Power System Stabilizer Design Research Articles

Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering applications

The novelty of this research lies in presenting a fresh stochastic algorithm enthused via ‘Velociraptor’ social intelligence in wildlife (or nature), known as the Velociraptor Group Optimization algorithm (VROA). In this strategy, ‘Velociraptor’ co-operative natural life cycle is mathematically framed, and novel mechanisms are presented to perform search (or exploration) and hunting (or exploitation). This suggests that the proposed method reveals a noteworthy ability in both exploitation and exploration. Furthermore, it successfully stabilities exploration and exploitation, supporting the search process. In the direction of evaluating the VROA, we utilized it on 51 CEC'17, CEC'20, and CEC'22 standard benchmark suites and six multidisciplinary engineering optimization functions. Furthermore, well-known statistical methods like Wilcoxon rank-sum test and Friedman's test have been used to verify the strength of the proposed algorithm against various optimizers. The tabulated numerical and statistical solutions show that VROA performs better than recent optimizers on most standard test suites and has efficiently attained the most competent resolution while concurrently upholding adherence to the designated constraints. The results validate that VROA can offer efficient and accurate optimal solutions in evaluation with recent metaheuristics.

Improved General Relativity Search Algorithm (IGRSA) for Designing Power System Stabilizers

This paper proposes a novel optimization algorithm for designing power system stabilizers (PSSs). The prospect of attaining higher stability motivated the authors to creat a new optimization algorithm for this study. A novel algorithm named the Improved General. Relativity Search Algorithm (IGRSA) was also developed by cloud theory to improve the performance of GRSA. The supremacy of the proposed approach is tested by comparing it with an introduced objective function in a medium multi-machine power system. The nonlinear simulation results and eigenvalues analysis has demonstrated that the proposed approach in this study is highly effective in enhancing the dynamic stability of the power system.

Optimal control design for power system stabilizer using a novel crow search algorithm dynamic approach

This paper proposes the first application of a novel improved metaheuristic optimization technique, dynamic crow search algorithm (DCSA) to find the optimal design of the power system stabilizer (PSS). Using the novel DCSA adaptive approach provides a global search capability as well as fastness and effectiveness to get the PSS best parameters under the search space constraints and local optimums. Moreover, PSS parameters has a crucial effect on power system stability that is a major nowadays engineering concern, where its best parameters could make a difference by damping small-signal stability oscillations effectively and in the smallest amount of time if they are obtained through a robust algorithm. As a result, PSS will prevent large types of instabilities which could reach up to some generation stations shedding. The novel proposed algorithm is for first time used and tested under different power system instabilities scenarios by using single-machine infinity bus model over multiple operating conditions to damp out perturbation signals effectively by means of getting optimal PSS design parameters, where Integral absolute error (IAE) performance index is used as an objective function to be minimized. Additionally, results obtained by the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version and other comparison methods. It is revealed that the proposed adaptive method is able to improve the power system stability dynamics and damp out perturbation oscillations successfully.

Probabilistic coordinated design of multi-band power system stabilizers using the unscented transformation

Probabilistic coordinated design of multi-band power system stabilizers using the unscented transformation

Optimal probabilistic design of multi-band power system stabilizers using the two-point estimate method

Optimal probabilistic design of multi-band power system stabilizers using the two-point estimate method

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.

Optimization Design of PSS and SVC Coordination Controller Based on the Neighborhood Rough Set and Improved Whale Optimization Algorithm

Aimed at reducing the redundancy of parameters for the power system stabilizer (PSS) and static var compensator (SVC), this paper proposes a method for coordinated control and optimization based on the neighborhood rough set and improved whale optimization algorithm (NRS-IWOA). The neighborhood rough set (NRS) is first utilized to simplify the redundant parameters of the controller to improve efficiency. Then, the methods of the Sobol sequence initialization population, nonlinear convergence factor, adaptive weight strategy, and random differential mutation strategy are introduced to improve the traditional whale optimization algorithm (WOA) algorithm. Finally, the improved whale optimization algorithm (IWOA) is utilized to optimize the remaining controller parameters. The simulation results show that the optimization parameters were reduced from 12 and 18 to 3 and 4 in the single-machine infinity bus system and dual-machine power system, and the optimization time was reduced by 74.5% and 42.8%, respectively. In addition, the proposed NRS-IWOA method exhibits more significant advantages in optimizing parameters and improving stability than other algorithms.

