Damping Power System Oscillations Research Articles

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

Over the last few years, machine learning tools have significantly progressed and attracted extensive applications in many parts of contemporary life. The power sector is one of the leading domains implementing such appealing and effective technologies for diverse applications as a part of the digital transformation of electric networks. A power system's low-frequency oscillation (LFO) is a non-threatening but slow-burning problem that might cause complete network failure unless adequately handled. This article proposes a state-of-the-art procedure of LFO damping in electric power networks via the sine cosine algorithm and deep learning (DL) technique. It uses two networks of power systems, in which the synchronous generator is fitted with a power system stabilizer (PSS) in the case of the first network; in the other, the synchronous machine is conjoined to the PSS that coordinates with a unified power flow controller. The proposal is developed based on the statistical assessment of the analyzed networks to improve the LFO damping via real-time adjustment of PSS parameters/variables. The proposed technique was evaluated using power system stability performance measuring criteria, such as the eigenvalue and minimum damping ratio. In the end, the effectivity of the stability-gaining procedure is also tested by time-domain simulation to implement in real-time. The study also dealt with a comparative investigation and discussion of the findings of some published works to conclude the capability of the proposed DL tool for stability improvement of the system in real-time by removing undesirable LFOs.

A robust fusion bus frequency estimation method to improve frequency oscillation damping in power systems

AbstractThis paper proposes a new robust method for accurately and reliably estimating remote bus frequencies in a power system. Two different measurement sources for the remote bus frequency are considered, that is, the ideal Frequency Divider (FD) and the Synchronous Reference Frame Phase Locked Loops (SRF‐PLLs). Each measurement signal encounters different uncertainties and data quality issues. In this paper, both data sources are employed and fused together to better estimate the remote bus frequencies. To this end, the model structure of the power system is selected and an Unscented Kalman Filter (UKF) is utilized together with a fusion covariance intersection method to enhance the accuracy of the estimated bus frequency. Since the fusion estimation method fails in case of quality issues on both channels, a robust Generalized Maximum‐likelihood UKF (GM‐UKF) using a novel outlier detection criteria is developed. The impact of the resulting robust fusion filter on the estimation of the remote bus frequencies and on the performance of WAPSS, which makes use of the estimated frequencies as the feedback signal, is examined via simulations. The results demonstrate the excellent performance and reliability of the proposed method in dealing with noise filtering and outlier suppression while ensuring a high statistical efficiency.

LSTM based deep neural network model of power system stabilizer for power oscillation damping in multimachine system

AbstractPower system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low‐frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID‐PSS) via a long short‐term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID‐PSS. To evaluate the performance of the proposed LSTM based PID‐PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID‐PSS is compared with traditional PSSs through time‐domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID‐PSS under diverse loading conditions.

Investigation of the Transfer Capability of the Nigerian 330 kV, 58-bus Power System Network using FACTS Devices

Over the years, the Nigerian power system is beset with lingering problems, which include severe power losses, as well as very low transfer capability of the transmission network to evacuate power from generating stations to the load at the distribution level. Presently, the Nigerian power industry is undergoing restructuring, especially in the generation and distribution systems. In view of the deregulation of electricity distribution and marketing, the traditional practices of the Nigerian power system are undergoing changes to address the identified problems in the existing power system. Specifically, better utilization of the existing power transmission network to improve on the system transfer capability and minimize cost is one of the key focuses of the deregulation agenda. This work deals with the enhancement of transfer capability of Nigerian 58-bus, 330kV network using FACTS (Flexible Alternating Current Transmission System) devices. FACTS devices are used for controlling transmission voltage, power flow, reducing reactive power losses, and damping of power system oscillations for high power transfer capability. Three FACTS devices; Thyristor Controlled Series Compensator (TCSC), Unified Power Flow Controller (UPFC), and Interline Power Flow Controller (IPFC) were used to investigate the transfer capability of the Nigerian 58-bus power system network. NEPLAN was employed in this work to model the Nigerian 58-bus system and optimally placed the FACTS devices at the weakest buses that were found out through the computation for available transfer capability (ATC) after continuation power flow (CPF) simulation was completed. MATLAB codes were developed and used to calculate power transfer capability of the network without and with FACTS devices. Comparing the three FACTS devices, the results obtained showed that UPFC enhanced the power transfer capability of the network than TCSC and IPFC.

Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization

Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.

