Machine Learning Based DC DC Converter for Photovoltaic (PV) Modules

Abstract

This research work describes the creation and testing of a machine learning based DC DC converter meant to improve the energy conversion efficiency of photovoltaic (PV) systems. PV modules are extremely sensitive to changing external factors including temperature, irradiance, and partial shade, causing power output to fluctuate. To address these issues, maximum power point tracking (MPPT) algorithms are commonly employed in DC DC converters to continuously modify the operating point and extract the most available power. Conventional MPPT approaches, such as Perturb and Observe (P&O) and Incremental Conductance (INC), might struggle to cope with rapid environmental changes, resulting in power losses. In this study, a machine learning model is integrated into the DC DC converter to forecast the ideal duty cycle in real time, using historical data and current environmental conditions. This method enables the converter to respond more efficiently to abrupt variations and partial shading, reducing tracking errors and oscillations. The suggested system learns patterns in PV behaviour using environmental information and simulations, resulting in faster and more accurate power point tracking than existing methods. This invention provides a machine learning based DC DC converter designed for use with photovoltaic (PV) modules. The converter utilizes real time data from environmental sensors and a machine learning model to optimize the PV module's power output. By continuously adapting to changes in irradiance, temperature, and load conditions, the system ensures maximum energy yield. The machine learning model is trained using historical data and is updated in real time, allowing the converter to maintain high efficiency even in dynamic environmental conditions.

Specification

Description:A Machine Learning (ML)-based DC-DC converter for PV modules integrates artificial intelligence to enhance power management and optimize energy harvesting in solar systems. The converter adjusts its operating parameters intelligently in response to changing environmental conditions such as solar irradiance, temperature, and load demands. Below is a comprehensive description of its architecture, operation, components, and advantage.
1. PV M odule:
 Converts sunlight into DC electrical energy.
 Produces variable voltage and current depending on environmental factors.
2. DC DC Converter:
 Adjusts the voltage and current from the PV module to match the load or storage requirements.
 Operates using pulse-width modulation (PWM) controlled by the ML algorithm.
 Boost Converter: Steps up the voltage.
 Buck Converter: Steps down the voltage.
 Buck-Boost Converter: Can both increase and decrease voltage.
3. Sensors:
 Measure critical parameters, including:
 PV output voltage (V)
 PV output current (I)
 Solar irradiance (W/m²)
 Temperature (°C) of the PV module
4. ML Controller:
 Hosts the ML algorithm and processes sensor inputs to predict the optimal duty cycle or MPP.
 The controller can be implemented on microcontrollers, DSPs (Digital Signal Processors), or FPGAs to ensure fast real-time processing.
 Algorithms used include:
 Supervised learning (e.g., neural networks, regression models) to predict the MPP.
 Reinforcement learning (RL) to adjust the converter in real-time based on continuous feedback.
 Hybrid approaches combining fuzzy logic with ML for improved decision-making under uncertainty. , Claims:A machine learning based DC DC converter for a photovoltaic (PV) module, comprising: A
DC DC converter connected to a PV module; A microcontroller or digital signal processor
(DSP) configured to control the converter; A machine learning algorithm stored in the
memory of the microcontroller, wherein the machine learning algorithm predicts the
maximum power p oint of the PV module based on real time environmental conditions and
past data; sensors configured to monitor environmental parameters, including irradiance and
temperature, wherein the microcontroller adjusts the operation of the DC DC converter
based on the machine learning predictions.
 The converter of claim 1, wherein the machine learning algorithm is selected from a group
comprising regression algorithms, neural networks, decision trees, or ensemble learning
methods.
 The converter of claim 1, further comprising a communication interface to send and receive
data from a remote server for enhanced predictive accuracy.
 The converter of claim 1, wherein the machine learning model is continuously updated with
real time data to optimize power output.
 The conv erter of claim 1, wherein the microcontroller is configured to adaptively adjust the
duty cycle of the DC DC converter to maintain optimal performance during changes in
irradiance and temperature.
 The converter of claim 1, further comprising an energy stor age device to store excess energy
during periods of low demand.
 The converter of claim 1, wherein the machine learning algorithm is trained using a
combination of historical environmental data and real time performance metrics from the
PV module.

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