BESS Adaptive PI Control for Microgrid Disturbance Recovery
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Solution Overview
Problem
Microgrid systems face challenges in improving power quality, particularly during disturbances such as sudden loss of power generation, short circuits, and sudden load changes, which can lead to instability in system frequency and voltage.
Innovation Solution
A hybrid control approach combining differential evolution optimization (DEO) and artificial neural networks (ANN) is used to optimize the parameters of a proportional-integral (PI) controller for a battery energy storage system (BESS) in a microgrid. This approach allows for online tuning of controller parameters based on the level of disturbance, enhancing the system's ability to restore frequency and voltage to normal operating limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional PID controllers are used in microgrid systems, then the control structure is simple and easy to implement, but the system cannot effectively restore frequency and voltage to steady-state conditions following disturbances
Solution Approach 1:
The patent applies dynamics by transitioning from a static PID controller to a dynamic adaptive controller that continuously adjusts its parameters based on real-time disturbance detection. The controller monitors system frequency and voltage deviations, and automatically modifies control parameters to optimize the restoration process, enabling the system to adapt to varying disturbance conditions rather than relying on fixed parameters.
Solution Approach 2:
The patent implements parameter changes by modifying the controller's gain parameters and control characteristics in response to detected disturbances. The controller analyzes the magnitude and type of disturbance (frequency deviation, voltage sag, etc.) and adjusts its internal parameters accordingly, transforming the control strategy from a fixed-parameter approach to a variable-parameter approach that optimizes performance for each specific disturbance scenario.
2Loss of time
If advanced control strategies are implemented to improve power quality, then the stabilization time and overshoot are reduced, but the control complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple control modes and parameter sets that are ready for immediate deployment when specific disturbance types are detected. Rather than computing optimal parameters in real-time during a disturbance, the controller has pre-prepared control strategies for common disturbance scenarios, allowing for rapid switching and immediate action that minimizes stabilization time without requiring complex real-time optimization calculations.
Solution Approach 2:
The patent implements feedback by continuously monitoring system frequency, voltage, and other critical parameters, and using this information to adjust the control strategy. The controller incorporates feedback loops that detect disturbance occurrence, assess the system's response to control actions, and dynamically modify control parameters to optimize stabilization performance, creating a closed-loop control system that adapts based on actual system behavior.
3Adaptability or versatility
If the controller parameters are fixed, then the control system is simple and stable, but it cannot adapt to different levels and types of disturbances
Solution Approach 1:
The patent applies dynamics by implementing a control system that transitions from static fixed parameters to dynamic adaptive parameters. The controller continuously monitors disturbance characteristics and automatically adjusts its control parameters in real-time, enabling it to adapt to different disturbance levels and types without requiring manual reconfiguration or complex parameter tuning procedures.
Data Source
AI summary
A microgrid system includes a synchronous generator configured to convert mechanical power into electric power, an energy storage system configured to store and supply electric power, a controller configured to control operation of the energy storage system; and a point of common coupling bus connecting the synchronous generator and the battery energy storage system, wherein a controller parameter of the controller is determined based on a level of a disturbance using a trained artificial neural network in response to occurrence of the disturbance in the synchronous generator.