Transient Stability Simulation Research Articles

A Grouping and Aggregation Modeling Method of Induction Motors for Transient Voltage Stability Analysis

Induction motors are the most common type of motor in power systems, constituting approximately 70–90% of the dynamic loads, making them significant contributors to system dynamics. In transient voltage stability analysis, dynamic equivalent models are commonly used to simplify the representation of a group of induction motors. This paper presents a method for the grouping and aggregation of induction motors at a common bus. Firstly, grouping rules are provided for clustering induction motors into several subgroups based on the mechanical principles of rotor force and motion, and aggregation rules are provided for aggregating a motor subgroup into a single-unit model based on the relationship between voltage drop and power transmission in distribution networks. Secondly, guided by the grouping rules, high-speed remaining electromagnetic torque and low-speed remaining electromagnetic torque are defined as new clustering indicators, and an adaptive K-means clustering method using silhouette coefficient verification is introduced to obtain the optimal motor subgroups. Thirdly, guided by the aggregation rules, a dynamic equivalent method is further introduced to obtain the equivalent single-unit model from a motor subgroup. Lastly, a transient voltage stability simulation in a typical distribution network is presented to illustrate that the proposed clustering and equivalent methods are more reasonable, accurate, and effective than traditional methods, as the obtained model has better dynamic characteristics and can more accurately reproduce the process of voltage collapse.

Grid impact analysis on wind power plant interconnection in strengthening electricity systems

The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.

Positive-Sequence Modeling of Droop-Controlled Grid-Forming Inverters for Transient Stability Simulation of Transmission Systems

Positive-Sequence Modeling of Droop-Controlled Grid-Forming Inverters for Transient Stability Simulation of Transmission Systems

Techno-economic Analysis of New Wind Power Plant Development in Southern Sulawesi Electrical Power System

Global warming due to carbon emissions has resulted in climate change, which negatively impacts the lives of living things. On the other hand, electricity demand is increasing every year due to the increasing human population, economic growth and equipment electrification. At the moment, most of the electricity generation in Indonesia uses fossil energy power plants which produce carbon emissions. PT. PLN (Persero) is a State-Owned Enterprise tasked with managing the electricity system in Indonesia. PT. PLN (Persero) through the General Plan for Electricity Supply (RUPTL) for 2021-2030 is targeting the construction of New and Renewable Energy Power Plants. One of possible plan is the Wind Power Plant (WPP) construction in the Southern Sulawesi electrical power system. The use of WPP is expected to reduce carbon emissions. However, the integration of WPP causes other problems in the existing system such as dropped voltage, increased equipment loading, increased short circuit current and decreased stability. So, it is necessary to conduct a study on the effects of integrating WPP into the existing system. Using DIgSILENT Power Factory software, Load Flow, Short Circuit and Transient Stability simulations were carried out. Then an economic analysis was carried out including calculating net present value (NPV), internal rate of return (IRR), profitability index (PI), payback period (PBP) and levelized cost of energy (LCOE. The use of WPP at a certain capacity will not disrupt the stability of the system's power because it is still supported by the fast response generators in the system during variable WPP output. Also voltage drop, equipment loading and short circuit current will not exceed the design value of the system. From financial aspect, with certain assumptions, WPP development can be proven to be economically feasible.

GPU-based transient analysis of modern grids deploying a hybrid DDM algorithm

Solving a significant number of differential-algebraic equations is essential for conducting transient stability simulations on a complex and interconnected electrical power system. Because of the increasing size and intricacy of modern grids, traditional sequential simulation methods are becoming more computationally challenging and time-consuming to use for dynamic simulations. This research aims to examine parallelism techniques from an innovative algorithm viewpoint to the hardware level by utilizing multiple graphics processing units as the main computational tool designed to simulate the dynamic behaviour of power systems on a large scale. Our approach features a novel hybrid algorithm to improve the dynamic simulations of grids with significant renewable energy integration. The algorithm employs a Schwarz-based approach in its initial decomposition phase to isolate synchronous machines and renewable sources from the grid. This separation enhances the algorithm's ability to manage these components effectively. Subsequently, each subsystem undergoes further division into subdomains in the second stage, with separate computations carried out using the Schur-complement technique. The simulations were conducted on various test systems, with a maximum of 25,000 buses, 8,000 synchronous generators, and 256 PV farms all modelled in detail. We introduce a GPU-oriented preprocessing and vectorization parallelization method for the implementation of our two-stage algorithm. This approach provides the flexibility to tailor parallelization techniques, including nested for loops and kernel pragmas, to craft a hardware solution that enhances the performance of our dynamic domain decomposition algorithm. Simulation outcomes have verified that this approach can yield a notable 7.8x acceleration in speed when executed on an NVIDIA GeForce RTX 2070 SUPER GPU.

