Artificial Intelligence-Based Power System Stabilizers for Frequency Stability Enhancement in Multi-machine Power Systems

Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superior damping. To address this issue, a Farmland Fertility Algorithm (FFA-PSSs controller) was used to solve an optimization problem for optimal design of PSSs system parameters, and its performance efficiency was compared to GA and PSO-based PSSs controllers. In addition to PSS, flexible current transmission (FACTS) devices are widely used. PSSs controllers and FACTS devices are frequently constructed in tandem to improve the dampening efficiency of the system. In this study, an Interline Power Flow Controller (IPFC) FACTS device will be added to the PSSs controller to improve the power system’s oscillatory stability. PSSs optimal design and supplemental controller of power fluctuations for IPFC were conducted out on WSCC multi-machine test systems using a linear system model. Using time-domain simulations and quantitative analysis, the proposed IPFC model was compared to the FFA-PSSs controller in terms of performance and efficiency. The main disadvantage of this technique is the difficulty in designing a dynamic IPFC model in test systems, as well as the burden of IPFC coordinated PSSs optimization. In both PSSs design using FFA method and FFA-optimized PSS with IPFC cases, rise in the computational and simulation costs was found unavoidable. To compensate for these flaws and obtain the research contribution, this paper proposes a Neuro-Fuzzy Controller (NFC) developed as a damping controller that can take the place of the two controllers (research objectives three). The application of the NFC substitutes the computational and simulation cost involved in designing multi-machine PSS and IPFC-FACTS systems simultaneously. With the availability of NFC in SIMULINK, a dynamic model of the WSCC three-machine system was developed under a variety of operating situations. Quantitative analysis results from the WSCC test system simulation show that when comparing the proposed NFC model to the IPFC model for the WSCC test system, the proposed NFC model was found to be 149 percent and 0 percent efficient in terms of the time to settle of rotor angle respond for G2 and G3, respectively, but 394 percent efficient when compared to the uncontrolled model. The decreased settling time values ensured the proposed NFC model’s efficacy in damping down the LFO and achieving superior stability over the two controllers. The proposed NFC model was shown significant performance improvement in both the transient and steady-state areas than when the system was designed with the two damping controllers

Optimal coordinated design of PSS and UPFC-POD using DEO algorithm to enhance damping performance

Low-frequency oscillations (LFO) are an inevitable problem of power systems and they have a great effect on the capability of transfer and power system stability. The power system stabilizers (PSSs) as well as flexible AC transmission system (FACTS) devices can help to damp LFO. The target of this study is to tackle the problem of a dual-coordinated design between PSS and unified power flow controller (UPFC) implementing the task of power oscillation damping (POD) controller in a single machine infinite bus (SMIB) system. So, dolphin echolocation optimization (DEO) technique is utilized as an optimization tool to search for optimal parameter tunings based on objective function for enhancing the dynamic stability performance for a SMIB. DEO an algorithm has a few parameters, simple rules, provides the optimum result and is applicable to a wide range of problems like other meta-heuristic algorithms. Use DEO gave the best results in damping LFO compared to particle swarm optimization (PSO) algorithm. From the comparison results between PSO and DEO, it was shown that DEO provides faster settling time, less overshoot, higher damping oscillations and greatly improves system stability. Also, the comparison results prove that the multiple stabilizers show supremacy over independent controllers in mitigationg LFO of a SMIB

Application of PSO to Design UPFC-based Stabilizers

Today, power demand grows rapidly and expansion in transmission and generation is restricted with the limited availability of resources and the strict environmental constraints. Consequently, power systems are today much more loaded than before. In addition, interconnection between remotely located power systems turned out to be a common practice. These give rise to low frequency oscillations in the range of 0.1-3.0 Hz. If not well damped, these oscillations may keep growing in magnitude until loss of synchronism results. Power system stabilizers (PSSs) have been used in the last few decades to serve the purpose of enhancing power system damping to low frequency oscillations. PSSs have proved to be efficient in performing their assigned tasks. The objective of this chapter is to investigate the potential of particle swarm optimization as a tool in designing UPFC-based stabilizers to improve power system transient stability. To estimate the controllability of each of the UPFC control signals on the electromechanical modes, singular value decomposition is employed. The problem of designing all the UPFCbased stabilizers individually is formulated as an optimization problem. Particle swarm optimizer is utilized to search for the optimum stabilizer parameter settings that optimize a given objective function. Coordinated design of the different stabilizers is also carried out by finding the best parameter settings for more than one stabilizer at a given operating condition in a coordinated manner

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers' parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods

Co-ordinated Design of PSS and TCSC Damping Controllers in Multi-machine Power System using PSO

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. Transient stability improvement is essential for maintaining system security that is the incidence of a fault should not lead to tripping of generating unit due to loss of synchronism. Flexible ac transmission system (FACTS) devices are found to be every effective in stressing a transmission network for better utilization of its existing facilities without sacrificing the desired stability margin. Amongst the available FACTS devices for transient stability enhancement, the TCSC is the most versatile one and it is a series FACTS device which allows continuous changes of the transmission line impedance. It has great application and potential in accurately regulating the power flow on a transmission line, damping inter-area power oscillations and improving the transient stability. Power System Stabilizer (PSS) which detects changes in the generator output power and by controlling the excitation value it reduces the power swings in the system. This Report presents coordinated control tuning of PSS with TCSC. The design of proposed coordinated damping controller is formulated as an optimization problem and the controller gains are optimized instantaneously using particle swarm optimization (PSO). Here single machine infinite bus system and the multi-machine power system employed with PSS and TCSC is considered. The coordinated tuning among the damping controllers is performed on the non-linear power system dynamic model. Finally, the proposed coordinated controller performance is discussed with time domain simulations. Different loading conditions are employed on the test system to test the robustness of proposed coordinate controller and the simulation results are compared with four different control schemes

A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems

The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Controlled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objective function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evaluation of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The findings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly