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

Yutian Fan, Yiqiang Yang (), Fan Wu, Han Qiu, Peng Ye, Wan Xu, Yu Zhong, Lingxiong Zhang and Yang Chen
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Yutian Fan: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Yiqiang Yang: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Fan Wu: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Han Qiu: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Peng Ye: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Wan Xu: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Yu Zhong: School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China
Lingxiong Zhang: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China
Yang Chen: School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000, China

Energies, 2025, vol. 18, issue 11, 1-21

Abstract: 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.

Keywords: active power loss; photovoltaic prediction; feature mode decomposition; reactive power optimization control; multi-objective optimization; MOPSO (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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