Fast Power System Transient Stability Simulation

Power system transient stability simulation is of critical importance for utilities to assess dynamic security. Most of the commercially available tools use the traditional numerical integration method to simulate power system transient stability, which is computationally intensive and has low simulation speed. This makes it difficult to identify any insecure contingency before it happens. It is already proven that power system transient stability simulation achieved using the differential transformation method (DTM) requires less computational effort and has improved simulation speed, but it still requires further improvement regarding its accuracy and performance efficiency. This paper introduces a novel power system transient stability simulation method based on the adaptive step-size differential transformation method. Using the proposed method, the step size is varied based on the estimated local solution error at each time step. The accuracy and speed of the proposed simulation approach are investigated in comparison with the classical differential transformation method and the traditional numerical integration method using the IEEE 9 bus and 39 bus test systems. The simulation results reveal that the proposed method increases the simulation speed by 20–44.57% and 83–92% when compared with the classical DTM and traditional numerical-integration-based simulation methods, respectively. It is also proved that compared with the DTM-based simulation, the proposed method provides 45.27% to 58.85% and more than 90% accurate simulation results for IEEE 9 and IEEE 39 test systems, respectively. Therefore the proposed power system transient stability simulation method is faster and relatively more accurate and can be applied for online transient stability monitoring of power system networks.

Co-simulation of real-time electromagnetic transient and transient stability simulations using dynamic phasor T-Line model

This paper presents techniques to interface a Transient Stability Analysis (TSA) based model to an ElectroMagnetic Transient (EMT) based model running on a Real Time Digital Simulator. Practical challenges of implementing such an interface are discussed. The proposed TSA model can be simulated using a substantially larger time-step compared to the EMT model. The interface between the EMT model and the TSA model is implemented using a transmission line modeled using Dynamic Phasors (DP). Time-step delay is the primary cause of numerical instability in co-simulations. In this work, a traveling wave model of a transmission line is used to decouple the EMT and TSA networks. The interface requires that the propagation delay of the interfaced transmission lines be greater than the EMT time-step even though the TSA is simulated using a much larger time-step than the EMT model. The proposed technique is validated using the IEEE 39 bus system and a power system with 500 buses.

Massively Parallel Modeling of Battery Energy Storage Systems for AC/DC Grid High-Performance Transient Simulation

Extensive integration of power electronics apparatuses complicates the modern power grid and consequently necessitates time-domain transients study for its planning and operation. In this work, a heterogeneous computing architecture utilizing the CPU and graphics processing unit (GPU) is proposed for the efficient study of interactions between a power grid network and massive utility-scale battery energy storage systems (BESSs). The device-level electromagnetic transient (EMT) simulation aiming at enhanced fidelity of the BESS is conducted simultaneously with electro-mechanical transient stability (TS) simulation which suffices system-level dynamic security assessment. Since the reservation of a large amount of energy storage units is computationally intensive for the CPU, the concurrent multi-streaming, multi-threading capability of GPU is exploited to achieve asynchronous sequential-parallel processing, so that the proposed EMT-TS co-simulation can flexibly harness all available computing resources. The multi-rate scheme is adopted for further computational burden alleviation in addition to achieving timely information exchange. It shows that the heterogeneous computation of an IEEE 118-bus system integrated with a substantial number of distributed batteries becomes feasible following the achievement of a remarkable speedup of over 200, and the device- as well as system-level accuracy are validated by MATLAB/Simulink^® and DSATools^TM/TSAT simulation, respectively.

