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LOVE EVOLUTION ALGORITHM-BASED TWO-STAGE FUZZY MULTI-OBJECTIVE FRAMEWORK FOR OPTIMAL DG AND EVCS PLANNING IN DISTRIBUTION NETWORKS CONSIDERING ROOFTOP PV UNCERTAINTY

DESIGN OVERVIEW
The increasing pe*******on of Distributed Generators (DGs), Electric Vehicle Charging Stations (EVCSs), and randomly deployed Rooftop Photovoltaic (RTPV) systems has significantly increased operational uncertainty and planning complexity in modern distribution networks. To effectively address these challenges, this MATLAB-based study proposes a Love Evolution Algorithm–based Two-Stage Fuzzy Multi-Objective (LEA-TSFMO) framework for the optimal siting and sizing of DGs and EVCSs under high RTPV variability.

In the first stage, a fuzzy multi-objective decision model is developed to aggregate multiple conflicting planning objectives, including real power loss minimization and voltage profile enhancement, into a unified fuzzy satisfaction index. This approach enables effective handling of uncertainties and trade-offs inherent in distribution system planning.
In the second stage, the Love Evolution Algorithm (LEA) is employed to solve the constrained optimization problem. LEA is a population-based metaheuristic inspired by the dynamics of human relationship evolution and interaction processes. The algorithm incorporates encounter, stimulus, value, role, and reflection phases to effectively balance global exploration and local exploitation within a nonlinear, multi-dimensional search space. Through adaptive interaction mechanisms, compatibility evaluation, and reflection-based diversification, LEA enhances convergence performance, avoids premature stagnation, and improves solution quality.

To accurately model the stochastic behavior of rooftop PV generation, probabilistic RTPV modeling is incorporated into both stages of the framework. The optimized network configuration obtained after RTPV-based DG integration is subsequently used as the base case for EVCS allocation, ensuring coordinated planning.

The proposed LEA-TSFMO framework is validated on the 33-bus radial distribution system under multiple operating scenarios and uncertainty conditions. Simulation results demonstrate that the proposed approach significantly reduces real power losses, improves voltage stability, and enhances renewable hosting capacity when compared with conventional optimization techniques. Owing to the effective integration of fuzzy decision-making and the robust search capability of LEA, the method exhibits superior convergence characteristics, high-quality solutions, and enhanced planning flexibility. Therefore, the proposed framework serves as a reliable and practical decision-support tool for future smart distribution systems with high renewable energy pe*******on and growing electric mobility demand.

MULTI-OBJECTIVE FUNCTION
The optimization problem aims to:
• Improve substation power factor
• Minimize real power losses
• Enhance voltage profile

To achieve these objectives, fuzzy membership functions are utilized:
• Triangular membership functions → for DG pe*******on and power factor
• Trapezoidal membership functions → for power loss and voltage constraints

TEST SCENARIOS
1. Base Case
2. Only Rooftop PV–DG
3. Only EVCS
4. Simultaneous RTPV–DG and EVCS

SIMULATION OUTPUTS
Graphical Results
• Voltage Profile
• Power Loss
• Convergence Characteristics

Numerical Results (MATLAB Command Window)
• Total Power Loss
• Optimal Roof-Top PV-DG Locations
• Optimal EVCS Locations
• Minimum & Maximum Bus Voltages
• Ex*****on Time

REFERENCES
1. W***y Stephen Tounsi Fokui, Michael J. Saulo, and Livingstone Ngoo,
“Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network with Randomly Distributed Rooftop Photovoltaic Systems,” IEEE, 2021.
2. Srinivasa Rao Gampaa, Kiran Jasthia, Preetham Golib, D. Das, and R.C. Bansal,
“Grasshopper Optimization Algorithm Based Two-Stage Fuzzy Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generations, Shunt Capacitors and Electric Vehicle Charging Stations,” Elsevier, 2019.

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ADVANCED CONTROL OF L–C FILTERED BLDC MOTOR DRIVES VIA MANTA RAY FORAGING OPTIMIZATION-BASED PARAMETER TUNING

DESIGN DETAILS
Brushless DC (BLDC) motor drives are extensively employed in high-performance industrial and automotive applications due to their high efficiency, superior power density, compact structure, and reliable operation. However, inverter-fed BLDC drives often suffer from torque ripple and current harmonic distortion, which adversely affect speed regulation, increase acoustic noise, and reduce overall drive performance. To address these challenges, this study proposes an advanced control strategy based on the Manta Ray Foraging Optimization (MRFO) algorithm for the effective reduction of torque ripple and harmonic distortion in an L–C filtered BLDC motor drive system.

An L–C filter is incorporated at the inverter output to suppress high-frequency switching harmonics and smooth the motor current waveform, thereby mitigating commutation-induced disturbances and improving power quality. The Manta Ray Foraging Optimization algorithm is employed to optimally tune the controller parameters owing to its unique chain foraging, cyclone foraging, and somersault foraging mechanisms, which provide an effective balance between exploration and exploitation while maintaining strong global search capability. The optimization objective is formulated to simultaneously minimize torque ripple, reduce harmonic distortion, and achieve accurate speed tracking under varying operating conditions.

The proposed MRFO-based control scheme is implemented and validated in the MATLAB/Simulink environment under different loading scenarios. Simulation results demonstrate a significant reduction in torque ripple and Total Harmonic Distortion (THD), along with enhanced transient response, improved steady-state performance, and superior speed regulation compared with conventional control approaches. Furthermore, the proposed method exhibits strong robustness against load disturbances, parameter uncertainties, and operating condition variations.
Overall, the integration of the Manta Ray Foraging Optimization algorithm with an L–C filtering mechanism provides an efficient, reliable, and robust solution for improving power quality, dynamic performance, and control effectiveness in BLDC motor drive applications.

REFERENCES
Reference Paper-1: Harmonics and Torque Ripple Minimization using L-C Filter for Brushless DC Motors
Author’s Name: A. Albert Rajan and Dr. S. Vasantharathna2
Source: IEEE
Year: 2009

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