Epri Smart Distribution and PQ 2012-06-06
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Epri Smart Distribution and PQ 2012-06-06

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  • Data has always been boringUnfortunately for many utilities, data is not considered glamorous, and has been neglected for many yearsIn the worst case, data is so bad, a field survey is warranted.This presentation talks about data quality as it relates to asset managementWith the smart grid there is a proliferation of automation on the feeder: this has led to a resurgence in asset management
  • Introduction – presents the goals and context of the paperAsset Management – discusses a common view of asset managementData Quality – offers an overview of data quality in generalGIS Data – discusses GIS data requirementsOMS Data – discusses OMS data requirementsCMMS Data – discusses CMMS data requirementsConclusion – presents concluding remarks.
  • Let’s pause and take a look at these quotes. Most utilities tell me that they’ve heard these before, ie did you get them from my employees?Ironically, while most utilities acknowledge that their data isn’t particularly good, they typically are very hesitant to invest any time or resources to improving it.
  • These are the systems of the smart grid.When we look at them, none of them are actually new to the utility industry. What’s different about today is that there are more sensors and control in the field, and more intelligent devices at the level of the customer.The volume of data required to make decisions exceeds the ability of any human. Analytics are the key to successful utility operations today. Algorithms are only as good as the data supplied to them.In this diagram, it becomes really apparent that the GIS is one of the key data hubs for all the systems of the smart grid. Perhaps most importantly, it is the owner and master data repository for all the other systems of the smart grid.
  • While all systems are critical, this diagram highlights how important the GIS is as a purveyor of data. We’re specifically going to talk about GIS as it relates to key asset management systems, DMS / OMS, and CMMS.
  • Asset management is the art and science of balancing the following goals.Balancing these goals is not easy. There are many conflicting priorities, needs, and most importantly political considerations.Many utilities have relied on the intuition of key resources to perform this balancing, more art than science.Moving forward, the experience of these key individuals may not be as effective as previously. In the Smart Grid era, we don’t necessarily know how equipment will age and fail, and we need to make asset management more of a science.Without quality GIS data, this is an unrealistic goal.
  • We often talk about “Bad” data. This is what “Bad” data really includes.Simply stated, if a utility cannot identify the location of their assets, asset management is an unrealistic goal.
  • Data issues are created upon initial data load, and over the course of data maintenance.Migration is often an opportunity to perform data cleansing.Industry expectation is that a conversion or migration is 98% accurate for visual validation, and 100% for automated checks, such as connectivity.It’s really, really, really hard to maintain data at that level of quality. Quite frankly most utilities will drop to 80 to 85% accuracy if they’re honest with themselves.
  • Often the GIS is used as a “garbage can” for data. This is not a sustainable situation. This diagram identifies the data that should reside within the GIS.Overloading the GIS with extraneous data is truly not a good idea: data maintenance processes cannot be optimized for GIS if non-standard data is resident.It is important to note that the GIS is the only system that considers not only the individual asset, as well, the aggregate of the assets in a system. As we all know, the key to asset management is criticality, and criticality is determined by the location of the asset with respect to customer load and the connectivity model.
  • Gaps, e.g. certain key data is missingRedundancies with other systems, e.g. data is captured in many systems and it is inconsistent or requires duplicate data entry to updateWorkflows pertaining to new construction and maintenanceLack of currency with system “as-built”, e.g. untimely work order completion / backlogInaccuracies with the field, e.g. GIS has data but does not represent the actual system in the fieldInaccurate or unavailable land-base, e.g. varying degrees of accuracy of land-base data based on the sourceCustomer to transformer connectivity by phase is in doubtGIS model itself allows for “bad” dataData dependencies and the “ripple effect” of bad GIS dataWhat is included in the GIS, level of detail, best practicesThe use of GIS to track communications infrastructure
  • Currently, identifying customer phase is a manual processDuring repairs, re-connections may be made hastilyIndividual customers, single phase lateralswe’ve had good luck with transformer identification, identifying transformers missing from models (step-down banks and service transformers), and errors where meters were on different
  • The taps are color coded by phase. Points are meters. After running a phase ID algorithm, any meters that show a mismatch are marked with a symbol of the phase color predicted by the phase ID algorithm. The utility field checked the circuit and confirmed the major tap mismatches. Overall, based on three circuits that have been field checked, the best algorithms have 95 to 99% accuracy.
  • Improved safety due to more accurate facilities records;Reduction in the overall cost of maintaining the GIS system as a whole;Efficiencies in implementing and troubleshooting AMI communications issues;Improved OMS and DMS benefit, e.g. reduced outage duration;Improved crew efficiencies due to improved distribution system representation;Improved load forecasting;More accurate system planning; andReduced work order cycle times.
  • When we look at the data required of a DMS/ OMS, it is impossible not to notice the dependencies on GIS data. The DMS/OMS is a critical system because it provides operational and failure data for asset management analytics. Simply stated, bad GIS data, bad DMS/OMS data, leading to bad asset management decisions.
