Asset maintenance planning in electric power distribution network using statistical analysis of outage data

Adoghe, A. U.; Awosope, C. O. A.; Ekeh, J.C.

doi:10.1016/j.ijepes.2012.10.061

Cited by 30 publications

(21 citation statements)

References 3 publications

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“…Several failure modes can be considered to evaluate reliability of maintenance planning (MP), as done in Yin et al (2013). Continuous-time model is applied in , Billinton and Grover (1975), Carnero and Gómez (2017); Cormen et al (2001), Dhople et al (2013), Endrenyi et al 1998, Nourelfath et al (2012, Ruiz-Castro (2014), Yin et al (2013) and Yssaad and Abene (2015) Monte Carlo Briš et al (2003), Endrenyi et al (1998), Hanai et al (2013), Hilber et al (2007), Melchor-Hernández et al (2015), Mollahassani-pour et al (2014, Salman et al (2017), Yssaad and Abene (2015) and Wang et al (2016) Acyclic graph Briš et al (2003), 2017and Briš and Byczanski (2013) Bayesian De Jonge et al 2015aBonferroni Yumbe et al (2016) Chi-squared Yumbe et al (2016) FMECA Yssaad and Abene (2015) and Yssaad et al (2014) Laplace Adoghe et al (2013) Monotone process Dehghanian et al (2013) Nelder-Mead algorithm Moradkhani et al (2014a) Nonhomogeneous Poisson Caballé et al (2015), Chen (2012), Doostparast et al (2014), Melchor-Hernández et al (2015 and Wang and Pham (2011) Serial coefficients test Adoghe et al (2013) Tree decision Abbasi et al (2009) and Cormen et al (2001), Mohammadnezhad-Shourkaei et al (2011 z-transformer...…”

Section: Methods For Reliability Analysismentioning

confidence: 99%

“…This step seeks to find causes of faults and provides the input parameters of the second step whose purpose is to forecast future failures. In Adoghe et al (2013), the Laplace method is used to identify the system's tendency to change reliability over time, and the serial correlation coefficient is used to identify whether the times between faults are independent of each other. A monotone process (Yeh 2005) is presented in (Zhang et al 2013) to model deterioration processes, and in (De Jonge et al 2015a), the weakest generating units are identified by applying Bayesian inference.…”

Section: Methods For Reliability Analysismentioning

confidence: 99%

“…,Lin and Wang (2012),Melchor-Hernández et al (2015),Velasquez-Contreras et al (2011),Zhang et al (2013),Yin et al (2013),Yssaad and Abene (2015) andYssaad et al 2014) MTTRAbbasi et al (2009),Adoghe et al (2013),Afzali et al (2019),Billinton and Grover (1975)"Briš and Byczanski (2013),Briš et al (2017),Canizes et al (2015),Chan and Shaw (1993),Carnero and Gómez (2017),Dehghanian et al (2013),Dhople et al (2013),Doostparast et al (2014),Endrenyi et al 1998, Estava (1987, Hilber et al(2007), Marquez et al (2013), Melchor-Hernández et al (2015), Mirsaeedi et al (2017a), Mirsaeedi et al (2017b), Moraes et al (2017), Mohammadnezhad-Shourkaei et al (2011), Moradkhani et al (2014a), Narimani et al (2018), Nourelfath et al (2012), Piasson et al (2016), Ruiz-Castro (2014), Salman et al (2017), Sim and Endrenyi (1988), Sittithumwat et al (2004), Silva et al (2014), Velasquez-Contreras et al (2011), Yin et al (2013), Xu and Hu (2013), Yssaad et al (2014), Yssaad and Abene (2015), Wang et al (2014), Wang et al (2016) and Wu and Clements-Croome (2005) SAIDI Abiri-Jahromi et al (2009), Afzali et al (20190, Aravinthan and Jewell (2013), Arya (2016), Bertling et al (2005), Canizes et al (2015), Chen (2012), Endrenyi et al (1998), Li and Brown (2004), Lin and Wang (2012), Mirsaeedi et al (2017b), Mohammadnezhad-Shourkaei et al (2011), Moradkhani et al (2015), Narimani et al (2018), Silva et al (2014), Sim and Endrenyi (1988), Velasquez-Contreras et al (2011), Wang and Pham (2011) and Wang et al 2014 SAIFI Abiri-Jahromi et al (2009), Afzali et al (2019), Aravinthan and Jewell (2013), Arya (2016), Bertling et al (2005), Canizes et al (2015), Li and Brown (2004), Mirsaeedi et al (2017b), Mohammadnezhad-Shourkaei et al (2011), Sittithumwat et al (2004), Velasquez-Contreras et al (2011) and Yssaad et al (2014) Table 3 Brazilian equivalent reliability indexes Index Reference DIC Adoghe et al (2013), Afzali et al (2019), Arya (2016), Bertling et al (2005), Billinton and Grover (1975), Canizes et al (2015), Dehghanian et al (2013), Hilber et al (2007), Mirsaeedi et al (2017b), Moraes et al (2017), Narimani et al (2018), Piasson et al (2016), Velasquez-Contreras et al (2011) FIC Afzali et al (2019), Aravinthan and Jewell (2013), Billinton and Grover (1975), Dehghanian et al (2013), Hilber et al (2007), Mirsaeedi et al (2017b), Moraes et al (2017) and Piasson et al (2016)…”

