Adaptive Power System Stabilizer Tuning for Low-Frequency Oscillations
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Solution Overview
Problem
Power system networks face instability due to low-frequency oscillations (LFO), which can lead to dynamic instability and network failures, especially with the integration of renewable energy sources like wind energy.
Innovation Solution
A method employing a fuzzy c-means clustering technique combined with a deep learning technique and a whale optimization algorithm to adjust Power System Stabilizer (PSS) parameters in real-time, effectively mitigating low-frequency oscillations in power system networks.
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 system stability deteriorates due to amplified LFO and reduced rotor damping torque
Solution Approach 1:
The invention dynamically adjusts PSS parameters (gain K and time constants T1, T2) based on real-time system operating conditions. The fuzzy logic controller continuously monitors system state and modifies PSS parameters to optimize damping performance across varying load conditions, preventing both LFO amplification and insufficient damping.
Solution Approach 2:
The invention implements a closed-loop feedback mechanism where the fuzzy logic controller continuously monitors rotor angle deviation and speed deviation, then adjusts PSS parameters accordingly. This feedback loop ensures that the system responds adaptively to changing conditions, maintaining stability while effectively damping oscillations.
2Productivity
If renewable energy sources are integrated to fulfill energy demands, then the energy supply capability is improved, but the system stability deteriorates due to volatile characteristics causing LFO
Solution Approach 1:
The invention introduces a fuzzy logic-based PSS as an intermediary control mechanism between renewable energy sources and the power system. This intermediary adapts to the volatile characteristics of renewable energy by dynamically adjusting damping parameters, thereby maintaining system stability while accommodating high renewable energy penetration.
Solution Approach 2:
The invention transforms the static PSS parameters into dynamic, adaptive parameters that respond to real-time system conditions. The fuzzy logic controller enables continuous adjustment of PSS gain and time constants based on operating conditions, allowing the system to accommodate variable renewable energy input while maintaining stability.
3Ease of operation
If conventional PSS parameter adjustment methods are used, then the implementation simplicity is improved, but the response speed to disturbances deteriorates
Solution Approach 1:
The invention replaces traditional mechanical or fixed-parameter PSS adjustment methods with an intelligent fuzzy logic control system. This substitution enables rapid, adaptive parameter adjustment based on real-time system conditions, significantly improving response speed to disturbances while maintaining ease of operation through automated control.
Solution Approach 2:
The invention pre-configures the fuzzy logic controller with rule bases and membership functions that enable immediate response to disturbances. By preparing the control logic in advance with comprehensive coverage of possible operating conditions, the system achieves rapid response without complex real-time calculations, balancing simplicity and speed.
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.