Multi-machine Power System Research Articles

SVC Control Strategy for Transient Stability Improvement of Multimachine Power System

The increase in renewable energy sources (RESs) in power systems is causing significant changes in their dynamic behavior. To ensure the safe operation of these systems, it is necessary to develop new methods for preserving transient stability that follow the new system dynamics. Fast-response devices such as flexible AC transmission systems (FACTSs) can improve the dynamic response of power systems. One of the most frequently used FACTS devices is the Static Var Compensator (SVC), which can improve a system’s transient stability with a proper control strategy. This paper presents a reactive power control strategy for an SVC using synchronized voltage phasor measurements and particle swarm optimization (PSO) to improve the transient stability of a multimachine power system. The PSO algorithm is based on the sensitivity analysis of bus voltage amplitudes and angles to the reactive power of the SVC. It determines the SVC reactive power required for damping active power oscillations of synchronous generators in fault conditions. The sensitivity coefficients can be determined in advance for the characteristic switching conditions of the influential part of the transmission network, and with the application of the PSO algorithm, enable quick and efficient finding of a satisfactory solution. This relatively simple and fast algorithm can be applied in real time. The proposed control strategy is tested on the IEEE 14-bus system using DIgSILENT PowerFactory. The simulation results show that an SVC with the proposed control strategy effectively minimizes the rotor angle oscillations of generators after large disturbances.

Power system stability enhancement through optimal PSS design

Low-frequency oscillations (LFO) are generated in interconnected electric networks because of the weak tie lines between the parts of the networks. Such oscillations have been a significant challenge for engineers that may lead to system instability if appropriate measures are not taken. This article proposes two new metaheuristic algorithms, the jellyfish search algorithm (JSA) and the tunicate swarm algorithm (TSA), to design the power system stabilizers (PSS) for single machine infinite bus (SMIB) and multimachine power system (MPS) networks. The proposed method uses the JSA and TSA to dampen out the LFOs by modifying the vital parameters of lead-lag type conventional PSS (CPSS). A damping ratio-based objective function improves both models' system damping. These methods are tested on an SMIB system and two distinct MPS networks exposed to the three-phase fault under various loading conditions. The effectiveness of the suggested approach has been studied by comparing the system eigenvalues and minimum damping ratio (MDR) produced from JSA and TSA-tuned PSS and the CPSS. In addition, time domain simulation results are compared, demonstrating that the new approach outperforms the standard techniques, such as particle swarm optimization (PSO) and backtracking search algorithm (BSA). In the SMIB system, MDR produced by JSA and TSA-based PSS is 2.55 times higher than that of CPSS. Similarly, in comparison to PSO and BSA-based PSS, JSA, and TSA-tuned PSS offer >1.4 times greater MDR in the Two Area Four Machine network and up to 8.4 times larger MDR for the IEEE-39 Bus network. Furthermore, the efficacy of the suggested JSA and TSA approaches' prediction capacity is validated by satisfying the values of statistical performance metrics.

Spiral-refraction mutation prairie dog algorithm: Optimization framework for engineering design of interconnected multimachine power system

Many real-world engineering problems, characterized by high-dimensionality, nonlinearity, nonconvexity, and multi-modality, demand advanced optimization methods. Traditional algorithms may struggle with these challenges. Prairie dog optimization (PDO) was proposed for function optimization but faces limitations in balancing exploration and exploitation due to two reasons. The first one is related to diversity features which may not be efficient, and the second one is related to having no leading mechanism for direct search feature towards the promising regions. With that in mind, this study introduces an enhanced PDO variant, improved PDO (IPDO), addressing PDO's drawbacks through two key strategies: refraction-based learning (RBL) and spiral search learning (SSL). RBL enhances exploration by generating refraction solutions, while SSL conducts deep local searches around the best solution, strengthening exploitation. IPDO's performance is evaluated on benchmarks, IEEE CEC2017/CEC2020 test suites, and real-world constraint engineering optimization problems. Comparative analyses, including statistical measures, convergence analysis, and boxplot analysis, demonstrate IPDO's superiority. Besides, the IPDO is applied to estimate more promising parameters of power system stabilizer utilized in an interconnected multimachine power system using Western System Coordinating Council three-machine, nine-bus power system. Results illustrate that the IPDO harvests the better estimation among the other methods, making it to be an efficient and powerful tool for dealing with the efficient operation of an interconnected multimachine power system which is a challenging real-world engineering problem.