Feasibility Study of Neural ODE and DAE Modules for Power System Dynamic Component Modeling

In the context of high penetration of renewables, the need to build dynamic models of power system components based on accessible measurement data has become urgent. To address this challenge, firstly, a neural ordinary differential equations (ODE) module and a neural differential-algebraic equations (DAE) module are proposed to form a data-driven modeling framework that accurately captures components' dynamic characteristics and flexibly adapts to various interface settings. Secondly, analytical models and data-driven models learned by the neural ODE and DAE modules are integrated together and simulated simultaneously using unified transient stability simulation methods. Finally, the neural ODE and DAE modules are implemented with Python and made public on GitHub. Using the portal measurements, three simple but representative cases of excitation controller modeling, photovoltaic power plant modeling, and equivalent load modeling of a regional power network are carried out in the IEEE-39 system and 2383wp system. Neural dynamic model-integrated simulations are compared with the original model-based ones to verify the feasibility and potentiality of the proposed neural ODE and DAE modules.

Robustly Cooperative Control of Transient Stability for Power System Considering Wind Power and Load Uncertainty by Distribution Preserving Graph Representation Learning (DPG)

Aiming at the influence of wind power and load uncertainty on the transient stability of a power system under low carbon mode, this paper first proposes a collaborative preventive and emergency control model of transient stability by distribution preserving graph representation learning (DPG). Second, the uncertainty set of wind power output and load demand is studied, and the mathematical form of the two-stage robust transient stability collaborative control model is proposed. Then, the latest artificial intelligence technology is embedded into the global optimization algorithm of the model so as to further improve the solving efficiency of the algorithm. Finally, based on the developed improved two-stage robust optimization framework, an effective collaborative control method for transient stability is developed. The transient stability prediction and control system developed in this project is not only conducive to large-scale wind power grid connection but also expected to make academic contributions to development of power system transient stability and practical simulation verification.

Voltage Stability Analysis of Power System with Photovoltaic Power Plant

Photovoltaic power plants usually do not provide reactive power output; hence the application of large photovoltaics in power systems will decrease the voltage stability level of the power system. Capacitor banks can provide reactive power to compensate the photovoltaic plants; therefore, capacitor banks can overcome the reactive power deficiency of photovoltaic plants. However, the effect of capacitor bank installation on the system’s voltage stability is unknown. Therefore, the research aims to investigate whether installing a capacitors bank can restore the level of system voltage stability. The study employs the method of Voltage Stability Margin and transient stability simulation to the IEEE 9 bus system. The IEEE 9 bus system is modified where one generator of the system is replaced with a photovoltaic plant, and a capacitor bank is also installed. The study results show that the modified system voltage stability level is lower than the original system. When the capacity of the capacitor bank is increased to the maximum allowable value, the voltage stability level rises. However, it is still unable to be restored to its original value.

Knowledge-enhanced deep reinforcement learning for intelligent event-based load shedding

Event-based load shedding (ELS) is an important emergency countermeasure against transient voltage instability in power systems. At present, the formulation of ELS measures is usually determined offline by the experience of experts, which is inefficient, time-consuming, and labor-intensive. This paper proposes a knowledge-enhanced deep reinforcement learning (DRL) method for intelligent ELS. Firstly, the Markov decision process (MDP) of the knowledge-enhanced DRL model for ELS is established based on transient stability simulation. Different from traditional response-based MDP, this MDP is event-based. Then, compared to conventional exponential decision space, a linear decision space of the DRL agent is established to reduce the decision space and training difficulty. Furthermore, the knowledge of removing repeated and negative actions is fused into DRL to improve training efficiency and decision quality. Finally, the simulation results of the CEPRI 36-bus system show that the proposed method can accurately give effective ELS measures. Compared with the pure data-driven DRL method, the knowledge-enhanced DRL method is more efficient.

