Health status perception of oil-immersed power transformers considering wind power uncertainty

• Published in: Electric power systems research 2024-09, Vol.234, p.110751, Article 110751
• Main Authors: Yi, Lingzhi, Su, Xingren, Wang, Yahui, Xu, Xunjian, Liu, Jiangyong, She, Haixiang
• Format: Article
• Language: English
• Subjects: Modeling of ash cloud evidence; New power system; Power transformer; State perception; Variable weight function
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2024.110751

•The optimal cloud entropy algorithm is utilizd to enhance the gray cloud model, and a dynamic weight allocation evaluation method is integrated based on variable weights for comprehensive state perception.•Dynamic indicators such as voltage deviation, voltage fluctuation, and other dynamic metrics as evaluation criteria are introduced, with a primary focus on the influence of dynamic indicators in scenarios with high new energy penetration rates.•A novel multi-scale information fusion method is implemented. This fusion method effectively addresses the issue of incomplete identification frameworks and reduces the impact of sensor errors.•Cases comparison is conducted in actual transformer operating environments with varying levels of new energy penetration rates to validate the superiority and scalability of the proposed model in transformer state assessment. The power transformer is the most important and critical component in the power grid of the electrical system. Its safe and stable operation is of great significance for the reliable transmission of renewable energy generation and the reliable power supply to end users. With the continuous development of new types of power systems, the load of the power system undergoes drastic changes, resulting in increased volatility and instability, which leads to issues such as overload, harmonics, and short circuits in transformers. Therefore, in order to accurately assess the operating status of transformers and promptly identify any existing conditions, a method is proposed in this paper. This method uses an optimal cloud entropy parameter calculation method and a variable weighting method to optimize the gray-cloud evidence model. Through a novel multi-source information fusion method, the results of various test items are fused to obtain the state awareness results. Meanwhile, different evaluation indicators are selected for different levels of renewable energy penetration and compared with other methods. The results are validated through examples, demonstrating that the method proposed in this paper can accurately reflect the operating status of transformers and has good scalability.