In the Utilities industry and outage management information systems, we generally divide outages into two areas: planned and unplanned outage management. This article will relate to both and explore how Outage Management Systems (OMS) empower utilities with smarter, faster decisions. Whether managing planned or unplanned outages, a CIM-based OMS increases efficiency through real-time data, seamless integration with SCADA, GIS, CRM, and more. Learn what features matter most and how to choose or optimize an OMS for your utility operations.
Outage Management Systems Requirements
Outage Management Systems are extensive, require lots of experience, and knowledge from the end user. How can an end-user really benefit from the outage management system implemented in the company? How should one setup the OMS system, and to which 3rd party systems should you integrate? What should be the most tangible benefits from the system, that a utilities-based company should look for? What are the technologies that we should consider as supported?
CIM Based Outage Management
Surely having a CIM based Common Information Model (CIM) Outage Management System plays a crucial role by providing a standardized framework for interacting and managing network connectivity and asset management data.
CIM also helps in network topology management, translating it into asset management hierarchies and optimizing planning for asset-related programs. It also enhances visualization and analysis through geo maps and schemes, improving outage information management.
Integration to Other Information Systems
An Outage Management System (OMS) is a specialized software solution used by operators of electric distribution systems to efficiently detect, manage, and restore power outages. An Outage Management System (OMS) should offer native CIM-based integration options with 3rd party systems such as:
- Enterprise Asset Management (EAM) systems,
- Supervisory control and data acquisition (SCADA) systems,
- Geographic information systems (GIS),
- Customer information systems (CRM),
- Lightning and weather data,
- Asset Investment Planning Systems for project planning,
- Mobile solutions.
Only fully integrated Outage Management System can fulfil key functionalities and show benefits such as outage detection, fault location identification, restoration prioritization, workforce management, and overall risk reduction.
Workforce Management
An OMS information system should fully support organizational business processes, especially the outage management process framework (Planned and Unplanned Outage Management, Switching Order Management, …).
OMS solutions should be designed for different power utility sector users responsible for maintenance, operations, and management. Users should dynamically plan and assign work based on resource availability. The system should auto-schedule outages by considering request details while geo-visualizing scheduled and ongoing tasks through dashboards, offering a clear view of related activities on a map.
OMS and the role of AI technology
What did AI technology changes bring in the last years, and what should solution providers and utilities search for in their landscape? How should we use available data and new technologies within outage management solutions?
In a recent paper, »Big Data Analytics for Predictive Lightning Outage Management Using Spatially Aware Logistic Regression, « authors (*1) investigated whether big data may be used to predict lightning outages.
Researchers used a comprehensive database that contains historical lightning-related events to train the prediction model. The study also used other data sources, such as Geographical Information System (GIS) data about utility assets (locations of substations, transmission lines, transmission towers, and substation transformers), utility historical outage records, etc.
They used a mixture-of-experts model incorporating multiple spatially aware logistic regression models to predict lightning strikes to the transmission lines one to three hours in advance. Different durations of lightning-related failures, including both temporary and permanent faults, were considered.
The results demonstrate the capability of the algorithm to predict lightning outage probability with high accuracy for a specific location. When tested on real utility data, applying a spatially aware logistic regression model yielded a prediction accuracy ranging from ~0.86 to ~0.94, with the Area Under the Curve being larger than 0.75 for all cases with prediction using spatial embedding.
Study results demonstrate that the model can isolate the area that will be affected by lightning with very high probability.
OMS Systems – Conclusion
When planning for an Outage Management System, put your priorities into already mentioned functionalities:
- CIM based OMS system,
- Integrated workforce management capabilities,
- Flexible integration options to 3rd party systems via API’s etc.,
- Secure and modern microservices built solution,
- Niche focused solution with extensive industry library support and experience,
- Solutions that utilize quality data models and the latest available technologies on the market.
(*1) M. KEZUNOVIC, T. DOKIC, Z. OBRADOVIC, M. PAVLOVSKI and R. SAID, »Big Data Analytics for Predictive Lightning Outage Management Using Spatially Aware Logistic Regression«, CIGRE 2020.