ENTERPRISE AI ANALYSIS: ENERGY SYSTEMS OPTIMIZATION
Hybrid Beluga Whale Optimization Based MPPT for Photovoltaic Powered Open End Winding Induction Motor Drives
This paper introduces a novel Hybrid Beluga Whale Optimization (HBWO) algorithm for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems integrated with an Open-End Winding Induction Motor (OEWIM) drive. The HBWO algorithm ensures fast and accurate maximum power extraction under dynamic conditions, including varying irradiance and partial shading. It incorporates an improved Direct Torque Control (DTC) scheme with a five-level torque hysteresis controller and an optimized switching table to reduce torque ripple and enhance system stability. Simulation results in MATLAB/SIMULINK show that HBWO outperforms traditional MPPT algorithms (P&O, PSO, COA, GPC) in DC link voltage rise time (0.11 s), overshoot (0.382%), settling time (0.17 s), steady-state ripple (0.50%), and steady-state error (0.04%). Motor speed control also benefits with superior rise time (0.1 s), overshoot (0.37%), settling time (0.18 s), and steady-state error (0.03%). Torque ripple is minimized to 0.5%, significantly lower than P&O (5.6%) and COA (2.2%). This validates HBWO's superiority in convergence speed, control accuracy, and robustness for renewable energy systems.
Enterprise Impact
Key metrics demonstrating the immediate and long-term value for enterprise adoption.
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DC Link Voltage Rise Time
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Motor Speed Rise Time
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Torque Ripple Reduction
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Overall Efficiency
Deep Analysis & Enterprise Applications
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The proposed Hybrid Beluga Whale Optimization (HBWO) algorithm achieves superior performance in Maximum Power Point Tracking (MPPT) for photovoltaic (PV) systems. It demonstrates significantly reduced DC link voltage ripple (0.50%) and motor speed ripple (0.53%) compared to conventional algorithms like P&O (5.6% torque ripple) and COA (2.2% torque ripple). HBWO ensures faster rise times, minimal overshoot, and lower steady-state error under dynamic irradiance, partial shading, and varying load conditions. Its convergence speed is notably faster than P&O, PSO, COA, and GPC, making it a robust and reliable solution for PV-fed OEWIM systems.
The motor drive is governed by an improved Direct Torque Control (DTC) scheme, featuring a five-level torque hysteresis controller and an optimized switching table. This advanced control method plays a pivotal role in reducing torque ripples and enhancing overall dynamic response of the motor drive, ensuring smoother operation during transients. This design minimizes torque ripple and improves flux control, essential for stable motor performance under varying environmental and load conditions.
Enterprise Process Flow
Initialize Parameters (n, Tmax, ub, lb, α, β, μ)
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Initialize Population with Hybrid Strategy
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Evaluate Initial Fitness
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Begin Optimization Loop (T = 1 to Tmax)
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Update Contraction Coefficient
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Perform Exploration, Exploitation, and Whale Fall Phases
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Update Position & Evaluate Fitness
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Update Global Best Solution & Best Fitness
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Repeat until Tmax
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Output Optimum Duty Cycle
| Algorithm | DC Link Voltage Performance | Motor Speed Performance | Torque Ripple | Efficiency | Convergence Time (s) |
|---|---|---|---|---|---|
| HBWO |
|
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0.5% | 99.89% | 0.192 |
| P&O |
|
|
5.6% | 97.25% | 1.38 |
| COA |
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2.2% | 98.75% | 0.686 |
| GPC |
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|
1% | 99.15% | 0.291 |
Advanced ROI Calculator
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Estimated Annual Savings
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Implementation Roadmap
Our structured approach ensures a seamless transition and maximum ROI.
Phase 1: System Assessment & Customization
Comprehensive analysis of existing PV infrastructure and OEWIM drive systems. Customization of HBWO algorithm parameters and DTC scheme for optimal integration.
Phase 2: Simulation & Validation
Extensive MATLAB/SIMULINK simulations to validate HBWO-MPPT and improved DTC performance under diverse real-world conditions (irradiance, shading, load variations). Hardware-in-the-loop (HIL) testing for robust validation.
Phase 3: Deployment & Optimization
Pilot deployment in a controlled environment, followed by continuous monitoring and fine-tuning of the HBWO algorithm for sustained peak performance and system stability.
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