Flexible AC Transmission System Devices Research Articles

Optimal DSSC’s deployment in power system using PSO

Owing to the high cost of installation and operation, distributed flexible AC transmission system (FACTS) technology gives opportunity to provide cost-effective solution in power system operation and control. A distributed static series compensator (DSSC) is a series FACTS device and it is placed equidistantly placed on the existing line to vary the line current. Active power can be controlled in the line with the help of DSSC. This paper presents DSSC for active power flow control to enhance system loadability index and to minimize reactive power generation by generators. Since DSSC is of low power. a small value of reactance is emulated in the line. Large number of DSSC’s are distributed along the transmission line at regular intervals to realize considerable change in the current. A particle swarm optimization is implemented to determine DSSC’s emulated reactance optimally. In this condition, all the lines flowing power in their limits with increased loading condition. Maximum system loadability index is evaluated by employing optimal number of DSSC’s on the line. A multiobjective problem is formulated. One objective function is formed such that no line would become overloaded even when loading is increased. Other objective is formulated to minimize reactive power generation by generators. A compromise solution is investigated for optimally connected of DSSC devices to achieve both the objective functions. IEEE 14 bus system is taken for MATLAB simulation of DSSC compensated system.

Multi-Objective Optimal Power Flow Analysis Incorporating Renewable Energy Sources and FACTS Devices Using Non-Dominated Sorting Kepler Optimization Algorithm

In the rapidly evolving landscape of electrical power systems, optimal power flow (OPF) has become a key factor for efficient energy management, especially with the expanding integration of renewable energy sources (RESs) and Flexible AC Transmission System (FACTS) devices. These elements introduce significant challenges in managing OPF in power grids. Their inherent variability and complexity demand advanced optimization methods to determine the optimal settings that maintain efficient and stable power system operation. This paper introduces a multi-objective version of the Kepler optimization algorithm (KOA) based on the non-dominated sorting (NS) principle referred to as NSKOA to deal with the optimal power flow (OPF) optimization in the IEEE 57-bus power system. The methodology incorporates RES integration alongside multiple types of FACTS devices. The model offers flexibility in determining the size and optimal location of the static var compensator (SVC) and thyristor-controlled series capacitor (TCSC), considering the associated investment costs. Further enhancements were observed when combining the integration of FACTS devices and RESs to the network, achieving a reduction of 6.49% of power production cost and 1.31% from the total cost when considering their investment cost. Moreover, there is a reduction of 9.05% in real power losses (RPLs) and 69.5% in voltage deviations (TVD), while enhancing the voltage stability index (VSI) by approximately 26.80%. In addition to network performance improvement, emissions are reduced by 22.76%. Through extensive simulations and comparative analyses, the findings illustrate that the proposed approach effectively enhances system performance across a variety of operational conditions. The results underscore the significance of employing advanced techniques in modern power systems enhance overall grid resilience and stability.

Optimal management in island microgrids using D-FACTS devices with large-scale two-population algorithm

Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management.

Implementation of ANFIS-based UPQC for Power Quality and Thermal Management Enhancement in the Distribution System

Conventional distribution systems generally operate with voltage and current waveforms being sinusoidal, though they slightly deviate from the ideal sinusoidal waveform, which is measured as distortion. The increase in power demand has led to the local generation and storage of power (Micro Grid systems). For its adoption, we need to implement smart grid architecture. Power electronics is a technologically sound way to limit different kinds of power quality (PQ) disturbances. It can also be used to control power flow, boost energy transmission capacity, enhance dynamic behavior and voltage stability, and ensure better power quality at distribution within established bounds. Unified Power Quality Conditioners (UPQC), one of the well-known Flexible AC Transmission System (FACTS) devices, are typically used to address problems in distribution systems such as voltage surges, flickers, neutral current reduction, and PQ. Additionally, thermal management is crucial for maintaining system stability and preventing overheating. While dealing with sensitive loads, a UPQC injects harmonics into the system, which can compromise system stability. This article describes an artificial neural network with harmonics elimination techniques for a modified UPQC connected with a Smart Grid. The performance of the proposed system is evaluated using MATLAB/Simulink software.