Enhancing Transient Stability in Multi-Machine Power Systems through a Model-Free Fractional-Order Excitation Stabilizer

Enhancing Transient Stability in Multi-Machine Power Systems through a Model-Free Fractional-Order Excitation Stabilizer

On the optimal multi-objective design of fractional order PID controller with antlion optimization

This study introduces an optimal multi-objective design approach for a robust multimachine Fractional Order PID Controller (FOPID) using the Antlion algorithm. The research focuses on the need for effective stabilizers in multimachine power systems by employing traditional speed-based lead-lag FOPID controllers. The study formulates a multi-objective problem, optimizing the damping factor and damping ratio of lightly damped electromechanical modes to maximize a composite set of objective functions, tackled through the Antlion algorithm. Stability analysis of Single-Machine Infinite-Bus (SMIB) and multimachine power systems is conducted based on rotor speed and power deviation minimization in the time domain response, along with damping ratio and eigenvalue analysis. The proposed approach is implemented and tested on three IEEE test cases, showcasing significant improvements in stability through the reduction of maximum overshoot (Mp) and settling time (ts) of speed deviation. Comparative analysis with other optimization-based FOPID controllers underscores the superiority of the proposed approach in enhancing stability in multimachine power systems. The main impact of this research lies in its contribution to the advancement of stability enhancement techniques in multimachine power systems, offering a systematic framework for optimal FOPID controller design and empowering decision-making processes in power engineering.

Improved chaotic Bat algorithm for optimal coordinated tuning of power system stabilizers for multimachine power system

Power systems exhibit nonlinearity. causing dynamic instability and complex power oscillations. This research proposes an innovative strategy using the Novel Bat Algorithm (NBA) to achieve ideal Power System Stabilizers (PSSs) in a multimachine power system. The approach shifts electromechanical modes to specific areas in the s-plane. Enhancing the multi-machine power system and establishing stabilizer parameters for dynamic performance. The study examines the designed approach aptitude for standard lead-lag PSSs configurations. In order to elevate the global search problem and transfer some static operators for the optimum optimization process. the chaos mapping. also known as CNBA. is introduced into NBA. Four different forms of chaos maps are compared in experiments to resolve unconstrained mathematical issues in order to illustrate CNBA performance. In any other case. the challenge of designing PSS under a wide range of loading situations is transformed into an optimization challenge with the damping ratio of electromechanical modes with low damping as the target function. The optimal stabilizers’ gains are gotten by employing the CNBA algorithm. Second plan. an effective technique is astutely established to delineate the PSS location and quantity using CNBA and another side using participation factor. To examine the efficacy of the proposed CNBA-based PSS on a large system; it is tested on the interconnected of New-England/New-York (16 generators and 68 buses) power grid. and verified by comparative study with NBA through eigenvalue analysis and nonlinear simulation to provide evidence the algorithmic competence of CNBA. The CNBA approach yields a minimum damping ratio of 37%. which is consistent with the its eigenvalue. In contrast, the NBA approach achieves a minimum damping ratio of 31%. The simulation results reveal the fine performance of the proposed CNBA-PSS in a convincing manner and its capacity to provide an excellent damping for inter-area and local oscillations under diverse operating cases compared to NBA-PSS then in the case of PSS location.

AC and DC Fault Ride-Through Strategies for Hybrid MMC Based HVDC Link and Interaction With Multimachine Power System Dynamics

AC and DC Fault Ride-Through Strategies for Hybrid MMC Based HVDC Link and Interaction With Multimachine Power System Dynamics

Simultaneous Policy Iteration for Decentralized Control of Multi-Machine Power Systems

Simultaneous Policy Iteration for Decentralized Control of Multi-Machine Power Systems

A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems

Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.

