Analysis of Inertia Characteristics of Photovoltaic Power Generation System Based on Generalized Droop Control

Zha, Yuxin; Lin, Jian; Li, Guanjun; Wang, Yue; YiZhang,

doi:10.1109/access.2021.3059678

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…However, due to the strong system uncertainty and converter interface power supply active transient characteristics by resources, operating conditions and other multiple factors, the system inertia time variability and uncertainty increased, thus affecting the system frequency stability. The photovoltaic unit is also unable to perform inertial response due to its own lack of rotating equipment and weak energy storage performance and complete decoupling from the system [15]. The current system uses a large number of energy storage devices with sag control or virtual inertia control to improve the active power disturbance capability of the system, which can supply a large amount of active power to the system in a short period of time and provide inertia support.…”

Section: B Spatial and Temporal Frequency Distribution Analysis Of Ne...mentioning

confidence: 99%

Research on Centre of Inertia Based Frequency Sampling and System Frequency Distribution Characteristics

You,

Zhao,

Hou

et al. 2024

IEEE Access

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As the amount of new energy injected into the power system increases, the system inertia decreases and the spatio-temporal characteristics become apparent. Due to the uneven distribution of inertia in the power system, the dispersion of the frequency response after disturbance is more pronounced, and the inertia estimation error has a random nature when different frequency measurement points are selected. For the above problems, a method is proposed to select the frequency measurement point based on the centre of inertia. Firstly, based on the frequency dynamic response characteristics, the main influencing factors of the frequency distribution characteristics are studied and the spatial and temporal distribution characteristics of the frequency of the new energy power system are analyzed. Secondly, the method of frequency sampling through the centre of inertia is proposed with full consideration of the electrical distance and generator inertia constants. Finally, a 10 machine 39 bus system is simulated as an example. Simulation results show that using the centre of inertia as the frequency sampling point during inertia evaluation can reduce the inertia evaluation error by up to 16.15%. The results verify the correctness of the above theoretical analysis and provide a favourable basis for further improving the accuracy of the power system inertia estimation.INDEX TERMS centre of inertia, frequency distribution characteristics, new energy

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Section: B Spatial and Temporal Frequency Distribution Analysis Of Ne...mentioning

confidence: 99%

Research on Centre of Inertia Based Frequency Sampling and System Frequency Distribution Characteristics

You,

Zhao,

Hou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…In addition, considering the nonlinear change in photovoltaic power and multiple environment sequence information, the convergence rate of the model slows down and overfitting appears with the increase in network input variables [23][24][25]. At the same time, the accuracy of photovoltaic power forecasting is also affected by time-varying factors [26,27]. Therefore, to guarantee the feasibility of photovoltaic power forecasting, it is beneficial to fully analyze the impact of environmental factors on the modeling of photovoltaic power forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

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“…Inertia, damping, and synchronization characteristics of a solar grid-tied and inverter-side DC voltage droop control system using the SSG mathematical model, and the electrical torque analysis technique were examined. Problems related to power quality issues, angular stability, voltage stability and various other technical aspects were also discussed in [20]- [22]. FLC is a specialized method of control that differs from traditional controllers like the proportional integral (PI) controller.…”

Section: Introductionmentioning

confidence: 99%

Analysis of inertia, damping, and synchronization characteristics in grid-connected photovoltaic systems with fuzzy logic control

Prakash,

Sahoo

2024

IJRA

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The integration of renewable energy sources (RES) into DC-distributed power systems (DC-DPSS) is gaining traction as a sustainable energy solution. However, the inherent variability of RES output can introduce instability into the grid, posing challenges for maintaining system reliability and stability. Fuzzy logic controllers (FLCs) have emerged as a promising approach to mitigate these instability issues, offering a robust and adaptable control strategy that can effectively handle the complexities of DC-DPSS. This paper examines the application of FLCs in DC-DPSS, exploring their effectiveness in addressing instability caused by RES fluctuations. FLCs are a control system that leverages fuzzy logic, a form of logic that utilizes linguistic variables to represent uncertainty, make decisions, and improve the stability of DC-distributed power systems. The research analyzes various system parameters, including inertia, damping, and synchronization characteristics, using a static synchronous generator (SSG) model. The study builds upon prior findings by adding a fuzzy logic controller to the existing system. The results showed better performance which resulted in improved inertia, damping, and synchronization characteristics. The efficiency of the proposed controller is demonstrated using MATLAB/Simulink.

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Analysis of Inertia Characteristics of Photovoltaic Power Generation System Based on Generalized Droop Control

Cited by 9 publications

References 23 publications

Research on Centre of Inertia Based Frequency Sampling and System Frequency Distribution Characteristics

Research on Centre of Inertia Based Frequency Sampling and System Frequency Distribution Characteristics

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Analysis of inertia, damping, and synchronization characteristics in grid-connected photovoltaic systems with fuzzy logic control

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