Periodic Disturbance Suppression Control for Power Systems
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Solution Overview
Problem
Conventional methods for suppressing periodic disturbances in power systems, particularly in dispersed power systems with smart grids and microgrids, face instability and convergence issues during large condition changes, as they do not adequately account for harmonic divergence and stagnation, leading to suboptimal control stability and compensation effects.
Innovation Solution
A periodic disturbance suppressing control apparatus that includes a sensing section, an estimating section, an adder, and a learning control section to correct the inverse model of the transfer characteristic during estimation, even under large condition changes, by using a multiplier and LPFs to calculate deviations and correct the reciprocal of the transfer characteristic based on differences in sensed and commanded periodic disturbances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional periodic disturbance suppression methods are used, then control stability is maintained under normal conditions, but the system becomes unstable and fails to converge during large condition changes due to not accounting for harmonic divergence and stagnation
Solution Approach 1:
The invention applies dynamics by making the transfer characteristic model adaptive and time-varying. The learning control section continuously updates the reciprocal of the transfer characteristic based on actual system responses, allowing the model to dynamically adjust to large condition changes in the power system rather than relying on a fixed predetermined model.
Solution Approach 2:
The invention implements feedback through the learning control section that uses the actual sensed periodic disturbance and suppressing command to compute the difference and update the transfer characteristic model. This closed-loop learning mechanism ensures the system adapts to changing conditions while maintaining stability.
2Measurement precision
If the reciprocal of the transfer characteristic is corrected using conventional learning methods, then model accuracy improves under small variations, but the system diverges and stagnates under large condition changes
Solution Approach 1:
The invention applies partial action by selectively updating only the reciprocal of the transfer characteristic model based on the difference between actual and expected periodic disturbance, rather than completely re-identifying the system. This incremental update approach prevents divergence while gradually improving model accuracy even under large condition changes.
Solution Approach 2:
The invention provides beforehand cushioning by using a predetermined transfer characteristic as a baseline model before large condition changes occur. This initial model serves as a stable foundation that prevents immediate divergence, while the learning control gradually adapts the model to new conditions, cushioning against potential instability.
3Measurement precision
If preparatory system identification is performed, then initial model accuracy is improved, but the system requires additional time and complexity for model identification before operation
Solution Approach 1:
The invention applies self-service by enabling the control system to automatically identify and update its own transfer characteristic model during normal operation through the learning control section. The system serves itself by continuously learning from actual system responses without requiring external system identification procedures or additional preparatory time.
Solution Approach 2:
The invention performs preliminary action by establishing a predetermined transfer characteristic model before operation, which provides immediate functionality. The model is then refined through continuous learning during operation, eliminating the need for extensive preparatory system identification while maintaining accuracy.
Data Source
AI summary
A periodic disturbance suppressing control apparatus is designed to estimate and correct an inverse model of a transfer characteristic of a real system successively even in case of large condition change in the real system, and to realize a stable control system.A periodic disturbance of an object to be suppressed is outputted as a sensed periodic disturbance ISdn, ISqn of a direct current component. A difference between a signal obtained by multiplication of the sensed periodic disturbance ISdn, ISqn with a multiplier using a reciprocal Qn of a transfer characteristic, and a signal obtained by adding only a detection delay to a periodic disturbance suppressing command I*dn, I*qn, to estimate the periodic disturbance dI^dn, dI^qn. Thee periodic disturbance suppressing command is calculated by calculating a deviation between the estimated periodic disturbance dI^dn, dI^qn. A learning control section 29 corrects the reciprocal Qn of the transfer characteristic in accordance with a quantity obtained by diving a difference of the periodic disturbance suppressing command I*dn, I*qn during one sample interval by a difference of the sensed periodic disturbance during the one sample interval.