Harmonic Detection for Shunt Active Power Filter Using ADALINE Neural Network

Janpong, Sarawut; Areerak, Kongpol

doi:10.3390/en14144351

Cited by 21 publications

(11 citation statements)

References 31 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]. This approach was previously analyzed in the area of passive filter optimization [163], but can also be found in the area of active power filters.…”

Section: Discussionmentioning

confidence: 99%

“…Further development of active power filters took place in the 1980s, with the development of technology and the development of new control methods, mainly related to the power pq theory [26][27][28] and the dq transformation [29][30][31]. Later, other control methods were developed [32][33][34], in particular, the use of Fourier transform [35][36][37], direct methods [38][39][40], and artificial neural networks [41][42][43][44][45]. This development also concerned the APF topology, and the converters used [46][47][48][49][50].…”

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confidence: 99%

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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%

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confidence: 99%

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

2022

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“…The current research focus for SAPFs concentrates on the control strategy [7][8][9][10] and harmonic detection methods [11,12], and considerable related research achievements have been obtained. By contrast, research on the negative effect of SAPFs on the power grid attracts relatively less attention.…”

Section: Introductionmentioning

confidence: 99%

Research on Oscillation Suppression Methods in Shunt Active Power Filter System

Hou

Wang

et al. 2022

Energies

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The shunt active power filter (SAPF) system oscillation is a massive threat to the security and stability of the power grid. This study classifies SAPF oscillation into two categories according to the difference in mechanisms. The SAPF oscillation in one category is caused by the resonant characteristics of a switching noise filter and is called external loop amplification. The SAPF oscillation in the other category is induced by the presence of a capacitor in the load current for SAPF and is called self-excited oscillation. Unlike previous studies, this study tried to reveal the internal relationship between the two kinds of SAPF oscillation, present a general shunt virtual-damping-based SAPF oscillation suppression strategy covering the previous resonant damping method, and provide the discrete domain stability criterion of the control system. The sampling frequency was at least six times the resonant frequency. The stability region was enlarged with an increase in the sampling frequency and narrowed with a rise in the resonant frequency. As to the harmful self-excited oscillation problem, this study proposes a composite control strategy combining selective harmonic compensation and grid-side current feedback. Moreover, this study considers the more general resistance–inductance–capacitance load situations and analyzes the stability of the SAPF–Thyristor Switched Capacitor (TSC) hybrid compensation system. Simulations and experiments demonstrated that the proposed compound control method can reduce the primary harmonics of the system by more than 90% and has a better oscillation suppression performance than previous suppression methods. In particular, if we selected the TSC series reactance rate following more than 6%, self-excited oscillation could usually be avoided.

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“…Conversely, the Adaptive Linear Neuron (ADALINE) network corresponds to a simple single-layer neural network that does not require an offline training stage, being trained online. Therefore, its potential has been shown in several works regarding not only filter parameter estimation [2,12,24,26] but also current/voltage harmonic components identification [27,28] and flux estimation in electrical machines [23], which have reported a good estimation accuracy. Furthermore, according to [5,25], ADALINE-based parameter estimation techniques typically require lower computational burden and present faster convergence than more conventional estimation methods, including those based on EKFs [17,18,20] and MRAS [11,[14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Online Filter Parameters Estimation in a Double Conversion UPS System for Real-Time Model Predictive Control Performance Optimization

et al. 2022

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Uninterruptible Power Supplies (UPS) represent the key technology to continuously feed and protect a wide range of critical applications in electric power systems. Due to their continuous operation, UPS components degrade over time, including their filtering elements, leading to decreased filtering capabilities. With these deviations, UPS performance is typically reduced. Moreover, if filter parameter deviations are not considered in the control system, performance degradation can be further increased and the critical load can be seriously compromised. This is especially important with Model Predictive Control (MPC), which heavily relies on system parameters accuracy. Thus, control performance optimization using estimated filter parameters in UPS systems can be extremely important. Nevertheless, this has not yet been covered in the literature for UPS systems. In light of these facts, this paper proposes a new mechanism that, by using online estimated filter parameters, optimizes the performance of an MPC strategy, in a UPS system. In the scope of this optimization mechanism, an estimation method that enables parameter identification and control optimization not only in balanced but also in highly unbalanced filter conditions (rarely studied in the literature) is proposed. Experimental results demonstrate the accuracy of the proposed estimators and the effectiveness of the proposed control performance optimization mechanism. Under severe filter parameter variations, the proposed performance optimization scheme enabled to reduce the degradation (caused by filter variation) of the grid current and load voltage THD by 29.41 % and 91.60 %, respectively. Furthermore, the degradation of the RMS load voltage value was also significantly reduced by 97.98 %.INDEX TERMS model predictive control, online inductance and capacitance estimation, power quality, real-time control performance optimization, uninterruptible power supply.

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Harmonic Detection for Shunt Active Power Filter Using ADALINE Neural Network

Cited by 21 publications

References 31 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

Research on Oscillation Suppression Methods in Shunt Active Power Filter System

Online Filter Parameters Estimation in a Double Conversion UPS System for Real-Time Model Predictive Control Performance Optimization

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