Analysis of sliding mode controller based DSTATCOM for power quality improvement in distribution power system

Jayaraman, Ramesh; Tummapudi, Suneel; Prakash, R.; Muni, T Vijay

doi:10.1016/j.matpr.2021.07.360

Cited by 6 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where 𝜔 𝑒 is the location of the power system's frequency, and what does it mean when "d" and "q" are used to indicate, respectively, the parts of the d-and q-axis in the revolving frame equations. The active power fitter's block diagram and the power converter's input voltage instructions will be derived using ( 5) and (6).…”

Section: Topology Of Dstatcommentioning

confidence: 99%

“…Hence, voltage source inverter-based multilevel inverters are used in most medium to high-power applications. The voltage source inverter base can be categorized as: i) a cascaded H-bridge MLI (CHB MLI), ii) a neutral point clamped MLI (NPC MLI), and iii) a flying capacitor MLI (FC MLI) [6], [7]. The CHB-MLI is most preferred due to the complexity posed by the neutral point clamped and flying capacitor multilevel inverter [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cascaded H-bridge multilevel inverter-based DSTATCOM with an artificial neural fuzzy inference systembased controller

Chenchireddy,

Jegathesan

2023

IJEECS

View full text Add to dashboard Cite

<span>In this paper, <span>a voltage source inverter is utilized as a distributed static compensator (DSTATCOM). The DSTATCOM is used for the elimination of harmonics caused by the nonlinear load. The reference currents of DSTATCOM were estimated with a synchronous reference frame (SRF). Conventionally, the SRF uses voltage, load current and filter current to compute the reference current to track desired source current for DSTATCOM. The sliding mode controller and feedback linearization are used to design the current controller. The feedback linearization technique and sliding mode control are used to cancel nonlinearities and offer invariant stability due to modeling uncertainties due to the DSTATCOM parameter and external load disturbance. The sliding mode controller requires a rigorous mathematical model of the system. To overcome this, an artificial neuro-fuzzy inference system (ANFIS) is devised for the control of DSTATCOM. The training data of the ANFIS controller is generated from the sliding mode controller algorithm. The overall system of DSTATCOM including the ANFIS algorithm is implemented in MATLAB/Simulink SimPower block sets. The simulated response of DSTATCOM was found to be improved and better than the sliding mode controller scheme.</span></span>

show abstract

Section: Topology Of Dstatcommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cascaded H-bridge multilevel inverter-based DSTATCOM with an artificial neural fuzzy inference systembased controller

Chenchireddy,

Jegathesan

2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…In addition, hardware was implemented to analyze the real-time performance of the proposed method [13]. However, to overcome the drawbacks of the conventional PI controller, the Sliding Mode controller was suggested for DSTATCOM to improve the THD and maintain the DC voltage constant during load variations [14]. The modern AC-DC microgrid with the intelligent FL-PI and FL-PID controller was designed with the view of minimizing PQ issues and improving voltage stability.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The battery error current reference i BS,err * is obtained by the PI of battery error current i BS,err . Where i BS,err is the difference between reference DC-Link current i re f dc and battery current i re f BS given in Equation ( 12) obtained from LPF given in Equations ( 13) and (14).…”

Section: Battery Storage (Bs)mentioning

confidence: 99%

Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter

Srilakshmi¹,

Koganti²,

Salkuti

2022

Algorithms

View full text Add to dashboard Cite

Nowadays, the integration of renewable energy sources such as solar, wind, etc. into the grid is recommended to reduce losses and meet demands. The application of power electronics devices (PED) to control non-linear, unbalanced loads leads to power quality (PQ) issues. This work presents a hybrid controller for the self-tuning filter (STF)-based Shunt active power filter (SHAPF), integrated with a wind power generation system (WPGS) and a battery storage system (BS). The SHAPF comprises a three-phase voltage source inverter, coupled via a DC-Link. The proposed neuro-fuzzy inference hybrid controller (NFIHC) utilizes both the properties of Fuzzy Logic (FL) and artificial neural network (ANN) controllers and maintains constant DC-Link voltage. The phase synchronization was generated by a self-tuning filter (STF) for the effective working of SHAPF during unbalanced and distorted supply voltages. In addition, STF also does the work of low-pass filters (LPFs) and HPFs (high-pass filters) for splitting the Fundamental component (FC) and Harmonic component (HC) of the current. The control of SHAPF works on d-q theory with the advantage of eliminating low-pass filters (LPFs) and phase-locked loop (PLL). The prime objective of the projected work is to regulate the DC-Link voltage during wind uncertainties and load variations, and minimize the total harmonic distortion (THD) in the current waveforms, thereby improving the power factor (PF).Test studies with various combinations of balanced/unbalanced loads, wind velocity variations, and supply voltage were used to evaluate the suggested method’s superior performance. In addition, the comparative analysis was carried out with those of the existing controllers such as conventional proportional-integral (PI), ANN, and FL.

show abstract

Enhancing power quality through DSTATCOM: a comprehensive review and real-time simulation insights

Govil,

Sahay,

Tripathi

2024

Electr Eng

View full text Add to dashboard Cite

Analysis of sliding mode controller based DSTATCOM for power quality improvement in distribution power system

Cited by 6 publications

References 18 publications

Cascaded H-bridge multilevel inverter-based DSTATCOM with an artificial neural fuzzy inference systembased controller

Cascaded H-bridge multilevel inverter-based DSTATCOM with an artificial neural fuzzy inference systembased controller

Design of Multi-Objective-Based Artificial Intelligence Controller for Wind/Battery-Connected Shunt Active Power Filter

Enhancing power quality through DSTATCOM: a comprehensive review and real-time simulation insights

Contact Info

Product

Resources

About