Stability Enhancement of DFIG Wind Farm Using SSSC With FOPID Controller

The wind power generation has become more important nowadays to meet the increase in power demand. DFIG based wind power generation is the recent and used in many countries due to its better power controllability. The controllers like Proportional Integral (PI) are used for the stabilization of the waveforms of the supply system. The change in controllers produces better oscillation damping in recent days. The effect of varying the wind input to generate power using the wind turbine resulting in instability in the power system because of the control is done on a grid supply. This paper aims to proposes an optimum FOPID controller for damping power system instability using a Static Synchronous Series Compensator (SSSC) system that takes into account the dynamics of wind energy conversion systems (WECS) connected to a infinite grid. The WECS model, which includes variations in wind supply to the wind turbine, has been developed to test the durability of the optimized controller that was developed to damping power system oscillations. The controller used to take the power system dynamics into account. A new controller is being designed to include a corrective measure for the damping the oscillations to adjust the instability caused by wind supply variations. The controller helps to tune the controller settings that lead to the achievement of the power oscillation damping objectives. These results are compared with conventional PMSM based wind turbine system.

Coordinated operation of fuzzy‐based TCSC and SMES for low‐frequency oscillation damping in interconnected power systems

SummaryModern power networks are enormously complex and frequently interconnected by weak tie‐lines, which results in a variety of stability issues such as low frequency oscillations (LFOs). LFOs can have a negative impact on power system stability and tie‐line transmission capacity, resulting in catastrophic blackouts if suitable damping control mechanisms are not in situ. This article demonstrates a robust design of damping control for inter‐area LFO damping in an interconnected power system. A new comprehensive approach is presented, which integrates the thyristor controlled series compensator (TCSC) and superconducting magnetic energy storage (SMES) to improve the damping of critical inter‐area modes. An intelligently designed fuzzy logic control (FLC) employing a global stabilizing feedback signal constitutes the supervisory controller. The stabilizing feedback signal is obtained based on the observability of critical modes using participation factor (PF) analysis. Moreover, a maiden application of a recently proposed optimization algorithm, K‐means optimizer (KO), is employed to simultaneously tune the parameters of FLC employed for coordinated operation of SMES and TCSC. To validate the robustness of the proposed scheme, several case studies are performed on Kundur's two‐area multi‐machine test system. A comparison with a residue‐based damping controller was also done to demonstrate the superior damping efficacy of the proposed KOFLC controller.

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.

Design of Coordinated PSS and SVC Damping Stabilizer for Low Frequency Oscillations Enhancement

This paper presents the application of PSS with based damping controller FACTS device and describes a coordinated tuning of parameters. The FACTS device considered here is Static Var Compensator (SVC). The method proposed to optimize the controller parameters is Particle Swarm Optimization (PSO) algorithm. The Single Machine Infinite Bus (SMIB) with PSS and SVC-based damping controller is selected as a case study system. In order to evaluate the effectiveness of these controllers, eigenvalues analysis and nonlinear simulation of this system are carried out under different loading conditions. The simulation results shows that PSS coordinated with SVC based stabilizer have outstanding fast damping of low frequency power system oscillations.

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.

Decentralized Data-Enabled Predictive Control for Power System Oscillation Damping

We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source converter (VSC)-based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping. Conventional optimal wide-area control is model based. However, in practice, detailed and accurate parametric power system models are rarely available. In contrast, the DeePC algorithm uses only input-output data measured from the unknown system to predict the future trajectories and calculate the optimal control policy. We showcase that the DeePC algorithm can effectively attenuate interarea oscillations even in the presence of measurement noise, communication delays, nonlinear loads, and uncertain load fluctuations. We investigate the performance under different matrix structures as data-driven predictors. Furthermore, we derive a novel Min-Max DeePC algorithm to be applied independently in multiple VSC-HVDC stations to mitigate interarea oscillations, which enables decentralized and robust optimal wide-area control. Further, we discuss how to relieve the computational burden of the Min-Max DeePC by reducing the dimension of prediction uncertainty and how to leverage disturbance feedback to reduce the conservativeness of robustification. We illustrate our results with high-fidelity, nonlinear, and noisy simulations of a four-area test system.

