As part of developing and implementing an integrated infrastructure asset management business plan for eThekwini Municipality in eastern South Africa, eThekwini Electricity embarked on an initiative to capture and model all of its electrical network assets.
This article explores the various components of this exercise with emphasis on the solutions deployed to address project challenges from both the municipality and the service provider’s perspectives. It also discusses the resulting benefits unlocked during and following project execution with improved detailed data availability and is how this project contributes to the smart grid initiatives of the municipality.
The primary objective was to comply with the South African Accounting Standards Board - Generally Recognised Accounting Practice - Section 17 (Property Plant and Equipment) (known as GRAP17) and to enable the municipality to effectively manage its assets by providing a solid foundation of reliable and detailed asset information. Secondary objectives included establishing a connected network model and enabling data integration between systems. eThekwini Electricity engaged the services of Aurecon to assist with the data collection and modelling exercise.
The project team had to find innovative solutions to a number of issues inherent to an exercise of this nature, including:
Conventional field capture projects focus on capturing attribute data while in the field. With the low cost of high definition cameras and the low cost of storage, this changes the data capture landscape.
The project team took a different approach that proved to reduce cost and enhance quality by moving most of the attribute capture work to office teams supervised by technical specialists.
High resolution aerial photography also proved to be highly effective as many asset points could be identified on the aerial photographs assisting in verifying positional accuracy of field data recorded, as well as identifying missed assets.
A network enabled Geographical Information System (GIS) environment was used to ensure data quality and to enable future benefit to eThekwini Electricity.
Field data capture exercises of a technical nature often contend with the following major challenges:
On this project, field work was limited to recording a GPS position, capturing detailed photographs of the assets and recording minimum attribute data. This resulted in significant time and cost savings as well as improved data quality.
The project team took a different approach whereby office modelling relied on domain specialists, a well-structured data capture process and state-of-the-art information systems in support of a largely unqualified, data capture and modelling team. The engineers and supervisors initially trained new data capturers and thereafter assisted with ad hoc queries and interpretation of data where the data capturers did not have the know-how.
Data modelling was carried out in a Geographical Network Information System (GNIS) supporting real connected network modelling. This greatly enhanced modelling quality and added value to the deliverable as a result of functionalities enabled from connectivity. Most municipalities in South Africa maintain a GIS model which simply represents equipment location but does not include actual connectivity that supports advanced functionality including capturing and managing a Property/Customer Network Link (PNL), which is of utmost importance for network operations as well as for smart grid planning and implementation projects. The model implemented allows full connectivity from the customer to any upstream device though network topology.
The GNIS environment allowed for development of automatic placement routines for template equipment which generated the relevant equipment at the GPS location. As an example a miniature substation shown in Figure 1 and Figure 2 consists of MV breakers, LV fuses, a busbar, transformer and container. Using the office captured attributes and the GPS position a complete and specific equipment model is automatically generated in the GIS. This reduced the modelling component to mainly establishing connectivity by connecting networks between equipment.
Figure 2: Minisubstation connected network model, allowing for 11kV cable connections to 11kV ring main unit (left), transformation from 11kV to 400V (upper right) and connection to LV cable circuits at LV fuses (right).
Network modelling includes rule sets with regards to connectivity enabling the utility to trace/follow networks from supply source to client connection adhering to actual network connectivity behaviour. This approach to modelling holds many benefits to the utility – it underlies all network planning activities and is essential for network operations.
As such, GNIS is the application of choice for networked utilities, including electricity, water, gas and telecommunications worldwide. GNIS software presents a realistic view of the network in terms of geographical location, how equipment connects to each other and supporting technical data for engineering analysis.
As a result of GNIS modelling, the project not only recorded asset data, but also delivered logical information regarding the assets, including:
Connected network modelling is still a relatively new concept for many local municipal GIS departments in South Africa who do not model connectivity.
Proper asset management can only be implemented with good data about those assets, including its location, technical attributes, dynamic attributes and logical attributes. In electrical utilities this is a big dataset to extend and keep updated with asset counts that could easily run into the millions. It is impossible to stay in control of your asset data without proper business processes supported by relevant information systems influencing asset data.
GNIS information is helpful in providing decision makers strategic information about the network equipment for example:
When analysing the functionality and requirements that utilities place on smart grid implementation, it is clear that the technology is more about managing assets and information about assets rather than protecting revenue or identifying illegal connections.
The various perspectives and requirements from smart grid implementations for the generation, transmission, distribution and customer sector point of view, relies heavily on accurate and readily available information about the customers, plant, network connections, energy sources and sinks, markets, real-time tariffs, network status, consumption, incidents, smart device location and “area of influence” and more.
To enable the above inter-operability, sufficient emphasis is needed to be placed on the requirement of network information availability and how the network is connected or related to the various devices in the field.
As a result of this project, eThekwini Electricity now has the ability to perform connected information analysis and unlock full smart grid requirements. Further enhancements can be implemented to ensure communication connectivity is also achieved.
Due to various reasons very few entities have all of their asset data under control, especially at the lower voltage levels. This can be corrected by:
Asset management is far greater than simply being compliant with guidelines such as GRAP17 or IFRS. It gives utilities the opportunity to really understand, optimally plan, effectively manage, operate and maintain their assets. Major investment is often made to ensure reporting on assets is in compliance with regulatory requirements, and utilities often miss opportunities to really address proper asset management, through projects and funding available. eThekwini Electricity realised the effort involved in enabling proper asset management, and will in future reap the benefits of investing in data capturing, implementing processes, systems and continuous training ensuring that through the unequivocal support of management the journey towards excellence in asset management is successful.
An enterprise asset management system is not just a single piece of software, but rather a system consisting of multiple parts; being processes, resources and various technical and non-technical software applications working in unity to realise asset management. There is a significant dependence on supporting systems to keep asset data up to date. Any entity serious about asset management should work through an exercise to define what asset data they need for proper asset management. This should be based on a holistic view of the assets from where a data attribute ownership mapping can be done which defines data ownership and responsibility for the various information systems.