Unified Power Quality Conditioner Research Articles

Compensation characterization of the UPQC system under an improved nonlinear controller based on the MSTOGI-PLL device

To solve the delay problem of a unified power quality conditioner (UPQC) system during the separation of the fundamental positive-order components and to better filter out the DC and harmonic components to realize accurate phase locking, a mixed second- and third-order generalized integrator phase-locked loop (MSTOGI-PLL) has been designed to replace the traditional synchronous reference frame phase-locked loop (SRF-PLL). Under the premise of adopting the new phase-locking device of MSTOGI-PLL, the active disturbance rejection control (ADRC) controller or the super-twisting algorithm (STA) sliding mode controller is used in the DC voltage control module instead of the traditional PI controller, and the suitability of the two nonlinear controllers with the MSTOGI-PLL device is investigated. First, the new MSTOGI-PLL device is designed, and the new phase-locking method is applied to make the UPQC realize accurate phase locking under the non-ideal situation of the grid voltage containing unbalance, DC component, harmonics, and so on. Based on the above foundation, the ADRC or STA controller is independently adopted to replace the PI controller to construct the UPQC system with the ADRC + MSTOGI phase-locking device and the second-order STA + MSTOGI phase-locking device. Finally, a simulation comparison is carried out in a Simulink simulation platform regarding the UPQC system under different phase-locking devices, and the simulation results demonstrate that MSTOGI-PLL can successfully filter out the DC component and resolve the phase-locking process delay issue. Additionally, the UPQC system with ADRC + MSTOGI-PLL exhibits superior immunity and response speed to the PI controller and the second-order STA controller.

Research on intelligent auxiliary regulation technology of large power grid section based on artificial intelligence

In the modern era, large-scale renewable energy systems are integrated with advanced power systems and provide efficient operations. Also, optimized power systems require accurate energy generation and effective control systems to manage and ensure a stable power supply. Nevertheless, uncertainties are the intermittent balance of supply and high electricity demand. In addition, conventional power sources are not applicable for this challenging task and increase electricity costs. Therefore, an efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems. Here, Neuro-fuzzy logic is used as dynamic reactive power compensation in the grid. Also, this logic can efficiently handle the Energy storage system (ESS). Then SP UPQC was used to enhance power quality in electrical distribution systems. It combines both series and shunt compensators to mitigate various power quality issues such as voltage sags, harmonics, and unbalance. After that MPPT was utilized to extract the maximum power from the grid system. Model Predictive Control (MPC) controller to determine the overall stability and performance of the system. Moreover, the developed model was implemented on the MATLAB platform and performance is analyzed in terms of voltage deviation, grid current, reactive power fluctuations and Total Harmonic Distortion (THD).

Performance analysis of three-phase solar PV, BESS, and Wind integrated UPQC for power quality improvement

UPQC (Unified Power Quality Conditioner) is indeed a Versatile power conditioner which could be utilized to adjust for a variety of power supply voltage disturbances, rectify voltage fluctuation, and minimize harmonic load current in accessing the power source. To ensure a continuous power supply. We are making use of UPQC technology in combination with shunt and series AFC (Active filter compensators) to counteract power quality problems resulting from grid harmonics and nonlinear loads. Therefore, the focus of this research is to analyze the UPQC's system performance under swell and sag with the variation of solar irradiation and temperature. To achieve these two distinct cases of PV system like temperature-25 °C, irradiance-700 W/m2, and temperature-45 °C, irradiance-800 W/m2, are considered respectively. The implementation of the FOPI controller purposes to bolster power quality of 3-phase HESS (hybrid energy storage system), which includes an integrated UPQC for outstanding performance. In order to manage harmonics resulting from non-linear loads and reduce grid power quality issues, the UPQC model integrates both shunt and series AFC. In the case of extended voltage interruptions, the integrated UPQC with BESS, wind and PV can effectively resolve power quality concerns. The UPQC shunt compensator efficiently harnesses power from HES (hybrid energy systems), whereas the series compensator acts as a guardian, protecting the load from power quality issues originating in the grid. In order to regulate the voltage of the DC connection at a desired level, the aim of this article is to develop a FOPI controller with isodamping properties. In particular, a novel hybrid algorithm called the Enhanced Seagull with Rooster Update (ES-RU) Algorithm, which combines the ideas of Chicken Swarm Optimization (CSO) and Seagull Optimization algorithm (SOA), optimally tunes the gain of the FOPI controller. For optimal performance, the FOPI controller's gain parameters (Kp, Ki, λ) are finely tuned using an Enhanced Seagull with Rooster Update (ES-RU) Algorithm. As a consequence, the performance of the adopted and extant models is examined during sag and swell conditions with respect to source voltage and load voltage and compared with other conventional models like PI, FF, CS and SG, respectively for both cases like temperature-25 °C, irradiance 700 W/m2, and temperature-45°C, irradiance-800 W/m2, correspondingly. The overshot and total harmonic distortion (THD) of the proposed ES-RU is superior to the existing models like PI, FF, CS and SG.

