Analysis of Power System Low Frequency Oscillation Based on Energy Shift Theory

Linear quadratic regulator design via metaheuristics applied to the damping of low-frequency oscillations in power systems

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated.

An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimodal and difficult combinatorial optimization problem, this paper presents a novel parameter automation strategy for Particle Swarm Optimization (PSO) called PSO with Time-Varying Acceleration Coefficients (PSO-TVAC). This optimization method has a strong ability to successfully control both global and local search in each iteration process for considerably increasing the probability of finding the global optimum solution. The PSO-TVAC algorithm is applied to optimal tuning PSS parameters problem in order to reduce the PSS design effort and find the best possible solution within a reasonable computation time. For this reason, the robustly selection of PSSs parameters is converted as an optimization problem based on the time domain-based objective function under different operating conditions. The robustness of the proposed method is demonstrated on a multi-machine power system in comparison with the classical PSO and conventional method based designed PSSs. It is shown through the nonlinear time domain simulation and some performance indices for a wide range of loading condition. The analysis of the results shows that the improved PSO-TVAC is not only very effective but also provides an excellent ability for damping low frequency oscillations and greatly enhance the dynamic stability of the power system. Moreover, the proposed PSO-TVAC algorithm is superior than that of the classical PSO one in terms of accuracy, convergence and computational effort. Key words: Power system stabilizer (PSS) design, particle swarm optimization, power system dynamics, time-varying acceleration coefficients.

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

Due to the increase in wind power accessing power system in recent years, the low-frequency oscillation of power system is more complicated. Therefore, a new adaptive switching control method combined with a nonlinear system is proposed for the complex operating conditions of the grid-connected power system. Firstly, this method decomposes the low-frequency oscillation control problem of the power system into two parts, which are the linear system control and the nonlinear system control. Then, in the linear system control part, the iterative identification method is used to control this part. In the nonlinear system control part, the nonlinear term is decomposed into a combination of the previous beating measurable and the nonlinear estimation increment. Secondly, the nonlinear system is divided into three different operating conditions by the nonlinear estimation increment. Thus, four different adaptive controllers are obtained. Then, the designed controller is switched according to the controlled object with four controllers by the switching function, which not only ensures the stability of the closed-loop system, but also improves the control performance of the controller under different operating conditions. Finally, the feasibility and effectiveness of this method are verified, and the controller can fit the control efficiency of the real power system well.

Design of output feedback UPFC controller for damping of electromechanical oscillations using PSO

A single machine-infinite-bus (SMIB) system including the interline power flow controllers (IPFCs) and the power system stabilizer (PSS) controller is addressed. The linearized system model is considered for investigating the interactions among IPFC and PSS controllers. To improve the stability of whole system again different disturbances, a lead-lag controller is considered to produce supplementary signal. The proposed supplementary controller is implemented to improve the damping of the power system low frequency oscillations (LFOs). Imperialist optimization algorithm (ICA) and shuffled frog leaping algorithm (SFLA) are implemented to search for optimal supplementary controllers and PSS parameters. Moreover, singular value decomposition (SVD) method is utilized to select the most effective damping control signal of IPFC lead-lag controllers. To evaluate the system performance, different operating conditions are considered. Reponses of system in five modes including uncoordinated and coordinated modes of IPFC and PSS using ICA and SFLA are studied and compared. Considering the results, response of system without controller shows the highest overshoot and the longest settling time for rotor angel at the different operating conditions. In this mode of system, rotor speed has the highest overshoot. Rotor angel in the system with only PSS includes lower overshoot and oscillation than system without controller. When PSS is only implemented, rotor speed deviation has the longest settling time. Rotor speed deviation in the uncoordinated mode of IPFC and PSS shows lower overshoot than system with only PSS and without controller. It is noticeable that in this mode, rotor angel has higher overshoot than system with only PSS. The superiority of the suggested ICA-based coordinated controllers is obvious compared with SFLA-based coordinated controllers and other system modes. Responses of coordinated PSS and IPFC SFLA-based supplementary controllers include higher peak amplitude and longer settling time compared with coordinated IPFC and PSS ICA-based controllers. This comparison shows that overshoots, undershoots and the settling times are reduced considerably in coordinated mode of IPFC based controller and PSS using ICA. Analysis of the system performance shows that the proposed method has excellent response to different faults in power system.

