Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine.
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| Title: | Photovoltaic Expansion Perception Method Based on GWO-PSO-Optimized Robust Extreme Learning Machine. |
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| Authors: | He, Houyu1 (AUTHOR) 20234460306@stu.usc.edu.cn, Sheng, Yifa1 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2350. 21p. |
| Subject Terms: | *Grey Wolf Optimizer algorithm, *Particle swarm optimization, *Photovoltaic power systems, *Extreme learning machines |
| Abstract: | Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). Firstly, the PV power generation data is preprocessed using cosine similarity and dynamic time warping (DTW) to reduce the impact of regional meteorological differences. Secondly, by combining the global search capability of the Gray Wolf Algorithm (GWO) with the fast convergence characteristics of the particle swarm optimization (PSO) algorithm, the hidden layer weights and biases of the robust extreme learning machine (MELM) are optimized to enhance the model's robustness to outliers. Finally, the dynamic diagnosis of capacity expansion intensity and time nodes is achieved by calculating the illegal capacity expansion coefficient K. Experiments based on actual PV data from Changsha show that the probability density analysis of the illegal capacity expansion coefficient can identify capacity expansion behaviors as low as 10%, with a positioning error of capacity expansion time nodes of ≤4%. In actual cases, three illegal capacity expansion users were successfully detected, and the detection deviation remained small under different capacity expansion ratios, verifying the effectiveness of the proposed method in PV capacity expansion detection. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | Addressing the safety risks to the distribution network caused by the unauthorized capacity expansion behaviors of distributed photovoltaic (PV) users, this paper proposes a PV capacity expansion detection model based on the gray wolf–particle swarm optimization hybrid optimization robust extreme learning machine (GWO-PSO-MELM). Firstly, the PV power generation data is preprocessed using cosine similarity and dynamic time warping (DTW) to reduce the impact of regional meteorological differences. Secondly, by combining the global search capability of the Gray Wolf Algorithm (GWO) with the fast convergence characteristics of the particle swarm optimization (PSO) algorithm, the hidden layer weights and biases of the robust extreme learning machine (MELM) are optimized to enhance the model's robustness to outliers. Finally, the dynamic diagnosis of capacity expansion intensity and time nodes is achieved by calculating the illegal capacity expansion coefficient K. Experiments based on actual PV data from Changsha show that the probability density analysis of the illegal capacity expansion coefficient can identify capacity expansion behaviors as low as 10%, with a positioning error of capacity expansion time nodes of ≤4%. In actual cases, three illegal capacity expansion users were successfully detected, and the detection deviation remained small under different capacity expansion ratios, verifying the effectiveness of the proposed method in PV capacity expansion detection. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 19961073 |
| DOI: | 10.3390/en19102350 |