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes.

ABC-WOA Optimization Strategy for UPQC Fuzzy PI Tuning

Growing numbers of power electronics-based non-linear loads have raised worries about power quality (PQ) problems, which include equipment failures, overheating, and unplanned shutdowns. Power filters have traditionally been used for harmonics elimination, but their limitations in dynamic compensation and size present challenges. The present research presents a new methodology using the Artificial Bee Colony - Whale Optimizations Algorithm (ABC-WOA) to optimize the Unified Power Quality Conditioner (UPQC) controller. Assignment aims to tackle obstacles that come with it. The suggested algorithm's efficacy is clear, as shown from the stabilization of objective function values. Highlighting the expertise of the Proportional-Integral (PI) controller in effectively regulating the DC-link voltage, this study successfully addresses power quality concerns. The analysis of voltage injection emphasizes the significant influence of the controller on the power system, showcasing seamless integration and enhanced power quality. This research provides valuable insights into the application of advanced control algorithms in power systems, specifically in addressing challenges related to non-linear loads and transient conditions.
  

Intelligent Solar PV Grid Connected and Standalone UPQC for EV Charging Station Load

The insertion of renewable energy sources into the grid, as well as the development of power electronics technology to regulate loads that are not linear, had an impact on power quality (PQ). This study focuses on the PQ enhancement of grid-connected and standalone solar PV systems (SPVS) with battery energy storage device (BESD) for the Electric vehicle (EV) charging station (EVCS) load in addition to the local load. Here, a hybrid control strategy that uses both the superior qualities of the sliding mode controller (SMC) and the fuzzy logic controller (FLC) is suggested for the unified power quality conditioner (UPQC's) shunt filter. It's major goal is to achieve steady DC capacitance voltage (SVDC) during load (like EVCS, non linear balanced/unbalanced etc.) and irradiation variations, diminish of total harmonic distortion (THD) in source current and load voltage, and mitigate sag, swell disturbances, and source voltage unbalances. The created model's performance is assessed using two scenarios (grid and island) under four case studies with varying combinations of loads and grid voltage circumstances. However, to establish the superiority of the proposed technology, comparative research with standard technologies such as proportional integral controller (PIC) and SMC controllers is required. THD is reduced by the proposed method to 2.25 %, 2.36 %, and 1.71 %, It is inferior to the current approaches found in the survey.

Power Quality (PQ) Analyses of DG Utilizing Unified Power Quality Conditioner (UPQC) by White Shark Optimizer and Recalling-Enhanced Recurrent Neural Network

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods.

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods.

Optimal power quality improvement in distribution system with UPQC using an improved strategy

AbstractA hybrid technique is proposed to enhance the power quality (PQ) on the distribution sides of the utility grid (UG) by controlling a unified power quality conditioner (UPQC) connected to hybrid sources (photovoltaic [PV] and wind turbine [WT]). The proposed hybrid method integrates the implementation of the pelican optimization algorithm and the Aquila optimizer; hence, it is called the improved aquila optimizer (IAO) technique. The objective of the proposed method is to lessen the total harmonic distortion (THD), voltage instability, and PQ issues during load fluctuation situations. The improved Aquila Optimizer technique optimizes the control parameters of the UPQC to achieve optimal PQ development. The series controller is attached to the grid‐side to enhance grid PQ, while the shunt hybrid active power filter (SAPF) and shunt active power filter (SHAPF) generate the best control pulses based on load and source conditions. The proposed solution addresses power loss, THD, and voltage instability problems during load fluctuation conditions. The series controller reduces voltage sag by 14% and voltage swell by 15%. The THD for the proposed technique is 0.8%. The PQ of the proposed technique is improved, and various characteristics are reduced. The efficiency of the proposed technique is examined by using MATLAB and is compared to existing approaches. The PQ characteristics are significantly improved, and the proposed technique is better than the existing techniques.

