Stability of Isolated Microgrids With Renewable Generation and Smart Loads

This paper presents a new method to suppress low frequency oscillation based on wide-area measurement. Since negative damping low frequency oscillation is usually caused by large disturbance that deteriorates system damping, relationships between oscillation modes and fault components are established through time-domain simulation. Then, abrupt change in electrical quantity of related components will be detected to distinguish negative damping low frequency oscillation and forced power oscillation. Forced power oscillation can be suppressed by isolating the disturbance source. However, direction of the oscillation energy flow can only identify multiple suspected areas when wide-area measurement is limited. Phase of tie-line active power for each suspected area is calculated during propagation stage to determine the area containing disturbance source. Dispatching actions should be given to enhance the system damping to secure value for low frequency oscillation. Therefore, control strategy is calculated to improve the damping of weakly damped modes to different values through online small signal stability analysis. Appropriate actions can be selected based on real-time measured dominant oscillation mode. Real engineering applications have fully confirmed the validity and practicability of this method.

This paper analytically derives oscillatory stability criteria of a grid-connected PMSG based on the block diagram model. The derivation reveals the general mechanism about how the condition of grid connection, loading and converter control parameters setting of the PMSG jointly affect the oscillatory stability of the PMSG. It logically explains why the condition of weak grid connection and heavy loading may cause destabilization. In addition, computationally simple and modal-computation free indices are proposed to identify the instability risk caused by the “improper parameters setting” of the control system of grid side converter (GSC) and the phase locked loop (PLL). Following analytical conclusions are obtained: (1) When the PLL is of high-frequency bandwidth, high-frequency oscillations are mainly caused by the PLL or the open-loop modal resonance between the PLL and current control of the GSC. In addition, the low-frequency oscillations may also occur as caused by the power control of the GSC. (2) When the PLL is of low-frequency bandwidth, low-frequency oscillations may be caused jointly by the power control of GSC and the PLL. An example grid-connected wind farm with eighteen similar PMSGs is presented to demonstrate and evaluate the stability criteria derived and analytical conclusions obtained in the paper.

Low-frequency oscillations of wind power systems caused by doubly-fed induction generators

The power system economy and safety demand is likely to force more and more power systems to interconnect. But the power transfer capability in the tie-lines is largely limited by small signal instability. A technique for low frequency oscillation analysis of bulk interconnected power system by combining sparse eigenvalue analysis with Prony method is presented in this paper, which uses Prony analysis to provide approximation of the oscillation modes for initial shift points of the sparse eigenvalue analysis. Based on the result of the sparse eigenvalue analysis, the PSSs that are installed on selected generators to damping the inter-area low frequency oscillation modes; time-domain simulations and sensitivity analysis of the power flow in the tie-line verify their performances. The simulation results of a practical power system demonstrate its effectiveness

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.

Low-frequency oscillation damping in the electric network through the optimal design of UPFC coordinated PSS employing MGGP

Low frequency oscillations (LFOs) in interconnected multi-machine power systems is a major concern in viewpoint of power system dynamic stability. Proposing high performance power system stabilizers (PSSs) to mitigate efficiently the LFOs is an attractive subject which has found remarkable consideration in latter literature. Owing to high potential of fractional order controllers (FOCs), a tilt-integral-derivative (TID) assisted stabilizer is proposed here. In doing so, IEEE type PSS2B is modified to establish a TID-based PSS2B stabilizer. The dynamic performance of the suggested TID-PSS2B is investigated in comparison with an ingeter order proportional integral derivative (PID) based PSS2B counterpart and a former TID-PSS. The objective of this paper is to improve the small-signal stability of an interconnected power system via preparing further damping torque to suppress the LFOs. The design procedure of the TID-PSS2B is mathematically formulated as a minimization problem with the aim of decreasing the machines speed deviations. An improved particle swarm optimization (IPSO) algorithm is used to find optimal parameters of the presented stabilizer. The time domain simulation results reveal that by employing the suggested TID-PSS2B, the speed oscillations are suppressed effectively. Furthermore, to evaluate the robustness behavior of the presented controller, sensitivity analysis is carried out.

Summary form only given. Low frequency oscillations (LFOs) have been obtained in a 3D time domain simulation for the first time. From our preliminary simulation studies, it is found that not only magnetic compression profile but initial velocity or velocity ratio play an important role in the operation of a MIG electron gun. In addition, the secondary emission effects on the LFOs are also studied. The frequency of LFOs has been estimated by a periodic asymmetric oscillator model, close to the experimental values.

In recent times, the shrinkage of fossil fuel reserves and increase in greenhouse emissions have compelled to switch to suitable eco‐friendly solutions. Electric vehicles (EVs), being one such solution, have been the center of attraction for the industries as well as the researchers. This paper deals with the inclusion of plug‐in EVs (PEVs) in isolated microgrid (I‐MG). An intelligent demand‐side management approach has been proposed for co‐ordination of the power generation from distributed generators, battery energy storage systems (BESSs), and other forms of distributed energy resources (DERs) to fulfill the daily load demand as well as energy demand of EVs. Medium voltage I‐MG integrated with DERs, BESSs and PEVs has been used for the study and the analysis has been done to justify the need for smart charging of PEVs in an I‐MG. The proposed technique combines smart charging and demand response which simplifies the consumer’s concerns and provides a cost‐effective solution.