Advanced UPFC Controllers to Improve Transient and Dynamic Stabilty of Power System

The industrial revolution is thought to have had its foundation in electricity. Electricity must be produced and transferred in greater quantities in order to meet the ever rising demand that must be delivered to numerous homes and businesses. it is slightly challenging to do so in a very flexible and efficient manner. So for transmission of such a large amount of electricity, In terms of the examination of the power system, (FACTS) will have been discovered to be a potential component. The comparative analysis of UPFC in the presence LG fault is described in this paper. Fuzzy logic controller has better performance in improving transient stability than conventional PI controller. System consist of multimachine power system with fault is simulated in MATLAB environment the results are also tested using prototype hardware model of set up 01 KVAR UPFC. There is improvement in rotor angle stability of power system using Fuzzy logic controller compared to conventional PI controller. Oscillations are also reduce with this FUZZY based UPFC controller

Modified Multimachine Power System Design with DFIG-WECS and Damping Controller

Rotor angle stability, which involves electromechanical oscillation damping and control, is very important in maintaining the stability of modern power grid systems. Renewable energy sources like wind energy are undergoing massive integration into modern power grid systems to meet energy demands and decarbonize power grid systems of carbon emissions from fossil fuel generators. To enable increased integration of wind renewable energy sources, precise models are needed for research and analytical purposes. Wind renewable energy is generated through a wind energy conversion system (WECS); one such conversion system is the doubly fed induction generator (DFIG) system. In this study, a precise model of a DFIG-WECS was modeled and integrated into the IEEE’s two-area Kundur power test system, which represents the available power grid system, and is also a multimachine power system using the Matlab/Simulink 2023 software. A damping controller known as the power system stabilizer (PSS), whose optimal parameters were obtained using artificial eco-system optimization (AEO), was also incorporated into the integrated power grid system to control and damp electromechanical oscillations. The results showed that the PSS damping controller effectively damped electromechanical oscillations in the integrated power grid system.

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Observer-based event-triggered [formula omitted] control for Hamiltonian systems

This paper investigates the problem of designing an observer-based event-triggered H∞ controller for a Hamiltonian system with delays incorporated in the underlying network. As our contributions, we first propose an event-triggered scheme which uses the Hamiltonian to decide whether to trigger the event generator at the sampling time. Additionally, when states are not exactly known globally asymptotically stable, we proceed to design an observer-based controller with which the resulting closed-loop system can be transformed into a time-delay Hamiltonian system. Based on the structural characteristic of the Hamiltonian systems, sufficient conditions are given to guarantee the closed-loop system to achieve the H∞ performance index with external disturbances in available and unavailable states, respectively. Finally, multi-machine power systems as simulation examples are illustrated to validate our proposed results.

Application of meta-heuristic methods to robust FACTS controllers design

This paper presents a simple method to simultaneously tune Flexible AC Transmission Systems (FACTS) in multimachine power systems. The proposed approach employs Genetic Algorithms (GA) and particle Swarm Optimization (PSO). The objective is to maximise the minimum damping ratios, subject to all combinations of their locations. The proposed approach has been examined and tested on the 2-area, 4-machine system which is close to realistic interconnected power systems model. The results are promising and show the effectiveness and robustness of the proposed approach.

An adaptive wide-area neuro-fuzzy based controller for variable frequency transformer in damping inter-area oscillations

Due to the dynamic, non-linear, and complex character of large-scale power networks, prolonged inadequately damped low frequency oscillations may develop, leading to loss of synchronisation of generating units. Flexible AC transmission controller when equipped with an appropriate additional damping mechanism offers substantial damping in mitigating these oscillations. In this context, a smart flexible transmission device namely variable frequency transformer (VFT) is integrated into a multimachine power system network to effectively damp low-frequency oscillations. An adaptive model free neuro-fuzzy inference system (ANFIS) based damping controller is developed for VFT in order to damp inter-area oscillations. The ANFIS is a resilient and intelligent system that combines fuzzy logic and neural network capabilities with attributes like robustness, adaptability, speed, and flexibility. The proposed damping controller, which provides additional damping proportionate to the frequency variation, was included into the VFT’s power loop. The controller input is determined as the center-of-inertia difference of generator rotor speeds and is a remote signal delivered by a wide-area measuring system. Depending on the observability of crucial modes, participation factor (PF) investigation is used to extract the appropriate feedback signal, which provides details on the overall the system’s dynamics. The ANFIS-based scheme for the VFT is designed to impart sufficient damping qualities to the critical modes of the examined system under a variety of operating conditions. Using extensive non-linear time-domain simulations and eigenvalue analysis, the effectiveness of the suggested technique was evaluated on a popular 2-area, 4-generator, 11-bus test system.

