Inteligent Monitoring of Distribution Automation

by S. E. Rudd, J. D. Kirkwood, E. M. Davidson, S. M. Strachan∗, V. M. Gutterson and S. D. J. McArthur, UK

Whilst the scope of definition for the ‘smart grid’ is wide and differs across territories, certain visions on  how our energy infrastructure is predicted to evolve are shared. It is envisaged that information and communications technologies will play a key role in the delivery of future networks. For the operation of future distribution networks, these visions translate into a number of changes from common practice:

  • An increasingly observable network - the proliferation of communications and monitoring equipment on distribution networks will result in greater observability at lower voltage levels
  • Bi-directional power flows, introduced through the connection of distributed energy resources - for networks originally designed for uni-directional power flows, this can lead to congestion and problems regulating voltage
  • Increased use or even reliance on distribution automation and active network management, as a means of providing reliable and cost effective supply of electricity
  • Controllable load through various demand-side management measures

 

 However, these changes to current practice are likely to result in a number of challenges for the utility personnel tasked with operating distribution networks. One such challenge is the increased volumes of data that such a highly monitored, active distribution system, with widespread use of automation, is likely to produce. Intelligent systems researchers within the power systems community have long understood the problems associated with deriving meaningful information from power systems data, especially under extreme conditions, such as during storms or other network events.

Over two decades of research has produced numerous expert systems, and model-based reasoning systems, for alarm processing for both transmission and distribution systems. However, the move to more observable distribution networks, which are active rather than passive, leads to a new set of challenges in understanding system behavior, health and performance of distribution automation and active network management schemes on a day-to-day basis.

Arguably, active network management is still in its infancy. Only a handful of schemes have seen deployment around the world, and utilities are still learning what the impact on the routine operation of the networks will be and what, from an operational perspective, the widespread roll-out of such schemes is likely to entail.
On the other hand, one area where much more experience has been gained is that of distribution automation. Regulatory pressure in the form of incentives relating to reliability of supply, e.g. customer minutes lost (CML) and customer interruption (CI) in the UK; and the customer average interruption duration index (CAIDI) in the USA, have resulted in utilities investing in distribution automation in a bid to increase their revenue.

Distribution automation to improve customer service, and in doing so meet or exceed regulatory targets, can take various forms: remote terminal unit (RTU) based schemes; automatic teleswitching schemes; and novel peer-to- peer communicating schemes, such as S&C Electric’s IntelliTeam2. However, regardless of the type of distribution automation used, understanding both the performance and health of such schemes is an operational requirement. In order to make a positive impact (and not a negative impact) on reliability of supply, distribution automation schemes must operate when needed. Identifying incipient failures, scheme performance issues or problems with equipment health before they result in failure of the scheme to operate when needed, is an important task: it ensures that schemes perform in such a way that justifies the investment in the first place.
This includes the health and performance of the communication systems on which they may rely. Often, such information is implicit in the power systems data that engineers use to make such assessments. Moreover, the volumes of data produced by a large number of schemes can make manual analysis of the data impractical. Symptoms of incipient failure may be seen over several hours, days or even weeks. Tracking such symptoms can be problematic.

This article discusses distribution automation in general and the requirement for automatic analysis of data relating to the health and performance of distribution automation schemes. A case study is included, which considers some of the data analysis problems seen by a UK utility after the widespread roll-out of a particular type of distribution automation scheme. This article outlines some of the decision support requirements from the perspective of the engineers tasked with maintaining and managing such schemes.
In terms of decision support technologies, the article examines the use of complex event processing and rule-based expert systems as a means of dealing with that data. Example rules for identifying a number of scheme health and performance issues, derived through knowledge elicitation, are presented. How these rules are used within a prototype alarm processing system, currently under development, is described. Future extensions to that prototype are also discussed.

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