Improving stability in power system network using tuned controller

Optimal tuning of power system stabilizer (PSS) parameters using genetic algorithm with single objective function is presented in this paper. A Single Machine Infinite Bus (SMIB) system is considered. The main objective of this research paper is to investigate the suitability of genetic algorithm for effective tuning of parameters of the power system stabilizer in a single machine infinite bus system. A conventional speed based lead‐lag PSS is used. A simple and effective method of tuning the parameters of PSS is proposed which is posed as an optimization formulation by maximizing the damping of modes of oscillations of the SMIB system over a wide range of loading conditions and different system configurations. It is found that GA based PSS with single objective design shows improved dynamic performance over Conventional PSS over a wide range of operating conditions and different system parameters.

The problem of transient stability and voltage regulation for a single machine infinite bus (SMIB) system is addressed in this paper. An improved Backstepping design method for transient stability enhancement and voltage regulation of power systems is discussed beginning with the classical Backstepping to designing the nonlinear excitation control of synchronous generator. Then a more refined version of this technique will be suggested incorporating the sliding mode control to enhance voltage regulation and transient stability. The proposed method is based on a standard third-order model of a synchronous generator connected to the grid (SMIB system). It is basically implemented on the excitation side of the synchronous generator and compared to the classical Backstepping controller as well as the conventional controllers which are the automatic voltage regulator and the power system stabiliser. Simulation results prove the effectiveness of the proposed method which ameliorates to a great extent the transient stability compared to the other methods.

This text proposes the design of the integer order IOPID Controller as Power System Stabilizer (PSS) on a SMIB System with AVR excitation and compare the performance by replacing it with fractional order FOPID controller as PSS on the same system. The performance and conduct of the controller adopted are differentiated and evaluated on a Single Machine Infinite Bus (SMIB) system under various distinct loading condition for which Heffron Phillip Model is adopted. The PSS is designed on the basis of its frequency responses by inspecting the system robustness by analyzing sensitivity function, gain margin (GM) and phase margin (PM). This paper first utilizes the IOPID as PSS and then its non-integer form is taken up and designed. It is seen that the performance achieved by using FOPID controller as PSS is better compared to IOPID controller.

This research evaluates the viability of the artificial eco-system optimization (AEO), a novel metaheuristic algorithm, for optimal power system stabilizers (PSSs) tuning in single-machine infinite bus (SMIB) and western system coordinating council (WSCC) multi-machine power systems. The PSS design problem was converted to an optimization problem to achieve optimal tuning, and an eigenvalue-based objective function was employed. The eigenvalue objective function was defined to optimally obtain the PSSs parameters and enhance the power system dynamic performance by improving the damping of electromechanical modes (EMs). The proposed AEO-based PSS performance was validated by comparing the results obtained with genetic algorithm (GA)-based PSS, particle swarm optimization (PSO)- based PSS design, and similar published work. AEO-based PSS method of tuning has been shown to damp electromechanical modes (EMs), control low-frequency oscillations (LFO), and provide better transient performance and convergence rate. Cite this article as: T. Ebuka Odoh, A. Sabo, N. Izzri Abdul Wahab and H. Beiranvand, "Artificial eco-system-based optimization algorithm for optimal design of single-machine infinite bus power system stabilizer," Electrica, 23(3), 522-533, 2023.

Low-frequency oscillations in power systems sometimes lead to larger disturbances leading to instability. Addition of power system stabilizer (PSS) is very effective in damping the oscillations. The main purpose of the PSS is to increase damping of synchronous machines in the excitation loop. In this paper a new meta-heuristic algorithm termed Quasi-oppositional symbiotic organism (QOSS) search has been proposed for designing the PSS parameters in an optimal manner. The technique is tested on both single machine infinite bus system as well as a multi-machine system. Various operating conditions like loading effect and line outages are also considered for establishing robustness of PSS. The PSS parameters are tuned with the help of other optimizations like Symbiotic organism search, Genetic algorithm and Particle swarm optimization and are compared with those obtained with the application of QOSOS, to show the superiority of the proposed algorithm.

