Non-conventional Transformers Cost Estimation Using Neural Network

Reza-Alikhani, Hamid-Reza; Salah, Peyman; Madahi, Seyed Siavash Karimi; Akhlaghi, Shahrokh

doi:10.1109/icfpee.2010.24

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The ANN method is like a black-box model, which provides an efficient way to model a complex nonlinear system. To model a system using an ANN model, there is no need to figure out the closed-form equations of the system or to know the complex relationship between input and output variables [24]. The neural network used in this study is a feed forward supervised learning model with one hidden layer.…”

Section: Fig 6 Correlation Between Solar Energy and Weather Variables...mentioning

confidence: 99%

Weather forecasting error in solar energy forecasting

Sangrody

Sarailoo

Zhou

et al. 2017

IET Renewable Power Generation

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Abstract:As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. In this paper, a study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for six days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting.

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Section: Fig 6 Correlation Between Solar Energy and Weather Variables...mentioning

confidence: 99%

Weather forecasting error in solar energy forecasting

Sangrody

Sarailoo

Zhou

et al. 2017

IET Renewable Power Generation

View full text Add to dashboard Cite

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“…In this paper, transformers loss of life is estimated via Adaptive Network-Based Fuzzy Inference System (ANFIS) which is an integration of Artificial Neural Networks (ANN) learning process and fuzzy inference system. ANNs are originally inspired by the biological structures of brains of humans and animals, which have extreme ability to solve complex problem in different disciplines [14]- [17].Authors in [14] use fuzzy modeling to strategize asset management in transformers, where the improvement in remnant life and the rate of aging in power transformer are achieved with fuzzy model system. To predict top oil temperature in transformers, an artificial neural network is modeled in [15].…”

Section: Subscriptsmentioning

confidence: 99%

Machine learning applications in estimating transformer loss of life

Majzoobi

Mahoor

Khodaei

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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Transformer life assessment and failure diagnostics have always been important problems for electric utility companies. Ambient temperature and load profile are the main factors which affect aging of the transformer insulation, and consequently, the transformer lifetime. The IEEE Std. C57.91-1995 provides a model for calculating the transformer loss of life based on ambient temperature and transformer's loading. In this paper, this standard is used to develop a data-driven static model for hourly estimation of the transformer loss of life. Among various machine learning methods for developing this static model, the Adaptive Network-Based Fuzzy Inference System (ANFIS) is selected. Numerical simulations demonstrate the effectiveness and the accuracy of the proposed ANFIS method compared with other relevant machine learning based methods to solve this problem. Index Terms-Adaptive Network-Based Fuzzy Inference System (ANFIS), transformer asset management, data-driven model, loss of life estimation.

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