FACTS: Present and Future

Flexible AC Transmission Systems (FACTS) are pivotal in modernizing power systems, enhancing their stability, controllability, and efficiency. Currently, FACTS devices such as Static VAR Compensators (SVCs), Static Synchronous Compensators (STATCOMs), and Unified Power Flow Controllers (UPFCs) are employed to manage power flow, mitigate system instabilities, and improve voltage regulation across transmission networks. These technologies address present challenges, including the integration of renewable energy sources, reduction of transmission losses, and enhancement of system reliability in the face of fluctuating power demands. However, the growing complexity of power grids, driven by the increasing penetration of intermittent renewable energy, electric vehicles, and distributed generation, necessitates advanced and scalable solutions. The future of FACTS lies in the development of more sophisticated, adaptive, and intelligent devices that leverage real-time data analytics, artificial intelligence, and machine learning to optimize power flow dynamically. Future advancements are expected to focus on enhancing the interoperability of FACTS with smart grid technologies, improving the resilience of power systems against cyber-physical threats, and facilitating the transition towards more decentralized and sustainable energy systems. Moreover, the integration of energy storage with FACTS devices could revolutionize their functionality, offering not only reactive power compensation but also energy balancing capabilities. This paper explores the current applications of FACTS and envisions their future role in addressing the evolving challenges of global power systems, emphasizing the importance of innovation and strategic investment in the ongoing transformation of electrical networks.

Testing and analysis of a D-STATCOM prototype connected to the electrical network

In this article Flexible AC Transmission Systems (FACTS) devices are addressed, especially the design and implementation of a D-STATCOM prototype, making a comparison between the theoretical behavior and the aforementioned prototype. A fuzzy logic-based strategy was used for voltage control, employing the Matlab Fuzzy Logic Designer tool. The neural network was trainned with the network voltage, the voltage of the inverter stage, the current, the active and reactive power, and the power factor as inputs. Initially, the load power factor was 0.62 and the prototype operation allowed the voltage to be increased above the grid voltage, which is reflected in the forward current, modifying the power factor to a value of 0.91 while maintaining the same voltage. Finally, the results demonstrate the relationship between the increase of the inverter stage of the prototype above the electrical power line, obtaining a variable capacitor thus adjusting the power factor.

VOLTAGE FLICKER MITIGATION USING GTOBASED STATCOM TO IMPROVE POWER QUALITY OF A MULTI MACHINE SYSTEM

Transmission networks of modern power systems are becoming increasingly stressed because of growing demand and restrictions on building new lines. One of the consequences of such a stressed system is the threat of losing stability following a disturbance. Flexible ac transmission system (FACTS) devices are found to be very effective in a transmission network for better utilization of its existing facilities without sacrificing the desired stability margin. FACTS such as Static Synchronous Compensator (STATCOM), employ the latest technology of power electronic switching devices in electric power transmission systems to control voltage and power flow. The STATCOM adjusts voltage at its terminal by managing the amount of reactive power injected into or absorbed from the power supply. When the system voltage is low, STATCOM creates reactive power; when the system voltage is high, STATCOM absorbs reactive power. In this paper STATCOM controllers are designed for improving transient stability of multi machine systems. Proposed controllers are implemented under MATLAB/SIMULNK environment. Results of PI based controllers installed with multi machine system is found to be better on comparison with conventional system.

