Power System Small-Signal Stability Enhancement Using Damping Controllers Designed Based on Evolutionary Algorithms

This paper presents the design of Power System Stabilizers (PSSs) using two Evolutionary Algorithm (EA) techniques, namely; Genetic Algorithm (GA) and Breeder Genetic Algorithm. BGA is a new form of evolutionary algorithm, which is based on the idea of survival of the fittest, but differs from the traditional Genetic Algorithm due to its artificial breeding nature. An eigenvalue based objective function is used in the design of the PSSs whereby the algorithms maximize the lowest damping ratio over specified operating conditions. For comparison purpose, the Conventional PSS (CPSS) is also included. The performance and effectiveness of the PSSs in damping the electromechanical modes is investigated. Eigenvalue analysis and time domain simulations show that BGA-PSS and GA-PSS perform better than the CPSS for all the operating conditions considered except at the nominal operating condition. However, BGA-PSS performs slightly better than the GA-PSS.

Stability enhancement of a multimachine power system using fuzzy logic based power system stabilizer tuned through genetic algorithm

Effects of various power system stabilizers on improving power system dynamic performance

Small signal stability is one of the factor to assesst the reliability of a power system. Small signal stability is related to the ability of a power system to maintain synchronization after being exposed by small disturbance. In Indonesia, the reliability of power system is one of the important aspect that has to be achieved. However, very scant attention has been paid on studied small signal stability performance on Indonesia electricity. Hence, this paper studied the small signal stability analysis and its enhancement. 150 kV Sulselrabar interconnected power system is used as test system. To enhance the performance of the system designing coordinated controller of power system stabilizer (PSS) and redox flow batteries (RFB) using cuckoo search algorithm (CSA) is conducted. Eigenvalue, damping ratio, participation factor analysis, and time domain simulation is performed to assess the small signal stability performance and the successfulness of the CSA to optimize the PSS and RFB parameter. From the cases studies, it is found that 150 kV Sulselrabar has 12 local mode and 4 inter-area mode. Furthermore, the coordinated controller between PSS and RFB based on CSA could enhance the small signal stability performance of 150 kV Sulselrabar interconnected system indicated by higher damping, smallest overshoot and fastest settling time.

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.

Small-signal stability is one of the main factors limiting power transmission in conventional power systems. This concern is primarily handled by adding damper windings of a synchronous generator and power-system stabilizer (PSS). However, due to the impact of the N-1 contingency, damper windings and a conventional PSS are insufficient to overcome this problem. Proper placement and design of the PSS are crucial for improving stability. One approach to optimizing the placement and tuning of the PSS is to use an artificial-intelligence method. Here, the cuckoo-search algorithm (CSA) is proposed to optimize the PSS tuning and placement. Based on simulation, it is found that a PSS based on the CSA can enhance the system's small-signal stability. The critical, local, and inter-area modes of the investigated system improve significantly. The oscillatory condition is more highly damped, as indicated by a smaller overshoot and faster settling time. It is also found that the CSA can be used to tune the PSS parameter under an N-1 contingency. From the analytical results of the N-1 contingency condition, optimal tuning of the PSS parameters was obtained, resulting in PSS-placement options for generators 1 to 7 and 12 to 15, producing a minimum damping of 0.611.

This paper presents a comparative analysis among various Power System Stabilizers (PSSs) for the enhancement of small signal stability of power systems. The most efficient approach of countering small signal instability dilemma is to add a supplementary controller known as PSS to the Automatic Voltage Regulator (AVR) on the generator. The conventional PSS seems to perform well at particular network configurations for which they are tuned. However, when system parameters change due to disturbance, unambiguous condition arises. The above limitations can be overcome by improving PSS design technique using artificial intelligence. In this paper, the effectiveness of various PSS design techniques has been analyzed in MATLAB/Simulink environment by performing nonlinear simulations utilizing a synchronous machine-infinite bus model. Among various PSSs the Adaptive Neuro Fuzzy Inference System (ANFIS) design technique is found better compare to the other design techniques.

In this paper, the Cuckoo Search Algorithm (CSA) is implemented on ten-machine thirty-nine bus New England power system for optimal tuning of Power System Stabilizer (PSS) parameters. The results are compared with Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSOA). For this, the design problem is formulated using an eigenvalue based multiobjective function that simultaneously control the damping ratio and damping factor for designing of PSS parameters. The optimized CSA based PSS (CSAPSS) parameters are used to shift the unstable or poorly damped electromechanical modes for severe line outage to a D-shape region in the left-half of the s-plane. The eigenvalue analysis, time-domain simulations and performances indices are used to observe the effectiveness of CSAPSS. The robustness of CSAPSS is compared with GA based PSS (GAPSS) and PSOA based PSS (PSOAPSS) under severe disturbances.

Population-based incremental learning (PBIL) has been recently applied to a range of optimization problems in controller designs with promising results. It combines aspects of genetic algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, the standard PBIL is improved by using a combination of adaptive and fixed learning rate that varies according to the generation. The adaptive-fixed (AF) algorithm can adjust the learning rate automatically according to the degree of evolution of the search. The objective of the power system stabilizer (PSS) design is to achieve adequate stability over a wide range of power system operating conditions. The proposed controller is compared with conventional PBIL with fixed learning rate (PBIL) and tested under various operating conditions. Simulation results show that the AFPBIL based PSS provides a more efficient search capability and gives a better damping and adequate dynamic performance of the system than the conventional PBIL based PSS.

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

This paper presents a new concept to design optimal power system stabilizer (PSS) as a damper on a multimachine power system control. This concept is developed 6om the proportional and integral (PI) control and linear quadratic regulator (LQR) method as a modem tool to design the PSS as optimal PSS. Using this concept, the stabilizers of the multimachine are designed for the turbine and excitation system side using the Riccati solution, as a character of LQR optimal control method solving. In this paper, the design of the optimal PSS is compared with the conventional PSS (CPSS) and No PSS system. In the final results, the application of optimal PSS as a damper of power system can greatly improve the performances of multimachine power system, and it shows the best results

Guest Editorial: Situational awareness of integrated energy systems

This paper presents a method to find the dynamic stability margin of power system using genetic algorithm. Power systems are subjected to wide range of operating conditions. Modern power systems are equipped with fast-acting protective devices for transient stability problems. Hence, power systems are operated above the transient stability limit. The dynamic behaviour of system can be evaluated using small signal stability analysis. The maximum loading to which the system can be subjected can be obtained by observing the eigenvalue variations of the system under different loading conditions. The loading for which system exhibits a pair of imaginary eigenvalues is the maximum loading limit. Beyond this limit, the system will become unstable. The loading for which the power system exhibits imaginary eigenvalues is evaluated by using genetic algorithm. The dynamic stability margin is evaluated for a 3-machine 9-bus system. The efficacy of the proposed method is tested for the power system including conventional power system stabilizers (CPSS).

Due to power system oscillations induced by complex interconnections, power system stability has been a major concern and will continue to be a major concern in power system operations. For improving power system stability, efforts have been directed toward damping power system oscillations. The small signal stability is the instability that has been influenced by complex interconnections. Small signal stability can be dampened with power system stabilisers (PSS). PSS, on the other hand, have certain disadvantages, such as significant variations in voltage profiles and the potential for leading power factor operations, which may reduce system stability in the face of severe disturbances. In wide multi machine power systems, using only traditional PSS can not be enough to dampen inter-area oscillations and the effect of voltage profile variation. Power electronic devices known as Flexible Alternating Current Transmission System (FACTS) devices, on the other hand, are good at damping inter-area oscillations and can handle voltage variations. As a result, based on computational intelligence techniques, this paper provides a thorough analysis and evaluation of PSS and FACTS power oscillation damping (POD) controllers in reducing low frequency oscillations. The importance of technical paper publications on PSS and FACTS-based controller design, optimum location, and parameter tuning is highlighted. Finally, the potential future research directions for novel PSS and FACTS devices is proposed as a reference for interested researchers.

Growth in power demand without increased network expansion necessitates that the stability of the power system with minimum of control systems is required to stay within the reference point of safe and reliable operation of the grid. In this paper, a three-machine nine-bus power system with a wind park is used for stability analysis and a H 2 /H ∞ robust power system stabilizer (PSS) is designed for damping oscillations. System performances after various disturbances are discussed by comparing the proposed PSS with conventional PSS. The results show that the robust PSS has stronger robustness and it is more reliable than conventional PSS (CPSS) under various disturbances.