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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.

This paper presents the shunt compensation performance of quasi-Z-source inverter (QZSI) and back to back connected QZSIs (BB-QZSI) to address the power quality (PQ) issues in the three-phase three-wire power utility network (PUN). Generally, these PQ issues are poor voltage regulation, low power factor (PF), source current distortion, unbalanced voltage, etc. The proposed BBQZSI-based distribution static compensator (DSTATCOM) consists of two QZSIs with a common dc-link capacitor. Because the QZSI could achieve buck/boost conversion as well as DC to AC inversion in a single-stage and the back to back configuration decreases the system down time cost (if a fault occurs in one QZSI the other can continue the shunt compensation). Particularly, icos ϕ control algorithmcontrol algorithm is implemented to generate proper switching pulses for the switches of DSTATCOM. The effectiveness of the BB-QZSI is verified through simulation studies over QZSI using MATLAB/Simulink software satisfying the recommended grid code.

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.

This article demonstrates the performance of back-to-back connected voltage source inverter (BTB-VSI)-based distribution static compensator (DSTATCOM). In the proposed system, a three-phase three wire electrical utility system (EUS) is integrated with distributed energy resources (DER) through BTB-VSI. It has drawn more attention due to enhanced active power filtering, simple design procedure and performs reliable operation. The objectives of the research work is to provide clean power for weak EUS with improved power quality (PQ) such as, better voltage regulation, improved power factor (P.F), reduced total harmonic distortion (THD), balanced voltage at point of common coupling (PCC) and better optimal active power flow control (OAPFC), and so on. An artificial neurocontrol technique Kernel Hebbian Least Mean Square (KHLMS) is chosen to determine the accurate reference source current for operation of voltage source inverter (VSI). The BTB-VSI performance is investigated on both shunt compensation and OAPFC. The proposed system is converted to a simulation model and hardware unit using MATLAB/Simulink software and SPARTAN−6 field programmable gate arrays (FPGA) controller. Finally, the Simulation and experimental results illustrate that the shunt compensating ability with OAPFC of BTB-VSI performs better over VSI as per IEEE Std 1159–2019 grid code. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive Kernel-Hebbian least mean square (KHLMS) controller for a dual voltage source inverter (VSI). The recommended topology consists of a distributed energy resource (DER) supported VSI called main VSI (MVSI) and split capacitor supported VSI termed as auxiliary VSI (AVSI). Both the MVSI and AVSI are used to serve the shunt compensation when DER is not integrated with MVSI. The DER scenario is considered to suppress the active power flow shortage in the utility grid. Here, optimal active power flow control (OAPFC) is managed by MVSI and shunt compensation is achieved by AVSI during DER operated mode. Hence, a dual VSI based distribution static compensator (DSTATCOM) facilitates the configuration merits such as reduction in system downtime cost, filter rating switching stress etc. Supremacy of both the neural network (NN) based controller and topology is presented by comparing VSI (called AVSI) in the context of harmonic reduction in source side, voltage balancing, power factor (PF) enhancement, better voltage regulation and OAPFC. The experimental results are obtained through field programmable gate array (FPGA) based hardware units which exhibit radical improvement in the power quality (PQ) conferring as per the international standard grid code (IEEE-519-2017). [ABSTRACT FROM AUTHOR]

This paper presents a dual voltage source inverter (DVSI)-based Distributed Static Compensator (DSTATCOM) to enhance the power quality in the power utility system (PUS). The proposed DSTATCOM consists of two numbers of two-level voltage source inverters (VSI) which controls the reactive power. One of them is regarded as the main voltage source inverter (MVSI) and the other is considered as auxiliary voltage source inverter (AVSI). The i cos ⁡ ϕ control algorithm is employed to generate the reference source currents as well as switching pulses. The detailed control algorithm is formulated by mathematical analysis using MATLAB/Simulink, and compared with different case studies. Among all the theoretical findings, the DVSI offers better voltage regulation, voltage balancing, source current harmonic reduction and power factor correction under various loading scenarios. Apart from these, reliability, stress balancing of the both MVSI and AVSI, cost reduction due to a decrease in the filter size and better operation of PUS is obtained. To check the effectiveness, the performance of the DVSI-based DSTATCOM is carried out as per the benchmark value of the IEEE-519-2014 and IEC-61000-1 grid code. [ABSTRACT FROM AUTHOR]

In this research paper, an inductively coupled distributed static compensator (IC-DSTATCOM) for three phase three wire (3P3W) electric power distribution system (EPDS) is proposed. The contraction of power quality (PQ) was marked as a perilous droop mode bump into direct coupled distributed static compensator (DC-DSTATCOM). To regain the PQ, inductive coupling transformer is assisted in conjunction with DC-DSTATCOM. The system equivalent circuit of IC-DSTATCOM is accomplished by take into account of impedance of both transformer and DC-DSTATCOM to reveal the filtering technique. The filtering icosØ mechanism is performed by following the generalized mathematical approach using MATLAB/Simulink. A case education is reviewed in detail to illustrate the performance of both DC-DSTATCOM and IC-DSTATCOM. The IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, good power factor, load balancing, and potential regulation. To examine the effectiveness, simulation outputs of the IC-DSTATCOM with different PQ parameter indices are presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 system code. [ABSTRACT FROM AUTHOR]

This research study explains the feasibility of implementing machine learning (ML) oriented inductively coupled distributed static compensator (IC-DSTATCOM) for power quality (PQ) enhancement. The issue involved in declining the power quality (PQ) using direct coupled static compensator (DC-DSTATCOM) was identified as a hazardous disappointment. Hence, to improve the quality, coupling transformer is served in unification with DCDSTATCOM. Also, the recent growth of machine learning (ML) systems and progression of computational resources, with unpredicted data obtainability, has inspired the researchers. In this study, density based spatial clustering of application with noise (DBSCAN) is employed by using its own learning mechanism (LM) using MATLAB/Simulink. This controller contains six subnets. Among them, six subnets are employed for active tuned weight extraction whereas other three subnets are used for reactive part. Moreover, the abovesaid devices are triggered with the help of generated reference supply current. A case education is reviewed in detail to demonstrate the operation of both DC-DSTATCOM & IC-DSTATCOM. Finally, the IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, upgrading in power factor, load balancing, and potential regulation etc. To examine the effectiveness, simulation outputs of the IC-DSTATCOM is presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 grid code. [ABSTRACT FROM AUTHOR]