Adaptive LMBP training‐based
            <i>icosϕ</i>
            control technique for DSTATCOM

Mangaraj, Mrutyunjaya; Panda, Anup Kumar; Penthia, Trilochan; Dash, Asish Ranjan

doi:10.1049/iet-gtd.2018.6295

Cited by 27 publications

(7 citation statements)

References 19 publications

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“…Voltages and currents from various parts of the system are sensed using hall-effect voltage transducer LEM LV25-P, current transducer LA55-P, current probes (A622) and voltage probes (RP1050D). The dSPACE module DS1104 along with MAT-LAB2014a/Simulink and connector panel module CP1104 for analog feedback signal are used to implement the proposed control algorithm [27][28][29][30]. The supply voltage and load current are tracked through the dSPACE 1104 panel via DAC (digital to Analog converter) channel.…”

Section: Resultsmentioning

confidence: 99%

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Sparse LMS algorithm for two‐level DSTATCOM

Mangaraj¹,

Panda

2020

IET Generation Trans & Dist

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Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean square is the improved version of adaptive least mean square learning mechanism with regards to sparsity. This innovative approach is utilized for better parameter estimation due to its algorithmic simplicity and parallel computing nature. Hence, sparse least mean square is expected to reduce the computation and storage requirements significantly. This suggested controller consists of six subnets. Three subnets for active and another three for reactive weight component are used to extract the fundamental component of the load current. Several factors like previous weight, normalizing weight and learning rate are involved in the sparse least mean square based weight‐updating rule to have better dynamic performance, reduced computational burden and better estimation speed etc. The detailed control algorithm is formulated using MATLAB/Simulink, and validated using experimental analysis. Among these two algorithms, the sparse least mean square offers better voltage regulation, voltage balancing, source current harmonic reduction and power factor correction under various loading scenarios.

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Section: Resultsmentioning

confidence: 99%

“…Here, sampling time of 20 µs is chosen to achieve efficient performance of the algorithm. The detailed execution process of d-SPACE is mention in the cited literature [25][26][27][28][29][30]. The current is determined with scale 25 A/div whereas the voltage scale is determined with 200 V/div.…”

Section: Resultsmentioning

confidence: 99%

Sparse LMS algorithm for two‐level DSTATCOM

Mangaraj¹,

Panda

2020

IET Generation Trans & Dist

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“…Mrutyunjaya Mangaraj Email: mrutyunjaya.m@srmap.edu.in 2 N Toushif Khan Email: tousif.k@srmap.edu.in 3 B Chitti Babu Email: bcbabu@iiitdm.ac.in 4 S M Muyeen Email: sm.muyeen@qu.edu.qa Indian Institute of Information Technology, Design and Manufacturing Kancheepuram, Chennai-600127. 4 Department of Electrical Engineering, Qatar University, Doha, Qatar-2713 These algorithms like Kernel Hebbian Least Mean Square [7], Levenberg Marquard back propagation [8], Hebbian Least Mean Square [9], Sparse Least Mean Square [10], Adaptive control [11], Recurrent neural network [12][13], CFNN-AMF [14], Neuro Fuzzy learning [15], neural network [16][17][18], Predictive control [19][20], PNK-LMF [21] are reported for harmoncs ellimination. But, few authors are addressed synchronous theory (abc-dq0) rotating model for SMES based VS-APF to improve the steady and dynamic state performance [3][4][5][6].…”

Section: Review Of Literature and Research Backgroundmentioning

confidence: 99%

Realization of Superconducting- Magnetic Energy Storage Supported DSTATCOM Using DeepBayesian Active Learning

Mangaraj,

Nizami,

Babu

et al. 2024

Preprint

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The Distributed Static Compensator (DSTATCOM) is being recognized as a shunt compensator in the power distribution networks (PDN). In this research study, the superconducting magnetic energy storage (SMES) is deployed with DSTATCOM to augment the assortment compensation capability with reduced DC link voltage. The proposed SMES is characterized by a DC-DC converter with different circuit elements like one inductor, two diodes and two insulated gate bipolar transitors (IGBTs). The Deep Bayesian Active Learning (DBAL) algorithm is suggested to operate SMES supported DSTATCOM for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor (PF), load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the DSTATCOM. To show the power quality (PQ) effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC- 61000-1 grid code.

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“…In view of this, various advanced and adaptive algorithms have been developed to control the active devices for the PQ improvement. Some of these algorithms are adaptive Neural network(NN) (Mangaraj et al, 2022), hybrid NN (Mangaraj et al, 2017;Panda and Mangaraj, 2017), Leaky LMS (Arya and singh 2013), adaptive neurofuzzy inference system (Badoni et al, 2016), Artificial NN-based discrete-fuzzy logic (Saribulut et al, 2014), LMBP (Levenberg-Marquardt back propagation) Training Based Control Technique (Mangaraj et al, 2020), KHLMS (Kernel Hebbian least mean square) algorithm (Mangaraj et al, 2019), Combined LMS-LMF-(least mean square-least mean fourth) Based Control Algorithm (Srinivas et al, 2016), Hebbian Learning (Siri et al, 2008) and Hebbian/Anti-Hebbian NN (Mangaraj 2021;Pehlevan et al, 2015). NN-based control algorithm has several attributes that make it very suitable for distributed static compensator (DSTATCOM).…”

Section: Introductionmentioning

confidence: 99%

VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach

Mangaraj¹,

Sabat²,

Barisal

et al. 2022

Power Electronics and Drives

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With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power quality (PQ) improvement has attracted significant interest due to its high accuracy. In this paper, six subnets are structured for the proposed deep learning approach (DL-Approach) algorithm by using its own mathematical equations. Three subnets for active and the other three for reactive weight components are used to extract the fundamental component of the load current. These updated weights are utilised for the generation of the reference source currents for VSC. Hysteresis current controllers (HCCs) are employed in each phase in which generated switching signal patterns need to be carried out from both predicted reference source current and actual source current. As a result, the proposed technique achieves better dynamic performance, less computation burden and better estimation speed. Consequently, the results were obtained for different loading conditions using MATLAB/Simulink software. Finally, the feasibility was effective as per the benchmark of IEEE guidelines in response to harmonics curtailment, power factor (p.f) improvement, load balancing and voltage regulation.

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Adaptive LMBP training‐based icosϕ control technique for DSTATCOM

Cited by 27 publications

References 19 publications

Sparse LMS algorithm for two‐level DSTATCOM

Sparse LMS algorithm for two‐level DSTATCOM

Realization of Superconducting- Magnetic Energy Storage Supported DSTATCOM Using DeepBayesian Active Learning

VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach

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