Appropriate models for designing frequency control schemes in power systems

Frequency response characteristics like frequency nadir and rate of change of frequency (ROCOF) are important in designing frequency control schemes. The so-called Frequency Response Model (FRM), widely used in power system frequency studies, has some limitations in calculating these metrics. This paper evaluates and analyses the performance of FRM and multi-area FRM in calculating performance metrics for small and multi-machine power systems while considering the effect of amplitude and duration of voltage deviations after a disturbance. The capabilities and limitations of FRM and multi-area FRM are inferred by comparing their performance with a detailed model. To overcome the shortcomings of simplified models, a partly-linearized detailed model is suggested that preserves the important nonlinearities in frequency studies but, to consider the impact of the network on the results of frequency studies with reduced computation time, linearizes the models of other system components. Also, based on the two-part characteristic curve of steam and gas power plants, modified models have been suggested for the turbines and included in the proposed model. Numerical results show that the accuracy of the proposed simplifications is close to the detailed model while its computation time is less than 10 % of that of the detailed model.

A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

A convolutional neural network framework to design power system stabilizer for damping oscillations in multi-machine power system

Enhancing the transient stability of interconnected power systems by designing an adaptive fuzzy-based fractional order PID controller

The reliable and efficient operation of the electrical energy transmission system heavily relies on maintaining stability as a key requirement. Interconnected power systems often experience low-frequency oscillations (LFOs), which have the potential to initiate instability and, subsequently, necessitate careful attention and investigation. To tackle this issue, researchers have focused on various controllers, including Fractional-Order Proportional-Integral-Derivative (FOPID) controllers. The FOPID controller, with its increased number of design parameters, has proven to be more successful than the traditional PID controller in various engineering applications. However, determining the optimal parameters for the controller becomes more challenging due to the increased design space. Moreover, the performance of fixed-gain FOPID controllers may be degraded when confronted with highly nonlinear and uncertain controlled objects such as power systems. This issue is primarily attributed to the static nature of fixed-gain FOPID controllers. To overcome these challenges associated with fixed-gain FOPID controllers and to instill an adaptive quality, this article introduces a novel power system control methodology. This adaptive approach combines a FOPID control strategy with a fuzzy logic system and is denoted as EOA-AFFOPID. The proposed adaptive controller dynamically tunes its coefficients through a fuzzy block, which significantly improves the overall performance of the control system. To establish a performance benchmark, we have also created an alternative controller, named EOA-FOPID, a version of the optima FOPID controller with fixed coefficients. The proposed methodology is applied to multi-machine interconnected power systems equipped with Static Synchronous Series Compensators (SSSCs), and comparative evaluations are conducted with other popular recent control strategies. The comparisons demonstrate the effectiveness of the EOA-AFFOPID control strategy in damping system fluctuations and achieving significant performance improvements in terms of different metrics including overshoot, undershoot, and settling time.

Advanced dynamic stability system developed for nonlinear load

&lt;span lang="EN-US"&gt;The conventional production of electrical energy gives rise to environmental problems and involves high production costs. To cope with the increasing demand for electrical loads and to optimize power transmission on the grid, it is imperative to shift towards renewable energies and their hybrid utilization. This planning study assesses the impact of fluctuating electric loads on bus voltages and frequency, with the objective of distributing energy in a more efficient and dynamic manner. Moreover, we delve into the implementation of artificial intelligence (IAPS) to curtail operations, minimize losses (GED), optimize production plans (EMSO), and synchronize decentralized multi-machine power systems (MMSA). Our study scrutinizes power dispatching on dynamic models of purely consuming loads, which are subject to dynamic constraints, by utilizing educational software that encompasses 13 cycles for 24 hours of frequency studies for multisource production in a standardized electrical system (EPSS). The control outcomes obtained through validation on an IEEE30 bus network display a resilient model for planned productions.&lt;/span&gt;

Cuckoo Search Approach for Optimal SVC Design in A Multimachine Power System

In this paper, a new metaheuristic method, the Cuckoo Search (CS) approach, based on the life of a bird family is proposed for the optimal design of a Static Var Compensator (SVC) in a multimachine environment. The SVC parameter tuning problem is converted to an optimization problem which is solved by the CS approach. The performance of the proposed CS-based SVC (CSSVC) has been compared with Particle Swarm Optimization (PSO) based SVC (PSOSVC) under various operating conditions. The superiority of the suggested technique in damping oscillations and enhancing voltage profile over a wide range of operating conditions and system configurations is confirmed through eigenvalue, performance indices, and time domain simulation results over the PSO.