Model‐based Predictive Control for Damping Electromechanical Oscillations in Power Systems

The localness of electromechanical oscillations in power systems

This study presents coordinate control tuning of power system stabilizer (PSS) with series and shunt FACTS controllers. Here thyristor controlled series compensator (TCSC) and static synchronous compensator (STATCOM) based controllers are coordinated to enhance the damping of power system inter area oscillations. The design of proposed damping controller is formulated as an optimization problem and the controller gains of a linearized power system are optimized instantaneously using genetic algorithm (GA). Here the power system employed with PSS and STATCOM as well as TCSC. The coordinated tuning among the damping controllers is performed on the linearized power system model. Finally, the proposed coordinated controller performance is tested with both eigen value analysis and time domain simulations. Three different control schemes are employed on the test system and the simulation results are compared.

This paper proposes an efficient method that tunes wide-area power system stabilizers (WPSSs) to have probabilistic robustness for damping inter-area electromechanical oscillations in power systems with incorporated random wind power. Specifically, the efficiency of this method benefits from unique consideration and deduction of the analytic forms of cumulative distribution functions (cdfs) of two types of random variables and their derivatives with respect to tunable parameters of the conventionally structured WPSSs, based on approximations of wind power probability density functions by Gaussian mixtures. These cdfs then compose the objective function of an optimization that can rapidly solve for optimal parameters of WPSSs by a sequential quadratic programming algorithm. The optimized WPSSs are probabilistically robust because they enhance the probability of two commonly desired control effects. Simulation studies on a modified IEEE 10-machine and 39-bus system validate the superior efficiency of the proposed tuning method and the excellent performance of the derived WPSSs.

Automatic generation control plays an important role in power system to maintain frequency and voltage stability in power system. Frequency of the electrical supply system should be maintained at a constant standard value inorder to maintain the proper working of different machinery and different components connected with the power system. Both real and reactive power control can be achieved through frequency and voltage control in the power system respectively. Today, power system complexity increases day by day due to integration of power electronic components and renewable energy generation units. This will lead to some issues in stability of the entire power system and power quality from utility side may be become a challenging one. This paper discusses a solution for this challenges by introducing AC DC parallel tie line with coordinated operation of super conducting magnetic energy storage(SMES) and thyristor controlled series compensator(TCSC). This type of method is analysed with taking thermal thermal two area power system and thermal hydro power system in MATLAB platform. Simulation results clearly validates that this methodology in AGC improve system performance.

In the category of small signal stability problem, one of the major causes of unpredictability of power transfer in the inter-connected power system is due to low frequency oscillations (LFO). These oscillations in the inter-connected power system, affects the power transfer capability and thereby, needed to be suppressed. To damp the oscillation and protect the power system, power system stabilizers are used. In this study, a Kho-Kho optimization (KKO) based proportional integral derivative (PID) power system stabilizer is put forward which can suppress the LFO in the inter-connected power system. Optimization methods are widely being used to solve number of optimization problem. Therefore, in this work the specification of the stabilizer are calibrated using KKO technique by optimizing a objective function constructed using the Integral time square error (ITSE) of the speed deviation. To test the effectiveness of the designed optimal controller, a one machine infinite bus (OMIB) with IEEE type DC-1 exciter unit is also taken into consideration. The superiority of the proposed technique is examined by comparing the obtained results with some other existing techniques.

This review article puts forward the phenomena of chaotic oscillation in electrical power systems. The aim is to present some short summaries written by distinguished researchers in the field of chaotic oscillation in power systems. The reviewed papers are classified according to the phenomena that cause the chaotic oscillations in electrical power systems. Modern electrical power systems are evolving day by day from small networks toward large-scale grids. Electrical power systems are constituted of multiple inter-linked together elements, such as synchronous generators, transformers, transmission lines, linear and nonlinear loads, and many other devices. Most of these components are inherently nonlinear in nature rendering the whole electrical power system as a complex nonlinear network. Nonlinear systems can evolve very complex dynamics such as static and dynamic bifurcations and may also behave chaotically. Chaos in electrical power systems is very unwanted as it can drive system bus voltage to instability and can lead to voltage collapse and ultimately cause a general blackout.

