Conventional Power System Stabilizer Research Articles

Power System Stabilizers Design Using BAT Algorithm

A new metaheuristic method, the BAT algorithm based on the echolocation behaviour of bats is proposed in this paper for optimal design of Power System Stabilizers (PSSs) in a multimachine environment. The PSSs parameter tuning problem is converted to an optimization problem which is solved by the BAT search Algorithm. An eigenvalues based objective function involving the damping factor, and the damping ratio of the lightly damped electromechanical modes is considered for the PSSs design problem. The performance of the proposed BAT based PSSs (BATPSS) has been compared with Genetic Algorithm (GA) based PSSs (GAPSS) and the Conventional PSSs (CPSS) under various operating conditions and disturbances. The results of the proposed BATPSS are demonstrated through time domain analysis, eigenvalues and performance indices. Moreover, the results are presented to demonstrate the robustness of the proposed algorithm over the GA and conventional one.

A Coordinated Pumped Storage Dual Compensated Hydro Governor with PSS Action to Damp Electromechanical Power Oscillations

Subject to increasing penetrations of renewable sources like solar photovoltaic (SPV) and wind energy sources, power system oscillation damping is going to be a critical challenge for system operators. This work proposes a new dual compensated governor (DCG) in coordination with a power system stabilizer (PSS) of a pumped storage hydro plant for power oscillation damping subject to intermittent SPV and wind penetration for a hydro, wind, and SPV integrated power system. The phase lag provided by the hydro governor is compensated by additional phase lead contributed by the dual compensation, where speed and real power deviations brought by uncertain SPV and wind penetrations are simultaneously controlled by two lead-lag controllers before being applied to conventional Proportional-Integral-Derivative (PID) governor. Again, subject to critical oscillatory unstable conditions, the DCG is coordinated with PSS through a multiobjective function employing a new modified Differential Evolutionary-Particle swarm optimization (MDEPSO) algorithm. Different case studies with sudden and random SPV and wind penetrations being executed with the proposed controller considering a two area four machine and 39 bus multimachine system with pumped storage hydro units to observe system oscillations are considered. The proposed damping control action has been implemented to damp these oscillations, and the damped response has been analyzed with eigenvalue distributions and Bode plots with sensitivity analysis. The proposed action is found to be much more efficient in contrast to conventional PID governor and PSS damping action. Also, the usage of present hydro governors can be much improved by this coordinated controller action.

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

A robust design of particle swarm optimization (PSO) and Taguchi algorithm-based power system stabilizer (PSS) is presented in this paper. It incorporates a novel concept in which Taguchi and PSO techniques are integrated for stabilization of single machine infinite bus (SMIB). The system tolerates uncertainty and imprecision to a maximum extent. The proposed controller's effectiveness is proved through experiments covering light load condition using MATLAB/Simulink platform. The performance of the system is compared without PSS and with a conventional PSS. The settling time of the optimal PSS is decreased by more than 75% to conventional PSS. The study reveals that the proposed hybrid controller offers enhanced performance with respect to settling time as well as peak overshoot of the system.

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System

Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gradient (TD3) agent controller to damp the oscillations without requiring an accurate system model. In this work, a Q-learning (QL)-based model-free wind speed DFIG was designed on the rotor-side converter (RSC), and a QL-based model-free DC-link voltage regulator was designed on the grid-side converter (GSC) to enhance the stability of the system. In the next step, the TD3 agent was trained to learn the system dynamics by replacing the inner current controllers of the RSC, which replaced the QL-based model. In the first stage, the conventional PSS and Proportional–Integral (PI) controllers were introduced to both the RSC and GSC. Then, the system was trained to become model-free by replacing the PSS and the PI controller with a QL algorithm under very small wind speed variations. In the second stage, the QL algorithm was replaced with the TD3 agent by introducing large variations in wind speed. The results reveal that the TD3 agent can sustain the stability of the DFIG system under large variations in wind speed without assuming a detailed control structure beforehand, while QL-based controllers can stabilize the doubly fed induction generator (DFIG)-equipped wind energy conversion system (WECS) under small variations in wind speed.

