Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization
Abstract
:1. Introduction
- (1)
- The BP neural network algorithm has the defects of slow convergence speed and local minimization, which are mainly due to the random selection of initial weights [26]. In this paper, the optimal initial neuron connection weights of the BP-PI controller are determined by combining IPSO with its fast convergence speed and global optimization features. Thus, the individual parameters of the PI controller are continuously adjusted by the improved BPNN algorithm, which can achieve better dynamic performance.
- (2)
- Coordinating modern artificial intelligence control with traditional PI control can effectively improve the efficiency and accuracy of an algorithm [27]. The proposed strategy applied to the wind turbine can effectively increase the anti-interference ability of the wind power region, enhance the stability of the power system, and thus have a promising development prospect in the field of new energy power generation.
2. System Dynamics
3. Frame and Algorithm of Wind Turbine
3.1. Particle Swarm Optimization Analysis
3.1.1. Basic Particle Swarm Optimization Algorithm
3.1.2. Improvement in Particle Swarm Optimization Algorithm
3.2. Principle and Framework of BP-PI Control Algorithm
3.3. Specific Implementation Process of IPSO-BPNN-PI-Based Secondary Frequency Control
4. Simulation and Discussion
- Case study one: Frequency adjustment in the presence of initial frequency errors
- Case study two: Adjustment process with application of random load disturbance
- Case study three: Frequency tuning with perturbations applied at subsequent intervals
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Parameter | Physical Significance |
---|---|
Nominal system frequency of the power system | |
Mechanical power of gas turbine | |
Sudden load disturbance | |
Steam valve position | |
Supplementary control action | |
Angular momentum | |
Speed governor time constant | |
Changing time constant (prime mover) | |
Reference setpoint | |
Equivalent damping coefficient of generator | |
Frequency bias constant | |
System measurement output |
Parameter | Max. Value | Min. Value | Stable Time | Stable Error |
---|---|---|---|---|
0.00086 | −0.01596 | 15.81 | −0.00024 | |
0.02611 | −0.00137 | 33.48 | 0.00012 | |
0.00047 | −0.00248 | 37.02 | 0.00007 |
Stable Time 1 | Stable Time 2 | Stable Time 3 | Stable Time 4 | Stable Time 5 | |
---|---|---|---|---|---|
PI | 46.86 | 30.39 | 28.47 | 38.25 | 35.17 |
BP-PI | 36.57 | 25.72 | 23.62 | 33.51 | 31.64 |
IPSO-BP-PI | 15.85 | 10.67 | 9.91 | 11.76 | 10.26 |
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Sun, J.; Chen, M.; Kong, L.; Hu, Z.; Veerasamy, V. Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization. Energies 2023, 16, 2015. https://doi.org/10.3390/en16042015
Sun J, Chen M, Kong L, Hu Z, Veerasamy V. Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization. Energies. 2023; 16(4):2015. https://doi.org/10.3390/en16042015
Chicago/Turabian StyleSun, Jikai, Mingrui Chen, Linghe Kong, Zhijian Hu, and Veerapandiyan Veerasamy. 2023. "Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization" Energies 16, no. 4: 2015. https://doi.org/10.3390/en16042015
APA StyleSun, J., Chen, M., Kong, L., Hu, Z., & Veerasamy, V. (2023). Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization. Energies, 16(4), 2015. https://doi.org/10.3390/en16042015