Coordination of Controllers to Development of Wide-Area Control System for Damping Low-Frequency Oscillations Incorporating Large Renewable and Communication Delay

The modern power systems incorporate high penetration of renewable is a large, composite, interconnected network with dynamic behavior. The small disturbances occurring in the system may induce low-frequency oscillations (LFOs) in the system. If the (LFOs) are not suppressed within a stipulated time, it may cause system islanding or even blackouts. Hence, it is essential to investigate the behavior of the system under various levels of disturbances and control action must be taken to damp these oscillations. The established approach to damping the LFOs is by installing power system stabilizers (PSS). PSS uses the local signals from generators to control the oscillations. The dominant source of inter-area oscillations in power systems is due to overloaded weak interconnected lines, converter-interfaced generation, and the action of the high gain exciter present in the system. Consequently, wide area control is needed to control the inter-area oscillations existent in the system. This paper developed a coordinated design of conventional PSS, static compensator, renewable converters, and wide area controller for damping the local and inter-area oscillations in renewable incorporated power systems. The performance of the developed controller is evaluated through the time domain analysis and eigenvalue analysis. A comparison of the introduced controller has been done with other standard conventional methods. The choice of input signals for the wide area controller from the wide-area measurement system is done based on the controllability index. Additionally, the location of the controller must be identified to dampen the inter-area oscillations in the system. In this paper, the controllability index is calculated to find out the highly affected wide area signals for considering it as the feedback signal to a developed controller. The location of the controller is recognized by computing the participation factor. The developed controller has experimented on renewable incorporated large study power systems when time delay and noise are present in wide area signals.

STABILISATEUR DE SYSTÈME D'ALIMENTATION ROBUSTE BASÉ SUR UN CONTRÔLE H∞ STRUCTURÉ POUR LA STABILITÉ D'UN SYSTÈME MULTI-MACHINE

A robust design of power system stabilizers (PSSs) using H∞ output feedback control has been introduced in this work. To facilitate the implementation of the designed PSSs, the proposed technique employs the H∞ to tune the fixed-structure conventional lead-lag PSS parameters of the multi-machine system. These PSSs are used to improve the damping of the local and inter-area low-frequency oscillations in power systems under different operating conditions. The proposed control is tested on a multi-machine system, which is a three-machine nine-bus system. A comparative simulation study shows a significant enhancement and good performance of the proposed design compared to an IEEE conventional power system stabilizer.

Design of Power System Stabilizer for DFIG-based Wind Energy Integrated Power Systems Under Combined Influence of PLL and Virtual Inertia Controller

Design of Power System Stabilizer for DFIG-based Wind Energy Integrated Power Systems Under Combined Influence of PLL and Virtual Inertia Controller

PMU-Based Power System Stabilizer Design Using Coati Optimization Algorithm

Low-frequency oscillation modes can destabilize the power system if they do not have adequate damping rates. The interconnection of large power systems and the complexity of operation has caused the increase in these modes and conventional control techniques have difficulty guaranteeing adequate damping rates. This article proposes a design method for a Power System Stabilizer damping controller using data from Phasor Measurement Units (PMUs) to ensure adequate damping rates for the modes even in the event of PMU data failures. Case studies were simulated and analyzed using the IEEE 68-bus system.

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

Robust multi-machine power system stabilizer design using bio-inspired optimization techniques and their comparison

This paper reports a comparative study among four bio-inspired meta-heuristic techniques i.e. Sooty-Tern Optimization (STO), Grey Wolf Optimization (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to tune the robust Power System Stabilizer (PSS) parameters of the multi-machine power system. These approaches are successfully tested on two bench-mark systems: sixteen-machine, sixty-eight-bus New England Extended Power Grid (NEEPG) and three-machine, nine-bus Western System Coordinating Council (WSCC). The efficacy of planned PSS via STO and GWO is validated by extensive non-linear simulations, eigenvalue analysis, and performance indices for numerous operating conditions under decisive perturbations, and outcomes are matched with those of GA and PSO techniques. In addition, the robustness is also tested for these algorithms. The results indicate that the PSS design using STO and GWO improves the small-signal stability and damping performance for mitigating inter-area and local area modes of low-frequency oscillations compared to GA and PSO.

