Randomised Field Trial for the Reliability Assessment of Power-Distribution Infrastructure in Nigeria
Fuente:
Zenodo
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2009
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866901769264562176 |
|---|---|
| author | Suleiman, Amina Okonkwo, Chinedu |
| author_facet | Suleiman, Amina Okonkwo, Chinedu |
| contents | <p>{ "background": "The reliability of power-distribution infrastructure in many developing nations remains poorly quantified, with assessments often relying on aggregated, non-experimental data. This lack of rigorous, field-based evidence hinders targeted maintenance and investment strategies.", "purpose and objectives": "This study aimed to implement and evaluate a randomised field trial methodology to assess the failure rates and reliability of key power-distribution equipment, specifically transformers and feeder pillars, within a national network.", "methodology": "A stratified randomised sampling design was employed, selecting 450 units from operational regions across the country. Units were monitored for performance failures over a continuous operational period. Reliability was modelled using a Weibull survival analysis, with the hazard function given by $h(t) = \\frac{\\beta}{\\eta} \\left( \\frac{t}{\\eta} \\right)^{\\beta-1}$, where $\\eta$ is the scale parameter and $\\beta$ the shape parameter. Robust standard errors were calculated to account for regional clustering.", "findings": "The estimated shape parameter $\\beta$ was 1.85 (95% CI: 1.62 to 2.11), indicating an increasing failure rate over time. Transformers in coastal regions demonstrated a 34% higher hazard rate compared to those in inland areas. The median time to failure for feeder pillars was significantly lower than for transformers.", "conclusion": "The randomised trial successfully generated precise, field-derived reliability metrics, revealing distinct wear-out failure patterns and significant geographical disparities in equipment performance.", "recommendations": "Infrastructure renewal programmes should prioritise coastal networks and adopt condition-based maintenance informed by the derived Weibull parameters. The methodological framework should be institutionalised for ongoing asset management.", "key words": "reliability engineering, survival analysis, power distribution, field trial, infrastructure asset management, Weibull distribution", "contribution statement": "This paper provides the first application of a randomised field trial with survival analysis to generate empirical reliability functions for electrical</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18966425 |
| institution | Zenodo |
| language | eng |
| publishDate | 2009 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Randomised Field Trial for the Reliability Assessment of Power-Distribution Infrastructure in Nigeria Suleiman, Amina Okonkwo, Chinedu Power-distribution reliability Randomised field trial Sub-Saharan Africa Infrastructure assessment Nigeria Condition monitoring Outage analysis <p>{ "background": "The reliability of power-distribution infrastructure in many developing nations remains poorly quantified, with assessments often relying on aggregated, non-experimental data. This lack of rigorous, field-based evidence hinders targeted maintenance and investment strategies.", "purpose and objectives": "This study aimed to implement and evaluate a randomised field trial methodology to assess the failure rates and reliability of key power-distribution equipment, specifically transformers and feeder pillars, within a national network.", "methodology": "A stratified randomised sampling design was employed, selecting 450 units from operational regions across the country. Units were monitored for performance failures over a continuous operational period. Reliability was modelled using a Weibull survival analysis, with the hazard function given by $h(t) = \\frac{\\beta}{\\eta} \\left( \\frac{t}{\\eta} \\right)^{\\beta-1}$, where $\\eta$ is the scale parameter and $\\beta$ the shape parameter. Robust standard errors were calculated to account for regional clustering.", "findings": "The estimated shape parameter $\\beta$ was 1.85 (95% CI: 1.62 to 2.11), indicating an increasing failure rate over time. Transformers in coastal regions demonstrated a 34% higher hazard rate compared to those in inland areas. The median time to failure for feeder pillars was significantly lower than for transformers.", "conclusion": "The randomised trial successfully generated precise, field-derived reliability metrics, revealing distinct wear-out failure patterns and significant geographical disparities in equipment performance.", "recommendations": "Infrastructure renewal programmes should prioritise coastal networks and adopt condition-based maintenance informed by the derived Weibull parameters. The methodological framework should be institutionalised for ongoing asset management.", "key words": "reliability engineering, survival analysis, power distribution, field trial, infrastructure asset management, Weibull distribution", "contribution statement": "This paper provides the first application of a randomised field trial with survival analysis to generate empirical reliability functions for electrical</p> |
| title | Randomised Field Trial for the Reliability Assessment of Power-Distribution Infrastructure in Nigeria |
| topic | Power-distribution reliability Randomised field trial Sub-Saharan Africa Infrastructure assessment Nigeria Condition monitoring Outage analysis |
| url | https://doi.org/10.5281/zenodo.18966425 |