| 引用本文: | 范 敏,杨 青,郭祥富,等.面向不平衡数据的配电网故障停电预测方法[J].电力系统保护与控制,2023,51(8):96-106.[点击复制] |
| FAN Min,YANG Qing,GUO Xiangfu,et al.Prediction method of power outage in a distribution network for unbalanced data[J].Power System Protection and Control,2023,51(8):96-106[点击复制] |
|
| 摘要: |
| 配电网故障停电事件会严重影响正常的社会经济生活。因此，迫切需要有效的配电网故障停电预测方法。采用人工智能方法分析配电网故障停电数据，发现存在配电网故障停电次数较少和引发配电网故障停电的原因分布不均等数据不平衡情况。为了及时、准确地预测配电网故障停电情况，从数据集质量和防止过拟合两方面入手改进故障停电预测模型。首先，设计了基于聚类的对抗神经网络来增强数据集质量。其次，构造了基于随机代价敏感卷积神经网络(RandomCost-CNN)的故障停电预测模型。RandomCost-CNN预测算法中采用有放回随机抽样思想设计了损失函数的随机选择策略，用以解决常规代价敏感过度拟合少数类(故障停电类)而使得大量多数类(正常类)被误报的问题，既保证少数类具有较好召回率与精确度，同时又提高了模型的泛化性能。实验证明所提方法能有效预测配电网故障停电事件发生概率，在配电网运维管理中能够发挥较好的预警作用。 |
| 关键词: 故障停电预测 不平衡数据分类 过拟合 RandomCost-CNN |
| DOI：10.19783/j.cnki.pspc.220907 |
| 投稿时间：2022-06-16修订日期：2022-10-11 |
| 基金项目:国家重点研发计划项目资助(2020YFB2009405) |
|
| Prediction method of power outage in a distribution network for unbalanced data |
| FAN Min1,YANG Qing1,GUO Xiangfu2,LIU Hao3,XIA Jialu1,PENG Yuwen1 |
| (1. College of Automation, Chongqing University, Chongqing 400044, China; 2. State Grid Henan Electric Power
Company, Zhengzhou 450052, China; 3. State Grid Henan Electric Power Company Electric
Power Research Institute, Zhengzhou 450052, China) |
| Abstract: |
| Power distribution network failure events will seriously affect the normal operation of social and economic life. Therefore, an effective method for predicting the power outage of a distribution network is necessary. The artificial intelligence method is used to analyze the power outage data of the distribution network. It is found that there are fewer times of power outages and the uneven distribution of the causes of the power outages in the distribution network. In order to predict the power outage situation of a distribution network in time and accurately, this paper proposes a modified power outage prediction model from the aspects of data set quality and overfitting prevention. First, it designs a cluster-based generative adversarial neural network to enhance the quality of the data set, then constructs a power outage prediction model based on a random cost-sensitive convolutional neural network (RandomCost-CNN). The RandomCost-CNN prediction algorithm adopts the idea of random sampling with replacement to design a random selection strategy of the loss functions, so that it can alleviate the minority class (power outage class) overfitting and avoid a large number of false positives for the majority class (normal class). It can ensure that minority classes have better recall & accuracy and improve the generalization performance of the model. Experiments show that the proposed method can effectively predict the probability of a power outage event in the distribution network. It can play a good early warning role in the operation and maintenance management of the distribution network. |
| Key words: power outage prediction imbalanced data classification overfitting RandomCost-CNN |