  • This list provides insight into the timeliness of acquisition of DMS/OMS data. A big problem that the industry is still tackling is the ability to understand the historical configuration of the system. This becomes a serious issue as more and more with self-healing and volt-var control the utility is running the distribution in a non-standard configuration (ie not as built as captured in the GIS). Being able to go back in time and determine the system configuration is of paramount importance.
  • Work orders: Scheduling jobs, assigning personnel, reserving materials, recording costs, and tracking relevant information such as the cause of the problem (if any), downtime involved (if any), and recommendations for future action Preventive maintenance (PM): Keeping track of PM inspections and jobs, including step-by-step instructions or check-lists, lists of materials required, and other pertinent details. Typically, the CMMS schedules PM jobs automatically based on schedules and/or equipment counter readings. Different software packages use different techniques for reporting when inspection and maintenance tasks should be performed. Asset management: Recording data about equipment and property including specifications, warranty information, service contracts, spare parts, purchase date, expected lifetime, and anything else that might be of help to management or maintenance workers. The CMMS may also generate asset management metrics such as the Facility Condition Index, or FCI. Inventory control: By means of interfacing with the utility materials management system (MMS), the CMMS coordinate the management of spare parts, tools, and other materials including the reservation of materials for particular jobs, recording where materials are stored, determining when more materials should be purchased, tracking shipment receipts, and taking inventory. Safety: Management of permits and other documentation required for the processing of safety requirements. These safety requirements can include lockout-tagout, confined space, operations safety, and others. CMMS packages can produce status reports and documents giving details or summaries of maintenance activities. The more sophisticated the package, the more analytics are available.
  • An Asset Management strategy can be an effective component in the overall corporate strategy of a T&D utility. Setting realistic objectives that demonstrate the benefits of the Asset Management program can ensure continued executive support. Leveraging incremental success will build both momentum and a better understanding of how the program should be tuned to meet the utility’s specific goals. Asset Management style and approach will change based on changing customer, regulatory, economic, and political factors. As a utility gains a better quantitative understanding of its Asset Management strategy, it will be more capable of reacting to the changing environment and context. In the Smart Grid paradigm, Asset Management becomes a requisite to success. It is important that utilities recognize that implementation of an Asset Management strategy does not resolve process, standards, or people issues. Successful implementation of an Asset Management strategy requires process, discipline, and a culture that promotes and empowers employees to treat assets as though they were their own. Employee buy-in is crucial to an Asset Management program as the T&D processes and data rely on human intervention and action. The principle success factor for any asset management program is however data. This paper identified critical data dependencies in the GIS, OMS, and CMMS applications: key asset management systems for the distribution utility.

Epri Smart Distribution and PQ 2012-06-06 Epri Smart Distribution and PQ 2012-06-06 Presentation Transcript

  • 1How Important is GIS Data Quality to the Modern Grid? Robert J. Sarfi, Michael K. Tao, J. Baker Lyon Boreas Group John J. Simmins EPRI 2012 PQSD Conference – San Antonio June 6, 2012
  • Contents 1. Introduction 2. Asset Management 3. Data Quality 4. GIS Data 5. DMS / OMS Data 6. CMMS Data 7. ConclusionEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 2
  • 1. Introduction “We react inconsistently “We react slowly to to information shifting work requests.” volumes due to “We execute “We have costly manual resource simple business and inconsistent allocation tasks with high asset processes.” skill and high management cost resources.” processes.” “Process automation is “We have minimal limited by our “Process ability to accurately incomplete and standardization is and quickly inaccurate limited by vertically measure our operational data.” business integrated systems.” performance.”EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 3
  • 2. Smart Grid Systems Distribution Demand Automation Response Asset Management AMI Mobile SCADA Materials Maint. Engineering GIS Mgt. Mgt. Analysis Graphic GIS Design Mapping Work DMS / OMS Mgt. Executive IVR SCADA GIS MDMS CMMS Information ManagementOperations System Central Databases BI / Analytics Work Optimization CIS CRM Mobile Workforce Mobile GIS Customer Customer Management Empowerment EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 4
  • 2. How Data Enables Workflows Service Restoration Work Order Drafting & Design OMS Network Analysis Planning & Engineering CMMS GIS AMI AMI Home WMS MDMS (MDM) Automation Maintenance & and Demand Construction Response DMS Schedule and Dispatch Wireless Mobile Distribution Enablement AutomationEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 5
  • 6 2. Smart Distribution - Operational Operatives • Reliability • Resource optimization • Demand / load optimization • Power quality optimization • Customer service • Equipment lifecycle cost of ownership • Equipment criticality • Staff utilization/skill set • Capital expenditures • Operations and maintenance expenditures.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 6