A special protection scheme for damping power system oscillations by controlling wind farms

This paper proposes a special protection scheme (SPS) as an additional tool for system operators to damp power oscillations and thereby improve the overall power system stability. This SPS is activated by an online power oscillation detection algorithm and utilizes a power system stabilizer (PSS) controlling a wind farm to damp power oscillations. Moreover, an adaptive time delay compensator (ATDC) compensates for the time delays in the control system. Different alternatives for the PSS input signals are compared by time-domain simulations and by small-signal analysis. The most significant improvement in the damping of the inter-area oscillations is achieved when the PSS uses the active tie-line power as input and controls the active power of the wind farm. The results also prove that the ATDC operates correctly if the total time delay remains below 300 ms. This SPS can improve the system stability when the conventional PSSs may be insufficient.

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%.

High Luminous Efficacy Phosphor-Converted Mass-Produced White LEDs Achieved by AlN Prebuffer and Transitional-Refraction-Index Patterned Sapphire Substrate.

Constant advance in improving the luminous efficacy (ηL) of nitride-based light-emitting diodes (LEDs) plays a critical role for saving measurable amounts of energy. Further development is motivated to approach the efficiency limit for this material system while reducing the costs. In this work, strategies of using thin AlN prebuffer and transitional-refraction-index patterned sapphire substrate (TPSS) were proposed, which pushed up the efficiency of white LEDs (WLEDs). The AlN prebuffer was obtained through physical vapor deposition (PVD) method and TPSS was fabricated by dry-etched periodic silica arrays covered on sapphire. Devices in mass production confirmed that PVD AlN prebuffer was able to improve the light output power (φe) of blue LEDs (BLEDs) by 2.53% while increasing the productivity by ~8% through shortening the growth time. Additionally, BLEDs on TPSS exhibited an enhanced top ηext of 5.65% in contrast to BLEDs on the conventional PSS through Monte Carlo ray-tracing simulation. Consequently, φe of BLEDs was experimentally enhanced by 10% at an injected current density (Jin) of 40 A/cm2. A peak ηL of 295.2 lm/W at a Jin of 0.9 A/cm2 and the representative ηL of 282.4 lm/W at a Jin of 5.6 A/cm2 for phosphor-converted WLEDs were achieved at a correlated color temperature of 4592 K.

Maiden Application of Skill Optimization Algorithm on Cascaded Multi-Level Neuro-Fuzzy based Power system Stabilizers for Damping Oscillations

Power system oscillation is a significant threat among interconnected power systems that may lead to instability. The success of oscillation damping is primarily responsible for a modern power system's safe operation. However, the development of damping controllers is a multimodal optimization problem with constraints which challenges the traditional optimization algorithms. This paper critically examines damping schemes and controller stability analyses to find solutions to these issues and improve the performance of a multi-machine power system (MMPS) . This paper reveals the technique of improving power system stability by employing the Skill Optimization Algorithm (SOA) to optimize the gains of the conventional power system stabilizer (PSS) and the Neuro-Fuzzy inputs-output scaling parameters. This study aims to propose a multi-level Neuro-Fuzzy power system stabilizers (MLNFPSS ) to reduce the instability of a multi-machine two-area power system (MMTAPS) under fault conditions. The simulations were run on a MMTAPS considering a transmission line fault in the middle. The analysis leads to the deduction that the proposed MLNFPSS response is more efficient than conventional PSS under symmetrical three-phase faults. All control strategies have been executed, and the simulation results have been assessed using MATLAB 2016b/Simulink.

Dingo optimization algorithm for designing power system stabilizer

The dingo optimization algorithm (DOA) adopts the social life of dingo dogs. The dingo is a breed of ancient dog originating from Australia. Dingo hunting strategies such as assault with persecution, flocking, and scavenging behavior became the inspiration for DOA. In this paper, DOA is applied to a power system stabilizer (PSS) to dampen low-frequency oscillations (LFO) in a single-machine infinite bus (SMIB). DOA is used to obtain optimal parameters for PSS. The damping controller is designed for optimal lead-lag control. To obtain the performance of the DOA method, the results were compared with the uncontrolled method, conventional PSS, Whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Simulation using MATLAB with three different operating conditions, namely light load (20%), medium load (50%) and high load (100%). From the simulation using MATLAB with SMIB modeling, it was found that the application of the DOA method on PSS has the ability to reduce the average undershoot value by 28.16% and reduce the average undershoot value to 65.57% compared to the conventional PSS 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.

