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Reference Number NIA_SPT_1602
Title UAV Platform Development for Automated Asset Condition Diagnosis
Status Completed
Energy Categories OTHER POWER and STORAGE TECHNOLOGIES(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
SP Energy Networks
Award Type Network Innovation Allowance
Funding Source ENA Smarter Networks
Start Date 01 March 2016
End Date 01 March 2017
Duration 12 months
Total Grant Value £77,385
Industrial Sectors Power
Region Scotland
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , SP Energy Networks (100.000%)
Web Site http://www.smarternetworks.org/project/NIA_SPT_1602
Objectives The scope of the project comprises the creation of a first UAV prototype integrating two sensors and a live demonstration to show the benefits of this technology in terms of improved imaging and accuracy of diagnosis, reduced operational risk, process efficiency cost savings and health and safety improvements. This project is divided in 8 stages: Stage 1. Procurement stage (1 month) Stage 2. Sensor testing (3 weeks)Stage 3. Evaluation of drone upgrade (3 weeks)Stage 4. De-energised field trial (2 weeks)Stage 5. Energised field trial (2 weeks)Stage 6. Evaluation of the information captured by the drone (4 weeks)Stage 7. Project Report (4 weeks)Stage 8: Project Dissemination Meeting (1 day) The success criteria for the project are: Successful sensors integration on UAV compatible hardware platform Identification of other innovative sensors to be integrated at a later stage Successful field trial: Capture high-quality images of components difficult to access and thermal images of live conductors Create an initial software package to be run on the drone platform which is open-source and can be developed further with academic collaboration on suitable automated analysis algorithms. Identification of issues when operating UAVs near high voltages lines. Identification of solutions Confidence for follow-on project, updated Cost Benefit Analysis (CBA), specification for UAV service tender
Abstract Unmanned Aerial Vehicles (UAVs) technology is developing rapidly. With the cost of deployment decreasing and the increasing possibility of adding relevant cost effective sensor technology which meets the weight restrictions for carriage, meaningful automated inspection is quickly becoming a reality. Some DNOs and TOs are starting to use UAVs for the inspection process. High-quality images of overhead lines and transmission towers are captured and analysed afterwards by experts. Reports are then created containing information on components health. These reports take considerable time to produce and are also subjective. This project addresses the practicalities of data collection, automation and prioritization rather than the physical aspects of drone flight. There are a number of issues that are preventing the widespread use of UAVs on Transmission Networks, such as the fact that UAVs are currently used Within Line of Sight (WLOS) due to Civil Aviation Authority (CAA) regulations. This project addresses some of the issues that are not already being tackled by other projects. The project focuses on the selection and deployment of innovative sensors on UAVs to improve diagnosis within the inspection process, and on the capture and initial processing of data coming from those sensors. Although a number of UAVs are equipped with different sensor technology, at present there is no bespoke solution available for overhead lines and transmission towers surveys designed for the purpose of automatic diagnostic of the degradation of their components. Research at PNDC and University of Strathclyde has been already undertaken in this area, and will set the basis for the development of the automatic diagnostic capability of the UAV. The project constitutes a first step towards a Business As Usual (BAU) solution where UAVs will be able diagnose a number of problems and broadcast information in real time during the inspection process of transmission towers and overhead lines. This will deliver a considerable reduction on the inspection costs and significantly reduce the health and safety risk. Furthermore, the automation of the diagnosis of defects will also deliver operational risk mitigation, as decisions can be made in a timely fashion based on more accurate information. The project aims are: Showcase UAVs as a flexible platform for the deployment of innovative sensors to improve asset condition monitoring and diagnosis, e.g. thermal, UV, hyperspectral. Display an innovative approach to integration of information coming from different sensors. The development of a hardware platform to do real-time lens focusing, continuous or triggered image acquisition, overlaying of HD and thermal imagery to create an integrated diagnostic picture is an innovative approach and importantly allows software innovation to add functionality in future work. An open source software platform and hardware interface avoids vendor lock-in and facilitates academic collaboration on algorithm development for diagnostics. Identification of potential issues when operating UAVs near high voltages lines. Implementation and trail of hardware solutions. This project constitutes the first step towards a BAU solution for inspection of transmission towers and distribution overhead lines. The output of the project will enable the following steps to achieve the BAU solution. The realisation of the BAU in several steps allows for a better management of the risks. The different steps for this realisation are given below: Step 1: Feasibility study: Sensors integration with new processing platform to be mounted on drone. Analysis and comparison of the platform’s performance and identification of a staged process to add more sensors, reduce platform weight and improve battery life. Step 2: Development of diagnostic capability through creation and adaption of algorithms for automatic classification of defects. Integration of other sensors of interest. Step 3: Incorporation with existing asset management software and strategy. Work towards simple automation of the inspection process. Work on automated guidance and integration with other means of remote asset monitoring -(e. g helicopter and satellite).This project aims to demonstrate the flexibility of UAVs for the deployment of innovative sensors and set the basis for a further development of the platform automatic diagnosis. i.e. turning the data captured through the sensors into information and therefore enabling an objective and faster decision making process. This project proposes the creation of a prototype system consisting of a commercially available UAV and adding a computer platform and sensor rig which will integrate two sensors: RGB and thermal cameras in the first instance. An open-source model will be adopted to acquire the sensor data, process, overlay and analyse the thermal content. Investigations will also take place on the possibility of triggering the image acquisition - this could involve a reduced definition live feed to the drone controller. In the first stage, offline analysis techniques can be used but a major focus of the platform development is to make it capable of automatically analysing and detecting relevant content in the acquired images. The automation of these techniques are innovative and have the potential to be transformative. The project is divided in the following stages: Stage 1. Procurement stage: Sensors suitability evaluated and weight/power requirements considered in the creation of a platform rig which can be attached to and powered by the SME (Eagle iSystems) drone. Stage 2. Sensor testing: Laboratory testing of sensor system as a whole. Integration at hardware level of visual and thermal imaging, and development of an analysis algorithm which can run in real-time on the drone hardware platform. A storage solution to keep relevant data and archive it so that it is easily identifiable and searchable after an inspection run will be developed. Stage 3. Evaluation of drone upgrade: Set up of power connections, design or procure an enclosure for the sensors, field tests. Stage 4. De-energised field trial: Test upgraded UAV on a SP Transmission approved transmission tower site, particularly to evaluate the performance in observing difficult-to-access components like insulators and feed-throughs. Stage 5. Energised field trial: Test upgraded UAV at PNDC overhead line, particularly to identify potential issues when operating UAVs near high voltage lines. Stage 6. Evaluation of the information captured by the drone: Comparison of acquired data against information captured by current inspections at SP Energy Networks. Identification of other innovative sensors to be incorporated at a later stage. Stage 7. Project Report: Detailed project findings delivered and suggestions for future phases. Stage 8. Project Dissemination MeetingNote : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 17/12/18