As a result of the multiple interruptions induced by low-frequency oscillations in power systems, they may lead to system instability. This research employs the tunicate swam algorithm (TSA), a metaheuristic optimization approach, to design the power system stabilizers (PSSs) for the multi-machine power system networks. To increase system damping, a damping ratio-based objective function is proposed and a commonly utilized traditional lead-lag type PSS structure is adopted. The effectiveness of the TSA-based PSS architecture is illustrated by two systems: one with four machines and another with ten machines. The effectiveness and robustness of the proposed method are demonstrated using many performance tests. An additional measure of trustworthiness is provided by the comparison of the simulation results to the well-known particle swarm optimization (PSO) approach for swarm intelligence in this field.

Ash behavior in power systems can have a significant impact on the design and performance of advanced power systems. The Energy & Environmental Research Center (EERC) has focused significant effort on ash behavior in conventional power systems that can be applied to advanced power systems. This initiative focuses on filling gaps in the understanding of fundamental mechanisms of ash behavior that has relevance to commercial application and marketable products. This program develops methods and means to better understand and mitigate adverse coal ash behavior in power systems and can act to relieve the U.S. reliance on diminishing recoverable oil resources, especially those resources that are not domestically available and are fairly uncertain.

Real time monitoring and control of low frequency oscillations is an important issue to be taken into consideration in modern interconnected power systems operation. A method to find the system small signal stability and coherent generators following a disturbance in real-time is proposed in this work The synchronously sampled data available from Phasor Measurement Units (PMUs) at a high sampling rate provides an opportunity to observe the system operation near to real-time. The first four cycles of post disturbance data comprising of bus voltage magnitudes and angles is measured from optimally placed PMUs. This data is fed to different Artificial Neural Networks to determine the damping index and coherent groups. The dimensionality reduction is performed using Principal Component Analysis. The suggested method is very fast and accurate in predicting the system damping and coherent groups in real-time for different operating conditions including topological variations and 3-phase faults. The efficiency of the proposed methodology is investigated on IEEE 39-bus test system.

This paper proposes a novel neural network approach for low frequency oscillation mode identification in power systems. After employing the fast Fourier transform in order selection, we then introduce a specific neural network whose topology strictly follows the exponentially damped sinusoidal model of low frequency oscillation signals. We thus turn the parameter estimation into an optimization problem. Simulations show that the proposed approach is superior to the state-of-art neural network schemes in anti-noise ability, parameter accuracy and computation speed.

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.

A novel tool for transient stability analysis of large-scale power systems: Its application to the KEPCO system

A PSO based unified power flow controller for damping of power system oscillations

The mode-based damping torque analysis (M-DTA) method for studying the effect of an external controller on power system low-frequency oscillations is proposed in this paper. Firstly, based on the interconnection model between the system and the controller in the frequency domain, the oscillation loop corresponding to the electromechanical oscillation mode is built, and the mode-based damping torque of the controller can be calculated. Then, the application of the M-DTA method in the power system is illustrated. The derivation shows that in the single-machine infinite-bus power system, the M-DTA method is completely equivalent to the classical damping torque analysis (C-DTA) method. In the multi-machine power system, the mode-based damping torque reflects the effect of the controller on the oscillation mode directly, overcoming the shortcomings of the C-DTA method in which there is no direct correspondence between damping torque and the oscillation mode. By deriving the relationship with the residue index, the M-DTA method shows higher accuracy than the residue method in applications such as controller parameter adjustment. Finally, two example power systems are presented to demonstrate the application of the proposed M-DTA method.