Dynamic switched crowding-based multi-objective particle swarm optimization algorithm for solving multi-objective AC-DC optimal power flow problem

In this paper, the multi-objective AC-DC optimal power flow (MO/AC-DC OPF) problem in the presence of renewable energy sources (RESs), flexible AC transmission system (FACTS) devices and multi-terminal direct current (MTDC) systems is introduced for the first time. Conflicting objective functions and the high complexity of the objective and constraint spaces are the main challenges in finding optimal solutions for MO/AC-DC OPF. To overcome these challenges, twelve different versions of the dynamic switched crowding-based multi-objective particle swarm optimization (DSC-MOPSO) algorithm are introduced in this paper. Studies on multimodal optimization problems have shown that all DSC-MOPSO versions have better performance metrics than the MOPSO algorithm. Using the developed DSC-MOPSO and its strong competitors, the Pareto-optimal solution sets of the MO/AC-DC OPF problem are investigated. In these investigations, the performances of the algorithms are tested for the minimization of dual and triple objectives such as fuel cost, voltage level deviation, emission and power loss in a modified IEEE 30-bus power grid. According to the simulation results, the proposed DSC-MOPSO achieved an improvement in fuel cost between 0.02 % and 5.05 % and a reduction in active power loss between 0.44 % and 30.74 % compared to its competitors. The Hypervolume (HV) performance metric was used to evaluate the Pareto-front coverage performance of DSC-MOPSO and other optimizers. The results from nine case studies of the MO/AC-DC OPF were statistically analyzed by the Friedman test according to the 1/HV metric. According to the Friedman test results, the rankings of DSC-MOPSO and MOMA are 1.984 and 3.079, respectively, ranking first and second among all competitors. Finally, in this study, feasible solutions for MO/AC-DC OPF problem are identified for the first time and the stability of competitive algorithms in finding these solutions is analyzed for the first time. The success rates and search times of DSC-MOPSO and MOMA algorithms in finding feasible solutions for MO/AC-DC OPF are 91.01 % (30.641 s) and 82.01 % (46.038 s), respectively.

Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes Optimization Algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm Algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plants along with FACTS devices.

Two-stage coevolutionary constrained multi-objective optimization algorithm for solving optimal power flow problems with wind power and FACTS devices

As a large amount of wind energy is integrated into the grid, the randomness it brings poses a challenge to modern power systems. The application of Flexible AC Transmission Systems (FACTS) in the grid is becoming more and more common, and it is necessary to consider how to choose suitable equipment in the appropriate locations. In this paper, a multi-objective optimal power flow (MOOPF) model with wind farms and FACTS devices is established. The Weibull probability density function is used to establish the wind speed model, and the cost problem brought by wind power is considered. The locations and ratings of thyristor-controlled series compensators, thyristor-controlled phase shifters, and static VAR compensators are added to the system as control variables. In addition, the constraints on the prohibited operating areas of thermal power generators and the valve point effect are also considered. Coevolutionary constrained multi-objective optimization algorithm (CCMO) is an advanced technology, and this paper improves it and names it two-stage coevolutionary constrained multi-objective optimization algorithm (TSCCMO). The proposed algorithm uses the constraint violation value as an additional objective function in the sub-population environmental selection process, and integrates a neighborhood selection strategy into the mating selection process. The population evolution process is divided into two stages, in the first stage the two populations cooperate weakly, and in the second stage the two populations will have strong cooperation. TSCCMO is used to solve this complex constrained MOOPF problem, and its results are compared and analyzed with CCMO, NSGA–II–CDP, C3M, and PPS. The comprehensive performance of TSCCMO is the best among the 6 cases.

