工程科学学报2026，Vol.48Issue(6)：1312-1326,15.DOI:10.13374/j.issn2095-9389.2025.09.30.002
基于TCN-LSTM-AAE和DPGMM的新型电力系统运行模式分析
Operating mode analysis of new power systems based on TCN-LSTM-AAE and DPGMM
摘要
Abstract
The rapid integration of renewable energy and the continuous expansion of its installed capacity have fueled profound and fundamental transformations in the operational mechanisms of modern power systems.These developments have introduced enhanced variability and volatility into system behavior,causing operational states to become increasingly complex,nonlinear,and d ynamic.Consequently,conventional operating mode identification approaches,which are predominantly based on empirical rules and static system assumptions,are inadequate for capturing the evolving patterns of new power systems,especially under high levels of uncertainty and renewable penetration.To address the increasingly prominent challenges in new power systems,this study proposes a systematic and data-driven framework for identifying and analyzing operating modes.The framework begins with a data preprocessing phase that fully accounts for the characteristics of multi-source data.Targeted strategies for outlier detection and missing-value imputation are applied based on the statistical distribution of different variables,which ensures the integrity,consistency,and reliability of the input data at the source and lays a robust foundation for subsequent modeling and analysis.To compensate for data sparsity and imbalance,which are common under high renewable penetration conditions,the framework incorporates a generative module based on an adversarial autoencoder that integrates temporal convolutional networks and long short-term memory.Through this hybrid architecture,the model can effectively learn the latent properties of the data while generating realistic and diverse augmented samples to address the problems of data imbalance.Additionally,a cosine annealing learning-rate schedule is employed during model training to enhance learning stability,prevent convergence to local minima,and improve overall training efficiency and representational quality.To address the issue of high-dimensional data and extract essential latent representations,an autoencoder is pre-trained to compress the operational data into a low-dimensional feature space.The resulting compact and informative features are then used as input to a Dirichlet process Gaussian mixture model(DPGMM),which is employed for clustering and operation mode identification.As a nonparametric Bayesian approach,DPGMM can adaptively infer the appropriate number of clusters without requiring manual specification.Such adaptive capability significantly enhances the model's flexibility,scalability,and generalization capacity.Furthermore,the framework employs the uniform manifold approximation and projection algorithm to perform dimensionality reduction and visualize the distribution of operating modes in a three-dimensional space.This enables deeper insights into the structural evolution of the system's operational states.The proposed framework is validated using real-world operational data from a power system in a city in North China.Experimental results demonstrate that the method exhibits excellent performance in identifying operation modes under complex and highly dynamic conditions.Specifically,as the penetration level of renewable energy increases,the number and dispersion of operating modes increase significantly.The number of modes increases from three in low-penetration scenarios to six under medium penetration and up to nine in high-penetration cases.Quantitative analysis further reveals that system dynamics evolve with stronger nonlinearity and increased uncertainty and that the randomness of transitions between modes substantially increases.These findings highlight the limitations of traditional rule-based dispatch strategies and emphasize the urgent requirement for intelligent,adaptive,and flexible control mechanisms to ensure a safe,stable,and efficient operation of future power systems.关键词
高可再生能源渗透/离群点检测/数据扩充/运行模式识别/电力系统动态分析Key words
high renewable energy penetration/outlier detection/data expansion/operating modes identification/dynamic analysis of power systems分类
信息技术与安全科学引用本文复制引用
杨明,卢玮钰,焦绪国,王文婷,王鑫,吴晓明..基于TCN-LSTM-AAE和DPGMM的新型电力系统运行模式分析[J].工程科学学报,2026,48(6):1312-1326,15.基金项目
国家重点研发计划资助项目(2023YFC3306304) （2023YFC3306304）
山东省高等学校青年创新团队发展计划资助项目(2022KJ290) （2022KJ290）
山东省自然科学基金面上资助项目(ZR2025MS1100) （ZR2025MS1100）
泰山学者青年专家资助项目(tsqn202408239) （tsqn202408239）
工业控制技术全国重点实验室开放课题资助项目(ICT2025B13) （ICT2025B13）
