Dual Input Power System Stabilizer Research Articles

Mitigation of Low-Frequency Oscillation in Power Systems through Optimal Design of Power System Stabilizer Employing ALO

Low-frequency oscillations are an inevitable phenomenon of a power system. This paper proposes an Ant lion optimization approach to optimize the dual-input power system stabilizer (PSS2B) parameters to enhance the transfer capability of the 400 kV line in the North-West region of the Ethiopian electric network by the damping of low-frequency oscillation. Double-input Power system stabilizers (PSSs) are currently used in power systems to damp out low-frequency oscillations. The gained minimum damping ratio and eigenvalue results of the proposed Ant lion algorithm (ALO) approach are compared with the existing conventional system to get better efficiency at various loading conditions. Additionally, the proposed Ant lion optimization approach requires minimal time to estimate the key parameters of the power oscillation damper (POD). Consequently, the average time taken to optimally size the parameters of the PSS controller was 14.6 s, which is pretty small and indicates real-time implementation of an ALO developed model. The nonlinear equations that represent the system have been linearized and then placed in state-space form in order to study and analyze the dynamic performance of the system by damping out low-frequency oscillation problems. Finally, conventional fixed-gain PSS improves the maximum overshoot by 5.2% and settling time by 51.4%, but the proposed optimally sized PSS employed with the ALO method had improved the maximum overshoot by 16.86% and settling time by 78.7%.

Smart DIPSS for Dynamic Stability Enchancement on Multi-Machine Power System

Disruption of the electric power system always results in instability. These disturbances can be in the form of network breaks (transients) or load changes (dynamic). Changes in load that occur suddenly and periodically cannot be responded well by the generator so that it can affect the dynamic stability of the system. This causes the occurrence of frequency oscillations in the generator. A poor response can cause frequency oscillations for a long period. This will result in a reduction in the available power transfer power. In a multi-machine power system, all the machines work in synchrony, so the generator must operate at the same frequency. Therefore, disturbances that arise will have a direct impact on changes in electrical power. In addition, changes in electrical power will have an impact on mechanical power. The difference in response speed between a fast electrical power response and a slower mechanical power response will result in instability. As a result of these differences, the system oscillates. The addition of the excitation circuit gain is less able to stabilize the system. To solve the problem, additional signal changes are required. The additional signal is generated by the Dual Input Power System Stabilizer (DIPSS) setting using the Ant Colony Optimization (ACO) method. Disruption of the electric power system always results in instability. These disturbances can be in the form of network breaks (transients) or load changes (dynamic). Changes in load that occur suddenly and periodically cannot be responded well by the generator so that it can affect the dynamic stability of the system. This causes the occurrence of frequency oscillations in the generator. A poor response can cause frequency oscillations for a long period. This will result in a reduction in the available power transfer power. In a multi-machine power system, all the machines work in synchrony, so the generator must operate at the same frequency. Therefore, disturbances that arise will have a direct impact on changes in electrical power. In addition, changes in electrical power will have an impact on mechanical power. The difference in response speed between a fast electrical power response and a slower mechanical power response will result in instability. As a result of these differences, the system oscillates. The addition of the excitation circuit gain is less able to stabilize the system. To solve the problem, additional signal changes are required. The additional signal is generated by the Dual Input Power System Stabilizer (DIPSS) setting using the Ant Colony Optimization (ACO) method.

Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

Electrical power system abnormalities may have several negative consequences on its stable operation. As a result, preserving its stability under such operational states has become an ongoing challenge for power engineers. PSSs are created as auxiliary controllers to address the instability issues produced upon disturbances. They dampen the oscillations induced by the disturbances by giving the system the necessary damping torque. This research aims at presenting a comprehensive study for the optimum tuning of power system stabilizer (PSS) of different structures. This aim is accomplished with the help of a novel modified optimization algorithm called Quantum Artificial Gorilla Troops Optimizer. Validation of the modified optimizer is firstly investigated with the well-known benchmark optimization functions and shows superiority over Gorilla Troops Optimizer and competitive algorithms. The research is extended to the application of the optimum tuning of various PSS structures of the single machine to the infinite bus model. The proposed optimization algorithm shows fast convergence over investigated optimization algorithms. Moreover, the Tilt-integral-derivative based PSS shows better performance characteristics in terms of lower settling time, and lower maximum and undershoot values over the conventional lead-lag PSS, dual input PSS, and fractional-order proportional-integral-derivative based PSS.

