A fuzzy inference-based approach for estimating power transformers risk index

Medina, Roxana Beatriz; Zaldivar, Diego A.; Romero, A. A.; Zuñiga, Jefferson; Mombello, Enrique E.

doi:10.1016/j.epsr.2022.108004

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…In step 12, (6) was applied for TUP or (7) for No-TUP, obtaining Table 3 that presents the tabulated useful life values for the extrapolated period.…”

Section: Case Descriptionmentioning

confidence: 99%

“…The probability of failure is a con-cept that merges the condition of the particular transformer unit and the external events that can trigger a final failure. Such external events can be of different nature, such as atmospheric discharges, sabotage, and other events such as short circuits or overloads [7]. Once, the transformer risk index is estimated.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for scheduling long‐term replacement of aging power transformers, considering risk

Piamba

Romero

Mayor

2023

IET Generation Trans & Dist

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This article presents an optimization methodology to schedule the replacement of power transformers (PT) into a fleet. The objective is the minimization of the summation of the risk indices of the PT. Each PT risk index is calculated from the estimation of the life used and the strategic importance of the unit. The PT life used is estimated as a relationship of the solid insulation polymerization degree, where aging processes due to oxidation, hydrolysis, and pyrolysis are considered from the calendar date when the unit starts its operation until different future scenarios. For the calculation of the PT strategic importance, financial, security, environmental, and network performance aspects are considered. Then, using the optimization model, together with the CAPEX and the available budget over a planning time, a strategy for optimally replacing the critical units is determined. The model was applied for a group of 102 units, demonstrating its applicability and effectiveness. The developed methodology serves to support the manager of these assets in making decisions in the long term.

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“…In step 12, (6) was applied for TUP or (7) for No-TUP, obtaining Table 3 that presents the tabulated useful life values for the extrapolated period.…”

Section: Case Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology for scheduling long‐term replacement of aging power transformers, considering risk

Piamba

Romero

Mayor

2023

IET Generation Trans & Dist

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show abstract

“…In (7), w is calculated using (8). Whereas, r1, r2, r3 are the random factors whereas c1 and c2 are the acceleration coefficients.…”

Section: T Wv T C R P T P T C R G T P T C R R P Tmentioning

confidence: 99%

Fault Diagnosis of a Transformer using Fuzzy Model and Unsupervised Learning

Dhiman¹,

Kumar²

2023

AJCT

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In this paper a power transformer fault diagnosis system (PDFDS) based on soft computing and computational intelligence is proposed. Fault diagnosis and analysis is an integral part of operational reliability. Systems like SCADA collect data of various equipment in power system network, however, fails to provide a critique fault diagnosis for the same which further leads to additional cost of replacing the equipment. This paper proposes a supervised-unsupervised predictive model for the data collected from various power transformers across Himachal Pradesh and IEC 10 database. To identify the different fault types in a transformer a fuzzy model is developed using the DGA interpretation techniques. Since, not all data samples in the collected dataset fall under the standards specified in the ratio tables it thus becomes difficult to identify the type of fault for such cases. To overcome this an improved fuzzy model with unsupervised clustering algorithm or Fuzzy Clustering means is used. Employing this improved model optimizes the data before feeding it to the different predictive machine learning models. Further, a particle swarm optimization algorithm with passive congregation is employed to optimize the performance of these machine learning models.

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“…The evaluation methods primarily consist of approaches based on uncertainty analysis theory [1][2][3][4],reliability theory [5][6][7][8][9], and artificial intelligence algorithms [10][11][12][13]. In recent years, the uncertainty analysis theory-based evaluation method has been widely applied in power quality assessment [14][15][16], power safety risk assessment [17,18], power system equipment condition assessment [19][20][21][22], and power grid operation risk assessment [23][24][25]. In reference [1], the risk assessment model of power communication network based on traditional matter-element fails to fully consider the fuzziness and randomness associated with determining the risk for the boundary value or measured value of each index.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

Chen,

Xu,

et al. 2024

IEEE Access

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The failure of the power communication network often leads to a cascading failure of the power system. Evaluating the potential risks in the power communication network is crucial to ensure the stable operation of the power system. This paper proposes a comprehensive risk assessment method for qualitative and quantitative measures. Firstly, an index system for risk assessment, considering both static structure and dynamic operation, is established. The optimal weight of each index is determined using the combination game weighting method. Additionally, a risk assessment model for the power communication network is established by coupling gray cloud theory and matter-element extension model. The fuzzy theory's triangular modulus operator is utilized to fuse three types of fault probabilities, quantify the risk value of the power communication network, and assign it a grade. Finally, experimental results demonstrate that this method improves discrimination degree by 1.2%, 92%, and 85% respectively compared to other traditional algorithms.

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A fuzzy inference-based approach for estimating power transformers risk index

Cited by 10 publications

References 20 publications

Methodology for scheduling long‐term replacement of aging power transformers, considering risk

Methodology for scheduling long‐term replacement of aging power transformers, considering risk

Fault Diagnosis of a Transformer using Fuzzy Model and Unsupervised Learning

Risk Assessment Method of Power Communication Network Based on Gray Cloud Theory and Matter-Element Extension Model Coupling

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