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Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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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.
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Maximum Power Transfer01:16

Maximum Power Transfer

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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.
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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.
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The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

278
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...
278
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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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:
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Power Factor01:11

Power Factor

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The power factor is defined as the ratio of average (or active) power to apparent power, as illustrated by the relation
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Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system.

Mohamed Ahmed Ebrahim Mohamed1, Shymaa Nasser Ahmed2, Mohamed Eladly Metwally2

  • 1Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt. mohamed.mohamed@feng.bu.edu.eg.

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The Arithmetic Optimization Algorithm (AOA) optimizes a grid-connected photovoltaic system

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Area of Science:

  • Renewable Energy Systems
  • Control Systems Engineering
  • Optimization Algorithms

Background:

  • Grid-connected photovoltaic (PV) systems require efficient Maximum Power Point Tracking (MPPT) for optimal energy harvesting.
  • Traditional MPPT methods may face challenges in dynamic conditions and parameter variations.
  • Optimizing MPPT controllers is crucial for maximizing the efficiency of PV systems.

Purpose of the Study:

  • To propose an optimal Maximum Power Point Tracking (MPPT) control scheme for grid-connected PV systems.
  • To utilize the Arithmetic Optimization Algorithm (AOA) for fine-tuning the parameters of a proportional-integral (PI) controller within an incremental conductance (IC) MPPT framework.
  • To enhance the performance and stability of PV systems through advanced optimization techniques.

Main Methods:

  • Development of a 100-kW benchmark grid-connected PV system model in MATLAB/SIMULINK.
  • Application of the Arithmetic Optimization Algorithm (AOA) to optimize the parameters of the PI controller in the Incremental Conductance (IC) MPPT algorithm.
  • Minimization of four standard benchmark performance indices to select the optimal controller parameters.
  • Comparative analysis against Modified Incremental Conductance (MIC), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) based MPPT methods.

Main Results:

  • The AOA-based PI-IC-MPPT significantly reduced rise time by up to 61% and settling time by up to 94% compared to other methods.
  • Demonstrated superior performance in MPPT extraction efficiency and system response.
  • Achieved notable improvements in transient response and stability metrics.

Conclusions:

  • The proposed AOA-based PI-IC-MPPT control scheme offers a highly effective and efficient solution for grid-connected PV systems.
  • AOA provides a robust optimization framework for enhancing MPPT performance.
  • The optimized MPPT controller leads to faster response times and improved energy yield from PV systems.