| 引用本文: | 王 玥,于 越,金朝阳.基于改进Att-LSTNet与无迹粒子滤波融合的主动配电网预测辅助状态估计[J].电力系统保护与控制,2024,52(8):98-110.[点击复制] |
| WANG Yue,YU Yue,JIN Zhaoyang.Forecasting-aided state estimation for active distribution networks based on improved Att-LSTNet and unscented particle filter fusion[J].Power System Protection and Control,2024,52(8):98-110[点击复制] |
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| 摘要: |
| 针对传统的无迹粒子滤波(unscented particle filter, UPF)存在不准确的新息向量及未知的量测噪声协方差矩阵导致估计精度低的问题，提出一种改进Att-LSTNet与UPF融合的主动配电网预测辅助状态估计(forecasting-aided state estimation, FASE)方法。首先，采用引力搜索算法(gravitational search algorithm, GSA)对支持向量回归(support vector regression, SVR)的关键参数进行优化处理，利用历史数据建立GSA-SVR模型，并将其引入至Att-LSTNet模型的输出层，构建一种增强预测模型。然后，利用UPF中的新息向量来训练该模型，并结合孤立森林算法和箱线图法对原始新息向量进行监控和修正。最后，针对量测噪声协方差矩阵未知的情况，结合修正后的新息向量和UPF计算出未知量测噪声协方差矩阵，并进行状态估计。基于IEEE33与IEEE118节点标准配电系统的算例结果表明，所提出的方法在估计精度、泛化能力和鲁棒性等方面具有优越性。 |
| 关键词: 主动配电网 预测辅助状态估计 Att-LSTNet 无迹粒子滤波 SVR |
| DOI：10.19783/j.cnki.pspc.231067 |
| 投稿时间：2023-08-18修订日期：2023-12-13 |
| 基金项目:国家自然科学基金项目资助(U22B20101 |
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| Forecasting-aided state estimation for active distribution networks based on improved Att-LSTNet and unscented particle filter fusion |
| WANG Yue,YU Yue,JIN Zhaoyang |
| (Key Laboratory of Power System Intelligent Dispatch and Control (Shandong University),
Ministry of Education, Jinan 250061, China) |
| Abstract: |
| In response to the issue of inaccurate innovation vectors and unknown measurement noise covariance matrices in the traditional unscented particle filter (UPF), a forecasting-aided state estimation (FASE) method for active distribution network is proposed, which integrates the improved Att-LSTNet and UPF. First, the key parameters of support vector regression (SVR) are optimized using a gravitational search algorithm (GSA), and a GSA-SVR model is established using historical data. This model is then introduced into the output layer of the Att-LSTNet model to create an enhanced forecasting model. Subsequently, the innovation vectors from UPF are used to train this model, and the isolation forest algorithm and box-plot method are employed to monitor and correct the original innovation vectors. Finally, in the case of unknown measurement noise covariance matrices, the corrected innovation vectors and UPF are combined to calculate the unknown measurement noise covariance matrices and perform state estimation. Case study results on the IEEE33-bus and IEEE118-bus test systems demonstrate the superiority of the proposed method in terms of estimation accuracy, generalizability, and robustness. |
| Key words: active distribution networks forecasting-aided state estimation Att-LSTNet UPF SVR |