Power quality stabilization system for grid connected large-scale solar power system

This paper presents solution to power quality issues when integrating a 1.5 MW photovoltaic array (PVA) with a unified power quality conditioner (UPQC) into the power grid. A modified unit vector template control scheme is used to generate a reference load voltage signal, and a synchronous reference frame (SRF) is created using a low-pass filter to control UPQC. Furthermore, the PVA is interfaced with the grid through a common DC link of shunt and series converters. The series converter mitigates power quality problems allied with the grid side, such as 3rd, 5th, and 7th harmonics, voltage sag, and voltage swell, by introducing signals in phase and out of phase voltage, respectively, at the point of common coupling (PCC). The shunt converter mitigates nonlinear load current harmonics and compensates for reactive power using SRF. The suggested methodology is implemented using MATLAB Simulink with both linear and nonlinear loads under different power quality conditions. Total harmonic distortions are maintained below 5% at PCC, as per IEEE-519 standards.

A novel fuzzy-logic controller-MDsUPQC topology for power quality improvement in multi-feeder distribution system

In the current situation, the significant usage of massive non-linear functioned power electronic loads has been increased in domestic and industrial applications. The main problem of power-quality (PQ) deterioration in both voltage quality and current quality from nominal values and also damaging the single/multi feeder distribution systems. The major contribution is to alleviate the PQ issues by employing novel multi-devices unified power-quality conditioner (MDsUPQC) topology in a multi-feeder distribution system. This MDsUPQC comprises multiple voltage source inverter (VSI) devices connected with a common direct current-link (DC-link) capacitor which is controlled by a proportional-integral (PI) controller. However, this controller has some technical issues that are not suited for the regulation of common DC voltage at the desired level because of improper selection of gain values. The contribution of this work is proposing an intelligent fuzzy-logic (FL) DC-link controller-driven MDsUPQC device which evidences the intelligent knowledge base for better regulation of PQ issues. The technique and performance of the suggested strategy for PQ improvement, load sharing between feeders, and simulation results are presented with comparative analysis utilizing the MATLAB/Simulink software tool.

Power Quality Conditioners-Based Fractional-Order PID Controllers Using Hybrid Jellyfish Search and Particle Swarm Algorithm for Power Quality Enhancement

Power quality (PQ) is a major issue in today’s electrical system that affects both utilities and customers. The proliferation of power electronics devices, smart grid technology, and renewable energy sources (RES) have all contributed to the emergence of PQ concerns in today’s power system. The Unified Power Quality Conditioner (UPQC) is a versatile tool that can be used to fix distribution grid issues caused by irregular voltage, current, or frequency. Several tuning parameters, however, restrict the effectiveness of the Fractional-Order Proportional Integral Derivative (FOPID) control technique, which is proposed to improve UPQC performance. To move beyond these restrictions and find the optimal solution for the FOPID controller problem, a hybrid optimization strategy called the Hybrid Jellyfish Search Optimizer and Particle Swarm Optimizer (HJSPSO) is employed. To meet the load requirement during PQ issue periods, the suggested model incorporates a renewable energy source into the grid system. Whether the load is linear or non-linear, the design maintains PQ problems to a minimum. Furthermore, the FOPID control technique is compared with other controllers. Results show that grid-connected RES systems using the proposed FOPID control approach for UPQC have fewer PQ problems. The presented UPQC with HJSPSO strategy significantly outperformed, with the shortest computing time of 127.474 s and an objective function value of 1.423.

Enhancing Power Quality with PDO-FOPID Controller in Unified Power Quality Conditioner for Grid-Connected Hybrid Renewables

Addressing the pressing research problem of power fluctuations and grid harmonics in the integration of renewable energy, our  proposed control strategy utilizes the Prairie Dog Optimization Fractional Order Proportional Integral Derivative (PDO-FOPID) controller  within a Unified Power Quality Conditioner (UPQC) system. This innovative approach is tailored to mitigate harmonics and meet load  requirements in grid-connected hybrid renewables. The UPQC system is instrumental in regulating coupling point voltage, countering  voltage and current harmonics to enhance overall power quality. The PDO-FOPID controller dynamically adapts control parameters to system dynamics and load changes, ensuring a stable power supply despite the variability of renewable sources. Simulations in MATLAB/ Simulink confirm its superiority over traditional control strategies, such as PI, sliding mode, and fuzzy control, in harmonics mitigation,  load fulfilment, and power stability. By effectively addressing these challenges, our proposed solution not only contributes to resolving a  critical research problem but also advances the seamless integration of Hybrid Renewable Energy Sources (HRES) into power systems,  thereby enhancing overall grid performance and the efficacy of renewable energy integration. 

