Power System Dynamics Research Articles

Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review

Wind-driven turbines utilizing the doubly-fed induction generators aligned with the progressed IEC 61400 series standards have engrossed specific consideration as of their benefits, such as adjustable speed, consistent frequency mode of operation, self-governing competencies for voltage and frequency control, active and reactive power controls, and maximum power point tracking approach at the place of shared connection. Such resource combinations into the existing smart grid system cause open-ended problems regarding the security and reliability of power system dynamics, which needs attention. There is a prospect of advancing the art of wind turbine-operated doubly-fed induction generator control systems. This section assesses the smart grid-integrated power system dynamics, characteristics, and causes of instabilities. These instabilities are unclear in the wind and nonlinear load predictions, leading to a provisional load-rejection response. Here, machine learning computations and transfer functions measure physical inertia and control system design’s association with power, voltage, and frequency response. The finding of the review in the paper indicates that artificial intelligence-based machine and deep learning predictive diagnosis fields have gained prominence because of their low cost, less infrastructure, reduced diagnostic time, and high level of accuracy. The machine and deep learning methodologies studied in this paper can be utilized and extended to the smart grid-integrated power context to create a framework for developing practical and accurate diagnostic tools to enhance the power system’s accuracy and stability, software requirements, and deployment strategies.

Data-Driven Identification of Nonlinear Power System Dynamics Using Output-Only Measurements

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements.

Square-root cubature Kalman filter based power system dynamic state estimation

The state estimator’s real-time state information plays a vital role in monitoring and control of the power system. As a result, one of the primary requirements of the estimator is to provide accurate state estimates with good convergence under varied system operating conditions. In this context, dynamic state estimation (DSE) consistently tracks the power system dynamics and delivers real-time system state evolution. In literature, this dynamic state estimation (SE) problem is generally solved using an extended Kalman filter (EKF). The EKF approach neglects the non-linear mathematical functions of network equations by linearly approximating the measurement equations. This leads to degradation of SE accuracy during a sudden change in load conditions. In this paper, a new derivative-free method based on square-root cubature Kalman filter (SCKF) is proposed to overcome the limitations of the EKF technique and achieves improved performance. The proposed SCKF technique has been tested on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems under various operating conditions. The effectiveness of the SCKF technique has been compared with the traditional EKF and cubature Kalman filter (CKF) techniques using different performance indices. The obtained results demonstrate the versatility of the proposed SCKF technique.

Effect of Optimum Sampling Rate on Uncertainty Quantification of Power System Inputs

This letter deals with the effect of downsampling in uncertainty quantification of stochastic input injections or fluctuations in the power system in the form of random load injections. The significance of carrying out such an analysis at proper time scales has been emphasized. It is observed that a downsampling mechanism becomes essential in order to study the impact of uncertainties on the overall dynamics of the system without being contaminated by the power system dynamics.

Effect of the Dynamics of the MTDC Power System on DC Voltage Oscillation Stability

Ignoring parts of the dynamics of a multiterminal DC (MTDC) power system can simplify its stability analysis but may miss some instability risks in the analyzed results. This study analyzed the stability of DC voltage oscillation by considering comprehensive dynamics of an MTDC system in a DC voltage time scale to reveal the stability mechanism of the DC voltage oscillation accurately. It demonstrated that a DC voltage oscillation loop is first formed by the DC voltage control of the voltage source converter (VSC). Subsequently, it is affected by the dynamics of the remaining DC system (RDCS) through two paths: (1) the equivalent conductance of the RDCS is equal to an additional damping added to the DC voltage oscillation loop, and (2) the equivalent susceptance forms a mirrored subsystem and results in an open-loop modal resonance, which always induces negative effects on DC voltage oscillation stability. These conclusions are also correct in parts of the MTDC system using DC voltage droop control. Moreover, a method was proposed to quickly determine the stable operating region of the RDCS. Finally, accuracy of the stability mechanism of the DC voltage oscillation and effectiveness of the proposed method are verified based on the SIMULINK platform.

