Review of AI applications in harmonic analysis in power systems

Eslami, Ahmadreza; Negnevitsky, Michael; Franklin, Evan; Lyden, Sarah

doi:10.1016/j.rser.2021.111897

Cited by 57 publications

(17 citation statements)

References 156 publications

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“…In the area of solving the problem of APF placement, a new trend can also be found related to the dynamic growth of machine learning, which is already used in other problems of widely understood power quality [161,162]. An example of such a trend may be the use of neural networks in APF control algorithms [41][42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

A Review on Optimization of Active Power Filter Placement and Sizing Methods

2022

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Distortions of current and voltage waveforms from a sinusoidal shape are, not only a source of technical problems, but also have serious economic effects. Their occurrence is related to the common use of loads with nonlinear current-voltage characteristics. These are both high-power loads (most often power electronic switching devices supplying high-power drives), but also widely used low-power loads (power supplies, chargers, energy-saving light sources). The best way to eliminate these distortions is to use active power filters. The cost of these devices is relatively high. Therefore, scientists all over the world are conducting research aimed at developing techniques for the proper placement of these devices, in order to minimize their investment costs. The best solution to this problem is to use optimization techniques. This paper compares the methods and criteria used by the authors of publications dealing with this topic. The summary also indicates a possible direction for further work.

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Section: Discussionmentioning

confidence: 99%

A Review on Optimization of Active Power Filter Placement and Sizing Methods

2022

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“…After identifying the harmonic sources in the network with the help of harmonic analysis software and by placing harmonic compensating equipment such as pas-sive and active filters in different locations in the network and evaluating their performance and role in reducing the level of harmonic distortion of the network, the most appropriate and economical methods of harmonic compensation can be determined. In other words, the power system engineers can design suitable mitigation equipment and choose the proper strategy to place the compensative devices [5][6][7][8][9]. Identifying harmonic sources helps to detect potential harmonic problems such as overheating in transformers, failure in circuit breaker function and increasing losses in the power system.…”

Section: Motivation and Incitementmentioning

confidence: 99%

Multiple harmonic sources identification including inverter‐based distributed generations using empirical Fourier decomposition

Motlagh

Foroud

2023

IET Generation Trans & Dist

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This paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter-based distributed generations (DGs) like microturbine (MT), battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly-fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion.

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“…ML is further divided into supervised learning, unsupervised learning, and reinforced learning for use in power electronics. Some of the research trends and an overview of artificial intelligence in power electronics can be found in [22][23][24][25][26][27][28][29][30].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

Kareem

Hur

2022

Electronics

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The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that age (deteriorate) under normal and diverse working conditions. This study presents a fault diagnostics framework integrated with long-term frequency for a switched-mode power supply aluminium electrolytic capacitor (SMPS-AEC). Long-term frequency condition monitoring (CM) was achieved using the advanced HIOKI LCR meter at 8 MHz. The data acquired during the experimental study can help to achieve the needed paradigm from various measured characteristics of the SMPS/power converter component to detect anomalies between the capacitors selected for analysis. The CM procedure in this study was bound by the electrical parameters—capacitance (Cs), equivalent series resistance (ESR), dissipation factor (DF), and impedance (Z)—-acting as degradation techniques during physical and chemical changes of the capacitors. Furthermore, the proposed methodology was carried out using statistical feature extraction and filter-based correlation for feature selection, followed by training, testing and validation using the selected supervised learning algorithms. The resulting assessment revealed that with increased data capacity, an improved performance was achieved across the chosen algorithms out of which the k-nearest neighbors (KNN) had the best average accuracy (98.40%) and lowest computational cost (0.31 s) across all the electrical parameters. Further assessment was carried out using the fault visualization aided by principal component analysis (PCA) to validate and decide on the best electrical parameters for the CM technique.

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Review of AI applications in harmonic analysis in power systems

Cited by 57 publications

References 156 publications

A Review on Optimization of Active Power Filter Placement and Sizing Methods

A Review on Optimization of Active Power Filter Placement and Sizing Methods

Multiple harmonic sources identification including inverter‐based distributed generations using empirical Fourier decomposition

Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

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