  • 3. Common Data Quality Issues • Corrupted (time sensitive, inaccurate) • Redundant data (unplanned) • Inability to correlate/cross- reference • Gaps • Lack of knowledge of available data • Access/securityEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 7
  • 3. Causes of Data Issues Initial Data Quality • Poor quality source data • Incomplete data migration and conversion from paper maps, asset ledgers and field data collection. Data Repositories Data Maintenance • Ambiguous definition of data ownership and access rights • Poor data quality control processes / practices • Deferred data update and maintenance.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 8
  • 4. Spatial (GIS) Data Registry Asset •Identification Customer •Attributes •Service Address •Connectivity •Service Facilities •Location H1 – Asset History C1 – Customer Information N1 – System Data Environmental •Landbase •Rights and Access •Administrative L1 – LandbaseEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 9
  • 4. GIS Data Issues • Gaps • Redundancies with other systems • Workflows pertaining to new construction and maintenance • Lack of currency with system “as-built • Inaccuracies with the field • Inaccurate or unavailable land-base • Customer to transformer connectivity by phase is in doubt • GIS model itself allows for “bad” data • Data dependencies and the “ripple effect” of bad GIS data • What is included in the GIS, level of detail, best practicesEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 10
  • 4. Automating Phase Identification SCADA AMI Voltage Voltage Data Data Correlating Voltage Customer Phase IDEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 11
  • 4. Phase Identification ExampleEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 12
  • 4. Benefit: Operational Efficiencies • Improved safety due to more accurate facilities records • Reduction in the overall cost of maintaining the GIS system as a whole • Efficiencies in implementing and troubleshooting AMI communications issues • Improved OMS and DMS benefit • Improved crew efficiencies due to improved distribution system representation • Improved load forecasting • More accurate system planning • Reduced work order cycle times.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 13
  • 5. DMS / OMS Data Facility •Identification Customer •Type •Service Address •Connectivity •Service Facilities •Location T1 – Technical C1 – Customer Information N1 – Network System Environmental Administrative •Landbase •Operations •Rights and •Procedures Access •Safety •Administrative L1 – Landbase A1 – AdministrativeEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 14
  • 5. DMS/OMS Data Attributes Type Network facility characteristics Non Real Time As designed network facility connectivity Non Real Time As operated network facility connectivity Real Time Line switch status Real Time Line protection devices (circuit switchers, Real Time reclosers, etc) Substation circuit breaker status Real Time Network status Real Time Name and service information Real Time and Non Real Time Outage call information Real Time Automated call information Real Time Service point (Smart Meter) status Real Time Landbase data Non Real Time Operating standards and policies Non Real TimeEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 15
  • 6. CMMS Data • Work orders • Preventive maintenance (PM) • Asset management • Inventory control • Safety CMMS packages can produce status reports and documents giving details or summaries of maintenance activities. The more sophisticated the package, the more analytics are available.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 16
  • 7. EPRI GIS Data Quality Survey – Phase 1 Preliminary Findings • Thirteen utilities participated in the survey. • Outage management and engineering analysis are the most common uses of GIS data. • Integration and dependencies vary widely. • No correlation between integration of the GIS and data quality. • User are generally confident in the data. • Utilities are doing a better job at „completeness‟ than „accuracy‟ of data. • Benefits of „good‟ data are seen, but repercussions of „bad‟ data are not.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 17
  • 7. EPRI GIS Data Quality Survey – Phase 1 Survey Result Statistics • Thirteen utilities participated in the survey. • 36% store all distribution data in GIS, but 66% make use of an asset management system. • 66% have unique asset IDs, only 27% physically tag the asset in the field. • 54% felt that data accuracy was 75-90% (64% user confidence in data). • 63% felt that data completeness was 75-90% (72% user confidence in data). • Only 9% of utilities have experienced a catastrophic problem due to data, but 56% have enjoyed a benefit of good data. • While 91% have programs to improve data, only 54% have dedicated staff. • 73% have automated quality assurance. • 91% have not seen quality deterioration over time.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 18
  • 7. Conclusions • Data is critical to T&D success • Leverage incremental success • Approach to data management will continue to evolve • Three things critical to success: 1. Data 2. Data 3. DataEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 19
  • Questions? We‟d really like your help with a data quality survey: http://www.surveymonkey.com/s/EPRI_GIS_Data_Quality_Project_1 Please complete the survey by Thursday, May 15.EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 20
  • 21 ContactsRobert J. Sarfi, Mike K. Tao, and J. Baker Lyon - Boreas Group LLC, Denver, CO 80209 (e-mail: rsarfi@boreasgroup.us, mtao@boreasgroup.us, blyon@boreasgroup.us )John J. Simmins - Electric Power Research Institute, Knoxville, TN 37932 USA (e-mail: jsimmins@epri.com ).EPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 21
  • Together…Shaping the Future of ElectricityEPRI Power Quality and Smart Distribution 2012 Conference and Exhibition© 2012 Electric Power Research Institute, Inc. All rights reserved. 22