A minimal architecture neuro adaptive predictive control scheme for power system stabilizer

The ever-increasing size and complexity of the present-day power systems puts a higher demand on the power system stabilizer for low-frequency oscillation damping. Alternate design methods of the conventional power system stabilizer or control structures to replace it, proposed over many decades, have not been widely adopted for practical use due to the impediments like non-linear and complex structure, and lack of fast adjustments. This paper proposes an improved neuro-adaptive control scheme, based on online system identification and simultaneous control, to replace conventional power system stabilizer. A simple, linear neural identifier, with a few adjustable connection weights, is used which ensures minimal computational burden and fast learning capability. The parameters of the controller, designed using optimal control with one-sample-ahead output prediction, are related to the identifier parameters. An adaptive learning rate, derived using Lyapunov stability theorems, guarantees stability of convergence of the learning algorithm as well as an optimal speed of convergence. Improved oscillation-damping performance over a wide range of operating conditions, in comparison with a well-designed conventional power system stabilizer and some other alternative controllers reported in literature, has been validated through simulation studies carried out on two different power systems. It is demonstrated that a simple linear neural identifier, which approximates a local linear model of a system, by adjustment of its parameters online, is faithfully able to track the varying dynamics of the system. Hence, it is not always appropriate to use complex structures and non-linear activation functions in neural network-based adaptive control applications which may pose difficulties in the implementation of these controllers. Also, the proposed control scheme is model-free, easier to implement and needs local measurements only.

A hypergraph-based approach for context-aware smart product-service system configuration

Smart product-service system (Smart PSS) is a heterogeneous and integrated system in which manufacturers/service providers deliver integrated and customized product-service bundles (PSBs) in a sustainable manner. Smart PSS configuration, a process of selecting proper PSBs based on user requirements, is expected to be further flexible to adjust the configuration results based on users’ straightforward requirements that contain user-preferred usage scenarios and technical attributes. However, the technical attributes provided by users might be inaccurate due to a lack of professional knowledge, thus prone to unaligned configuration results. Besides, sorely technical attributes other than a comprehensive scope of information was emphasized in the conventional PSS configurators. To address these problems, this paper proposes a novel hypergraph-based Smart PSS configuration framework. Contrary to the conventional configurators that emphasize the mapping between technical attributes and PSBs, the proposed framework introduces usage scenario information as the auxiliary information, which is usually straightforwardly expressed by users. To represent the heterogeneous information with less local information loss, a hypergraph model based on the requirement attribute-product-service bundles-usage scenario (RA-PSB-US) data model is established. Besides, a hypergraph ranking algorithm is adapted to rank the PSBs. To mitigate the proneness of selecting settled PSBs other than returning adaptable results (i.e., the bias of hypergraph-based Smart PSS configuration) during the configuration process, an unbiased hypergraph ranking algorithm is proposed by normalizing the hyperedge degree. Finally, a comparative study is conducted based on an online 3D printing service review dataset to validate its effectiveness and advantages.

Improvement of Dynamic Stability of a Single Machine Infinite-Bus Power System using MPC based Power System Stabilizer

Abstract— Power system stabilizers (PSSs) are used to enhance the damping during low frequency oscillations. This paper presents a study of power system stabilizer using fuzzy logic to enhance the stability of single machine infinite bus system. In this paper the problem of conventional power system stabilizer for stability enhancement is presented which is traditionally used as well as, an effective techniques using Artificial Intelligence (AI) based on fuzzy logic and Model Predictive Control (MPC) is used in designing the control system for PSS in order to achieve recognized improvement in Dynamic Stability. In the last few years fuzzy logic and MPC are used in power system applications as a powerful tool and shows recognized improvement in the stability performance This paper presents a solution for enhancing the improvement in the performance of single machine infinite bus system with fuzzy power system stabilizer of triangular membership function. Speed deviation (w) and acceleration are taken as an input variable. The performance of conventional, fuzzy based power system stabilizer with triangular membership function and MPC responses are studied and compared with conventional lead-lag compensator. The simulations are implemented using MATLAB - SIMULINK as a simulation program tool.