Faster-Than-Real-Time Hardware Emulation of Extensive Contingencies for Dynamic Security Analysis of Large-Scale Integrated AC/DC Grid

The rapid expansion of modern power systems has brought a tremendous computational challenge to dynamic security analysis (DSA) tools which consequently need to process extensive contingencies. In this work, hardware emulation is investigated to accelerate the DSA solution of a large-scale AC/DC system deployed on the field-programmable gate arrays (FPGAs) faster-than-real-time (FTRT) execution. Electromagnetic transient (EMT) modeling of the DC grid is conducted since the fast converter dynamics require a small time-step for accuracy; in contrast, the transient stability (TS) simulation is applicable to the AC grid which tolerates a much larger step size. To coordinate the 2 different types of simulation, an interface based on dynamic voltage injection is proposed to integrate the AC and DC grids, in addition to maintaining a low hardware latency. An emulation platform consisting of multiple FPGA boards is established so that with a proper allocation it has a sufficient capacity to accommodate the system under study which has 6 ACTIVSg 500-bus systems interconnected by a 6-terminal DC grid. The efficacy of the proposed FTRT hardware emulation platform is demonstrated by 2 case studies with more than 5500 contingencies analyzed in total, where an FTRT ratio of more than 208 is achieved for the hybrid AC/DC grid, while it is over 277 times for a single 500-bus system. Furthermore, the FTRT dynamic emulation results, including the security indices, are validated by the simulation tool DSATools/TSAT.

Hybrid Parallel-in-Time-and-Space Transient Stability Simulation of Large-Scale AC/DC Grids

The increasing complexity of modern AC/DC power systems poses a significant challenge to a fast solution of large-scale transient stability simulation problems. This paper proposes the hybrid parallel-in-time-and-space (PiT+PiS) transient simulation on the CPU-GPU platform to thoroughly exploit the parallelism from time and spatial perspectives, thereby fully utilizing parallel processing hardware. The respective electromechanical and electromagnetic aspects of the AC and DC grids demand a combination of transient stability (TS) simulation and electromagnetic transient (EMT) simulation to reflect both system-level and equipment-level transients. The TS simulation is performed on GPUs in the co-simulation, while the Parareal parallel-in-time (PiT) scheduling and EMT simulation are conducted on CPUs. Therefore, the heterogeneous CPU-GPU scheme can utilize asynchronous computing features to offset the data transfer latency between different processors. Higher scalability and extensibility than GPU-only dynamic parallelism design is achieved by utilizing concurrent GPU streams for coarse-grid and fine-grid computation. A synthetic AC/DC grid based on IEEE-118 Bus and CIGRÉ DCS2 systems showed a good accuracy compared to commercial TSAT software, and a speedup of 165 is achieved with 48 IEEE-118 Bus systems and 192 201-Level detail-modeled MMCs. Furthermore, the proposed method is also applicable to multi-GPU implementation where it demonstrates decent efficacy.

A risk-based machine learning approach for probabilistic transient stability enhancement incorporating wind generation

ABSTRACT Power systems are becoming more complex than ever and are consequently operating close to their limit of stability. Considering its significance in power system security, it is important to propose a novel approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables. Moreover, the time-domain simulation approach for transient stability evaluation can be very computationally intensive, especially for a large-scale system. The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this paper proposes a risk-based, machine learning decision-making approach, for probabilistic transient stability enhancement, by replacing circuit breakers, including the impact of wind generation. The IEEE 14-bus test system was used to test and validate the effectiveness of the proposed approach. DIgSILENT PowerFactory and MATLAB were utilised for transient stability simulations (for obtaining training data for machine learning), and applying machine learning algorithms, respectively.

Simulation of Transient Stability using Model of Non-utility Generator for Large Consumer's Power Systems on a Real Power System

Simulation of Transient Stability using Model of Non-utility Generator for Large Consumer's Power Systems on a Real Power System

Real-Time Transient Instability Detection in the Power System With High DFIG-Wind Turbine Penetration via Transient Energy

