变压器热点温度估计通过自适应的神经模糊推理系统方法
Edwell T Mharakurwa1, Dorothy W Gicheru1
1Department of Electrical & Electronic Engineering Dedan Kimathi University of Technology (DeKUT), Private Bag, 10 143, Nyeri, Kenya.
Heliyon
|February 23, 2024
概括
这项研究引入了一个自适应性神经模糊推理系统 (ANFIS),以准确估计功率变压器绕线热点温度 (HST). 通过提高热分析准确度,ANFIS模型提高了变压器可靠性和资产管理.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 由于动态负载和环境变化的热应力,功率变压器的绝缘降解.
- 精确的绕热点温度 (HST) 估计对于保持电力系统可靠性和防止加速绝缘老化至关重要.
- 传统的热模型用于HST计算是复杂的和非线性的,需要改进的估计方法.
研究的目的:
- 为了提高电力变压器中最高油温 (TOT) 和绕线热点温度 (HST) 估计的准确性.
- 开发和验证适应性神经模糊推理系统 (ANFIS) 模型,用于实时热性能监测.
- 为资产管理人员提供一种可靠的方法来评估变压器负载,并确保运营寿命.
主要方法:
- 开发了一种子ANFIS模型,用于使用负载和环境温度作为输入来估计顶部油温 (TOT).
- 实施混合优化技术来微调ANFIS会员功能以提高准确性.
- 在动态运行条件下使用60/90MVA,132kV功率变压器的现场数据验证了ANFIS模型.
主要成果:
- ANFIS模型在估计TOT和HST方面表现出高准确度,分别达到0.98和0.96的确定系数.
- 该模型的平均平方误差为7.8的TOT和10.3的HST,低于传统的热模型.
- 与现场数据和类似模型的验证证实了精确的输入-输出相关性和模型的可靠性.
结论:
- 适应性神经模糊推理系统 (ANFIS) 提供了一种高度准确和可靠的方法来估计电力变压器绕热点温度 (HST).
- 使用ANFIS进行准确的HST监控有助于有效的资产管理,使负载建议得到优化并提高变压器的整体寿命.
- 这种方法解决了传统模型的局限性,在功率变压器热分析和运行安全方面取得了重大进展.
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