Neural Network-Based Aggregated Equivalent Modeling of Distributed Photovoltaic External Characteristics of Faults
Abstract
:1. Introduction
2. Photovoltaic System Modeling Techniques
2.1. Modeling of Photovoltaic Modules
2.2. Mathematical Model of PV Inverter
2.3. PV Inverter Fault Ride-Through Control Strategy
3. Distributed Power Aggregation Equivalent Modeling
3.1. LSTM Networks
3.1.1. Forward Computation of LSTM
3.1.2. Backpropagation of LSTM
3.2. Aggregate Equivalent Modeling Process
4. Simulation Example
4.1. Physical Modeling
4.2. Neural Network Model Training and Testing
4.3. Comparison of Training Model and Testing Real Model
5. Conclusions
- (1)
- The proposed method does not rely on a specific physical mechanism but rather builds a model through a data-driven approach and utilizes neural networks to capture the nonlinear relationships of complex systems, which improves the efficiency, accuracy, and flexibility of the aggregated equivalence of fault characteristics.
- (2)
- The proposed method is applicable to the aggregated modeling of distributed power supply fault characteristics at all levels, such as lines, buses, regional grids, etc., and has a certain degree of universality.
- (3)
- The proposed method can realize the distributed power aggregation fault characteristic modeling of any output scenario and can adapt to the real-time modeling needs of new energy generation output fluctuations.
- (4)
- The proposed method provides an accurate and reliable reference for relay protection analysis and decision-making, helps optimize and improve relay protection strategy, facilitates the setting calculation of protection value, and improves the stability and reliability of the power grid.
- (1)
- The current proposed method can obtain the electrical steady-state quantities after a fault but it is not yet possible to equate the transient process, and in the future, we will try to predict the complete transient process after a fault using LSTM training.
- (2)
- The proposed method is currently applicable to aggregation modeling after steady state and three-phase faults and can be treated analogously for training modeling under asymmetric faults.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Reference Value | ||
---|---|---|
Voltage: Ub = 380 V | Power: Sb = 150 kVA | Frequency: fb = 50 Hz |
Circuit parameters (per-unit values) | ||
Filter inductor: L = 0.08 | Filter capacitor: C = 0.05 | Resistance: R = 0.02 |
Control parameters (per-unit values) | ||
kpp = 0.5 | kqp = 0.5 | kip = 0.3 |
kpi = 40 | kqi = 40 | kii = 10 |
PV Contribution | Steady State Error/% | |||
---|---|---|---|---|
U = 1.0 | U = 0.8 | U = 0.5 | U = 0.1 | |
[1.0, 0.8, 0.4, 0.9, 0.7] | 0.18 | 0.16 | 0.26 | 0.36 |
[0.3, 0.2, 0.1, 0.6, 0.5] | −1.27 | 1.48 | −0.23 | −0.65 |
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Li, K.; Huang, Q.; Fan, R.; Gao, S.; Wang, A.; Huang, T.; Sun, R. Neural Network-Based Aggregated Equivalent Modeling of Distributed Photovoltaic External Characteristics of Faults. Electronics 2024, 13, 3232. https://doi.org/10.3390/electronics13163232
Li K, Huang Q, Fan R, Gao S, Wang A, Huang T, Sun R. Neural Network-Based Aggregated Equivalent Modeling of Distributed Photovoltaic External Characteristics of Faults. Electronics. 2024; 13(16):3232. https://doi.org/10.3390/electronics13163232
Chicago/Turabian StyleLi, Kuan, Qiang Huang, Rongqi Fan, Shuai Gao, Anning Wang, Tao Huang, and Ruichen Sun. 2024. "Neural Network-Based Aggregated Equivalent Modeling of Distributed Photovoltaic External Characteristics of Faults" Electronics 13, no. 16: 3232. https://doi.org/10.3390/electronics13163232