Grid-forming (GFM) energy storage can be utilized as a backup power source for the power grid to ensure the security of the power grid. GFM energy storage can also enhance the strength of the power grid and improve its stability. However, the GFM energy storage inherits the characteristics of the synchronous generator. Low-frequency oscillations may occur in GFM energy storage, which affect the stable operation of the power system. This paper proposed an additional damping control strategy for GFM energy storage to address the low-frequency oscillation. Firstly, this paper builds the state-space small-signal mathematical model of the GFM energy storage grid-connected system to analyze the participation factors of the low-frequency oscillation mode and clarify the key control parameters affecting the GFM energy storage grid-connected system the low-frequency oscillation. Then, this paper proposed an additional damping control strategy to increase the damping ratio of the low-frequency oscillation mode and improve the stability of the GFM energy storage grid-connected system. Finally, semi-physical experiments verified the effectiveness of the proposed additional damping control strategy.

The subject of this thesis is the dynamics of granular materials. Granular matter is defined as collections of macroscopic, dissipative particles. The size of the individual particles (grains) must be large enough so that thermal fluctuations may be ignored. The loss of kinetic energy at every grain-grain collision implies the need of an external energy source to keep grains in movement. This thesis centres on a specific energy injection method: vibrated systems, where the grains container is shaken such that particles gain energy through collisions with the walls. As farfrom-equilibrium dissipative systems, vibrated granular matter presents many distinct out-of-equilibrium stable states and complex transitions between them. In this thesis both particle simulations and different continuum models are used to investigate further the relation between discrete and continuum descriptions of particle systems, a subject of fundamental scientific interest. A collective, semi-periodic movement of the grains inside vertically vibrated containers is for the first time identified and characterized. A simulational study of these oscillations is presented in Chapter 2, and Chapter 3 mainly describes an experimental observation of it. The oscillations take place in density-inverted states, such as the granular Leidenfrost effect, where grains separate in a high temperature region near the moving bottom wall and a dense region on top. The quasiperiodic movement is usually orders of magnitude slower than the energy injection shaking frequency, thus they are named low-frequency oscillations (LFOs). Furthermore, from the equations of mass and momentum conservation in continuum media an expression for the typical natural oscillation frequency is derived, in good agreement with both simulations and experiments in the high energy injection limit. Increasing the energy input and system size takes the system from the granular Leidenfrost state to a buoyancy-driven convective state. Chapter 4 presents an indepth study of this transition, revealing the existence of fluctuating convective flows far before the transition, as also suggesting a reinterpretation of the dynamics that includes the influence of LFOs. The characteristic length and time-scales of precursory fluctuations are measured, and the amplitude of the critical mode is observed to be consistent with a quintic supercritical amplitude equation. The last two chapters of this thesis study the granular Leidenfrost to convection transition using granular hydrodynamics. Chapter 5 deals directly with the relation of discrete and continuum descriptions of granular systems. A methodology is proposed to quantify the finite-number effects on fluctuations, involving scalings that leave the granular hydrodynamic equations invariant while varying the total number of particles. This method allows us to conclude that LFOs are a finite-number, discretization phenomena, although the mode of oscillation is seen to be a macroscopically determined quantity. In Chapter 6 the granular hydrodynamic equations are numerically solved, and the solutions compared with particle simulations. Deviations of the continuum model due to finite-size and higher order effects are discussed. Finally, it is observed that to first order the transition can be understood as a Rayleigh-Bernard instability described by Navier-Stokes-like equations in the Boussinesq approximation.

In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO.

In this paper, a systematic method is presented to research the Low Frequency Oscillation (LFO) in bulk power system based on the Wide Area Measurement System (WAMS). This method contains two different approaches: the nonlinear analysis method (NAM) and energy analysis method (EAM). The NAM extracts the oscillatory parameters and oscillation mode based on the nonlinear viewpoint of each single measured signal and the EAM analyzes the LFO phenomena from the energy aspect based on the ensemble measurement matrix. Two methods work as a complementary manner and their calculated results check each other. The near-real time application of proposed method is described based on the instantaneous computation ability of NAM and the recursive relationships of the covariance matrix of ensemble measurement matrix. The actual measured data from WAProtector is utilized to evaluate the feasibility and validity of the proposed method.

Power System Stabilizer controller (PSS) and Static Var Compensator (SVC) with damping controller can effectively damp power system oscillations. In order to eliminate the adverse effects between controllers caused by improper setting of parameters, coordination control of PSS and SVC is studied in this paper using bacterial colony chemotaxis (BCC) optimization algorithm, to improve dampings of electromechanical oscillation modes. Case study based on Two-Area with Four-Machine System, in both eigenvalue analysis and time domain simulation, confirms that the method is valid, and the simulation result shows that the optimal scheme obtained by method proposed in this paper can effectively damp low frequency oscillation and enhance the system small signal stability.

Detection of low-frequency oscillation using synchrophasor in wide-area rolling blackouts