Today, a big number of businesses have most of their value tied to the data they possess instead of physical assets like premises. That creates a need to preserve the data from unauthorized access or a loss. Actually, data protection is nothing new but what has changed the most during the last decade are potential consequences of a single incident, especially regarding sensitive and often personal data. If realized, this can dramatically affect your business for years or even worse, destroy the business completely. All of this only for often poorly designed data protection.
You can drive any car but wouldn’t it be nice to drive a Ferrari? In information systems, data migration makes it possible to transform any car into a Ferrari.
The exterior of a car can be changed and it can look like a Ferrari but from the inside, it would be the same old car. It would be much nicer to drive if the car would be the Ferrari also from the inside but that would require every car part to be transformed to the Ferrari form. In the end, even a Lada can be transformed to a Ferrari - both from the outside and inside. After the transformation, you can fine-tune your driving skills and improve the performance of the Ferrari.
Typically, utilities have several different IT systems for network asset data management. Modular solutions allow rapid implementation using basic datasets of network asset data, offering a wide variety of technical integration possibilities for a fluent flow between utility business processes and systems.
During decades of deliveries I’ve participated in, several different systems have been integrated with Trimble Energy solutions. Technically, various technologies can, and have been used, and integrations have been implemented either as point to point or using the utility’s different integration platforms. For example, integrating the intelligent network model with CIS to transfer customer data, MDM to synchronize meter data and alarms, or SCADA to transfer events from the network, providing real time data of customers, meters and events, as well as mobile solutions and utility outage maps. Additionally, the same data is used for an external communication gateway to provide real time data for end customers, web portals, or external services.
Although the key process of identifying grow-in and fall-in hazards to both distribution and transmission lines are similar, the regulations, environments and client priorities will differ significantly between the two.
For example, transmission lines in the US are often able to maintain large clear cut corridors so the numbers of vegetation grow-in hazards will be very small. For distribution lines, which are much more prevalent, it is impractical to hope to maintain a clear cut and so the number of grow-in and fall-in hazards are more significant. Because of this, different kinds of analyses that assess the volume and likelihood of areas of vegetation hazards are valuable to aid in work management prioritisation.
From the very beginning, the core of Trimble's solution for managing electrical distribution networks has been about combining asset data with geospatial information and electro-technical data. Knowing what your assets are, where they’re located and how they connect to each other is more than just documenting your network, it is about composing a model out of the network. And on top of that model, you have an ever expanding set of features from searching and analyzing to planning and running your network operations.
When it comes to the work of the maintenance foreman, there is one very limited resource: time. We all probably recognize the maintenance foreman: the guy sitting at the office with his phone ringing every ten minutes with linemen calling for instructions, addresses, tools and project numbers. At the same time, he should also be planning and preparing for the work of the linemen for coming days and assigning reactive maintenance tasks to available resources. Don’t get me wrong, all these tasks are needed to keep the process ongoing. Or are they?
RNA (Reliability Network Analysis) enables quantifying various reliability indices (e.g., customer interruption cost, SAIDI, SAIFI) for a given distribution network area. The evaluation is based on the componentwise failure impact and failure probabilities applied over the whole network. Network components in this context are, e.g., lines, disconnectors and distribution substations. Any component that can fail can potentially cause supply interruptions for the customers in the network; however, the likelihood and severity of these contributions strongly depend on where and which type of component is in question. For example, a failure on a trunk line of a radial feeder near to a feeding substation will likely contribute more customer interruptions than a failure of a line at some peripheral part of the feeder. This all, of course, is affected by the topological details of the network in question; whether there are adequate reserve connections available, how fast the fault can be located and isolated using the switches with different operating times (manual, remote of recolser), standby generators, etc.
In-depth, topical writings on utility software solutions written by various experts.