微型电脑应用

变压器作为电力系统的核心设备，对电网的稳定运行和安全至关重要。针对变压器故障诊断的准确性和实时性要求，提出一种基于蜘蛛蜂优化算法(SWO)优化的最小二乘支持向量机(LSSVM)模型，并将其应用于数字孪生变电站变压器故障诊断。采用数字孪生技术构建变压器的高精度虚拟模型，实现对物理设备状态的实时映射和监控。利用SWO对LSSVM中的惩罚因子进行优化，以提高故障诊断的准确性和效率。通过数字孪生环境中的实时数据监控和模型预测，实现了变压器故障的早期预警。实验结果表明，所提出的模型在故障诊断准确率和诊断速度方面均优于现有模型，验证了所提出的模型在数字孪生变电站中的实用价值和潜在应用前景。

The transformer, as a core equipment of power systems, is crucial for the stable operation and safety of the power grid. Aiming at the accuracy and real-time requirements for transformer fault diagnosis, this paper proposes a model based on the spider wasp optimizer(SWO) optimized least squares support vector machine(LSSVM), and applies it to transformer fault diagnosis in digital twin substations. A high-precision virtual model of the transformer is constructed using digital twin technology to achieve real-time mapping and monitoring of physical equipment status. The SWO is utilized to optimize the penalty factor in the LSSVM, thereby enhancing the accuracy and efficiency of fault diagnosis. Early warning of transformer faults is realized through real-time data monitoring and model prediction in the digital twin environment. Experimental results demonstrate that the proposed model outperforms existing models in terms of fault diagnosis accuracy and diagnosis speed, validating practical value and potential application prospects of the proposed model in digital twin substations.