Enhancement of power system transient stability and voltage regulation performance with decentralized synergetic TCSC controller

In this paper, a new nonlinear control approach for thyristor-controlled series capacitors (TCSCs) has been proposed to schedule TCSC line active power transfer, improving transient stability, and obtain acceptable voltage regulation performance of the power system. This control method for the TSCS has been presented based on synergetic control theory. According to the control targets, a linear combination of the TCSC voltage variation, generator rotor speed, TCSC active power, and TCSC reactance is chosen to design a decentralized synergetic TCSC controller (DSTC). A dynamic parameter adaption approach is also presented for updating the DSTC parameters to satisfy control objectives under different power system operating conditions. In the studied system, each generator is equipped with a decentralized improved synergetic excitation controller (ISEC). Dynamic characteristic of the presented DSTC is studied in a single machine infinitive bus and a WSCC-type 3-machine 9-bus power system. A restructured improved synergetic excitation controller (RISEC) has also been formulated to investigate the impact of ISEC on the multi-machine system. Simulation results show that the proposed controller can provide better transient stability, active power flow of the TCSC line, and voltage regulation performance than the ISEC with or without conventional TCSC controller and conventional power system stabilizer without any TCSC controller.

Application of a neuro-fuzzy controller for single machine infinite bus power system to damp low-frequency oscillations

Generally, power systems experience a variety of disturbances that can result in low frequency electromechanical oscillations. These low frequency oscillations (LFOs) take place among the rotors of synchronous generators connected to the power system. These oscillations may sustain and grow to cause system separation if no adequate damping is provided. Power system stabilizers (PSSs) are one of the alternative devices used to solve this rotor oscillation problem. The major limitation of using PSSs at the excitation system of synchronous machine is that the conventional PSS is a permanent parameter type operating under a particular system operating condition, and its parameters are acquired through trial and error. An efficient way of operating the PSS has been by designing the PSS parameters using a powerful optimization procedure. However, designing PSS damping controller is a cumbersome task as it needs a comprehensive test system modeling and a heavy computational burden on the system. In this research, a novel, model-free neuro-fuzzy controller (NFC) is designed as the LFOs’ damping controller to substitute the traditional PSS system making the power system simple without complications in PSS design and parameter optimization. The proposed controller application implements the LFOs’ control without a linearized mathematical model of the system, as such it makes the system less complex and bulky. Single machine infinite bus (SMIB) test system was simulated in SIMULINK domain with the PSSs and with the proposed controller to compare the NFC effectiveness. The simulation outcome for the eigenvalue study with NFC stabilizer yields steady eigenvalues that enhanced the damping status of the system greater than 0.1 with decreased overshoots and time to rise via the proposed NFC process than with the conventional FFA-PSS. Similarly, the generator transient reaction also presents the ω and δ based on the time to settle was improved by 64.66% and 28.78%, respectively, via the proposed NFC process than with the conventional FFA-PSS. Finally, the conventional PSS was found to be complicated in its design, parameter optimization and less effective than the proposed controller for the LFOs’ control.

Simultaneous design of fuzzy PSS and fuzzy STATCOM controllers for power system stability enhancement

The low frequency oscillations have always been the main problem of power system and can lead to power angle instability, limiting the maximum power to be transmitted on tie-lines and system separation. For boosting power system stability limits, the most effectiveness way is to install supplementary excitation control, power system stabilizer (PSS) to add a supplementary feedback stabilizing signal into the automatic voltage regulator (AVR). This article investigates the coordination and optimization of fuzzy controllers for designing PSS and STATCOM controllers for more attenuation of power system fluctuations. The designed fuzzy controller replaces the STATCOM AC voltage regulator. Moreover, for more damping a fuzzy power system stabilizer (FPSS) is placed on all machines. Coordination between Fuzzy Based STATCOM (FSTATCOM) and FPSS is achieved by Self-Adaptive Learning Bat Algorithm (SALBA) in two stages. At first, scaling factors and then fuzzy sets of membership functions (MFs) will be tuned based on a performance index. To indicate the effectiveness of the proposed scheme, the coordinated optimized FPSS and FSTATCOM are compared with conventional design approaches like conventional PSS (CPSS) and proportional-integral controller based STATCOM (PISTATCOM). The simulations clearly demonstrate the effectiveness of the coordinated fuzzy controllers in terms of transient and dynamic stability.