mentioning

confidence: 99%

“…Adoghe et al (2013),Aravinthan and Jewell (2013),Bao et al (2018),Beaumont and Geary (1944),Bertling et al (2005),Briš and Byczanski (2013),Briš et al (2017),Campelo et al 2016, Carnero andGómez (2017),Dhople et al (2013),Endrenyi et al (1998),Endrenyi et al (2001),Hanai et al (2013),Hilber et al (2007),Li and Brown (2004),Marquez et al (2013), Melchor-Hernández et al 2015, Mirsaeedi et al (2017a, Mirsaeedi et al (2017b), Mohammadnezhad-Shourkaei et al (2011), Moradkhani et al (2014a), Moradkhani et al (2015), Moraes et al (2017), Narimani et al (2018), Piasson et al (2016), Sim and Endrenyi (1988), Sittithumwat et al (2004), Velasquez-Contreras et al (2011), Zhang and Gockenbach (2011), Yssaad and Abene (2015), Yssaad et al (2014), Yumbe et al (2013), Yumbe et al (2016) and Wang et al (2014) Breaker Adoghe et al (2013), Aravinthan and Jewell (2013), Beaumont and Geary (1944), Bertling et al (2005), Endrenyi et al (1998), (2001), Hanai et al (2013), Hilber et al (2007), Li and Brown (2004), Melchor-Hernández et al (2015), Piasson et al (2016), Sim and Endrenyi (1988), Zhang and Gockenbach (2011), Yssaad and Abene (2015), Yssaad et al (2014) and Wang et al (2014) Branch Abiri-Jahromi et al (2009), Adoghe et al (2013), Aravinthan and Jewell (2013), Bertling et al (2005), Briš and Byczanski (2013), Briš et al (2017), Canizes et al (2015), Carnero and Gómez (2017), Hanai et al (2013), Hilber et al (2007), Li and Brown (2004), Mirsaeedi et al (2017a), Mirsaeedi et al (2017b), Moraes et al (2017), Moradkhani et al (2014a), Moradkhani et al (2015), Narimani et al (2018), Piasson et al (2016), Sittithumwat et al (2004), Yssaad and Abene (2015), Yssaad et al (2014), Yumbe et al (2013), Wang et al (2014) and Wang et al (2016) Battery Beaumont and Geary (1944), Masteri et al (2018) and Narimani et al (2018) Bus Adoghe et al (2013), Bertling et al (2005), Hilber et al (2007), Yssaad and Abene (2015), Yssaad et al (2014) and Wang et al (2014) Capacitor Canizes et al (2015), and Piasson et al , (2013), Hilber et al (2007), Yssaad and Abene (2015) and Yssaad et al (2014) Generator Bao et al (2018) and Endrenyi et al (1998), (2001), Mirsaeedi et al (2017a) Insulator Adoghe et al (2013), Moradkhani et al (2014a, Moradkhani et al (2015), Yssaad and Abene (2015), Yumbe et al (2013) and Yumbe et al (2016) Jumper Moradkhani et al (2014a) Motor Sittithumwat et al (2004) Pole Salman et al (2017), Moradkhani et al (2014a, Yumbe et al (2013) and Yumbe et al (2013), Aravinthan and Jewell (2013), Briš and Byczanski (2013), Mirsaeedi et al (2017b), Piasson et al (2016), Yssaad and Abene (2015), Yssaad et al (2014) and Wang et al (2016) Surge arrester Yssaad and Abene (2015), Yumbe et al (2013)Voltage regulatorPiasson et al (2016...…”

mentioning

confidence: 99%

See 3 more Smart Citations

Maintenance Planning of Electric Distribution Systems—A Review

Trentini

Guedes

Oliveira

et al. 2020

J Control Autom Electr Syst

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Electric distribution systems have the objective of supplying electricity with quality and reliability to the final consumers. In order to meet both criteria, efficient maintenance programs have a vital importance mainly due to the actual increase in the requirements for distribution service quality and in technologies related to electrical networks. In this sense, the number of options and criteria for developing effective programs makes the related decision-making process a complex task. This paper presents a comprehensive review on maintenance planning in electrical distribution systems covering different criteria such as economic and reliability. More specifically, this work summarizes contributions up to now through a holistic framework that comprises the premises of predictive, preventive and corrective maintenance. The work is organized by relevant aspects of researches in the field, as criteria, probability functions, constraints and methods that have been applied, within a comprehensive classification.

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Section: Methods For Reliability Analysismentioning

confidence: 99%

Section: Methods For Reliability Analysismentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Maintenance Planning of Electric Distribution Systems—A Review

Trentini

Guedes

Oliveira

et al. 2020

J Control Autom Electr Syst

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“…Another conventional method of restoring customer interruption is by repairing or replacing the faulty network component [8][9][10][11]. The selection of either repairing or replacing a faulty network component depends on the class of group demand outage, types of network components, network component availability, transportation, geographical area of faulty area, and others.…”

Section: Introductionmentioning

confidence: 99%

Energy Regulator Supply Restoration Time

Ridzuan

Djokić

2019

Energies

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In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.

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