An improved decentralized finite-time approach for excitation control of multi-area power systems

The complexity of modern power systems has introduced more challenging aspects for stability. These challenges are due to the increase in stressed and constraint conditions. To resolve this issue, a novel adaptive fuzzy decentralized-based finite-time excitation control scheme has been proposed. The proposed scheme is aimed to be deployed for a large scale power systems with output and input constraint requirements. To handle these constraints, the backstepping design with a tan-type barrier Lyapunov function (BLF) is presented. Also, the parametric system uncertainties that model the power system dynamics are approximated using the fuzzy logic systems. This would eventually make the design completely decentralized due to bound-free interactions and constraints. While considering the time-varying constraint requirements on the power system outputs, a finite-time convergence of the power system output tracking errors into a small set around zero is further guaranteed. The proposed control scheme is then validated on a four-area interconnected power system subject to load fluctuations. Numerical results show the efficacy of the proposed approach.

Propagating Uncertainty in Power System Initial Conditions Using Data-Driven Linear Operators

In this paper, we propose a data-driven approach for uncertainty moment propagation through the non-linear power system dynamics. The proposed approach relies on the linear representation of a nonlinear system using the Perron-Frobenius & Koopman operators for propagating the moments. Data from non-linear simulations is used to approximate the linear operators by matrices, thereby enabling moment propagation by matrix multiplication. Results for a large-scale system are presented to demonstrate the accuracy and speed-up of the proposed method.

The Influence of SMES Magnet Operation Parameters on Voltage Distribution Characteristic

The voltage distribution on the magnet of superconducting Magnetic Energy Storage (SMES) system are the result of the combined effect of system power demand, operation control of power condition system (PCS) and magnet parameters, which is a key issue that influence the stability and security of SMES magnet. This paper mainly focuses on the interaction between the different components and the influence factors on voltage distribution on the magnet. Firstly, the hybrid model which integrate the power system dynamics, the detailed device of PCS and the transient model of magnet is built. Then, from the perspective of power requirement of power system, operation mode and control strategy of PCS, the voltage distribution characteristics are comprehensively analyzed. Finally, the optimization design method of operation mode to reduce the unevenness of voltage distribution is proposed.

Impact of decentralized microgrids optimal energy management on power system dynamics

This paper proposes a unified framework between the optimal energy management (OEM) problem of grid-connected microgrids and the frequency control of power systems through time-domain simulations. The proposed formulation is aimed at analyzing how different objectives for the microgrid OEM and control strategies affect the system frequency regulation. Each OEM model minimizes the microgrid local objective while ensuring energy reserves and operational limits by solving a two-stage stochastic optimization mathematical model. Two control approaches of microgrids are considered, namely, greedy and cooperative. The proposed framework is evaluated considering the IEEE 39-bus test system, including multiple microgrids with different energy storage capacities, and control strategies. Results show an improvement in system’s frequency deviation when MGs operate in the cooperative configuration while simultaneously reducing their operational costs.

Modelling of water conduit dynamics using the electromagnetic transient program line model

Almost all electrical power systems literature assumes that the shaft mechanical torque of a water turbine is linearly proportional to the wicket gate position and ignores the dynamics in the shaft torque due to the propagation of the water from the reservoir to the turbine. This propagation is usually modelled very simply using the water start-time equation and ignoring the travelling wave dynamics along possibly long conduits. This simplified modelling may introduce significant errors in the frequencies of oscillation which are in the range of 0.5–2 Hz and often fall within the range of slow oscillations in power system dynamics. This paper introduces the EMTP-H model for water dynamics based on the analogy between the hydraulic equations and the EMTP transmission line equations. The proposed model is validated with a field test and is applied to a simple power system frequency dynamics case.

Stochastic aggregated dynamic model of wind generation with correlated wind speeds

The paper describes a method to study the transient behaviour of power systems with correlated wind speeds. With this aim, a generalised method to extract correlation from real-world measurement data is proposed. This method allows accommodating data with any sampling rate and any probability distribution. The case study discusses the impact of correlated wind speeds on power system dynamics by utilising the well-known two-area system modified to include a distribution network with wind power generation. The correlation of wind speeds is set up using real-world data. Simulation results show that a high level of correlation among the wind speeds worsens the dynamic performance of the system.

