Performance Evaluation of BB-QZSI-Based DSTATCOMUnder Dynamic Load Condition

The presence of low frequency components in the common mode voltage can cause harmful electromagnetic interference. A critical study on various causes of low frequency components in the common mode voltage of a space vector pulse width modulated Quasi–Z– source three–phase H–bridge voltage source inverter fed from a three–phase diode bridge rectifier is presented in this paper. The Quasi–Z–source network is utilized in boosting the rectified dc voltage which increases the overall voltage gain. The study considers the effect of boosting on the low frequency components. The input three-phase diode bridge rectifier has its influence in modulating the instantaneous common mode voltage and contributes low frequency components. The unbalanced three-phase supply can contribute additionally ac supply frequency component in the common mode voltage. The major contribution of this paper is the analytical, simulated and experiment-based study on various causes of the low frequency common mode voltages due to the combined action of the input non-ideal three phase grid, the front-end diode bridge rectifier as well as the load-end Z-source H-bridge three-phase inverter feeding a three-phase inductive load, while operated through a space vector pulse width modulation strategy.

Section: Introductionmentioning

confidence: 99%

A Study on Various Causes of Low Frequency Components in Common Mode Voltage of a Space Vector Pulse Width Modulated Three– Phase Quasi–Z–Source H–Bridge Inverter Fed from a Three–Phase Diode Bridge Rectifier

Nanda,

Syam,

Mukherjee

2024

“…So, to dampen the power oscillations, the SVC in the transmission system plays a very significant role. The load currents are mainly collected or extracted from the PCC (Mangaraj, 2021;Mangaraj et al, 2022b;Mangaraj et al, 2022c;Puhan et al, 2021;Sabat et al, 2021;Wang et al, 2017c;ZangenehBighash et al, 2018).So, here Voltage Source Inverter (VSI) or DSTATCOM is the main custom power device that can be used for the mitigation of the harmonics and other PQ issues to get a reliable power supply from the power system. The foremost objective of DSTATCOM is to control the flow of the reactive power.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of SVC-Coupled Hybrid DSTATCOM using JLMS Algorithm for Enhancement of Power Quality

Mangaraj¹,

Sabat²

2023

Self Cite

This research study explains the design and performance of a Static VAR Compensator (SVC) coupled Hybrid Distribution Static Compensator (DSTATCOM) for low voltage power distribution system (PDS). A comparative analysisis done between Voltage Source Inverter (VSI)-based DSTATCOM and SVC Hybrid DSTATCOM using Jordan Least Mean Square (JLMS) algorithm to alleviate the shunt-related power quality (PQ) glitches. The specific design is obtained as compared to the existing neural network toolbox algorithm to improve the estimation speed and accuracy. The main objective is to extract the tuned weighted values of fundamental active and reactive power components of distorted load currents for the switching signal generation using an adaptive JLMS algorithm.Several functions of DSTATCOM and hybrid DSTATCOM using this JLMS controller are showcased for harmonics curtailment, power factor (p.f.) improvement, load balancing, voltage regulation, etc. Simulation results on an SVC Hybrid DSTATCOM have shown an acceptable level of performance under unbalanced loading situations. Finally, some comparative results are provided to validate the feasibility and effectiveness of the suggested topology.

“…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

Self Cite

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.