Analysis of Power Quality issues of different types of household applications

Thentral, T. M. Thamizh; Palanisamy, R.; Usha, S.; Bajaj, Mohit; Zawbaa, Hossam M.; Kamel, Salah

doi:10.1016/j.egyr.2022.04.010

Cited by 16 publications

(3 citation statements)

References 19 publications

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“…Tis design incorporates most of the CMC regular operations, such as frequency control, voltage magnitude, and input power factor. For CMC output and input voltage, current basic components, the CMC must be within the dqo reference frame (9)(10)(11)(12)(13)(14). Te transformation matrix must meet the following conditions.…”

Section: Modeling Of Matrix Convertermentioning

confidence: 99%

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Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System

Amarendra

Pandian

Reddy³

et al. 2023

International Transactions on Electrical Energy Systems

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Advanced power converters are being developed due to the latest developments in power electronics switches. The matrix converter (MC) assessment is the product of this drastic growth. MC is a bidirectional power flow device with a single-step energy conversion mechanism that operates at variable voltage and frequency. The main drawback of MC is the presence of harmonics due to power electronic device switching. A lot of research has been done to decrease the harmonics content present in the MC, but it is restricted to a certain level only. To overcome this limit, the harmonics are minimized in the MC by incorporating soft computing controllers. This article presents particle swarm optimization (PSO), modified PSO (MPSO), and modified hybrid PSO (MHPSO) based controllers for different loads. The proposed MC with a soft computing controller reduces the harmonics by selecting suitable switching pulses for every sample. The simulations are made using the MATLAB/Simulink environment. The results of the proposed MHPSO controller reduce the THD value from 8.654 with a PSO approach to 7.536 in the presence of nonlinear loads.

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Section: Modeling Of Matrix Convertermentioning

confidence: 99%

“…With proper control of the switches, the required frequency and output voltage are accessible [8]. Te output of CMC contains diferent types of harmonics and various controllers are used to minimize the same [9]. By adjusting the duty cycle and switching pattern, the pulse width modulation (PWM) approach reduces the harmonics [10].…”

Section: Introductionmentioning

confidence: 99%

Modified Hybrid PSO Algorithm for Efficient Control of the Matrix Converter-Fed Electrical Drive System

Amarendra

Pandian

Reddy³

et al. 2023

International Transactions on Electrical Energy Systems

Self Cite

View full text Add to dashboard Cite

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“…The experiments were done in the capital city of a tropical, fast-developing country (Dhaka, Bangladesh), where using solar power is quite significant considering the poor economic condition of many people (Thentral et al, 2022) (Mirza et al, 2022). Therefore, the cost-effectiveness of a realistic MPPT approach is vital for real-life implementation and our method can track the maximum power point without adding to any significant installment cost.…”

Section: Conclusion and Scope Of The Future Workmentioning

confidence: 99%

Implementation of an MPPT technique of a solar module with supervised machine learning

et al. 2022

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Automated calibration of a maximum power point tracking (MPPT) algorithm for the photovoltaic (PV) system is pivotal for harnessing the maximum possible energy from solar power. However, most existing calibration methods of such an MPPT system are cumbersome and vary greatly with the environmental condition. Hence, an automated pipeline capable of performing suitable adjustments is highly desirable. We proposed a method using supervised machine learning (ML) in a solar PV system for MPPT analysis. For this purpose, an overall schematic diagram of a PV system is designed and simulated to create a dataset in MATLAB/Simulink. Thus, by analyzing the output characteristics of a solar cell, an improved MPPT algorithm on the basis of a neural network (NN) method is put forward to track the maximum power point (MPP) of solar cell modules. Moreover, we implemented the algorithm in a hardware setup and verified the theoretical result with the empirical data. Typically, the performance accuracy of the NN models is around 97∼98%. But our proposed model shows an even higher efficiency (99.8% approximately) without adding to any extra computational cost.

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