The Implementation of Space Vector PWM in 36-Pulse D-STATCOM for Power Quality Improvement

Introduction: This paper addresses the ongoing challenge of harmonics reduction in power systems, particularly focusing on Flexible AC Transmission Systems (FACTS) devices. Instead of employing a conventional multilevel converter, an alternative configuration is proposed: a 36-pulse gate turn-off thyristor-based converter (GTO-VSC). This converter comprises three 12-pulse Voltage Source Converters (VSCs) as fundamental units, operating collectively on the frequency mode of GTO switching control within a Distribution Static Synchronous Compensator (D-STATCOM). The D-STATCOM dynamically compensates reactive power in the system. The 36-pulse configuration results in AC output voltage waveforms containing harmonics such as the 35th, 37th, 71st, 73rd, etc., leading to higher total harmonic distortion (THD) levels. background: Reduced number of switches in flexible AC transmission. Method: In response, the paper presents a new model featuring a 3-level, 36-pulse, 25kV, ±3MVAr STATCOM configured with 12-pulse GTO-VSCs. This model incorporates PWM gate switching and employs a PI-control methodology to optimize harmonics in simulation, specifically targeting harmonics at the 35th, 37th, 71st, 73rd, etc., orders using Sim Power Systems toolbox in Math Works software. Result: The compensator's operational performance is controlled through switching angle optimization, which minimizes harmonics, regulates voltage, and manages reactive power in the electrical transmission system. Conclusion: The simulation results demonstrate that the proposed model is acceptable for enhancing the flexibility and performance of the FACTS device in mitigating harmonics in power systems.

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

Gradient pyramid mechanism and Nelder-Mead simplex enhanced Colony Predation Algorithm for optimal power flow problems

The escalating human energy demand, particularly in the realm of electricity consumption, imposes a substantial burden on power system planning, resulting in the emergence of the optimal power flow (OPF) problem. To address this challenge, researchers have implemented a Flexible AC Transmission System (FACTS) in modern power systems to optimize network congestion. This paper presents a new application of the Colony Predation Algorithm (CPA) in the domain of Optimal Power Flow (OPF) through the introduction of an enhanced Colony Predation Algorithm (PNMCPA). PNMCPA incorporates the gradient pyramid mechanism and the Nelder-Mead simplex (NMs), marking the first utilization of CPA in the OPF field. The proposed approach presents a novel hierarchical gradient pyramid mechanism that combines the approximate gradient descent method with the construction of a pyramid fall rate, thereby optimizing the population as a whole within a framework that enhances population diversity. Additionally, the NMs algorithm is employed to optimize the optimal set of solutions for the population, thereby accelerating the convergence efficiency of the algorithm. The primary objective of this study is to optimize multiple factors, including the generation cost and transmission loss of the power system when multiple FACT components are present. Utilizing the Weibull probability density function, the model captures the uncertain and unstable nature of wind energy resources, encompassing direct, penalty, and standby costs. The proposed algorithm is applied to the IEEE30 bus test system, and the experimental results demonstrate that PNMCPA achieves a final optimization cost of 806.0132 $/h, surpassing other optimization algorithms. Concerning the power loss matter within the bus system, the PNMCPA algorithm significantly effectively mitigates total power loss, reducing from 1.44 MW to 1.41 MW instead of the initial CPA and substantially enhancing convergence speed. Empirical simulation outcomes clearly endorse PNMCPA as a highly efficient approach to tackling optimal power flow problems in the presence of multiple FACTS devices. The significant advancements presented in this paper mark a significant contribution to the field, offering a novel and superior solution to optimize power systems in the face of escalating energy demands.

Optimal location of FACTS devices with EVCS in power system network using PSO

The appeal of Electric Vehicles (EVs) is steadily growing, driven by the numerous advantages they offer to both owners and the environment. However, this surge in EV adoption places an increasing demand on the power system network for electric charging. The consequential strain on the power system may lead to instability and potential contingencies within the network. To mitigate these challenges, this paper recommends the installation of Flexible AC Transmission System (FACTS) devices. Specifically, two types of FACTS devices, Static Var Compensator (SVC) and Thyristor-Controlled Series Capacitor (TCSC), are employed to enhance the stability and loading conditions of the power system network. The study focuses on an IEEE 39 bus system network chosen for the installation of Electric Vehicle Charging Stations (EVCS). The objective is to assess the maximum loading factor of each bus within the network. This paper introduces a methodology for determining the optimal locations for both EVCS and FACTS devices. Notably, a Particle Swarm Optimization (PSO) algorithm is employed to optimize the size of FACTS devices and associated installation costs, ensuring an efficient and cost-effective enhancement to the power system's stability and loading conditions.