A number of techniques have been proposed to dampen the power system oscillations in the electric power systems. Flexible alternating current transmission system (FACTS) devices are becoming one of them. Among the FACTS family, the static synchronous compensator (STATCOM), a shunt connected FACTS device, has been widely used to provide smooth and rapid steady state, limit transient voltage, and improve the power system stability and performance by absorbing or injecting reactive power. However, the influence ability depends on its placement, control signal, and place of receiving-signal in the network. In order to satisfy these issues, this paper proposes a method for optimal setting and signal position of the STATCOM into the multi-machine power systems with the aim for damping the electromechanical oscillations. This method is developed from the energy approach based on Gramian matrices considering multiple tasks on the Lyapunov equation, in which the observability Gramian matrix is used to seek an optimal location for STATCOM placement. The another is the controllability one used to determine the best local input signal placement that is chosen as a feedback signal for the power oscillation damping (POD) of STATCOM. In addition, the Krylov-based model reduction method is introduced to shorten the calculation time. The proposed method has been verified on the IEEE 24-bus system by analyzing the small-signal stability to search several feasible placements, and then the transient stability is analyzed to compare and determine an optimal placement through testing various cases. The obtained result is also compared with other optimal method.

The paper explores the influence of power system loads on static VAr compensator (SVC) damping effectiveness. Equipped with properly tuned additional stabiliser SVCs have been successfully used for improving the damping of electromechanical oscillations in power system. The damping improvement is usually achieved through indirect voltage influence through voltage dependent loads. Tuning of additional compensator stabiliser (ACS) traditionally assumes that loads are statically voltage dependent. However load parameters are generally uncertain and loads often exhibit some dynamic response. This uncertain dynamic behaviour of loads can introduce an additional phase shift between voltage and load response and such can detune the ACS. Example of robust tuning of ACS to compensate influence of load dynamics and uncertainty is presented and discussed.

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

This paper presents a dynamic model of the equivalent Great Britain (GB) 36-zone power system, which can be used for reliable and realistic assessment of emerging load frequency control mechanisms. Flexible architecture of the presented dynamic test system permits a broad range of security of supply and small-signal stability studies for design of future power grids. It can be particularly useful for academic research, but also for undertaking feasibility studies in power industries. The proposed dynamic test system, which is obtained through network reduction of the original full-scale GB transmission power system developed by National Grid Electricity System Operator (NGESO) Company, provides detailed information about the GB power system. In this regard, the required data and modelling approaches to develop the 36-zone system are provided in detail. The presented dynamic test system represents the system topology, impedance characteristics and electromechanical oscillations of the original GB power system however, it is not an exact equivalent of the master GB system. Illustrative dynamic models of the key system components, including synchronous generators, automatic voltage regulators, power system stabilizers, hydro and steam turbines models along with speed governing systems are presented. Dynamic behavior of 36-zone test system in response to infeed loss contingencies is investigated. Particularly, the impact of changes in the system inertia on the system electromechanical modes is examined using the modal analysis approach. In this context, the mode shape concept is employed to determine dominant generators and contribution of different zones in the low frequency oscillations. Moreover, time-domain simulations are undertaken to validate the modal analysis results. Additionally, the condition of different zones from the viewpoint of frequency nadir and maximum rate of change of frequency for various contingencies and extreme cases are examined.

Model-based network-wide optimizations in real-time emergency control of power systems are largely bottlenecked by the fast computational requirements. Furthermore, the operational aspects of modern power and energy systems are going through transformative changes to tackle global renewable energy expansion-related issues. Decentralized operations in power systems are becoming essential with the influx of distributed energy resources (DERs), dynamic loads, and electric vehicles (EVs). This research considers a well-known model-based strategy, Model Predictive Control (MPC), for real-time operation and controls of power systems and proposes novel methods utilizing machine learning, data-driven methods, and operator theory to solve computational challenges by making those real-time for the first time. Additionally, we develop a distributed MPC framework to address the recent needs of power systems' operation and control. In Part I, we first present a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. This method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times. Next, we present an alternative data-learned linear Koopman embedding approach for model predictive emergency voltage control in a power grid. This approach involves a "basis-dictionary free" method to lift the nonlinear power system dynamics to a higher dimensional linear space where MPC is exercised in a real-time fashion. We propose a novel data-driven encoder-decoder architecture, Koopman-inspired deep neural network (KDNN), which supports end-to-end learning from trajectory data. The MPC optimization in linear space makes the problem scalable and rapid for real-time implementations. Part II of this research focuses on distributed MPC design for quasi-static power flow-based voltage control problems under short-term (15-minute) variations of load and generation profiles. To realize the proposed distributed control, first, a centralized voltage control problem assuming grid observability is formulated and next transferred to the distributed versions based on both bus-wise and area-wise decomposition of the network. Distributed versions are solved via the alternating direction method of multipliers (ADMM), utilizing only neighbor-to-neighbor communications to attain optimal solutions. The implementation, efficacy, performance, and robustness of proposed research approaches are validated with IEEE test system benchmarks.

