Runge–Kutta optimization‐based selective harmonic elimination in an H‐bridge multilevel inverter

Sajid, Injila; Sarwar, Adil; Tariq, Mohd; Bakhsh, Farhad Ilahi; Hussan, Reyaz; Ahmad, Shafiq; Mohamed, Adamali Shah Noor; Ahmad, Akbar

doi:10.1049/pel2.12507

Cited by 6 publications

(3 citation statements)

References 28 publications

(72 reference statements)

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“…Injila Sajid et al in 17 implemented the Runge Kutta Optimization (RKO) algorithm to validate the SHE-PWM method in several topologies of MLIs. Various Modulation Indices (MIs) are used to obtain the switching angles.…”

Section: Introductionmentioning

confidence: 99%

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

Nasser,

Refky,

Shatla

et al. 2024

Sci Rep

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The Multilevel inverter (MLI) plays a pivotal role in Renewable Energy (RE) systems by offering a cost-effective and highly efficient solution for converting DC from Photovoltaic (PV) sources into AC at high voltages. In addition, an innovative technology holds immense significance as it not only enables the seamless integration of PV systems into the grid but also ensures optimal power generation, thereby contributing to the widespread adoption of RE and fostering a sustainable future. This paper presents a modified sinusoidal pulse width modulation (SPWM) control scheme for a three-phase half-bridge cascaded MLI-powered PV sources. The selection of the MLI configuration is motivated by its reduced number of switching components, which enhances system reliability and simplifies experimental implementation. Compared to the SPWM schemes which require (m−1) carriers that make the generation of the pulse circuit very complex, the proposed control scheme requires only three signals: a carrier signal, a triangular waveform, and a modulating signal. This approach significantly reduces the complexity of control and facilitates practical implementation. The proposed control scheme simulation is verified using MATLAB/SIMULINK Software. The grey wolf optimization (GWO) algorithm is implemented to determine the optimal switching angles of the proposed control scheme. The Total Harmonic Distortion (THD) objective is selected to be the fitness function to be minimized for improving the quality of the output waveforms. For verification, the results of the proposed GWO-based modified SPWM control scheme are compared with those obtained using both the Particle swarm Optimization (PSO) and Genetic algorithm (GA) used in the literature. Simulation results declared that the proposed control scheme improves performance, especially THD which is minimized to 6.8%. Experimental validation has been conducted by building a laboratory prototype of the proposed system. The experimental and simulation results gave acceptable and limited convergent results considering the experimental difficulties.

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Section: Introductionmentioning

confidence: 99%

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

Nasser,

Refky,

Shatla

et al. 2024

Sci Rep

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“…The presence of a maximum power point (MPP), representing the optimal functioning point for achieving the highest output, is particularly significant. However, factors like clouds, trees, buildings, dust deposition, etc., causing partial shading can lead to its formation on PV panels, resulting in diminished performance [6][7]. To address this issue, a frequently employed approach suggests integrating bypass diodes into designated cells within the series circuit.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Hybrid MPPT Using Artificial Intelligence for an Autonomous PV System

2024

ijSmartGrid

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This study compares traditional Maximum Power Point Tracking approaches, such as Perturb and Observe and Incremental Conductance, with a novel hybrid strategy incorporating Artificial Neural Networks. The hybrid algorithm synergizes the strengths of Perturb and Observe, Incremental Conductance, and Artificial Neural Networks by dynamically adjusting control parameters using historical data. The primary objective is to demonstrate the superior performance of the hybrid approach, highlighting its quick adaptation to changes in solar conditions, improved power quality and tracking accuracy, sustained stability, and a significant boost in power extraction compared to established techniques. Real-time simulations are conducted on representative solar energy systems, evaluating performance indicators under various scenarios, including rapid irradiance fluctuations and transient conditions. The results confirm that the hybrid MPPT approach, empowered by Artificial Neural Networks, outperforms traditional methods across key benchmarks such as response time, power quality, tracking precision, stability, and power extraction and even against stand -alone neural network approach. The ultimate aim is to identify the most effective hybrid MPPT technique based on comprehensive performance assessments.

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“…The first group consists of utility-shared PV systems, which encompass hybrid power systems, grid-tied systems, power plants, and various interconnected applications. The second group comprises stand-alone PV systems, which are utilized to power electrical pumps, electric vehicles (EVs), space applications, and street lights, as well as other independent devices [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

Sajid,

Gautam,

Sarwar

et al. 2023

Processes

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This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions.

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Runge–Kutta optimization‐based selective harmonic elimination in an H‐bridge multilevel inverter

Cited by 6 publications

References 28 publications

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

A grey wolf optimization-based modified SPWM control scheme for a three-phase half bridge cascaded multilevel inverter

Adaptive Hybrid MPPT Using Artificial Intelligence for an Autonomous PV System

Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

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