Multi-objective Snow Ablation Optimization Algorithm: An Elementary Vision for Security-Constrained Optimal Power Flow Problem Incorporating Wind Energy Source with FACTS Devices

This study delves into the exploration of a novel Multi-objective Snow Ablation Optimizer (MOSAO) algorithm, tailored for addressing expansive Optimal Power Flow (OPF) challenges inherent in intricate power systems. These systems are often complemented with the integration of renewable energy modalities and the state-of-the-art Flexible AC Transmission Systems (FACTS). Building upon the foundational framework of a previously documented single-objective Snow Ablation Optimizer, we have evolved it into the MOSAO paradigm. This transformation is achieved by harnessing the potency of non-dominated sorting coupled with the crowding distance strategy. The task of OPF magnifies in complexity when integrating renewable energy resources due to their inherent unpredictability and intermittent nature. As the modern power landscape evolves, FACTS devices are witnessing an increasing deployment to mitigate network demand and alleviate congestion issues. Within the ambit of this research, we've incorporated a stochastic wind energy source, working synergistically with an array of FACTS instruments. These encompass the static VAR compensator, thyristor-controlled series compensator and thyristor-driven phase shifter, all operating within the confines of an IEEE-30 bus framework. Strategic placement and calibration of these FACTS devices aim to optimize the system by minimizing the cumulative fuel expenditure. The capricious essence of wind as an energy source is elegantly depicted through the lens of Weibull probability density graphs. To distil the optimal middle-ground solutions, we've employed a fuzzy decision-making matrix. When benchmarking our findings against those derived from other esteemed optimization algorithms, we observe a notable distinction. The results from the modified IEEE-30 bus system accentuate the superior convergence, diversity and distribution attributes of MOSAO, especially when scrutinizing power flows. The MOSAO source code is available at: https://github.com/kanak02/MOSAO.

Hyper-heuristic strategies for optimal power flow problem with FACTS devices allocation in wind power integrated system

This research provides hyper-heuristic methodologies for solving Optimal Power Flow (OPF) issues in power system networks with Flexible AC Transmission Systems (FACTS) devices. OPF can be treated as one of the demanding challenges in the power system operating networks. To address the problems of loss and cost reduction, three types of FACTS devices will be studied in this paper: Static VAR Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Thyristor-Controlled Phase Shifter (TCPS). Two high level hyper-heuristic (HHH) approaches, called Exponential Monte Carlo with counter (EMCQ) and randomly select-Only Improving (OI), are employed as high-level metaheuristic to select and leverage the effectiveness of four low-level metaheuristics (LLH). These low-level metaheuristics comprise the Moth-Flame Optimizer (MFO), Barnacles Mating Optimizer (BMO), Teaching-Learning Based Optimization (TLBO) and Gradient-Based Optimizer (GBO). The usage of HHH solving the OPF problem is tested on the modified IEEE 30 bus system that integrates the thermal generators with the wind power. Findings of the study demonstrated the promising results by HHH which manages to outperform all the selected LLH algorithms.

Non-Linear Synergetic Control of UPFC for Efficient Damping of Local and Inter-Area Oscillations

This paper suggests a novel non-linear synergetic control for a unified power flow controller (UPFC). UPFC is a member of flexible AC transmission system devices, that controls power flow and regulates the voltage by enhancing the transient stability of the power system. UPFC comprises of two voltage source converters (VSCs). Equations of VSC are inherently non-linear. In this paper, we propose a non-linear approach to control the UPFC based on the synergetic control technique. It is simple to implement and valid for a variety of operations. It is also effective in suppressing inter-area oscillations. To verify the effectiveness of the proposed controller, various case studies are simulated and the results are compared with the traditional linear and non-linear controllers.

