Dynamic framework for crew dispatch optimization in power distribution fault inspection
提出一个动态双目标模型，利用贝叶斯定理结合报修电话和现场信息更新故障概率，并采用字典序方法优化巡检人员路径，以减少故障检测时间。
• Fault location: This paper presents a framework for locating faults using inspection crews. • Bayes’ Theorem: Trouble calls are used to estimate the posterior probability of faults. • Multi-objective model: A multi-objective model is proposed for dispatching inspection crews. • Dynamic framework: A dynamic method updates the posterior fault probabilities using field information provided by crews and updates routes accordingly. • Efficiency: The use of posterior probability can reduce inspection time. The increasing frequency of natural disasters in recent years has significantly affected electrical distribution networks. A growing body of research has focused on dispatching crews to inspect and repair the electrical grid. While some studies estimate fault probabilities, others incorporate trouble calls or propose dynamic, multi-objective approaches. However, existing studies do not integrate all these aspects into a unified framework that simultaneously refines fault probabilities based on trouble calls, updates them dynamically using field inspection information, and embeds the updated probabilities into a multi-objective optimization model. Most existing approaches address these elements separately, for example by estimating prior fault probabilities without dynamic updating or by optimizing crew routing primarily based on shortest paths. As a result, such approaches may delay the inspection of the most likely fault locations and limit the effectiveness of inspection. This gap is addressed in this work and validated through numerical experiments. This paper proposes a dynamic bi-objective model for dispatching crews to inspect outaged areas, aiming to minimize routing efforts while prioritizing areas with higher fault probabilities. The problem is addressed using a lexicographic method, which first optimizes one objective and then solves the second without altering the result of the first. To support the model, posterior fault probabilities are estimated using Bayes’ theorem and updated with trouble call and field information, allowing adaptive crew routing. Numerical experiments on the IEEE-34 and IEEE-126 test systems demonstrate that dynamic refinement of fault probabilities, driven by inspection crew feedback, leads to substantial improvements in fault detection time.