Robust Design of Dual-Input Power System Stabilizer Using Chaotic JAYA Algorithm

This work aims to improve the stability of a system that consists of one generator using a dual-input stabilizer of a power system. For this purpose, a new design method based on a chaotic JAYA algorithm is proposed. This algorithm hybridizes the original JAYA algorithm with chaos to improve its exploration and exploitation capabilities. Indeed, the disordered numbers generated by the chaotic map are exploited to control the standard JAYA algorithm’s search equations. The issue of design is stated as an optimization problem. The proposed technique is involved in the selection of optimal values of the dual-input power system stabilizer (PSS) parameters. The efficiency of the proposed controller is assessed under various loading conditions and compared with the original JAYA, cuckoo search and particle swarm optimization (PSO) algorithms. The target function consists of the integration of absolute error multiplied by the time, including the speed deviation is considered for the controller design method. The controller tuned by a chaotic JAYA algorithm is tested using a single machine infinite bus (SMIB) power system. The findings of the simulation results demonstrate the robustness and effectiveness of the suggested method in damping oscillations over an extended range of loading conditions.

Seeker optimized SVC-PID controller for reactive power control of an isolated hybrid power system

The modeling and controlling approach of a stand-alone wind–diesel hybrid model has been focused in this paper while adopting coordinated tuning of static VAR compensator (SVC)-PID controller and power system stabilizer (PSS) for controlling the reactive power. Seeker optimization algorithm (SOA) has been used for tuning the parameters of the SVC-PID and PSS. To find out the optimal parameters of the studied model, the SOA has been implemented successfully. The synchronous generator (SG) is equipped with IEEE type-I excitation system along with dual input PSS like IEEE-PSS3B in the studied hybrid model. On-line performance analysis of the studied hybrid model is achieved by using Takagi–Sugeno fuzzy logic (TSFL) based controller. In real-time, the TSFL helps to track the accuracy of reactive power compensation for any sort of input disturbance. It is found that the proposed SOA-TSFL is more effective to get the nominal optimal parameters of SVC-PID and PSS for on-line optimal reactive power control and terminal voltage response of the investigated power system model. It has also been investigated that the performance of the proposed controller is effectively influenced by the transmission delay signal.

Power System Stabilizer PSS4B Model for Iraqi National Grid using PSS/E Software

To damp the low-frequency oscillations which occurred due to the disturbances in the electrical power system, the generators are equipped with Power System Stabilizer (PSS) that provide supplementary feedback stabilizing signals. The low-frequency oscillations in power system are classified as local mode oscillations, intra-area mode oscillation, and interarea mode oscillations. Double input multiband Power system stabilizers (PSSs) were used to damp out low-frequency oscillations in power system. Among dual-input PSSs, PSS4B offers superior transient performance. Power system simulator for engineering (PSS/E) software was adopted to test and evaluate the dynamic performance of PSS4B model on Iraqi national grid. The results showed that after installing the PSS in a specific plant the oscillation of rotor angle, bus frequency, speed, power flow is better than without PSS during the disturbances that occurred during the simulations. All the PSS/E simulation and tests were done in the National dispatch center (NDC) laboratory, Ministry of Electricity. 

A multiobjective tuning approach of power system stabilizers using particle swarm optimization

This work presents an optimal tuning approach of power system stabilizers (PSSs) using multiobjective particle swarm optimization. Two types of PSSs are investigated, the conventional speed-based PSS type and a dual-input PSS type that uses the accelerating power as an additional input. The tuning problem of these PSSs is formulated as a minimization problem of a vector objective function characterizing the damping and the transient performance of the closed-loop system. A 3-machine 9-bus power system example is considered, and the speed-constraine multiobjective particle swarm optimization algorithm is used to solve the optimization problem. The results show that trade-offs exist between the 2 objective functions of the problem, and that the best trade-off is obtained with the dual-input PSS. The performance of the resulting PSSs is illustrated through numerical simulations considering different scenarios.