A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses

With the expansion of transmission systems, the devices that contribute to the overall power system performance must be adequate to the increasing modeling complexity and requirements. In this sense, the Flexible AC transmission system (FACTS) devices are often employed to improve stability and power quality, while the Thyristor-Controlled Series Compensator (TCSC) is a common example, able to change the equivalent transmission line impedance, improving power flow. This work discusses two established techniques for the control of TCSC devices based on the lead-lag model, whose parameters are defined through metaheuristic techniques, such as Genetic Algorithm and Particle Swarm Optimization. The results found in these publications are implemented in the Matlab/Simulink environment and confronted with the proposed Fuzzy logic application, written with linguistic inference, simplified rules, and simple membership functions. The two published models and the proposed Fuzzy logic performed satisfactorily with very similar results in all scenarios simulated considering a Single Machine Infinite Bus (SMIB) equivalent system model. The big advantage of the use of Fuzzy logic is its modeling simplicity, unlike the heuristic techniques that require much more modeling time and, sometimes, a large number of iterations to achieve acceptable parameters.

AUTOMATIC ADJUSTMENT OF REACTIVE POWER BY FACTS DEVICES UNDER CONDITIONS OF VOLTAGE INSTABILITY IN THE ELECTRIC NETWORK

This article describes the problem of automatic regulation of reactive power using electronic devices FACTS (Flexible AC Transmission Systems): static synchronous compensator (STATCOM) and unified power flow controller (UPFC). With the help of a complex writing form, the following are determined: voltages at the installation nodes of the FACTS device and loads, currents of loads, power sources and electronic compensators in case of voltage instability at the load node of the electrical network. Voltages and currents are determined using the node-voltage method. The task of STATCOM is partial or full compensation of reactive power. During the reduction of the voltage at the load node, the reactive power generated by the power source decreases. The STATCOM should partially or fully compensate for the reactive power imbalance as quickly as possible. However, at the same time, it is not possible to fully compensate for the voltage reduction. A series-parallel or parallel-series UPFC can be used to solve this problem. As a result of using the UPFC, it is possible to automatically raise the voltage level to acceptable values with the help of the UPFC series compensator. The analysis shows that the parallel-serial UPFC is characterized by the stability of operation. In the case of using a series-parallel UPFC, there are restrictions on the ability to adjust the imaginary voltage component of the series compensator, since the angle of the voltage vector changes, which causes a failure in the operation of the regulator of the parallel compensator UPFC.

Optimal allocation of flexible AC transmission system (FACTS) for wind turbines integrated power system

AbstractThe importance of flexible alternating current transmission system (FACTS) devices is increasing day by day as they provide compensation for power systems. The installation of such devices solves many issues regarding the stability and power transferability of power systems. However, for optimizing the performance of FACTS devices, the location and sizes of FACTS devices must be selected very carefully. On the other side, the utilization of optimization techniques is increasing daily, as it is capable of solving nonlinear, random problems by its stochastic nature. As a result, it became a reliable tactic for solving stochastic problems almost in every aspect of our lives. Therefore, it is adopted for solving power system problems, like, FACTS devices allocation, distributed generators allocation, determining component's parameters, and more. In this paper, the optimal size and location of several FACTS devices are selected to achieve the following single objective functions separately: fuel cost minimization and power losses minimization, then combining the mentioned objective function into one multiobjective function for gross cost minimization. The power system optimized is the Institute of Electrical and Electronics Engineers 30‐bus standard system, and the FACTS devices installed are static VAR compensator, thyristor control series compensator, and thyristor‐controlled phase shifter, while the optimization is performed using different conventional and recent optimization algorithms, including a newly developed algorithm, typically Leader Walrus Optimization Algorithm “LWaOA,” making a comparison among such algorithms to investigate the algorithm that possesses the best performance. The results of the optimization processes show the superiority of LWaOA.