Journal Article

Coordinated Reactive Power–Voltage Control in Distribution Networks with High-Penetration Photovoltaic Systems Using Adaptive Feature Mode Decomposition

Fan, Yutian, Zhang, Lingxiong, Wu, Fan, Ye, Peng, Qiu, Han, Xu, Wan, Chen, Yang, Yang, Yiqiang, Zhong, Yu

2025

Overview

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case.

• Publisher: MDPI AG
• Subject: Accuracy / active power loss / Alternative energy sources / Artificial intelligence / China / Efficiency / Electric power systems / Energy consumption / Energy resources / feature mode decomposition / Foot-and-mouth disease / Forecasting / Mathematical optimization / MOPSO / multi-objective optimization / Network management systems / Neural networks / Optimization algorithms / photovoltaic prediction / reactive power optimization control / Renewable resources / Solar energy industry / Systems stability / Time series / Variables / Wu, Fan