Performance Investigation of Unified Power Quality Conditioner Using Artificial Intelligent Controller

Jayachandran, J.; Sachithanandam, R. Murali

doi:10.15866/iremos.v8i1.5396

Cited by 3 publications

(3 citation statements)

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“…Currently, artificial intelligence e.g., artificial neural network (ANN) is considered as a latest tool to design control circuits for power quality appliances. Employment of ANN approach in PV-UPQC has been mentioned in [38] to improve power quality. Using ANN delivers fast response compared to traditional PID circuits.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Renewable Energy Based Distributed Generation System Using ANN Tuned UPQC

Zanib

Batool²,

Riaz

et al. 2022

IEEE Access

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Emerging trends and advances in techniques in power electronics, Unified Power Quality Conditioner (UPQC) has a superior performance compared to other methods. The paper proposes the application of a ANN based UPQC to enhance the power quality of a three-phase Low-voltage network connected to a hybrid distribution generation (DG) system. The proposed work emphases the detailed performance analysis of a distributed generation system that integrates a solar PV and wind energy system by utilizing Unified Power Quality Conditioner (UPQC) with an artificial neural network (ANN) controller with respect to proportional-integral (PI) controller. The core objective of the proposed ANN is to offer good steady and dynamic state performance compared to the PID controller. The system called UPQC-ANN-RE feeds energy generated by a photovoltaic array and a wind turbine into the electrical grid and loads attached to a system of 3-phase 4-wire electrical distribution. In addition to inserting active/real power in the utility grid, the system of UPQC-ANN-RE functions as a UPQC, improving power quality signs e.g., voltage and current harmonics and power factor. A detailed analysis of the active-real power flow by converters is carried out to allow a good understanding of the operation of the UPQC-ANN-RE. The simulation outcomes are presented to assess the dynamic and steady state performance of the system of UPQC-ANN-RE connected to an electrical distribution system and to compare the consequences with the PI controller.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Renewable Energy Based Distributed Generation System Using ANN Tuned UPQC

Zanib

Batool²,

Riaz

et al. 2022

IEEE Access

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“…The authors have provided a comparative analysis between the PI and FLC techniques and concluded that the FLC technique gives better results for the PV-UPQC-BSS system relative to the other technique based on PI. The implementation of the NN approach in PV-UPQC for power quality improvement has been mentioned [31], [32]. This technique requires proper learning and validation before implementing it to the electrical system.…”

Section: Introductionmentioning

confidence: 99%

Power Enhancement With Grid Stabilization of Renewable Energy-Based Generation System Using UPQC-FLC-EVA Technique

Sarita

Kumar

Vardhan³

et al. 2020

IEEE Access

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The proposed work focuses on the power enhancement of grid-connected solar photovoltaic and wind energy (PV-WE) system integrated with an energy storage system (ESS) and electric vehicles (EVs). The research works available in the literature emphasize only on PV, PV-ESS, WE, and WE-ESS. The enhancement techniques such as Unified Power Flow Controller (UPFC), Generalized UPFC (GUPFC), and Static Var Compensator (SVC) and Artificial Intelligence (AI)-based techniques including Fuzzy Logic Controller (FLC)-UPFC, and Unified Power Quality Conditioner (UPQC)-FLC have been perceived in the existing literature for power enhancement. Further, the EVs are emerging as an integral domain of the power grid but because of the uncertainties and limitations involved in renewable energy sources (RESs) and ESS, the EVs preference towards the RES is shifted away. Therefore, it is required to focus on improving the power quality of the PV-WE-ESS-EV system connected with the grid, which is yet to be explored and validated with the available technique for enhancing power quality. Furthermore, in the case of the bidirectional power flow from vehicle-to-grid (V2G) and grid-to-vehicle (G2V), optimal controlling is crucial for which an electric vehicle aggregator (EVA) is designed. The designed EVA is proposed for the PV-WE-ESS-EV system so as to obtain the benefits such as uninterruptible power supply, effective the load demand satisfaction, and efficient utilization of the electrical power. The power flow from source to load and from one source to another source is controlled with the support of FLC. The FLC decides the economic utilization of power during peak load and off-peak load. The reduced power quality at the load side is observed as a result of varying loads in the random fashion and this issue is sorted out by using UPQC in this proposed study. From the results, it can be observed that the maximum power is achieved in the case of PV and WE systems with the help of the FLC-based maximum power point tracking (MPPT) technique. Furthermore, the artificial neural network (ANN)-based technique is utilized for the development of the MPPT algorithm which in turn is employed for the validation of the proposed technique. The outputs of both the techniques are compared to select the best-performing technique. A key observation from the results and analysis indicates that the power output from FLC-based MPPT is better than that of ANN-based MPPT. Thus, the proper and economical utilization of power is achieved with the help of FLC and UPQC. It can be inferred that the EVs can play a vital role in imparting the flexibility in terms of power consumption and grid stabilization during peak load and off-peak load durations provided that the proper control techniques and grid integration are well-established. INDEX TERMSPV system, Energy storage system (ESS), electric vehicles (EVs), UPQC, grid stability, fuzzy logic control (flc), electric vehicle aggregator (EVA), MPPT, power improvement.The associate editor coordinating the review of this m...