“Enhancing power system stability: an innovative approach using coordination of FOPID controller for PSS and SVC FACTS device with MFO algorithm”

This paper investigates an optimal methodology for mitigating low-frequency oscillation concerns in power systems. The study explores the synergistic integration of a power system stabilizer (PSS) and a flexible alternating current transmission system (FACTS) to formulate an intelligent controller. A comprehensive analysis encompasses various PSS design strategies, including lead-lag (LL), proportional-derivative-integral (PID), and fractional-order proportional-integral-derivative (FOPID) controllers. The FACTS device selected for this investigation is a static VAR compensator (SVC), highlighting the exceptional efficacy of FOPID-based PSS over alternative strategies with a power oscillation damper. The study extends its scope to encompass a comparative assessment of two distinct optimization algorithms: the moth flame optimization (MFO) and the antlion optimization (ALO). The research is conducted using a single-machine infinite bus power system (SMIB) as the case study platform. A total of four diverse test scenarios are executed under varying operating conditions. The evaluation of the developed method employs six distinct performance indices to investigate the developed controller thoroughly. The outcomes reveal that the MFO-optimized FOPID-PSS and SVC controller outperforms other control schemes. This optimized configuration demonstrates substantial improvements across all performance indices. These findings underscore the superior capabilities of the proposed approach in enhancing power system stability and performance.

Power System Stability Enhancement Using Grasshopper Optimization Approach and PSSs

A new meta-heuristic algorithm namely Grasshopper Optimization Approach (GOA) for Power System Stabilizer (PSS) design problem is investigated in this paper. The parameters of PSSs are optimized by GOA to minimize the time domain objective function. The performance of the designed GOA based PSSs (GOAPSS) has been has been compared with Differential Evolution (DE) based PSSs (DEPSS) and the Particle Swarm Optimization (PSO) based PSSs (PSOPSS) under various loading events. The results of the proposed GOAPSS are confirmed via eigenvalues, damping ratio, time domain analysis, and performance indices. Moreover, the robustness of the GOA in getting good damping characteristics is verified.

Recurrent neural network based design of fractional order power system stabilizer for effective damping of power oscillations in multimachine system

The complexity of modern power systems has risen due to the growing demand for electricity. To mitigate this problem, the role of the electric utility in supplying safe and reliable electricity has become increasingly crucial. Low-frequency oscillations (LFOs) in a power system are an inevitable aspect. Therefore, the reliability of the system requires appropriate damping to overcome these LFOs. This paper proposes a novel design methodology for a fractional-order power system stabilizer (FO-PSS) to enhance the stability of the power system network. FO-PSS is designed to effectively dampen LFOs in both single-machine infinite bus systems (SMIB) and multi-machine power systems (MMPS). Recurrent neural network (RNN) is used to predict the parameters of proposed PSSs. To evaluate the efficacy of the proposed PSS, various test cases are considered. The results are compared to those obtained from traditional PSSs and optimization based PSSs. The effectiveness of the proposed RNN-based FO-PSS is demonstrated under diverse loading conditions.

Artificial Eco-System-Based Optimization Algorithm for Optimal Design of Single-Machine Infinite Bus and Multi-Machine Power System Stabilizers

Artificial Eco-System-Based Optimization Algorithm for Optimal Design of Single-Machine Infinite Bus and Multi-Machine Power System Stabilizers

LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

AbstractPower system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low‐frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID‐PSS) via a long short‐term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID‐PSS. To evaluate the performance of the proposed LSTM based PID‐PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID‐PSS is compared with traditional PSSs through time‐domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID‐PSS under diverse loading conditions.

Power System Stabilizer Design using Genetic Algorithms and Particle Swarm Optimization

in this paper, Meta-heuristic techniques using genetic algorithms GA and particle swarm optimization PSO to tuning optimal design of power system stabilizer PSS proposed. This latter have been used for many years to add damping to electromechanical oscillations of power system, Based on this idea we have proposed multiobjective function composed with tow function, first maximize stability margin by increasing the damping factors while minimizing the real parts of the eigenvalues. Simulation results to comparative study between genetic algorithms and particle swarm optimization obtained by our realized graphical user interface (GUI) proved the efficiency of PSS optimized by genetic algorithms in comparison with Particle Swarm Optimization, showing stable system responses almost insensitive to large parameter variations and under different operating regime (under-excited, nominal and over excited regime).

Power system stabilizer design for ideal AVR using local measurements

In this work, the application of an Ideal automatic voltage regulator is presented for an interconnected power system. The Ideal AVR is designed using the feedback linearization technique and it is shown that the voltage regulation response with the Ideal AVR is superior to conventional linear AVR. Subsequently, a power system stabilizer is added to improve the small signal stability. The proposed control scheme is demonstrated to provide vastly superior voltage regulation and damping of the rotor mode compared to conventional control. Simulation results are presented using a sub-transient (6th order) model of the IEEE 14 generator 59 bus system.