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Related Concept Videos

Maximum Power Transfer01:16

Maximum Power Transfer

213
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
213
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

91
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
91
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

148
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
148
Control of Power Flow01:30

Control of Power Flow

246
There are several methods to control power flow in power systems:
246
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

148
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
148
Power Factor Correction01:20

Power Factor Correction

150
The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
150

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Updated: May 24, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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GWO and WOA variable step MPPT algorithms-based PV system output power optimization.

Abderrahim Zemmit1, Abdelouadoud Loukriz2, Khaled Belhouchet3

  • 1Electrical Engineering Department, Electrical Engineering Laboratory (LGE), University of M'Sila, M'Sila, 28000, Algeria. abderrahim.zemmit@univ-msila.dz.

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|March 6, 2025
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Summary
This summary is machine-generated.

This study introduces novel Maximum Power Point Tracking (MPPT) algorithms using Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO) to boost photovoltaic (PV) system efficiency. These bio-inspired methods significantly reduce ripple and overshoot, enhancing overall energy output.

Keywords:
GWOMPPTOptimizationVariable step size MPPT algorithmWOA

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Area of Science:

  • Renewable Energy
  • Electrical Engineering
  • Optimization Algorithms

Background:

  • Photovoltaic (PV) systems face challenges with nonlinear characteristics and low efficiency.
  • Advanced Maximum Power Point Tracking (MPPT) is crucial for optimizing PV energy generation.

Purpose of the Study:

  • To propose and evaluate innovative MPPT algorithms based on Whale Optimization Algorithm (WOA) and Grey Wolf Optimization (GWO).
  • To enhance PV system efficiency, tracking accuracy, and stability using adaptive step-size optimization and a novel fitness function.

Main Methods:

  • Development of two MPPT algorithms utilizing WOA and GWO with adaptive step-size optimization.
  • Implementation of a novel fitness function designed to minimize ripple and overshoot while improving tracking accuracy.
  • Simulation and field validation using real-world data from a PV station.

Main Results:

  • Proposed algorithms achieved significant reductions in ripple (up to 99%) and overshoot (up to 67%) compared to fixed-step methods.
  • The PO-WOA algorithm demonstrated the highest efficiency (98.87% simulation, 98.94% real data) and minimal power loss.
  • Experimental validation confirmed the effectiveness of WOA and GWO-based MPPT under dynamic environmental conditions.

Conclusions:

  • WOA and GWO-based MPPT algorithms offer robust and efficient solutions for enhancing PV system performance.
  • These bio-inspired optimization techniques significantly improve energy output and stability in photovoltaic systems.
  • The study highlights the potential of advanced algorithms to overcome critical challenges in PV energy generation.