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“…Artificial neural network (ANN) based on SRF theory as a control to compensate for PQ problems of 3P3W system through UPQC for various balanced/unbalanced/distorted conditions at load and source has been proposed [9]. The proposed model has successfully mitigated harmonics/reactive currents, unbalanced source and load, and unbalanced current/voltage.…”

Section: Introductionmentioning

confidence: 99%

Load Active Power Transfer Enhancement Using UPQC-PV-BES System With Fuzzy Logic Controller

Amirullah¹,

Adiananda²,

Penangsang³

et al. 2020

IJIES

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This paper presents enhancement of load active power transfer using Unified Power Quality Conditioner-Photovoltaic-Battery Energy Storage (UPQC-PV-BES) system. This system is connected to a three phase three wire (3P3W) system with a voltage of 380 V (line to line) and 50 hertz. The proposed model is also compared with UPQC and UPQC-PV respectively. The parameters investigated are load voltage, load current, load active power, and efficiency. BES functions to save excess energy generated by PV, distribute it to the load, avoid interruption voltage, and regulate the charging process and energy utilization. The fuzzy logic controller (FLC) is proposed and compared with proportional integral (PI) method to control DC voltage variable and input DC reference voltage, to produce a reference current source on hysteresis current controller on shunt active filter in 12 disturbance scenarios (scns). In Scenario (Scn) 1 to 5, the 3P3W system uses three combinations of UPQC with PI controller and FLC, still keeps load voltage and load current above 300 V and 8 A. Whereas in Scn 6, only the UPQC-PV-BES with FLC is able to maintain load voltage and load current higher compared to UPQC and UPQC-PV combinations as 304.1 V and 8.421 A, respectively. In Scn 1 to 5, the 3P3W system uses three combinations of UPQC with PI controller and FLC, capable of producing load active power above 3600 W. Whereas in Scn 6, only a combination of UPQC-PV-BES with PI controller and FLC is able to produce a load voltage of 3720 W and 3700 W, respectively. In Scn 1 to 6, UPQC-PV-BES results in lower efficiency compared to using UPQC and UPQC-PV. However, in Scn 6, UPQC-PV-BES with FLC is able to produce load voltage, load current, and load active power higher than UPQC-PV and UPQC. Thus, the UPQC-PV-BES model using FLC is able to compensate load voltage and load current, as well as to enhance load active power, especially for an interruption on source bus. This research is simulated using Matlab/Simulink.

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Performance Investigation of Unified Power Quality Conditioner Using Artificial Intelligent Controller

Cited by 3 publications

References 0 publications

Performance Analysis of Renewable Energy Based Distributed Generation System Using ANN Tuned UPQC

Performance Analysis of Renewable Energy Based Distributed Generation System Using ANN Tuned UPQC

Power Enhancement With Grid Stabilization of Renewable Energy-Based Generation System Using UPQC-FLC-EVA Technique

Load Active Power Transfer Enhancement Using UPQC-PV-BES System With Fuzzy Logic Controller

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