Multi-Area Hybrid Power Frequency Control With Cascaded Fractional MPC
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing load frequency control (LFC) methods in multi-area hybrid renewable energy power systems struggle to achieve rapid response to frequency disturbances, leading to high settling times, undershoot, and overshoot.
Innovation Solution
A hybrid control system incorporating a cascaded fractional model predictive controller (CFMPC) and fractional-order proportional-integral-derivative (FOPID) controllers, along with a sooty terns controller, to predict future power outputs, minimize controlled fitness equations, and optimize gain parameters for rapid frequency correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional control techniques (PI, I, PID) are used for load frequency control, then the control structure is simple and easy to implement, but the response speed is slow and settling time is high
Solution Approach 1:
The controller is segmented into multiple independent modules: CFMPC module for predictive control and primary regulation, FOPID-1 module for fractional-order correction, and FOPID-2 module for fine-tuning. Each module operates with specific gain parameters (Kp1, Ki1, Kd1 for FOPID-1; Kp2, Ki2, Kd2 for FOPID-2) that can be optimized separately using sooty terns optimization algorithm, enabling faster response without increasing overall implementation complexity
Solution Approach 2:
The CFMPC module performs preliminary action by predicting future frequency deviations and generating proactive control signals before disturbances fully impact the system. The model predictive control algorithm calculates optimal control actions in advance based on system dynamics and constraints, reducing the time required for frequency stabilization
2Device complexity
If conventional control techniques (PI, I, PID) are used for load frequency control, then the control structure is simple, but the response accuracy is low with high overshoot and undershoot
Solution Approach 1:
The controller transitions from static gain parameters to dynamic adaptive parameters. The sooty terns optimization algorithm continuously adjusts the gain parameters (Kp1, Ki1, Kd1, Kp2, Ki2, Kd2) based on real-time system conditions, frequency deviations, and rate of change. This dynamic adaptation enables the controller to optimize performance for each operating condition, significantly reducing overshoot and undershoot while maintaining manageable complexity through automated parameter tuning
Solution Approach 2:
The controller implements multi-level feedback mechanisms: the CFMPC uses feedback from frequency measurements and tie-line power flow to predict future deviations, while the FOPID-1 and FOPID-2 modules provide cascaded feedback correction. The sooty terns optimization algorithm also uses feedback from system performance to iteratively refine gain parameters, achieving high precision through continuous adaptation
3Object-affected harmful factors
If renewable energy sources are integrated into the power system, then the environmental impact is reduced and carbon footprint decreases, but the power quality deteriorates due to weather-dependent intermittent generation
Solution Approach 1:
The controller implements comprehensive feedback from renewable energy generation measurements, tie-line power flow, and frequency deviations. The CFMPC uses this feedback to predict future generation variations and adjust control signals proactively. The FOPID-1 and FOPID-2 modules provide additional feedback correction layers, enabling the system to maintain power quality despite intermittent renewable generation by continuously adapting to actual generation conditions
4Adaptability or versatility
If the control system uses multiple areas with tie-lines for power sharing, then the system flexibility and load distribution improve, but the frequency disturbance propagation and control complexity increase
Solution Approach 1:
The multi-area control system is segmented into independent area controllers, each implementing the same CFMPC-FOPID-1-FOPID-2 structure. This modular segmentation allows each area to autonomously manage its frequency control while the tie-line provides coordination. The standardized module design reduces overall complexity despite multiple areas, as each controller follows the same structure with locally optimized parameters
Solution Approach 2:
The controller design implements universality by using the same CFMPC-FOPID-1-FOPID-2 architecture for all areas in the multi-area system. This universal structure performs multiple functions: local frequency regulation, tie-line power flow control, and coordination with renewable energy sources. The standardized design simplifies implementation across multiple areas while maintaining flexibility through local parameter optimization using the sooty terns algorithm
Data Source
AI summary
A hybrid controller and method for mitigating frequency disturbances in a multi-area power plant including multiple thermal energy generators and renewable energy sources includes a cascaded fractional model predictive controller (CFMPC), first and second fractional-order proportional-integral-derivative controllers (FOPID-1 and FOPID-2) and a sooty terns controller. The CFMPC generates a minimized area central error (ACE) signal based on minimizing a controlled fitness equation, an ACE signal and a load power disturbance signal (ΔPL). The FOPID-1 generates a frequency disturbance correction signal based on the frequency disturbance value (Δfi). A combined frequency correction signal is generated by adding negative values of the minimized ACE signal and the frequency disturbance correction signal. The FOPID-2 receives the combined frequency correction signal and generates a frequency error correction signal. The sooty terns controller generates optimized gain parameters and transmits the optimized parameters to the FOPID-1 and the FOPID-2 to mitigate the frequency disturbances.