Optimal Evaluation of Power System Harmonics in Smart Grid Based on Grey Wolf Optimizer

Abdullah, Muhammad; Malik, Tahir Nadeem; Ashraf, ⋅Muhammad Mansoor; Shah, Muhammad Athar; Iqbal, Qasim; Sabir, Muhammad

doi:10.1109/icomet48670.2020.9074102

Cited by 2 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complete test system stating the harmonics present in the test signal are given in Ref. [30]. The given continuous time test signal is subjected to sampling and discretization as per Nyquist criterion considering, 64 samples per cycle with the sampling frequency of 3.4 kHz.…”

Section: Estimation Of Integral Harmonics In Vfd 131mentioning

confidence: 99%

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

et al. 2023

View full text Add to dashboard Cite

The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS–MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS–MVO framework is tested and validated by estimating the harmonics present in multiple test signals with different noise levels. Amplitudes and phases of harmonics present in the polluted signal were estimated, and the framework computational time was compared with the previously developed technique’s results which are reported in the literature. There was 80% reduction in computational time and 82% improvement in terms of accuracy in estimating harmonics using WLS–MVO as compared to previously developed techniques. The performance of the developed framework is further validated by estimating the harmonics present in the real-time voltage and current waveforms obtained from axial flux permanent magnet generator (AFPMSG), uninterruptible power supply (UPS), and light-emitting diode (LED). The purposed technique technique outperforms the already-developed techniques, in terms of accuracy and computational time.

show abstract

Section: Estimation Of Integral Harmonics In Vfd 131mentioning

confidence: 99%

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Grey wolf optimizer (GWO) has gained significant attention in recent years due to its flexibility, scalability, and few parameters [ 61 ]. It is applied in various applications such as gait analysis [ 62 ], structural strain reconstruction [ 63 ], engines [ 64 ], renewable energy systems [ 65 ], robotics [ 66 ], deep learning [ 67 ], wireless sensor networks [ 68 ], smart grid [ 69 ], medical [ 70 ], and energy management [ 71 ]. Even though GWO has been utilized in different applications, due to the complexity of real-world optimization problems, various improvements have been made in GWO in terms of updating mechanisms, hybridization, encoding schemes, multi-objective, and new operators.…”

Section: Introductionmentioning

confidence: 99%

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

et al. 2023

View full text Add to dashboard Cite

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

show abstract

Optimal Evaluation of Power System Harmonics in Smart Grid Based on Grey Wolf Optimizer

Cited by 2 publications

References 24 publications

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads

Variants of Chaotic Grey Wolf Heuristic for Robust Identification of Control Autoregressive Model

Contact Info

Product

Resources

About