificial Intelligence (AI)) to provide standalone diagnostics and prognostic maintenance for hybrid microgrids. The architecture consumes sensor-based data streams and uses supervised and unsupervised machine learningMachine learning models to detect and assess anomalies on inverters, solar PV arrays, and wind turbines near real time. Integrating edge computingEdge computing reduces cloud dependency and we observe on site, low latency, real time processing of data. Here in this chapter we discuss AIArtificial Intelligence (AI) model design, data gathering strategy, prototype creation, and system architecture. For performance we discuss accuracy, latency, and fault recovery time. We observe that it also holds true which small-scale AIArtificial Intelligence (AI) models at the edge can be utilized to enhance energy access, operation dependability, and upkeep within rural electrificationRural electrification schemes. Also, we examine what the future holds for scale deployment, federated learning methods, and policy concerns regarding sustainable energy systems.

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AI-Driven Predictive Maintenance and Fault Diagnosis in Hybrid Solar-Wind Microgrids Using Edge Computing for Rural Electrification

  • 章节

  • 发表日期：

    2026年

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  • R. Poornima,
  • S. Kannaki,
  • P. Gayathri

摘要

The trend todayHybrid solar-wind micro grid ofArtificial Intelligence (AI) decentralizedPredictive maintenance renewable energyRenewable energy hasFault diagnosis made hybrid solarEdge computing windRural electrification microgrids the flagship in remote and rural areas. However, maintaining their dependability is still challenging, withheld by the lack of telemetries accessibility and the availability of experts. This chapter proposes a new solution that incorporates edge computingEdge computing and artificial intelligence (AIArtificial Intelligence (AI)) to provide standalone diagnostics and prognostic maintenance for hybrid microgrids. The architecture consumes sensor-based data streams and uses supervised and unsupervised machine learningMachine learning models to detect and assess anomalies on inverters, solar PV arrays, and wind turbines near real time. Integrating edge computingEdge computing reduces cloud dependency and we observe on site, low latency, real time processing of data. Here in this chapter we discuss AIArtificial Intelligence (AI) model design, data gathering strategy, prototype creation, and system architecture. For performance we discuss accuracy, latency, and fault recovery time. We observe that it also holds true which small-scale AIArtificial Intelligence (AI) models at the edge can be utilized to enhance energy access, operation dependability, and upkeep within rural electrificationRural electrification schemes. Also, we examine what the future holds for scale deployment, federated learning methods, and policy concerns regarding sustainable energy systems.