JCMB Technology Inc.

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Smart Data, Smart Grid.

Even if you could replace every experienced electric operator by a set of intelligent switches tomorrow, what will those switches do without an accurate knowledge of the network that is to be switched? An experienced operator can make sense of network model and data errors and make appropriate decisions in times of need. Intelligent devices however do not have this ability, they are quite inflexible and fault intolerant with regards to the data they use to make decisions.

We learned from our past implementations of distribution information systems such as GIS and OMS that good and accurate data is the foundation upon which successful systems are built. Data is a deceptively simple word but it can hide many complex concepts:

  • Knowing what information is available
  • Knowing what is missing
  • Understanding the structure in which the information is kept
  • Understanding the relationships that exist between those data objects
  • And finally, the integrity of it all

JCMB has created a structured method for gathering this information, the DQAP process. DQAP stands for Data Quality Assessment Process.

All along the DQAP process, the results of the analysis is documented in the DCSM. DCSM stands for Data Certification Standard Manual. This manual is the specification that is prepared in light of the analysis of the first and second phases of the DQAP. It contains the specifications for the data correction process needed to bridge the gap between the actual state of the data discovered herein and the requirements of the new system(s) being implemented.

The Fusion environment also provides the opportunity of visualizing the data in new and meaningful ways for detecting possible use problems.

JCMB has created the Dataport technology to facilitate the format conversion of utility data for import or export. This technology has a rule base engine that runs ‘Data Enhancement Rules’ in the Fusion jargon. The ability to create translation, transformation and enhancement rules in this engine without having to program a new interface is the reason for its high efficiency.

JCMB can often leverage an already made rule set when importing data from often encountered source such as Smallworld, ESRI, Oracle and others. JCMB is constantly adding base rule sets to its Dataport technology. When starting from one of those off the shelf base rule set the configuration of the import translation can be done quickly.

Standardizing data at this level enables JCMB to use off the shelf algorithms for visualization, analysis and validation. It also provide our technician with a constant user interface for applying edits and additions. In fact, creating the initial Dataport translation rule sets provide actual labor but mostly large time saving when considering the benefits of holding the data set in our universal engine.

  • Rich tool set designed especially for the certification of utility data sets
  • Off the shelf analysis algorithms
  • Off the shelf validation
  • Off the shelf editor
  • No need to train analysis and correction staff
  • Minimal custom code, stable certification code base

Taking data into Fusion leverages a well defined, stable and constant certification process that relies on repeatable technologies.

Example of validations performed in a typical DQAP process:

Electrical: Phase, De-energized, Meshed, Meter-Transformer, Connection rules, Propagation rules
Data: Mandatory attributes, Normalized values, Data integrity , Cross validation, Inter-system conflicts
Spatial: Equipment location, Landbase reference, Proximity rules, Annotation, Overstri Visualization, Node reduction
Model: Incompatibilities, Limitations

This structured method for dealing with large data pools is an essential part of planning any new distribution system, especially in the context of a smart grid initiative. It can be used to assess the effort in bringing data to the requirements of the new smarter grid as well as provide an accurate measure of quality in every day data maintenance operations.

Smart Data Smart Grid whitepaper

Data Quality vs. Data Integrity

 
At JCMB, we differentiate Data Quality and Data Integrity:
 
Data Quality is ensuring a high-fidelity reproduction of source Data that adheres to a clearly defined corrective action plan for error/omission resolution. The deliverable is subject to an independent QA/QC test and validation with respect to mutually agreed standards and procedures with the customer.

The result is a sound rendition of the Distribution Network with:
  • A Data Model that supports full connectivity
  • Accurate complex device behavior and manipulation
  • Consistent attribution
  • Standardized symbology
  • A certified product that conforms to the specification          
Data Integrity goes beyond Data Quality. It is an on-going business practice that aims to achieve Data symmetry with the real-time state of the Distribution Network.

Idaho Power: power company develops and sustains quality data
 

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