Performance Analysis of an ANN-Based MPPT Controller for a Solar PV and Battery-Powered BLDC Motor Drive
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Performance Analysis of an ANN-Based MPPT Controller for a Solar PV and Battery-Powered BLDC Motor Drive
Abstract
The integration of intermittent renewable energy sources, such as solar photovoltaics (PV), into applications requiring a consistent power supply presents a significant engineering challenge. This paper presents a comprehensive performance analysis of a system designed to power a Brushless DC (BLDC) motor drive using a solar PV array integrated with a Battery Energy Storage System (BESS). The proposed methodology employs an Artificial Neural Network (ANN) to execute Maximum Power Point Tracking (MPPT), ensuring optimal energy harvesting from the PV array under variable atmospheric conditions. A bidirectional DC-DC converter, governed by a proportional-integral (PI) controller, manages the power flow to and from the battery, thereby regulating the common DC bus voltage. Simulation results, conducted under a step-change in solar irradiance, demonstrate the system's efficacy. The ANN-based controller successfully extracts maximum power from the PV array, and the power management system effectively maintains a stable DC bus voltage, ensuring continuous operation of the motor load. The battery seamlessly transitions between charging and discharging states to compensate for fluctuations in solar power generation. This study validates the proposed control strategy as a viable and robust solution for reliably powering motor drive applications with renewable energy.
Keywords
Solar Photovoltaic (PV), Maximum Power Point Tracking (MPPT), Artificial Neural Network (ANN), BLDC Motor, Battery Energy Storage System (BESS), Bidirectional Converter
I. Introduction
The global imperative to transition towards sustainable energy has catalyzed the widespread adoption of renewable energy systems, with solar photovoltaics (PV) at the forefront. However, the inherent intermittency of solar energy, which fluctuates with changing atmospheric conditions, poses a significant challenge for applications that demand a stable and continuous power supply. This variability necessitates intelligent power management systems to bridge the gap between volatile energy generation and consistent load requirements.
Brushless DC (BLDC) motors are increasingly utilized in a diverse range of applications due to their high efficiency, reliability, and excellent control characteristics. Ensuring the operational reliability of BLDC motor drives when powered by intermittent PV sources presents a significant control and power management challenge. The core task lies in simultaneously maximizing the energy harvested from the PV array while ensuring the delivery of a regulated voltage to the motor drive, irrespective of solar irradiance levels.
To address these challenges, system designs typically incorporate two key technologies: Maximum Power Point Tracking (MPPT) algorithms and Battery Energy Storage Systems (BESS). MPPT techniques are essential for dynamically adjusting the operating point of the PV array to maximize its power output. Concurrently, a BESS acts as an energy buffer, absorbing surplus power when generation exceeds demand and supplying stored energy when generation is insufficient. The effectiveness of the entire system hinges on the sophistication of the control strategies governing these components.
This paper presents the design and simulation-based validation of an integrated system featuring an Artificial Neural Network (ANN) based MPPT controller. The primary contribution of this work is to demonstrate the dynamic performance of this intelligent control strategy in managing power flow between a solar PV array, a battery, and a BLDC motor load. The system is designed to maintain a stable DC bus voltage, ensuring uninterrupted motor operation even during significant variations in solar irradiance.
The subsequent sections of this paper are organized as follows: Section II details the architecture of the proposed system. Section III describes the control strategies for MPPT and DC bus voltage regulation. Section IV presents the simulation model and its parameters. Section V provides a detailed analysis of the simulation results. Finally, Section VI concludes the paper and suggests directions for future research.
II. Proposed System Configuration
The proposed system is architected to create a stable and resilient power source for a BLDC motor by synergistically combining a solar PV array with a battery energy storage unit. The topology is strategically designed to decouple the variable power generation from the constant power demand of the load, with each component playing a distinct role in achieving the overall objective of reliable power delivery.
System Architecture Overview
The system comprises five primary components interconnected via a common DC bus. The solar PV array serves as the primary energy source. It is interfaced with the DC bus through a unidirectional DC-DC boost converter, which is controlled by the MPPT algorithm. The battery storage unit acts as the energy buffer and is connected to the same DC bus via a bidirectional DC-DC buck-boost converter. This converter manages the charging and discharging of the battery to maintain bus voltage stability. The common DC bus provides a regulated voltage to the inverter of the BLDC motor drive, which constitutes the system's load.
Component Descriptions
• Solar PV Array: The energy source is a PV array configured with two parallel strings to deliver a peak power of approximately 100 W under standard test conditions (1000 W/m² irradiance). The voltage at the maximum power point (Vmpp) is 17 V.
• Battery Energy Storage System (BESS): A lead-acid battery with a nominal voltage of 12 V and a capacity of 100 Ah is used for energy storage. It is responsible for absorbing excess PV energy and supplying power during periods of low or no solar generation.