Artificial electric field algorithm based superconducting magnetic energy storage system for damping inter area oscillations

This paper presents an artificial electric field algorithm (AEFA) based optimal control of superconducting magnetic energy storage (SMES) system for damping inter area oscillations. The artificial electric field algorithm is based upon Coulomb’s law of electrostatics and Newton’s law of motion. The proposed technique is used to optimize the SMES controller parameters for effective damping of inter area oscillations. Because of the potential for system damage and the subsequent constraints on power transmission along select lines, low frequency inter-area oscillations have been identified as a serious problem in utility systems. With the advancement in power electronics technologies, a new option for damping of power system oscillations has been created by integrating energy storage devices to it. The proposed scheme is tested on a two area interconnected power system which is suitably modelled and it’s modal analysis is done. The participation of each area towards the inter area mode is analyzed by using participation matrix and the suitable placement of SMES units is decided accordingly. MATLAB Simulations are carried out to prove the efficacy of proposed scheme. The simulation results show that proposed SMES controller along with the incorporation of SMES units to each generator bus effectively damps out the inter area oscillations.

Power Flow and Power Quality Enhancement by TCSC using five different Novel Methods.

The demand of electricity is continuously increasing due to increase in population and urbanisation. Due to lack of generation and transmission facilities there is over exploitation of present resources. The existing transmission lines are overloaded which make the system prone to stability problems. In the proposed work the power flow is increased in the existing system after using FACTS device using five different methods. They are firing by TCR pulse generator, using control subsystem based on RS Flip Flop, using Synchronised 6 pulse generator, using Thyristor -6 pulse generator with PLL, and pulse generator block of MATLAB.TCSC is used to control the dynamic power flow and also to enhance the power quality of the system. The power quality of the system is polluted due to use of common harmonic loads like computers, motor drives, arc and induction furnaces, battery charges, rectifiers, induction heating equipment. So, power electronics-based device TCSC is used in the proposed work. increases the efficiency of the line and improve the stability and security of the system. The active power at the receiving end is enhanced using the above different methods and the various results of power flow enhancement and THD analysis is shown. The THD analysis of the system is done in the present work using all the different methods. There provide other benefits to the system like damping power system oscillations, mitigating sub synchronous resonance, providing voltage support, improving transient stability.

Improved Salp Swarm Optimization Algorithm for Damping Controller Design for Multimachine Power System

This paper proposes an improved salp swarm algorithm (ISSA) as an effective metaheuristic method for tackling global optimization issues and damping power system oscillations. In the suggested ISSA, new equations are introduced to update the location of the leader and followers. This modification improves the method’s exploration possibilities while also preventing it from converging prematurely. Benchmark test functions are used to confirm the proposed algorithm’s performance, and the results are compared to SSA and other effective optimization algorithms. According to the extensive comparisons, the enhanced ISSA algorithm has higher convergence accuracy and stability than the original SSA and other researched algorithms. Furthermore, the feasibility and efficiency of the proposed method were demonstrated by the simultaneous coordinated design of UPFC based damping controllers. For the two-area, four-machine system, the experimental findings are provided. Simulation experiments reveal that ISSA designed controllers outperform those created using other methods.

An Improved Immune Clone Selection Algorithm for Parameters Optimization of Marine Electric Power System Stabilizer

In the marine electric power system, the marine generators will be disturbed by the large change of loads or the fault of the power system. The marine generators usually installed power system stabilizers to damp power system oscillations through the excitation control. This paper proposes a novel method to obtain optimal parameter values for Power System Stabilizer (PSS) to suppress low-frequency oscillations in the marine electric power system. In this paper, a newly developed immune clone selection algorithm was improved from the three aspects of the adaptive incentive degree, vaccination, and adaptive mutation strategies. Firstly, the typical PSS implementation type of leader-lag structure was adopted and the objective function was set in the optimization process. The performance of PSS tuned by improved immune clone selection algorithm was compared with PSS tuned by basic immune clone selection algorithm (ICSA) under various operating conditions and disturbances. Then, an improved immune clone selection algorithm (IICSA) optimization technique was implemented on two test systems for test purposes. Based on the simulations, it is found that an improved immune clone selection algorithm demonstrates superiority over the basic immune clone selection algorithm in getting a smaller number of iterations and fast convergence rates to achieve the optimal parameters of the power system stabilizers. Moreover, the proposed approach improves the stability and dynamic performance under various loads conditions and disturbances of the marine electric power system.

Optimal node selection of damping controller for power systems with high wind power penetration

With the increasing penetration of doubly fed induction generator (DFIG) wind turbines in power systems, they should be capable of damping the power oscillations. DFIGs equipped power oscillation damper (POD) combing synchronous generators equipped with power system stabilizer (PSS) are being deployed to damping power system oscillations. However, a major gap in this field is the selection of the optimal controller node in the system. This paper aims to propose an optimal node selection approach for POD and PSS in power systems, assisted by an event triggered node augmentation scheme to cope with cascading system contingencies. A submodular function is constructed to evaluate the distance between the system unstable eigenvectors and the controllability Gramian, and the base input set as well as the candidate nodes are selected. Subsequently, an event triggered condition is formulated to allow the candidate controller nodes to participate in damping the system oscillation. Four case studies are conducted and the results indicate that the proposed approach can effectively select the optimal PSS/POD nodes to improve the performance of the controllers.