O presente trabalho propõe a aplicação de técnicas de controle robusto para o desenvolvimento de uma metodologia sistemática de projeto de controladores multiobjetivo para o amortecimento de oscilações eletromecânicas em sistemas elétricos de potência. A técnica de controle adotada considera as incertezas referentes às variações das condições de operação do sistema elétrico de potência. A imposição de um limite superior para a energia da saída do sistema em malha fechada e o posicionamento regional de pólos são utilizados como objetivos de projeto na metodologia proposta. O limite superior para a energia da saída é proposto como índice de desempenho no projeto de controlador de amortecimento para sistemas de potência. A metodologia de projeto é estruturada de forma a gerar controladores que atendam a vários requisitos práticos do problema de oscilações eletromecânicas em sistemas elétricos de potência. Os resultados obtidos demonstram a eficiência dos controladores projetados pela metodologia proposta.

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.

Guest Editorial: Situational awareness of integrated energy systems

Power system is one of the largest man-made systems, which over the years has become the vital infrastructure for modern life. The foundation of today’s power system is three-phase alternating current (AC), which is adopted as the main stream technology for its advantages in voltage transforming, wide-area networking and long-distance power transmitting. The AC power system is a self-sustained oscillatory system operating at nominal frequency, i.e., 50 or 60 Hz. However, the multi-time-scale dynamics in a power system, mechanical, electrical or their coupled are prone to cause the exchange of energy at wide range of frequencies other than the nominal one, which is collectively known as the oscillation issues in a power system. Such oscillations would lead to power quality degradation, equipment failure, or even worse, system instability. Actually, the development history of power system is also a history of continuous “battle” against the oscillation problems. This article reviews the basic concepts and classification of the classical as well as emerging power system oscillations. The mechanism, origin and research advances of various oscillations are re-visited and discussed, from the earlier and long-standing low frequency oscillation (LFO), and then subsynchronous oscillation/resonance (SSO/SSR) associated with turbo-generators and wind turbine generators, to the latest wide-band oscillations involving the sophisticated dynamics of increasing renewables and power electronics in a power system. LFO incident was reported for the first time in 1964 in American Western Interconnection, where the formation of a larger power system by combining two smaller one, i.e., Northwest and Southwest systems, caused power oscillation at about 0.1 Hz. Later studies showed that the multiple machines with mechanical inertial but electrically connected through networks function like a mass-spring system, leading to LFOs at a frequency ranging from 0.01 to 2.5 Hz, depending on the size of the system. Initially, excitation control was improved by adding the function of power system stabilizer (PSS) to mitigate LFO. Later, advanced control, including linear optimal control, nonlinear control and optimal decentralized coordinated control, were developed to solve the problem. Since 2000, the continuous increase of system size and coverage causes very-low-frequency oscillation issues, to address which, several new technologies, for instance, wide-area monitoring and control, flexible AC transmission system (FACTS) control, has been commissioned in China and around the world. SSR/SSO is induced by the interactions between the multi-mass shaft of a turbo-generator and the series-compensated AC or HVDC transmissions. The frequency of power oscillation is subsynchronous, or less than the nominal frequency but generally above the 2.5 Hz. The first SSR incident was captured at Mohave Power generating Station in 1970. In China, however, SSR/SSO has been a severe stability concern since 2000, the time witnessing the widely usage of series compensation and HVDC to transfer electricity from remote coal-fueled power plants. After more than ten years’ continuous research, we have developed effective tools and countermeasures, for instance, multi-dimensional analysis, optimized control and coordinated protection, to solve practical SSR/SSO problems in China and some other countries. The integration of wind power introduces new interactive dynamics that involve wind turbine converters and AC grids. It is called sub-/super-synchronous control interaction (SSCI), which would result in a different type of oscillation from several Hz to double nominal frequency. Such sub/super-synchronous oscillations associated with wind power has entered people’s vision since 2009 and gained widespread attention after the occurrence of several actual incidents in US and China. To address this new issue, we have developed novel frequency-domain modeling and analysis methods to quantify the risk first, and then control strategies on either generator/converters side or grid side to modify the interaction to stabilize SSCI. These methods and strategies have been put into practice, for instance in Guyuan and Hami systems in China, to verify their effectiveness. At this moment, power system is undergoing profound changes, one of which is the rapid growth of renewables and power electronics in all sections, including generation, transmission and consumption of electricity. Such a change is triggering power system oscillations in a very wide band, i.e., from several Hz to several thousand Hz. The underlying reason is the multi-time-scale dynamic interactions among the huge number of converters and the increasingly complicated power grid. This leads to open challenges that are yet to be solved, including the need of: (1) Wide-spectrum models to represent the multi-time-scale dynamics; (2) advanced analysis methods to quantify the stability of wide-band oscillations; and (3) adaptive and coordinated controls to mitigate the oscillation under varying system conditions. In a nutshell, systematic and in-depth researches are required to establish new theories and methodologies for this emerging issue so as to promote the sustainable growth of renewable energy and the low-carbon development of power system.