• BLDC Motor: The system load is a 24 V, 39 W BLDC motor. It is designed to operate under a constant torque demand, requiring a stable input voltage from the DC bus.
• Power Converters:
◦ Unidirectional Boost Converter: This converter steps up the 17 V output from the PV array to the higher DC bus voltage. Its duty cycle is actively controlled by the ANN-based MPPT to ensure the PV array operates at its maximum power point.
◦ Bidirectional Buck-Boost Converter: This crucial component manages the power flow between the 12 V battery and the 24 V DC bus. It operates in buck mode to charge the battery when surplus PV power is available and in boost mode to discharge the battery and support the load when PV power is insufficient.
The seamless operation of this hardware configuration is governed by the sophisticated control strategies detailed in the following section.
III. Control Strategy and System Modeling
The stability and efficiency of the proposed system are contingent upon two independents but coordinated control loops. The first loop focuses on maximizing energy harvesting from the solar PV array, while the second is dedicated to regulating the DC bus voltage and managing the overall power flow.
A. ANN-Based Maximum Power Point Tracking
The primary objective of the MPPT controller is to continuously adjust the electrical operating point of the PV array to ensure it produces the maximum possible power under dynamically changing solar irradiance and temperature.
1. Principle of Operation: This system employs an intelligent MPPT technique based on an Artificial Neural Network (ANN). The ANN is trained to recognize the relationship between environmental conditions and the PV array's maximum power point voltage.
2. ANN Controller Implementation: The ANN controller takes two real-time inputs: solar irradiance (W/m²) and ambient temperature. Based on these inputs, the network generates a corresponding reference voltage signal (V_ref), which represents the optimal operating voltage for the PV array under those specific conditions.
3. Control Loop: The V_ref generated by the ANN is compared with the actual measured voltage of the PV array. The resulting error signal is fed into a Proportional-Integral (PI) controller. The PI controller processes this error to generate the appropriate duty cycle for the Pulse Width Modulation (PWM) signal. This PWM signal drives the switching element (IGBT) of the PV-side boost converter, thereby adjusting the array's operating voltage to match the V_ref and achieve maximum power extraction. The function of the PI controller can be represented as:
B. DC Bus Voltage Regulation and Power Management
1. Control Objective: The main goal of this subsystem is to maintain a constant and stable DC bus voltage of 24 V, which is the rated voltage required by the BLDC motor drive. This regulation must be maintained despite fluctuations in PV power generation and load demand.
2. Voltage Control Loop: The control mechanism for the bidirectional converter is based on a feedback loop. The actual DC bus voltage is measured and compared to a fixed 24 V reference value. The resulting error is processed by a PI controller, which generates the duty cycle for the PWM signals that control the bidirectional converter's switches. This action modulates the power transfer between the battery and the DC bus to eliminate any voltage deviation.
3. Power Flow Logic: The direction of power flow through the bidirectional converter is determined implicitly by the PI controller based on the state of the DC bus, which in turn depends on the balance between PV power generation and total load consumption. The total system load, including the BLDC motor and inverter losses, is approximately 60 W.
◦ Charging Mode: When the power generated by the PV array exceeds the ~60 W required by the load, the DC bus voltage tends to rise. The PI controller responds by directing the surplus power to charge the battery. In this state, the bidirectional converter operates in buck mode, stepping the 24 V bus voltage down to the battery's charging voltage.
◦ Discharging Mode: When the PV power generation falls below the ~60 W load requirement, the DC bus voltage tends to drop. The PI controller detects this and commands the battery to supply the deficit power to the load. In this state, the bidirectional converter operates in boost mode, stepping the 12 V battery voltage up to the 24 V DC bus voltage.
This dual-loop control strategy enables the system to autonomously adapt to changing conditions, ensuring both maximum energy yield and a stable power supply for the load.
IV. Simulation Model and Parameters
To evaluate the dynamic performance and validate the efficacy of the proposed control strategies, a comprehensive system model was developed and simulated using the MATLAB/Simulink environment. The model faithfully implements the system architecture and control algorithms described in the preceding sections, utilizing standard blocks from the Simulink library to represent the physical components and controllers. The key parameters used in the simulation are detailed in Table I.
Table I: System Simulation Parameters
Component | Parameter | Value |
Solar PV Array | Peak Power | ~100 W |
Voltage at MPP (Vmpp) | 17 V | |
Current at MPP (Impp) | 5.88 A (2.94 A per string) | |
Configuration | 2 Parallel Strings | |
Battery Storage | Nominal Voltage | 12 V |
Capacity | 100 Ah | |
BLDC Motor | Rated Voltage | 24 V |
Rated Power | 39 W | |
Rated Speed | 3000 rpm | |
Rated Torque | 0.125 Nm | |
Torque Constant | 0.05 Nm/A | |
Line-to-Line Resistance | 1.35 Ω | |
Line-to-Line Inductance | 0.93 mH | |
Pole Pairs | 4 | |
DC Bus | Nominal Voltage | 24 V |
Converter Parameters | L & C Values | Calculated based on standard design equations for the respective power, voltage, and switching frequency ratings. |
The results obtained from the simulation of this model are presented and analyzed in the following section.
