Charge Controller Step Gradations for Fluid Turbine Voltage Ranges
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Solution Overview
Problem
Clusters of fluid turbines face challenges in coordinating operations and maintaining efficiency, especially under low fluid conditions, which can result in suboptimal energy production and non-compliance with electrical grid standards.
Innovation Solution
Implementing a system that uses a processor to determine a power curve for each fluid turbine, applying MPPT-compliant loads to adjust rotational velocity, and controlling DC voltage outputs to meet performance criteria, thereby optimizing energy generation and compliance with grid requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If clusters of fluid turbines are constructed to aggregate energy production, then green energy production capacity is improved, but operational coordination and maintenance complexity worsen
Solution Approach 1:
The patent divides the cluster of fluid turbines into multiple groups, with each group having its own charge controller that independently manages a subset of turbines. This segmentation allows each controller to handle fewer turbines, reducing the coordination complexity for each controller while maintaining the aggregated energy production capacity of the entire cluster.
2Duration of action of moving object
If fluid turbines operate under low fluid conditions, then operational continuity is improved, but energy production efficiency worsens
Solution Approach 1:
The charge controller dynamically adjusts the rotational velocity of fluid turbines based on real-time fluid conditions. Under low fluid conditions, the controller reduces rotational velocity to match the available fluid energy, preventing stalling and maintaining operational continuity. This dynamic adjustment optimizes the balance between operational continuity and energy production efficiency across varying fluid conditions.
3Productivity
If MPPT protocol is applied to fluid turbines, then energy extraction efficiency is improved, but load management complexity worsens
Solution Approach 1:
The charge controller implements Maximum Power Point Tracking (MPPT) algorithm that automatically monitors fluid conditions and independently adjusts the electrical load on each turbine to maximize power extraction. The system self-regulates the load management without requiring external intervention, thereby improving energy extraction efficiency while keeping the load management complexity contained within the automated control system.
Data Source
AI summary
Systems, methods, and computer program products are disclosed for controlling a voltage output of a turbine. Controlling a voltage output of a turbine includes a rectifier for converting an AC output of a turbine to a DC voltage; a sensor for measuring the DC voltage; a charge controller for receiving the DC voltage from the rectifier and produce a DC output, including: a first circuit meeting a first performance criterion for a first DC range, a second circuit meeting a second performance criterion for a second DC operating range, a switch channeling the DC voltage between the first and second circuits; and a processor for receiving the DC voltage measurement from the sensor, and use the switch to channel the DC voltage between the first and second circuits, thereby meeting the first and second performance criteria when the DC voltage is in the first and second DC ranges, respectively.