H-robust technique based ant optimal power system for solar energy power system stabilizer

The prime objective of the proposed work is to create a method for solar power stabilizer under deregulated environment. A robust thermal power system was designed and implemented in the present work. In this an advancement procedure enhanced Artificial Bee Colony (ABC) algorithm was investigated to settle the recurrence and tie-line power motions successfully. The robust solar power system stabilizer (RPSS) was planned to utilize the upgraded ABC for the planning of regulators for dynamical frameworks for electrical designing. The main objectives of this research are to reduce the oscillation and to increase the overall robust performance of the system. The proposed technique adopts the stability margin (ε) as the index of robustness and performance of the controlled system. As an outcome a correlation were made between the conventional force framework stabilizer (CFFS) and PSS with H∞ improvement as an additional source. An improvement was seen in the ABC in contrast with different methods that leads to produce an efficient result and to generate the desired response.

An optimised Interval type-2 fuzzy PID-based PSS for stability improvement in solar-PV integrated power system considering uncertainties

This paper presents a robust Interval type-2 Fuzzy Proportional Integral Derivative (IT2FPID)-based Power System Stabiliser (PSS) to lessen low-frequency oscillations (LFOs) in the power system with Solar Photovoltaic (SPV) systems. Uncertainties in the power system do not allow the PSS to function at its maximum capacity and also have adverse impacts on dynamic stability. The proposed ITFPID-PSS can easily handle the uncertainties of load and PV systems in Single Machine Infinite Bus (SMIB) and Multimachine Power System (MMPS) due to its unique shape of its membership function. The gains of the proposed IT2FPID-PSS are optimised using Jaya algorithm. The performance of the proposed controller is evaluated under different operating conditions and also compared with the conventional PSS. Both graphical and comparative investigations suggest the superiority of the proposed Jaya algorithm-optimised IT2FPID-PSS over CSA-optimised IT2FPID-PSS and FA-optimised IT2FPID-PSS under various operating conditions and load disturbances.

A novel approach of intensified barnacles mating optimization for the mitigation of power system oscillations

AbstractOptimization is the process of attaining the best solution from the available set of prioritized constraints to maximize or minimize the desired function involved in it. It is imperative in engineering to plan and design to find the best out of confined resource and time. A recently recommended barnacles matting optimization (BMO) has been recognized in computing the optimal parameters of the electric power system with excellent performance characteristics. In this paper, the intensified version of BMO has been proposed with the aid of cuckoo search (CS) and traditional particle swarm optimization (PSO). An in‐depth analysis was made on the BMO technique and proposed suitable modifications to deal with the electrical power system stability enhancement issue efficiently. The proposed technique in the power system stabilizer (PSS) parameter computation is validated on the 23 benchmark functions to examine for its suitability. The robustness of the proposed method is presented via statistical analysis and boxplot of the 23 benchmark functions. The PSS parameters are computed in a benchmark two area four machine system using intensified BMO under self‐clearing fault conditions. A multi‐objective function is designed to improve the damping nature offered under system uncertainties, and the comparative analysis is presented among conventional PSS, BMO, intensified BMO with CS and PSO (BMO‐CS and BMO‐PSO), and harris hawks optimizer.

Perbaikan Stabilitas Dinamik Sistem Tenaga Terintegrasi Pembangkit Listrik Tenaga Mikro Hidro dan Diesel Menggunakan PSS Berbasis ANFIS

Power generating units should be built closed to load centers according to electric power company regulation. These units are generally small in capacity, so that makes power transfer among inter- and outer-areas is easier to be done. This study aims to improve dynamic stability of a Micro Hydro Power Plant (MHPP) on perspective of 5% load increased in Diesel Power Plant (DPP). The DPP is equipped by an ANFIS-based Power System Stabilizer (PSS) in this scheme. Two inputs with Gauss2mf membership function (MF) type and 5-number of the each MF are used to implement the ANFIS model. Simulation results show that overshoot of MHPP power angle deviation is reduced to the values of 0.023, 0.0138 and 0.0132°, for the DPP without PSS, conventional and ANFIS PSS, respectively. Also, reduction in setting time is achieved at times >6, 4.051 and 3.553 s. It is shown that the ANFIS PSS is more effective to enhance the dynamic stability compared to other PSS