Modeling and seeker optimization based simulation for intelligent reactive power control of an isolated hybrid power system

Seeker optimization algorithm (SOA) is a novel heuristic population-based search algorithm based on the concept of simulating the act of human searching. In SOA, the acts of human searching capability and understanding are exploited for the purpose of optimization. In this algorithm, search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, effectiveness of the SOA has been tested for optimized reactive power control of an isolated wind–diesel hybrid power system model. In the studied power system model, a diesel engine based synchronous generator (SG) and a wind turbine based induction generator (IG) are used for the purpose of power generation. IG offers many advantages over the SG but it requires reactive power support for its operation. So, there is a gap between reactive power demand and its supply. To minimize this gap between reactive power generation and its demand, a variable source of reactive power such as static VAR compensator (SVC) is used. The SG is equipped with IEEE type-I excitation system and dual input power system stabilizer (PSS) like IEEE-PSS3B. The performance analysis of a Takagi–Sugeno fuzzy logic (TSFL)-based controller for the studied isolated hybrid power system model is also carried out which tracks the degree of reactive power compensation for any sort of input perturbation in real-time. In time-domain simulation of the investigated power system model, the proposed SOA–TSFL yields on-line, off-nominal coordinated optimal SVC and PSS parameters resulting in on-line optimal reactive power control and terminal voltage response. The performance of the proposed controller, with the influence of signal transmission delay, has also been investigated.

Improving power system dynamic performance using attuned design of dual-input PSS and UPFC PSD controller

The objective of this work is the coordinated design of controllers that can enhance damping of power system swings. With presence of flexible AC transmission system (FACTS) device as unified power flow controller (UPFC), three specific classes of the power system stabilizers (PSSs) have been investigated. The first one is a conventional power system stabilizer (CPSS); the second one is a dual-input power system stabilizer (dual-input PSS); and the third one is an accelerating power PSS model (PSS2B). Dual-input PSS and PSS2B are introduced to maintain the robustness of control performance in a wide range of swing frequency. Uncoordinated PSS and UPFC damping controller may cause unwanted interactions; therefore, the simultaneous coordinated tuning of the controller parameters is needed. The problem of coordinated design is formulated as an optimization problem, and particle swarm optimization (PSO) algorithm is employed to search for optimal parameters of controllers. Finally, in a system having a UPFC, comparative analysis of the results obtained from application of the dual-input PSS, PSS2B, and CPSS is presented. The eigenvalue analysis and the time-domain simulation results show that the dual-input PSS & UPFC and the PSS2B & UPFC coordination provide a better performance than the conventional single-input PSS & UPFC coordination. Also, the PSS2B & UPFC coordination has the best performance.

Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers

Seeker optimization algorithm (SOA) is a new heuristic population-based search algorithm. In this paper, SOA is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). In SOA, the act of human searching capability and understandings are exploited for the purpose of optimization. In SOA-based optimization, the search direction is based on empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (namely PSS2B, PSS3B and PSS4B) are optimally tuned to obtain the optimal transient performances. From simulation study it is revealed that the transient performance of the dual-input PSS is better than the single-input PSS. It is further explored that among the dual-input PSSs, PSS3B offers the best optimal transient performance. While comparing the SOA with recently reported optimization algorithms like bacteria foraging optimization (BFO) and genetic algorithm (GA), it is revealed that the SOA is more effective than either BFO or GA in finding the optimal transient performance. Sugeno fuzzy logic (SFL)-based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.

Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables.

Bio-inspired fuzzy logic based tuning of power system stabilizer

In this paper, bacteria foraging optimization (BFO) – a bio-inspired technique, is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. A comparative performance study of these four variants of PSSs is also made. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. A comparison between the results of the BFO and that of genetic algorithm (GA) is conducted in this study. The comparison reveals that BFO is more effective than GA in finding the optimal transient performance. For on-line, off-nominal operating conditions Sugeno fuzzy logic (SFL) based approach is adopted. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer parameters.

Intelligent dual input power system stabilizer

This paper presents a new approach for real-time tuning of a dual input power system stabilizer using neuro-fuzzy system (NFS). Intelligent dual input power system stabilizer (IDIPSS) comprises of NFS and conventional dual input power system stabilizer. The NFS is a fuzzy inference system implemented in the framework of multi-layered feed forward adaptive network. NFS network is trained using hybrid training algorithm for real-time tuning of the dual input power system stabilizer. The generator real power ( P e), reactive power ( Q e), and terminal voltage ( V t) characterizing the operating condition are input signals to the network while optimum DIPSS parameters K S1 and T 1 are the outputs. Investigations have been carried out considering three, five and seven membership functions (MFs) of Triangular, Trapezoidal, and Gaussian shapes. Studies reveal that for real-time tuning of the dual input PSS, the NFS network with three MFs of any shape is adequate. The proposed IDIPSS exhibits quite a robust performance to wide variations in loading condition, system parameters and large perturbations.