Determination of Power Transformers Health Index Using Parameters Affecting the Transformer’s Life

Padmanaban, Sanjeevikumar; Khalili, Mohsen; Nasab, Morteza Azimi; Zand, Mohammad; Shamim, Ahmad Ghaderi; Khan, Baseem

doi:10.1080/03772063.2022.2048714

Cited by 20 publications

(6 citation statements)

References 40 publications

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“…Health Index The Health Index (HI) is a measure that could be used to assess the overall condition of a power transformer. This value is derived using some of the most representative variables of state or diagnostic that describe the activity and status of the transformer, and it is converted into a quantitative index that gives insight about its health status [75]. The necessity to assess the influence of individual findings from tests performed on the technical state of the transformer and the aging process progression is the motivation for developing a Health Index.…”

Section: Life Lossmentioning

confidence: 99%

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

et al. 2022

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After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning.

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Section: Life Lossmentioning

confidence: 99%

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

et al. 2022

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“…Fuzzy intelligence method and neural networks have been employed for thermal modelling and real-time monitoring of transformer. These models rely on the data-driven black-box method that requires a large data set with long time series for both model development and testing [11,12].…”

Section: Introductionmentioning

confidence: 99%

Real‐time monitoring of transformer hot‐spot temperature based on nameplate data

Rezaeealam,

Askary

2023

IET Generation Trans & Dist

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This paper proposes a real‐time hot‐spot monitoring method for three‐phase two‐winding oil‐filled transformers under steady‐state conditions, where the corresponding test certificates are not accessible to the customers. For this purpose, the separate determination of iron‐core and copper losses is achieved by real‐time measuring of voltages and currents in both primary and secondary sides and also employing nameplate data of the transformer, including the winding resistances. Subsequent calculations based on IEEE Std C57.110‐2018 provide the top‐oil and hot‐spot temperatures. It is shown that the estimated top‐oil and winding hot‐spot temperatures and also load and no‐load losses match with the measured ones with the maximum difference of 3.5%, 2%, 2.7%, and 9%, respectively.

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“…The power flow in RDS is hence unidirectional. Large voltage drops, poor voltage stability, and substantial power losses are caused by distribution lines with high impedance ratios [3]. In order to increase system reliability and voltage regulation, a number of techniques have recently been put up for enhancing the passiveness of RDS by incorporating power generation sources with minimum range [4].…”

Section: Introductionmentioning

confidence: 99%

A Holistic Approach to Voltage Profile Improvement and Power Loss Mitigation through Distributed Generation Placement and Sizing

Aman,

Ahmad,

Khan

et al. 2023

Preprint

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One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

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Determination of Power Transformers Health Index Using Parameters Affecting the Transformer’s Life

Cited by 20 publications

References 40 publications

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers: A Novel Approach for Smart Grid-Ready Energy Management Systems

Real‐time monitoring of transformer hot‐spot temperature based on nameplate data

A Holistic Approach to Voltage Profile Improvement and Power Loss Mitigation through Distributed Generation Placement and Sizing

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