混合GOA和PSO优化用于可再生多源双区域电力系统的负载频率控制
Muhammad Zubair Yameen1, Abdul Khalique Junejo2, Zhigang Lu1
1Key Laboratory of Power Electronics for Energy Conservation and Drive Control of Hebei Province, Yanshan University, Qinhuangdao, 066004, China.
Scientific reports
|May 20, 2025
概括
一个新的混合型草优化算法-粒子群优化 (GOA-PSO) 优化的比例-整数-导数 (PID) 控制器增强了可再生能源电网中的负载频率控制 (LFC). 这种先进的控制器显著提高了稳定性,并减少了单区域和双区域系统的波动.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 整合可再生能源的整合
背景情况:
- 将太阳能,风能和电动汽车 (EV) 集成到电网中,挑战了频率稳定性和连线电流.
- 来自非传统能源的波动可能会降低最终用户的电力质量和可靠性.
研究的目的:
- 提出一种新的比例整合导数 (PID) 控制器,通过混合的草优化算法-粒子群优化 (GOA-PSO) 进行优化,以实现增强的负载频率控制 (LFC).
- 评估控制器在单区域和双区域互联电力系统中的性能,使用各种可再生能源和电动汽车.
主要方法:
- 开发了一种混合GOA-PSO算法,以优化PID控制器参数,利用GOA的勘探和PSO的开发.
- 在单一和双区域电力系统网络中集成的基于模糊的MPPT光伏,P&O MPPT PMSG风力和电动汽车模型.
- 用完整时间绝对误差 (ITAE) 作为控制器调整的适应性函数.
主要成果:
- 在一个单一区域系统中,GOA-PSO-PID控制器表现出优于传统PSO-PID的性能,在超标 (高达79.95%) 和低于超标 (高达92.78%) 中实现了显著的降低,并在结算时间 (高达98.91%) 中实现了改进.
- 在双区域系统中,控制器可以大幅减少超标 (高达76.73%) 和低标 (高达87.62%),并改善上升时间 (高达75.68%).
- 控制器在±40%的参数变化和负载波动中被证明是稳健的.
结论:
- 该GOA-PSO-PID控制器为管理可再生能源主导的电力网络的频率稳定提供了强大的和可适应的解决方案.
- 这种优化的控制策略显著提高了LFC性能,确保了现代智能电网的可靠电力供应.
相关概念视频
Load-frequency control
112
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
112
Fast Decoupled and DC Powerflow
148
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
148
Maximum Power Flow and Line Loadability
91
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
91
Distributed Loads: Problem Solving
609
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
609
Multimachine Stability
129
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
129
The Power Flow Problem and Solution
148
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk, phase angle δk, real power Pk, and reactive power Qk. Two of these four variables are inputs, while the...
148