Guest Editorial: Situational awareness of integrated energy systems

This paper aims to present the design of power system stabilizer (PSS) as a second-order sliding mode controller (SOSMC). Power systems have always been growing into more dynamic and complex systems. This is because of continuously occurring disturbances and parametric uncertainties in power systems due to changing loads. One of the major problems with this area is small frequency oscillations which lead to the system instability if these oscillations are not damped properly and also reduce power transfer capability. Therefore, it is essential to use power system stabilizers (PSS) to damp out small frequency oscillations. In this paper, single machine infinite bus (SMIB) system is used as study system. Sliding mode control (SMC) is a nonlinear and very robust control technique. Advantages of this controller can be used to control the power systems in order to increase the stability. But SMC has a major disadvantage of chattering. SOSMC overcomes the disadvantages produced by SMC. Therefore, in this paper, PSS is designed as SOSMC using twisting algorithm and applied to SMIB. The robustness of PSS as SOSMC is demonstrated by carrying out comparative simulation under different operating conditions of the SMIB power system.

In this paper, low frequency oscillations in power system under the different operating conditions have been solved through PID based power system stabilizer. The tuning of the parameters of the PSS are considered as optimization problem, and the parameters are tuned using genetic search algorithm. Here forth-order linear and non-linear model of the synchronous machine (model 1.1) which includes both the generator main field winding and the damper winding in q-axis is considered for finding out the sensitivity of electromechanical modes of single machine infinite bus system. The effectiveness of automatic voltage controller, and PID-power system stabilizer are identified through eigen value analysis and participation factor method. The non-linear simulation results show the effectiveness and capability of PID-PSS for power system stability improvement under various disturbances.

Design of Robust Power System Stabilizer using Genetic Algorithm-based Fixed-Structure H∞ Loop Shaping Control

In this paper, the Authors present the designing of power system stabilizer (PSS) and static var compensator (SVC) based on chaos, particle swarm optimization (PSO) and shuffled frog leaping (SFL) Algorithms has been presented to improve the power system stability. Single machine infinite bus (SMIB) system with SVC located at the terminal of generator has been considered to evaluate the proposed SVC and PSS controllers. The coefficients of PSS and SVC controller have been optimized by Chaos, PSO and SFL algorithms. Fi-nally the system with proposed controllers is simulated for the special disturbance in input power of genera-tor, and then the dynamic responses of generator have been presented. The simulation results show that the system composed with recommended controller has outstanding operation in fast damping of oscillations of power system and describes an application of Chaos, PSO and SFL algorithms to the problem of designing a Lead-Lag controller used in PSS and SVC in power system.

A new SPARLS algorithm for tuning power system stabilizer

In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO.

Improvement of low-frequency oscillation damping in power systems using a deep learning technique

This paper presents the application of Fuzzy Logic Controller (FLC) for Thyristor Controlled Series Capacitor (TCSC) to enhance over all stability of electric power systems. The proposed hybrid controller combines the advantages of FLC and a conventional PI controller. In this work, we consider Single Machine Infinite Bus (SMIB) system installed with Power System Stabilizer (PSS) and Fuzzy-PI based TCSC Controller. In order to test effectiveness of these controllers, simulations are carried out in different loading conditions under a symmetrical three-phase short circuit fault at generator terminals. The response of Fuzzy-PI controller is compared with that of the PI controller. The simulation result shows that the Fuzzy-PI controller has outstanding operation in fast damping of power system oscillations.

Steadiness of power system is a significant issue in power system operation. In this article design of Neural Network tuned Fuzzy logic power system stabilizer (NNTFLPSS) for single machine infinite bus (SMIB) system is proposed to settle down low frequency swinging that improves small signal stability in power system. The speed deviance and variation in speed deviance of the rotor of synchronous generator from the trained neural network were considered as the feedback to the fuzzy logic power system stabilizer (FLPSS) to recover the power system from small signal stability problem by refining damping oscillations. The comparative reading was noted for rotor speed deviances and rotor angle deviances using conventional PSS (CPSS), Fuzzy logic based power system stabilizer (FLPSS) and NNTFLPSS. The MATLAB simulation results obtained indicates the improved performance of NNTFLPSS over the CPSS and FLPSS.