Two-stage robust optimization of unified power quality conditioner (UPQC) siting and sizing in active distribution networks considering uncertainty of loads and renewable generators

This paper proposes a robust optimization siting and sizing strategy for the unified power quality conditioner (UPQC) in the active distribution network (ADN) integrated with renewable energy generators. The optimization objectives including investment cost, line loss, and voltage deviation are integrated into an overall cost for both the planning and operating stages. A novel UPQC equivalent power injection model is proposed based on the auxiliary branch method. A set of linear and second-order conic (SOC) constraints are employed to accurately describe the steady-state operation of UPQC. An improved branch current flow model is developed to incorporate radial distribution networks with UPQCs. The UPQC siting and sizing planning is formulated as a mixed integer second-order conic programming (MISOCP) problem. Accounting for the uncertainty in loads and distributed generators, a two-stage robust optimization model is established to address worst-case operational scenarios. A solving strategy is developed based on the strong duality theory and the column-and-constraint generation (C&CG) algorithm, and then commercial solvers can solve the problem accurately and efficiently. Case studies on an IEEE 33-node distribution network demonstrate this strategy's robustness in dealing with loads and renewable energy uncertainties, making it applicable across different UPQC topologies and operation modes. The proposed method has superior performance over the representative meta-heuristic approaches in terms of global optimality as well as an over 7.5 times faster computation speed. This work has significance in enhancing the quality and efficiency of modern power systems highly integrated with renewable energy.

Green energy-sourced AI-controlled multilevel UPQC parameter selection using football game optimization

The power quality (PQ) has been significantly affected by the integration of intermittent non-conventional sources (NCS) into the local distribution system in addition to the adoption of power electronic technologies to regulate non-linear loads. This article combines the H-bridge cascade five-level unified power quality conditioner (5L-UPQC) with the wind power generation system (WPGS), solar photovoltaic power generation system (SPVGS), and battery storage system (BSS) as an effective approach to address PQ problems. The utilization of the Levenberg–Marquardt backpropagation (LMBP)-trained Artificial neural network controller (ANNC) in the UPQC is recommended for generating appropriate reference signals for the converters. This eliminates the requirement for conventional complex conversions, such as abc, dq0, and αβ. Moreover, the artificial neuro-fuzzy interface system (ANFIS) is recommended for achieving a DC-link balance. Football game optimization (FBGO) is utilized to determine the optimal shunt and series filter characteristics. The major objectives of the proposed system are to reduce the current waveform irregularities, resulting in a decrease in the total harmonic distortion (THD), an enhancement in the power factor (PF), the mitigation of supply voltage imbalances and disturbances, and the maintenance of a steady direct-current link capacitor voltage (DLCV), despite the variations in the load, solar irradiation, and wind velocity. The efficiency of the suggested strategy is assessed using four case studies that involve different loads, variable wind velocities, and source voltage balancing conditions. Based on the simulation studies and obtained results, the suggested method significantly decreases the THD to values of 2.91%, 3.63%, 3.75%, and 3.50%. Additionally, it achieves a power factor of unity, which is considerably lower compared to other multilevel schemes that use the traditional symmetrical reference frame (SRF) and instantaneous reactive power (pq) methods. This design has been executed using the MATLAB/Simulink program.

Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC

The current paper presents an original approach into the microgrid control framework by incorporating LSTM-based optimization with specific emphasis on refining the gain parameters of a Proportional-Integral-Derivative (PID) controller. This integration represents a significant advancement in improving the overall efficiency of microgrid control systems. By creatively applying LSTM optimization, the paper achieves dynamic adjustments of the PID controller's parameters, resulting in more precise regulation of output power quality. Through the utilization of the Unified Power Quality Conditioner (UPQC) in conjunction with LSTM-based optimization, the paper establishes a compelling link between improved power quality and the resultant tariff rates. This highlights their combined influence on enhancing power quality and calibrating tariff rates, providing a fresh perspective on optimizing microgrid operations.

A control method for the single-phase three-leg unified power quality conditioner without a phase-locked loop

The single-phase three-leg unified power quality conditioner (UPQC) can achieve the functions of voltage compensation, reactive power compensation, and harmonic compensation. However, traditional control algorithms require a phase-locked loop to obtain the real-time phase angle of the grid voltage, which undoubtedly increases algorithm complexity. To simplify the phase-locked calculation, this paper proposes a control method without the phase-locked loop for the single-phase three-leg UPQC. In the proposed scheme, the instantaneous value of the grid voltage is employed to realize the grid integration control. Then, the load voltage reference is calculated in real time using a second-order generalized integrator. Moreover, a simple algorithm for reactive power and harmonic compensation is discussed, further simplifying the control algorithm. Finally, a small-scale experimental platform is built, and the effectiveness of the proposed method is verified by the experimental results.

Optimal design of solar/wind/battery and EV fed UPQC for power quality and power flow management using enhanced most valuable player algorithm

The behavior and performance of distribution systems have been significantly impacted by the presence of solar and wind based renewable energy sources (RES) and battery energy storage systems (BESS) based electric vehicle (EV) charging stations. This work designs the Unified Power Quality Conditioner (UPQC) through optimal selection of the active filter and PID Controller (PIDC) parameters using the enhanced most valuable player algorithm (EMVPA). The prime objective is to effectively address the power quality (PQ) challenges such as voltage distortions and total harmonic distortions (THD) of a distribution system integrated with UPQC, solar, wind, BESS and EV (U-SWBEV). The study also aims to manage the power flow between the RES, grid, EV, BESS, and consumer loads by artificial neuro-fuzzy interface system (ANFIS). Besides, this integration helps to have a reliable supply of electricity, efficient utilization of generated power, and effective fulfillment of the demand. The proposed scheme results in a THD of 4.5%, 2.26%, 4.09% and 3.98% for selected four distinct case studies with power factor to almost unity with an appropriate power sharing. Therefore, the study and results indicate that the ANFIS based power flow management with optimal design of UPQC addresses the PQ challenges and achieves the appropriate and effective sharing of power.

A New Design of UPQC-Based Hybrid Multi-Carrier Modulation for Transformer Less Grid Connected PV-Based Active Power Filter

In recent times, there has been a growing concern over the augmented power consumption, leading to a pressing need for compensating devices to maintain power quality in grid-connected systems. One extensively employed solution is the Unified Power Quality Conditioner (UPQC), which incorporates both shunt and series active power filters (APFs) connected through a DC-link capacitor. However, traditional systems using compensating devices can be costly and cumbersome, often relying on transformers to interface with the grid. To address these drawbacks, this report proposes a transformer-less networking approach using a multi-level inverter (MLI) that reduces the number of switches in the shunt and series APF. To enable the efficient operation of the MLI in UPQC, the report utilizes Synchronous Reference Frame Theory and Hybrid Multi-Carrier Pulse Width Modulation (H-MCPWM) techniques for reference signal generation and gate control, respectively. MATLAB/Simulink simulation version 2019a is employed to validate the performance of the proposed system, along with a hardware prototype featuring a motor load. A key aspect of the investigation involves conducting harmonic analysis for various modulation indices with a non-linear load. The resulting data is then compared to the outcomes of the conventional model. By exploring this approach, the report aims to showcase the viability of the transformer-less networking of MLI in UPQC, thereby paving the way for more cost-effective and efficient grid-connected systems with improved power quality.

Optimal allocation of open unified power quality conditioner in integrated distribution system environment with renewable energy sources

ABSTRACT The growing utilization of renewable energy sources (RES) that rely on inverters has adverse impacts on distribution systems, resulting in challenges such as power quality (PQ) issues and increased losses in the distribution network. One potential remedy for these issues involves the strategic allocation of open unified power quality conditioner (UPQC-O). This paper introduces a comprehensive two-layer integrated optimization model designed for the allocation of UPQC-O and RES in an active distribution network. The primary objective is to enhance energy efficiency, PQ, and economic benefits simultaneously. Furthermore, an innovative solver known as the multi-improved whale optimization method (multi-IWOA) has been implemented to address the optimization problem at hand. The efficacy of this approach was validated through testing on both the standard IEEE 69-bus radial distribution system and a real-world Indian 52-bus system. The evaluation results underscore the significance of optimally distributing UPQC-O resources while taking into consideration the integration of RES within the distribution system. It’s worth noting that the proposed methodology achieved a substantial 56.72% cost reduction for the IEEE 69-bus radial distribution system and a significant 76.092% cost reduction for the Practical System Indian 52-bus system. This highlights the substantial advantages of the proposed methodology for the efficient management of UPQC-O and RES integration in distribution systems.