Multiobjective Optimization Applied to Maintenance Policy for Electrical Networks

Hilber, Patrik; Miranda, Vladimiro; Matos, Manuel; Bertling, Lina

doi:10.1109/tpwrs.2007.908468

Cited by 81 publications

(20 citation statements)

References 8 publications

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“…6 is calculated using (13). In (13), the numerator is the cumulative probability of failure and the denominator is the cumulative frequency of failure [7] …”

Section: Probabilistic Assessment Of During-fault Failure Ratementioning

confidence: 99%

Preventive Maintenance Scheduling Based on Short Circuit and Overload Currents

Behzadirafi

Salehfar

2015

IEEE Trans. Smart Grid

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One of the important aspects of smart grid is to make the best use of existing assets. One way (among others) to achieve this objective is by scheduling effective maintenance routines. Abnormal conditions, including short circuits or overloads, would affect not only the exposed components, but also those farther away from the fault locations. This exposure may cause some of the components fail right away or at a later time. Therefore, the severity, frequency, and timings of system faults are important factors in planning maintenance programs. In this paper, the impact of increased current flows, in the form of short circuits and overloads, on component failure rates are studied and a maintenance scheduling model taking into account such impacts is developed. Using the developed model, the optimum mean time to preventive maintenance is derived and used to maximize the components availability. Results of this paper would help in preventing damage to the equipment by optimally scheduling maintenance programs based on short circuit current levels of the system.

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“…6 is calculated using (13). In (13), the numerator is the cumulative probability of failure and the denominator is the cumulative frequency of failure [7] …”

Section: Probabilistic Assessment Of During-fault Failure Ratementioning

confidence: 99%

Preventive Maintenance Scheduling Based on Short Circuit and Overload Currents

Behzadirafi

Salehfar

2015

IEEE Trans. Smart Grid

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“…In [30], a criticality factor was defined for components based on their impact on the system failure rate, system outage duration, and the total energy not served. Hilber et al [31] proposed a multi-objective optimization approach for devising maintenance policies in the power system. They analyzed the impact of failure of each individual component on the customer interruptions and used this data to develop an importance index for each component.…”

Section: Maintenance Scheduling -State Of the Artmentioning

confidence: 99%

Maintenance-centric energy management of industrial plants assisted by demand response

Mohagheghi

Raji

2014

2014 IEEE Industry Application Society Annual Meeting

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Asset management is an integral part of every industrial plant. Here, the goal is to improve the efficiency of the system while reducing the cost of maintenance and risk of failure. Traditionally, asset management has been performed as time-based maintenance or corrective maintenance; however, in order to reduce the consequences of component failures on the operation of the overall system many plant managers have moved towards more advanced condition-based or reliabilitycentric maintenance techniques. Here, the asset manager would adopt a more proactive maintenance approach based on the condition of the component and its importance for the overall production plan. Although shutting down a process for maintenance purposes has long-term benefits, it may accrue costs associated with the lost revenue, inventory buildup or wasted labor. A solution is put forth in this paper that counteracts financial losses due to preventive maintenance with direct benefits gained from demand response (DR). Within this context, maintenance scheduling is coordinated with DR, which is used as a tool at the asset manger's disposal in order to offset these financial losses. A case study is presented for a sample industrial plant in order to further explain the design concepts.Index Terms-Asset management, demand response, energy management, industrial plant demand scheduling, maintenance scheduling, preventive maintenance.

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“…Taking the widely used RCM technology into account, supposing that a series of PM strategies S with maintenance rate are already constituted, the resulting composite failure rate under a certain PM strategy s ∈ S can be estimated as :

λ^{i} (s) = λ_{italicequal} (1 - \sum_{k = 1}^{m} (x_{italicsk} / 100))

. Where, supposing that the failure rate of different transmission line i ( i = 1, 2, ⋯, n ) is equal to the average failure rate λ equal , and that s can reduce the failure rate of relevant failure cause k ( k = 1, 2, ⋯ m ) with the same percentage x sk %, the resulting composite failure rate under S which is also regarded as the corrective maintenance frequency can be therefore given as:

λ^{i} (S) = \sum_{s \in S} (\sum_{k \in s} λ^{i} (s) + \sum_{k \notin s} λ_{italicequal})

.…”

Section: Lccmentioning

confidence: 99%

Multi-objective multi-stage transmission network expansion planning considering life cycle cost and risk value under uncertainties

Liu

Cheng

Yao

et al. 2012

Int. Trans. Electr. Energ. Syst.

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SUMMARY Compared with traditional transmission network expansion planning, the exact life cycle cost is worth further studying. A three‐dimensional life cycle cost (LCC) model for entire transmission network which consists of time dimension, component dimension and cost dimension is proposed. The device layer, the system layer and the cost of externalities of cost dimension are described and formulated in detail. The conditional value at risk (CVaR) of social welfare is established through optimal power flow calculation so as to take social responsibility into consideration. The multi‐objective multi‐stage transmission network expansion planning model is constituted with two objectives which are minimum LCC and minimum CVaR of social welfare. Four uncertain factors including wind power are considered and simulated through Monte Carlo method. An effective normal boundary intersection algorithm integrated with improved niche genetic method is presented to solve the proposed model. The case studies carried out on the 18‐bus system and 77‐bus system not only generate even distributed pareto set but also recommend the optimal planning scheme. The comparisons between the proposed scheme and existing method verify the feasibility and validity. Copyright © 2012 John Wiley & Sons, Ltd.

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Multiobjective Optimization Applied to Maintenance Policy for Electrical Networks

Cited by 81 publications

References 8 publications

Preventive Maintenance Scheduling Based on Short Circuit and Overload Currents

Preventive Maintenance Scheduling Based on Short Circuit and Overload Currents

Maintenance-centric energy management of industrial plants assisted by demand response

Multi-objective multi-stage transmission network expansion planning considering life cycle cost and risk value under uncertainties

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