基于秃鹰搜索算法优化支持向量机的电力系统故障预测方法研究

Power System Fault Prediction Method Based on Support Vector Machine Optimized by Bald Eagle Search Algorithm

针对电力系统故障预测准确率低、预测模型受参数限制等问题,笔者通过对人工智能算法优化的研究,提出基于秃鹰搜索算法优化支持向量机的电力系统故障预测方法。通过采用Matlab仿真软件建模,模拟生成电力系统多种故障,应用秃鹰搜索算法优化计算支持向量机最优的核函数和惩罚因子,实现对电力系统故障进行预测和诊断。实验结果表明:该方法在电力系统中预测故障的准确率高达99.94%,相比其他算法,其准确率均大幅提高,预测效果优势显著。该方法的应用能够提升电力系统故障预测的有效性。

Aiming at the problems of low accuracy of power system fault prediction and parameter limitation of prediction model, this paper proposes a power system fault prediction method based on support vector machine optimized by Bald Eagle Search algorithm through the study of artificial intelligence algorithm optimization. By using Matlab simulation software modeling, a variety of faults in power system are simulated and generated. The Bald Eagle Search algorithm is used to optimize the optimal kernel function and penalty factor of support vector machine to realize the prediction and diagnosis of power system faults. The experimental results show that the accuracy of this method in predicting faults in power systems is as high as 99.94 %. Compared with other algorithms, its accuracy is greatly improved, and the prediction effect is significant. The application of this method can improve the effectiveness of power system fault prediction.