Power System Stabilizer Tuning for Low-Frequency Oscillation Damping
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Solution Overview
Problem
Power systems experience instability due to low-frequency oscillations (LFO) caused by weakly damped oscillation modes, speed control system instability, improper primary frequency modulation settings, excitation system defects, and the volatile nature of renewable energy sources, leading to potential network failures.
Innovation Solution
A method employing fuzzy c-means clustering and a combination of deep learning and whale optimization algorithms to adjust power system stabilizer (PSS) parameters in real-time, using data sets from power networks to mitigate LFO.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If synchronous generators with high-gain AVR are used to damp low-frequency oscillations, then the damping capability is improved, but the oscillations are amplified and rotor damping torque is reduced
Solution Approach 1:
The invention modifies PSS parameters (gain K and time constant T1) to optimize the damping performance of synchronous generators. By adjusting these parameters, the system achieves effective LFO damping without the harmful oscillation amplification caused by high-gain AVR, thus resolving the technical contradiction between damping capability and oscillation amplification.
2Productivity
If renewable energy sources are added to power networks to fulfill energy demands, then the energy supply capability is improved, but system instability and low-frequency oscillations increase
Solution Approach 1:
The invention introduces a power system stabilizer (PSS) as an intermediary device to counteract the destabilizing effects of renewable energy sources. The PSS processes terminal voltage, real power, and reactive power data to generate control signals that dampen LFOs caused by the volatile characteristics of renewable energy, thereby maintaining system stability while preserving energy supply capability.
3Ease of operation
If conventional LFO mitigation methods are used, then the implementation simplicity is maintained, but the response time and damping effectiveness are insufficient
Solution Approach 1:
The invention replaces conventional mechanical or simple electronic LFO mitigation methods with an intelligent control system based on deep learning and whale optimization algorithms. This substitution enables faster response times and more effective damping by automatically adjusting PSS parameters in real-time based on system conditions, while maintaining ease of operation through automated control.
Data Source
AI summary
A method and system for mitigating low-frequency oscillations of a power system network (PSN). The method includes receiving multiple data sets from the PSN, comprising values of terminal voltage, a real power, and a reactive power. The method further employs the multiple data sets to a fuzzy c-means clustering technique, a deep learning technique and a whale optimization algorithm to generate a pair of parameter values for a power system stabilizer controlling a steady-state of the power system network.