By increasing the penetration of wind power in the modern power systems, to investigate transient stability is of special importance. This article presents an innovative index based on the concept of the potential energy boundary surface along with kinetic energy, which aims to detect the transient instability of a power system comprising doubly fed induction generator (DFIG)-based wind farms. Accordingly, transient instability detection (TID) is performed without directly calculating the unstable equilibrium point and thus the computational load is decreased. Since the proposed approach requires only the postfault data that can be easily measured by the phasor measurement units, it is suitable for real-time applications. Therefore, the proposed approach can be applied as a general tool to any power system with any change in topology and operating conditions. Moreover, considering that the current-balance form is the preferred industry model for the implementation of transient stability simulation, the network equations in the current-balance form are extracted for grid-connected DFIG and synchronous generators. Different scenarios are simulated in the Western System Coordinating Council 3-machine, 9-bus system, and the 10-generator New England system. To validate the new index for TID, simulation results are compared with the transient stability index and out-of-step distance relay. The obtained results validate the correctness and effectiveness of the presented new index.

Development of a method for calculating the transmission‐line constants of cables installed in a rectangular tunnel

AbstractAccurately calculating the transmission‐line constants of underground cables is important for various types of power system simulations such as electromagnetic transient, power flow, and transient stability simulations. The transmission line constants of cables installed in a tunnel must be calculated taking the skin and proximity effects into account, since the cables are spaced closely to each other and also to a wall face of the tunnel. However, the conventional method, which is capable of considering the skin and proximity effects, has been restricted to circular cross‐section conductors such as cables and a circular tunnel. Therefore, the method cannot handle a tunnel with a rectangular cross section. In this paper, a method for calculating the transmission‐line constants of a conductor system in which both circular and rectangular cross‐section conductors present in the same space is proposed. Then, the walls of a rectangular tunnel are represented by eight rectangular conductors, and cables represented by circular conductors are placed inside the eight rectangular conductors. In this paper, the proposed method is verified by experiments using a conductor system in which three circular conductors are arranged in a rectangular conductor. Calculated results obtained by the proposed method agree well with the measured results.

Machine Learning Based Transient Stability Emulation and Dynamic System Equivalencing of Large-Scale AC-DC Grids for Faster-Than-Real-Time Digital Twin

Modern power systems have been expanding significantly including the integration of high voltage direct current (HVDC) systems, bringing a tremendous computational challenge to transient stability simulation for dynamic security assessment (DSA). In this work, a practical method for energy control center with the machine learning (ML) based synchronous generator model (SGM) and dynamic equivalent model (DEM) is proposed to reduce the computational burden of the traditional transient stability (TS) simulation. The proposed ML-based models are deployed on the field programmable gate arrays (FPGAs) for faster-than-real-time (FTRT) digital twin hardware emulation of the real power system. The Gated Recurrent Unit (GRU) algorithm is adopted to train the SGM and DEM, where the training and testing datasets are obtained from the off-line simulation tool DSAToolsTM/TSAT®. A test system containing 15 ACTIVSg 500-bus systems interconnected by a 15-terminal DC grid is established for validating the accuracy of the proposed FTRT digital twin emulation platform. Due to the complexity of emulating large-scale AC-DC grid, multiple FPGA boards are applied, and a proper interface strategy is also proposed for data synchronization. As a result, the efficacy of the hardware emulation is demonstrated by two case studies, where an FTRT ratio of more than 684 is achieved by applying the GRU-SGM, while it reaches over 208 times for hybrid computational-ML based digital twin of AC-DC grid.

An Efficient Rapid Method for Generators Coherency Identification in Large Power Systems

With steadily increasing interest in enhancing large power systems’ transient stability, generator coherency identification has become critical for the dynamic equivalents, controlled-islanding, and wide-area control of these systems. This paper presents an approach based on two classical and powerful techniques. The proposed method comprises the slow coherency method followed by the time-domain-based simulation of transient stability to identify the coherent groups of generators. In this regard, various operating conditions of the system are considered to obtain the updated coherency information between groups of generators by analyzing the chosen generator rotor angle. The proposed approach’s merits are tested on the New England IEEE 39-Bus and modified IEEE 118-Bus test systems in the PowerFactory software tools through Python. Corresponding simulation results validate the proposed paradigm’s effectiveness by enhancing the transient stability speed of a large power system without decreasing its coherency behavior accuracy. It is also observed that the proposed scheme tends to be more consistent in determining the coherent groups of generators in the presence of disturbances and different operational conditions.