V. Results and Discussion
The system's performance was evaluated under a dynamic simulation scenario designed to test its response to a significant change in operating conditions. The BLDC motor was operated under a constant load torque of 0.125 Nm, while the solar irradiance was subjected to a step change from 1000 W/m² down to 600 W/m² to simulate the effect of passing cloud cover.
System Performance at High Irradiance (1000 W/m²)
• PV System Analysis: Under an irradiance of 1000 W/m², the ANN-based MPPT controller effectively maintains the PV array's operating voltage at its optimal point of 17 V. Consequently, the array generates its maximum power of approximately 100 W.
• Power Flow and Battery State: The 100 W generated by the PV array is more than sufficient to meet the demand of the BLDC motor drive and its associated inverter, which together consume approximately 60 W. The power management system automatically diverts the surplus power of approximately 35 W to the battery. This is confirmed by the simulation results, which show a negative battery current of around -2 A, indicating charging. Correspondingly, the battery's State of Charge (SoC) exhibits a steady increase from its initial 50% level.
System Dynamic Response to Irradiance Change
• Transition Analysis: The critical test of the control system occurs when the irradiance is abruptly reduced from 1000 W/m² to 600 W/m².
• PV Power Reduction: As expected, the power generated by the PV array drops significantly, stabilizing at a new maximum power point of approximately 60 W.
• Battery Mode Reversal: At this new operating point, the PV-generated power precisely matches the load demand. The control system, in its primary mission to maintain a rigid 24 V bus, responds instantly to the cessation of surplus charging power. It commands the battery to transition seamlessly from charging mode (negative current) to discharging mode, supplying a small current of approximately 1 A. This action ensures uninterrupted power delivery to the load and compensates for any system losses, demonstrating the high sensitivity and robust regulation of the voltage control loop. This immediate reversal is reflected in the SoC trend, which shifts from a positive (charging) slope to a negative (discharging) slope.
• DC Bus Stability: Most importantly, throughout this significant disturbance in the primary power source, the DC bus voltage remains tightly regulated around the 24 V setpoint. This highlights the effectiveness of the PI-based voltage control loop in managing the bidirectional converter to ensure a continuous and stable power supply to the load.
BLDC Motor Performance
• Operational Stability: Due to the effective regulation of the DC bus voltage, the BLDC motor operates continuously and stably throughout the simulation. The motor's speed, torque, stator current, and back-EMF waveforms remain consistent, reflecting the successful isolation of the load from the power source fluctuations. The power management system successfully guarantees that the motor receives the required input power at its rated voltage at all times.
In summary, the simulation results provide strong validation for the proposed control strategy, demonstrating its ability to manage power flow effectively and maintain system stability under varying environmental conditions.
VI. Conclusion and Future Scope
This paper has presented a detailed performance analysis of an integrated solar PV and battery storage system designed to power a BLDC motor drive. The simulation results successfully demonstrate that the proposed control scheme, which combines an ANN-based MPPT controller for the PV array and a PI controller for DC bus voltage regulation, provides a robust and efficient solution. The system effectively harvests maximum power from the PV source, intelligently manages the battery's state of charge, and maintains a stable voltage supply to the load, even during abrupt changes in solar irradiance. The seamless transition of the battery between charging and discharging modes underscores the effectiveness of the power management logic. The results affirm that intelligent control paradigms, such as ANN-based MPPT, are critical for unlocking the full potential of integrated PV-BESS systems in high-reliability applications.
Future Scope
While the simulation results are promising, further research could enhance and validate this work. Potential future directions include:
• Experimental Validation: The development and testing of a hardware prototype to experimentally verify the simulation results and assess the real-world performance of the controllers under various operational and environmental conditions.
• Comparative Algorithm Study: A comprehensive comparative analysis of the ANN-MPPT controller against other advanced MPPT algorithms, such as those based on Fuzzy Logic or Particle Swarm Optimization, to benchmark its performance in terms of tracking speed and steady-state efficiency.
• Dynamic Load Profile Investigation: An investigation into the system's transient response and stability margins under dynamically varying load profiles, representative of real-world motor applications, to assess the robustness of the DC bus voltage regulation.
• Battery Health Management: The integration of advanced power management algorithms focused on optimizing the charging and discharging cycles to improve the long-term operational health and lifespan of the battery energy storage system.
VII. YouTube Video
VIII. Purchase link of the Model
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