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PD IEC TR 62059 — Electricity Metering Equipment Dependability Technical Report Series (General Concepts and Field Data Collection)
Part 11: General concepts; Part 21: Collection of meter dependability data from the field
📈 PD IEC TR 62059 is the UK adoption of technical reports from the IEC TR 62059 series, with UK responsibility held by Committee PEL/13 (Electricity meters). The series addresses dependability of electricity metering equipment. This batch contains two documents: Part 11 explains the general concepts of dependability management, and Part 21 specifies the collection of meter dependability data from the field. Electricity meters operate for long periods at customer premises, and their dependability directly affects measurement accuracy, billing fairness and grid operating efficiency. A unified dependability concept and data-collection methodology is essential for manufacturers, utilities and regulators alike.
💡 “Dependability” of metering equipment is broader than “quality”: it includes the reliability of remaining fault-free over the life cycle, the availability of operating correctly when needed, the maintainability of rapid recovery after failure, and the management system and methods that unify these elements.
1. Scope and Series Structure 📌
This series belongs to the IEC TR 62059 family (electricity metering equipment — dependability), organised along “concepts — methods — applications”. This batch contains two documents:
| Document | Subject | Content | Year |
|---|---|---|---|
| PD IEC TR 62059-11:2002 | General concepts | Electricity metering equipment — Dependability — General concepts | 2002 |
| PD IEC TR 62059-21:2002 | Field data collection | Electricity metering equipment — Dependability — Collection of meter dependability data from the field | 2002 |
⚠️ Technical reports (TR) are informative, guiding documents that do not specify mandatory clauses. Dependability management is typically implemented together with statistical method standards (e.g. IEC 61649 Weibull analysis, IEC 60605 reliability testing) and quality management systems (ISO 9001).
2. Part 11 — General Concepts of Dependability Management 🧠
Part 11 (2002) explains the basic concepts and methods of dependability management for electricity metering equipment. Core content includes:
- Relationship between reliability, maintainability and dependability: defining reliability, availability and maintainability and their interrelationships, and explaining that “dependability management” is the coordinated management of the three;
- Reliability data collection and analysis: sources, collection principles and analysis methods (life distributions, failure rates, MTBF) for reliability data;
- The role of stakeholders: analysis of the responsibilities and collaboration of suppliers/manufacturers, utilities/energy providers/meter operators, and electricity users in dependability management;
- Dependability management methods: management activities and interfaces across design, production, installation, operation and maintenance.
💡 The three dimensions of dependability are inseparable: reliability (probability of not failing) determines “does it fail”, availability (fraction of time working) determines “how long it works”, and maintainability (speed of recovery after failure) determines “how fast it is repaired”. Metering management should optimise all three rather than a single indicator.
3. Part 21 — Collection of Meter Dependability Data from the Field 📊
Part 21 (2002) specifies a unified method for collecting dependability data from meters in the field. Core content includes:
- Classification of metering equipment by complexity: classification by functional complexity to set appropriate data-collection and management requirements per class;
- Operating conditions: recording the environmental and electrical conditions under which the equipment operates, providing background for failure-rate assessment;
- Field report: standardising the recorded content and format of field failure reports, including device identification, failure phenomenon, time and field information;
- Classification of failures: classification by nature (critical, non-critical) and cause (design, manufacture, use, environment) with a common terminology for statistics;
- Root cause of the failure: tracing and analysing the fundamental cause, providing the basis for improvement;
- Criticality of failures: assessing the impact of failures on metering, billing and grid operation;
- Data collection through sampling plans: representative data collection through statistical sampling plans, balancing cost and statistical confidence.
🚨 Field dependability data is the “gold mine of improvement”: if failure reports are inconsistent, root causes unclear, or sampling unrepresentative, the estimated failure rates and life will be distorted. Unified classification, standardised reporting and scientific sampling are the prerequisites for data usability.
4. Engineering and Application Practice 🛠️
- 🔍 Unified report template: deploy a standardised (electronic) field failure report form with fields for device identification, operating conditions, failure classification, root cause and field handling, ensuring data consistency.
- 📊 Data analysis: analyse batch failure-rate curves (bathtub curve) and abnormal batches using life distributions (e.g. Weibull analysis) and failure-rate estimation methods.
- 🎯 Sampling design: design field data collection per the Part 21 sampling plans, balancing representativeness and cost; increase sampling intensity for high-risk equipment.
- 🤝 Multi-party collaboration: establish data-sharing and feedback mechanisms among manufacturers, utilities and meter operators, closing the loop from field data back to design improvement and preventive maintenance.
5. Frequently Asked Questions ❓
❓ Q: How do Parts 11 and 21 relate?
A: Part 11 provides the conceptual framework of dependability management (relationships among reliability/availability/maintainability and stakeholder responsibilities); Part 21 provides the concrete methods of field data collection (classification, reporting, failure classification, sampling). They form a “concepts—methods” pair.
A: Part 11 provides the conceptual framework of dependability management (relationships among reliability/availability/maintainability and stakeholder responsibilities); Part 21 provides the concrete methods of field data collection (classification, reporting, failure classification, sampling). They form a “concepts—methods” pair.
❓ Q: What is the difference between reliability and availability?
A: Reliability is the probability that equipment does not fail within a specified time under specified conditions; availability is the fraction of time the equipment is in a working state when needed, combining reliability (failure rate) and maintainability (repair time).
A: Reliability is the probability that equipment does not fail within a specified time under specified conditions; availability is the fraction of time the equipment is in a working state when needed, combining reliability (failure rate) and maintainability (repair time).
❓ Q: Why do field data need a sampling plan?
A: Collecting long-term data from all devices is costly and impractical. A scientific sampling plan obtains statistically representative failure-rate and life estimates from fewer samples, balancing cost and confidence.
A: Collecting long-term data from all devices is costly and impractical. A scientific sampling plan obtains statistically representative failure-rate and life estimates from fewer samples, balancing cost and confidence.
🔍 Q: How are “critical” and “non-critical” failures defined in failure classification?
A: Typically by impact on metering function and safety: a critical failure causes metering failure or endangers safety, while a non-critical failure only degrades performance or causes a recoverable anomaly. The specific classification should be uniformly defined considering equipment function and billing impact.
A: Typically by impact on metering function and safety: a critical failure causes metering failure or endangers safety, while a non-critical failure only degrades performance or causes a recoverable anomaly. The specific classification should be uniformly defined considering equipment function and billing impact.