A PMU-Based Data-Driven Approach for Enhancing Situational Awareness in Building A Resilient Power Systems

A data-driven approach for improving wide-area situational awareness (SA) in modern power systems for building a more resilient grid is proposed in this article. SA is of paramount importance when it comes to accurate sensing of events for better visualization of the power system dynamics. Various events uniquely excite particular states of the power systems resulting in a maximum change in the entropy for certain power system components. A new conceptual framework utilizing structure preserving energy function (SPEF) for identifying the specific component of the energy function and correlating it with the appropriate event has been suggested. Cubature Kalman filter-unknown input based dynamic state estimation technique has been employed for the extraction of states and unknown inputs from the phasor measurement unit (PMU) measurements for the derivation of the SPEF-based event indicators. In order to demonstrate the efficacy of the proposed technique, it has been validated on the IEEE 39-bus system and also on actual PMU data obtained from the Indian power grid. The results indicate the suggested technique to be accurate and robust to noise and thus can be a promising tool for real-time power system monitoring.

The mathematical model of a wind power plant and a gas power plant

To enable conduction of quality research of power system dynamics using computer simulation software, appropriate mathematical and simulation models have to be developed. In the last two decades, an exponential increase of installed wind power capacity can be observed, while the gas power plant capacity and energy production has also seen an increase in the recent years. Consequently, their impact on power system dynamics is no longer negligible. Furthermore, the increased penetration of wind power has led to a lot of research concerning frequency support capabilities from wind power plants (WPPs). In this paper, after a brief theoretical introduction, simplified system frequency response (SFR) simulation models of a generic wind power plant and a gas power plant have been developed in MATLAB. These generic models have been integrated with existing SFR models of a steam and hydro unit. The model contains a representation of under-frequency load shedding (UFLS) and demand response, as well. Graphical user interface (GUI) has been developed to control this expanded model.

System Dynamics and Frequency Regulation of a Multi-Area Power System Using an Optimal Controller

For system frequency control, the current work proposes a multi-area power system integrating various renewable sources. An effective controller with intrinsic superior capabilities of restricting peak deviation, steady state errors, and oscillatory behavior of a dynamic system is an apparent alternative for autonomous generation control of interconnected power systems. The most difficult part of using a controller is figuring out how to get the most out of it. Because of its space complexity, the trial and error method of determining the benefit by indirect optimization with an acceptable performance index appears to be insufficient. As a result, it appears that using an effective algorithm technique to find the maximum gains for the controllers is the best option. The gains of the controllers are addressed in this work and thus are optimized using a meta-heuristic technique in this study.

Impact analysis of energy-based control structures for grid-forming and grid-following MMC on power system dynamics based on eigenproperties indices

This paper addresses the impact of the modular multilevel converter (MMC) energy control in the system dynamics when grid-forming and grid-following control schemes are used in the same system. The MMC total energy control can be implemented in different approaches. Depending on the control architecture and the level of energy buffering capability the MMC can be used to mitigate the propagation of disturbances from the AC to the DC side and vice-versa. In order to study the effect of this MMC capability in the system dynamics and interactions, a point-to-point link including one grid-forming and one grid-following converter is studied in detail for four different energy-based control architectures combinations. In order to compare them, two eigenproperties indices have been derived. The first index quantifies the participation of the eigenvalues in the system response and the second one, quantifies which states rule on the system dynamics. This study is accompanied with detailed nonlinear models implemented in MATLAB Simulink used to verify the results via time-domain simulations.

Parameter identification of small distributed hydro generators during excitation system start‐up

Concerning the penetration of renewable energy resources into the power grid, investigation of the power system dynamics to meet the power demand and maintain stability has gathered momentum recently. On the other hand, evaluating the generator parameters in the distributed generation (DG) units as a subsystem of a large-scale power grid is crucial for control and stability analysis purposes. To this aim, a simple and efficacious algorithm to identify the operational parameters of synchronous generators (SGs) in small hydro DGs is proposed here. The required data for the SG identification using the recommended approach is gathered during excitation system start-up while the DG is disconnected from the grid. Using the proposed test procedure, the adverse impact of magnetic saturation on estimation results can be avoided. Furthermore, a straightforward parameter estimation (PE) procedure is established here to obtain the intended parameters from the recorded data. Finally, the proposed method was applied to a 10 MW hydro DG, and the results are discussed.

