Optimization Models for the Inspection of Electric Power System Assets Using Drones

Abstract

To inspect electric power system assets, electric utilities have relied on inspection personnel and costly equipment. For example, the utilities inspect a power transmission tower by dispatching line crews and helicopters. Wind turbines are inspected by a technician using rope access. These practices are ineffective due to high operation costs, safety concerns, and low inspection rate. Recently, there has been an increasing interest in using a drone, or also known as unmanned aircraft system (UAS). We explore the drone routing optimization models for inspecting these electric power system assets. In the first chapter, we formulate an optimization model to find an efficient drone flight path for inspecting a transmission tower. The objective of the model is to maximize a function involving three performance ratios, namely, flight time, image quality, and tower coverage. This optimization problem is solved by using two metaheuristic algorithms which are a particle swarm optimization (PSO) based-algorithm and a simulated annealing (SA) based-algorithm. In the second chapter, we develop a routing optimization model to minimize the total operation time for inspecting a wind farm by using one drone and one ground vehicle for carrying the drone. This optimization problem is decomposed into two steps. The first step clusters the wind turbines and optimizes the drone routing in each cluster by solving the classical traveling salesman problem (TSP) using an integer linear programming model. The second step optimizes the ground vehicle routing by solving the equality generalized traveling salesman problem (E-GTSP) using an integer linear programming model. We test our approach using three case studies created by using actual wind farm locations. Finally, in the third chapter, we extend the optimization model presented in the previous chapter by considering the visual line-of-sight regulation and a multi-drone system. The multi-drone routes are optimized by solving the multiple traveling salesman problem (mTSP) using a mixed integer linear programming model. Also, the proposed model is tested on three case studies.