Chaotic Power System Stabilization Based on Novel Incommensurate Fractional-Order Linear Augmentation Controller

The nonlinear dynamics of an incommensurate fractional-order single-machine infinite-bus (SMIB) power system benchmark model are explored and studied by means of modern nonlinear analysis theories, such as bifurcation, chaos, power spectral density (PSD), and bicoherence methods. The effect of incommensurate order derivatives on power system dynamics is presented. The study reveals that the power system undergoes interesting dynamics such as periodic motion, chaotic oscillations, and multistability whenever the system parameter values fall into particular ranges. A new fractional-order linear augmentation-based control scheme is applied to damp out the power system’s chaotic oscillation, change the stability of the coexisting states, and drive the system from multistability to monostability. The stability of the proposed control system is derived using Lyapunov theory. Simulation results confirmed the effectiveness and robustness of the proposed control scheme in damping power system oscillations and achieving good overall performance. The results in this paper will give a better understanding of the nonlinear dynamic behaviors of the incommensurate fractional-order SMIB power system.

Online DMDc Based Model Identification Approach for Transient Stability Enhancement Using Wide Area Measurements

This letter proposes an online control strategy to enhance the overall damping of low-frequency power system oscillations of large inter-connected electrical networks. To achieve this objective, the proposed approach employs wide area measured signals to estimate an accurate online model of power system dynamics using the dynamic mode decomposition with control (DMDc) method. The DMDc technique applies singular value decomposition (SVD) to the collected data matrices such that an accurate low-order approximation of system dynamics is obtained. The estimated model is thereafter utilized in designing the wide area damping controller (WADC) using discrete linear quadratic regulator (DLQR). Furthermore, kalman filtering is deployed to update the states of the designed controller and to remove the noise that is contained in the measured signals. The effectiveness of the proposed method is tested on the 10 machines, 39-bus New England test system.

Operational planning steps in smart electric power delivery system

This paper presents a comprehensive review of advanced technologies with various control approaches in terms of their respective merits and outcomes for power grids. Distributed energy storage control is classified into automatic voltage regulator and load frequency control according to corresponding functionalities. These control strategies maintain a power balance between generation and demand. Besides, three basic electric vehicle charging technologies can be distinguished, i.e. stationary, quasi-dynamic and dynamic control. For realizing charge-sustaining operation at minimum cost quasi-dynamic and dynamic strategies are adopted for in-route charging, while stationary control can only be utilized when the electric vehicle is in stationary mode. Moreover, power system frequency stability and stabilization techniques in non-synchronous generator systems are reviewed in the paper. Specifically, a synchronverter can damp power system oscillations and ensure stability by providing virtual inertia. Furthermore, it is crucial to manage the massive information and ensure its security in the smart grid. Therefore, several attack detection and mitigation schemes against cyber-attacks are further presented to achieve reliable, resilient, and stable operation of the cyber-physical power system. Thus, bidirectional electrical power flows with two-way digital control and communication capabilities have poised the energy producers and utilities to restructure the conventional power system into a robust smart distribution grid. These new functionalities and applications provide a pathway for clean energy technology. Finally, future research trends on smart grids such as IoT-based communication infrastructure, distributed demand-response with artificial intelligence and machine learning solutions, and synchrophasor-based wide-area monitoring protection and control (WAMPC) are examined in the present study.

Wide-area oscillation damping in low-inertia grids under time-varying communication delays

Wide-Area Control (WAC) can be efficiently used for oscillation damping in power systems. However, to implement a WAC, a communication network is required to transmit signals between the generation units and the control center. In turn, this makes WAC vulnerable to time-varying communication delays that, if not appropriately considered in the control design, can destabilize the system. Moreover, with the increasing integration of renewable energy resources into the grid, usually interfaced via power electronics, power system dynamics are becoming drastically faster and making WAC more vulnerable to communication delays. In this paper, we propose a design procedure for a delay-robust wide-area oscillation damping controller for low-inertia systems. Its performance is illustrated on the well-known Kundur two-area system. The results indicate that the obtained WAC successfully improves the oscillation damping while ensuring robustness against time-varying communication delays.