Fuzzy logic approach in power transformers management and decision making

Arshad, Muhammad; Islam, Syed; Khaliq, Abdul

doi:10.1109/tdei.2014.003859

Cited by 77 publications

(60 citation statements)

References 20 publications

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“…(i) It is generally accepted that hydrogen is generated (in the oil) by nearly all transformer faults [6], [20]. This is not true of the other four dissolved gases whose concentrations are used in our method [15].…”

Section: Fuzzzy Inferencementioning

confidence: 94%

“…We determined the values of a and b through repeated trials, the final values being chosen on the basis of an accurate oil health index output [20]. Our values of a and b differ from those quoted in [6] and [20] by 5-10%.…”

Section: Membership Functions and Degrees Of Membershipmentioning

confidence: 99%

“…Table 2. Lower and upper dissolved gas concentration limits a and b (in ppm) for each of the five input Membership Functions, and for each dissolved gas, for transformers rated 5-50 MVA and voltage 6-220 kV [6], [20]. The output of our FL method, i.e., the oil health index, covers the range 0-1, and is divided into five Gauss2 type MFs, as shown in Figure 3.…”

Section: Membership Functions and Degrees Of Membershipmentioning

confidence: 99%

“…These gases have been studied by researchers to assess the condition of the oil, expressed as a health index [4], [5]. Over the past two decades dissolved gas analysis (DGA) has also proved to be useful as an early indicator of transformer incipient faults [6]. Incipient faults are internal faults which constitute no immediate hazard to the transformer [5], but could eventually lead to major failure if ignored.…”

Section: Introductionmentioning

confidence: 99%

“…Changes in the individual concentration of the gases serve as an indicator for the identification of various types of faults. During the past few decades, several diagnostic methods such as Key gas [6], Rogers ratios [7], [8], Duval Triangle [9], [10], Dornenburg ratios [11], [12], modified Rogers ratios [10], [11] and IEC/IEEE ratio code methods [12], [13] based on the concentration of gases have been developed. These methods use the ratios of the concentrations of specific pairs of gases to identify a specific fault condition [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

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Expert System for Health Index Assessment of Power Transformers

Ranga¹,

Chandel²

2017

ijeei

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A fuzzy logic based technique using dissolved gas analysis to determine the health index of the oil in a power transformer is proposed in the present paper. The proposed fuzzy model integrates the DGA standards and the practical knowledge of transformer diagnostics experts in making a consistent decision on the health condition of the transformer oil. Twenty different transformer oil samples of a power utility validate the reliability of the proposed diagnostic models. Furthermore, a fuzzy logic based overall transformer health assessment model has been proposed in this paper. This model uses several diagnostic tests data of transformers including water content, break down voltage, interfacial tension. It is envisioned that the proposed fuzzy models will prove very convenient even for inexperienced engineers to determine the health index of the transformers. This shall help in initiating suitable action for proper maintenance of the transformers and enhancing their remnant life.

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Section: Fuzzzy Inferencementioning

confidence: 94%

Section: Membership Functions and Degrees Of Membershipmentioning

confidence: 99%

Section: Membership Functions and Degrees Of Membershipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Expert System for Health Index Assessment of Power Transformers

Ranga¹,

Chandel²

2017

ijeei

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Multiagent trajectory decision‐making for complex systems based on transformer large model structure

Ma,

Gong,

Wang

2024

Intl J Robust & Nonlinear

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The article explores the application of transformer‐based large models for trajectory decision‐making in complex decision systems, focusing on multiagent nodes in a topological structure. By leveraging the capabilities of large models, the proposed approach enhances decision‐making in model‐free control environments characterized by numerous agents, diverse internal tasks, and unknown conditions. The proposed framework integrates advanced transformer models with a sophisticated decision‐making framework to enhance the efficiency, safety, and reliability of multiagent operations in dynamic environments. The results demonstrate the superior performance of the transformer model, achieving a trajectory decision‐making accuracy of 97.8%, significantly outperforming other models such as long short‐term memory (LSTM), gate recurrent unit (GRU), and traditional approaches. The visualizations highlight the close alignment between the true and predicted outcomes of complex decision‐making systems, showcasing the model's ability to capture complex dependencies and interactions within the multiagent environment. High task completion rates and low collision rates further underscore the effectiveness of the decision‐making framework in optimizing agent coordination and ensuring safe operations.

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