Decision strategies in sequential power markets with renewable energy

With direct electrification at the core of global decarbonization efforts, it is key for policy makers to adequately evaluate market dynamics in renewable power systems. The ongoing integration of renewable energy sources fundamentally changes these dynamics, with variable production profiles, lower marginal production costs and increased price volatility. This motivates us to study how an increasing supply from renewable energy sources impacts decision making of intermittent and non-intermittent producers in sequential forward and spot markets. We do this by examining prices and volumes observed in an experimental trading environment that allows us to vary the market share of intermittent renewable energy production with a high degree of control. The results show that non-intermittent power producers, who can flexibly adjust volumes in the short-term, can retain their profits by moving focus from forward to spot markets. We find empirical support for our experimental results by validating data from German short-term power markets in 2021 and create awareness on the convenience yield for flexibility in power systems with a high market share of intermittent renewable energy sources. The study thereby guides policy makers to align supporting the uptake of intermittent renewable energy production with taking away hurdles faced by low-carbon flex technologies.

Robust Dynamic State Estimator of Integrated Energy Systems Based on Natural Gas Partial Differential Equations

The reliability and precision of dynamic database are vital for the optimal operating and global control of integrated energy systems. One of the effective ways to obtain the accurate states is state estimations. A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on Kalman filter. To take full advantage of measurement redundancies and predictions for enhancing the estimating accuracy, the dynamic state estimation model coupling gas and power systems by gas turbine units is established. The exponential smoothing technique and gas physical model are integrated in Kalman filter. Additionally, the time-varying scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The proposed method is applied to an integrated gas and power systems formed by GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating results show that the method can obtain the accurate dynamic states under three different measurement error conditions, and the filtering performance are better than separate estimation methods. Additionally, the proposed method is robust when the measurements experience bad data.

Estimating the Region of Attraction for Power Systems Using Gaussian Process and Converse Lyapunov Function

The significant penetration of renewable energy resources into power systems introduces intermittent disturbances that drive the system away from its stable operating point (i.e., equilibrium). Nevertheless, as long as the system states are still contained inside a region of attraction (ROA), the system will finally return to the normal operating equilibrium. Thus, the ROA plays a crucial role in the stability assessment of dynamical power systems under uncertainties. This brief introduces a novel framework to construct the ROA of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytic Lyapunov functions, which are subject to two limitations: the analytic Lyapunov functions may not be always readily available and the resulting ROA may be overly conservative. This work overcomes these two limitations by leveraging the converse Lyapunov theorem in control theory to eliminate the need for an analytic Lyapunov function and learning unknown Lyapunov functions with the Gaussian process (GP) approach. In addition, a GP upper confidence bound (GP-UCB)-based sampling algorithm is designed to reconcile the tradeoff between the exploitation for enlarging the ROA and the exploration for enhancing the confidence level of the sample region. Within the constructed ROA, it is guaranteed in the probability that the system state will converge to the stable equilibrium with a confidence level. Extensive simulations and experimental validations are conducted to substantiate the assessment approach for the ROA of IEEE test systems and a real smartgrid using phasor measurement unit (PMU) data. It is demonstrated that the proposed approach can significantly enlarge the estimated ROA compared to that of the analytic Lyapunov counterpart.

Direct Participation of Dynamic Virtual Power Plants in Secondary Frequency Control

This paper proposes a novel control strategy in which Renewable Energy Sources (RES) considered in a new Dynamic Virtual Power Plant (DVPP) concept directly participate to Secondary Frequency Control (SFC). This allows full participation of these generators to SFC, i.e., in the same manner as classic synchronous generators by fulfilling identical specifications from both control and contractual points of view. An internal real-time redispatch has been proposed to account in DVPP in order to determine the amount of active power injection by each RES unit for the provision of frequency support at the secondary level. The whole control scheme is designed to take into account both rapid and slow dynamics of modern power systems which contain both classic synchronous generators and rapid power electronics for renewable energy sources in which DVPP is supposed to be inserted. The performance of secondary frequency control strategy has been validated through simulation studies on a two-area benchmark with mixed wind power plants and classic synchronous generators. This work is part of the H2020 POSYTYF project