Impact of Inverter Based Resources on Critical Clearing Time

Changing the steady state parameters of power system before fault occurrence is a new approach for increasing the transient stability of the system. With the presence of PMUs in smart grid and the possession of system information at any given time, FACTS devices can be set up online so that total critical clearing time (CCT) of the network is maximized as the most accurate transient stability criterion, by changing the system's steady state variables before the fault occurrence. Calculation of CCT requires iterative fault analysis and time-domain simulation of the network. In this paper, a novel prefault online optimal FACTS tuning is presented for transient stability enhancement of smart grid by CCT maximizing. Also the optimal type, number and location of FACTS devices are determined for long term planning. FACTS devices modeling and New England 39-bus system simulation are done by DIgSILENT software. DPL is used for CCT calculation and proposed approach implementation based on PSO algorithm. Also investment cost of these devices are considered for determining the optimal number of them.

This paper presents a transient stability investigation with solar-PV generation. In this study solar-PV generation is represented by an aggregated solar-PV model, which is an efficient analytical startegy to investigate transient stability of a power system. The study was conducted using the IEEE 3-machine 9-bus test system in DIgSILENT Power Factory. In this study, two solar-photovoltaic (PV) integration scenarios are considered. First, traditional synchronous generators (SGs) are connected to the grid and then the critical clearing time (CCT) are determined after creating faults at various locations in the network. Second, the traditional SGs are replaced by the aggregated solar-PV system with similar capacity, and then CCT is calculated in order to accurately investigate and quantify the impact of the solar-PV generation. From the simulation studies, it has been found that integration of the solar-PV into the power system have a beneficial impact on transient stability, especially when the solar-PV systems are partially loaded and operated at the voltage control mode. Aggregated representation of the solar- PV generation would not be a realistic representation, as the individual solar-PV system response would not act on a single busbar as represented in this study using an aggregated model. Therefore, future studies are directed towards developing a more accurate solar-PV model to represent the aggregated effect from distributed solar-PV generation.

The growing integration of inverter-based resources (IBRs) introduces new challenges to power system stability. In this context, virtual synchronous machine control-based IBRs (VSMIBRs) are anticipated to replace some conventional synchronous generators (SGs) and reshape future power systems. In such grids, IBRs must provide stability services such as dynamic voltage recovery (DVR). Accordingly, this paper presents a comprehensive analysis of the impact of VSM-based IBR integration on DVR and load shedding amount, considering the western electricity coordinating council (WECC) voltage recovery criteria (as protection constraints) across various penetration levels. Furthermore, the influence of DC-link voltage limitations on the DVR performance of VSM-IBRs is examined, and a dedicated algorithm is proposed for calculating their spinning reserve to enhance DC-link stability. For the simulations in this study, the IBR and its controllers are accurately modeled in the DSL (dynamic simulation language) environment of DIgSILENT PowerFactory software. The IEEE 39-Bus test system is utilized for time-domain simulations to demonstrate the effectiveness of the proposed method.

In recent years, power systems have become larger and more complicated. Deregulation of electric power industries has begun in some countries, and power system operations have become more difficult. However, the stability of the power supply is the most important consideration for customers. Therefore, power system operators should consider not only economic load dispatch but also on-line stability aspects. In this paper, the authors pay attention to one of the most important transient stability indices [critical clearing time (CCT)] and propose a new transient stability preventive control method using linear relationships between CCTs and generator rotor angles. At first, CCT calculations, as contingency screenings, are carried out to find contingencies that have smaller CCTs than predefined target values. The target values are taken as larger values than the actual circuit breaker operating times in the power system. A preventive control to achieve a more stable power system operating point is carried out by generator output rescheduling and generator terminal voltage control, which are determined using the relationships between CCTs and generator rotor angles. This proposed method is demonstrated using a Japanese standard power system called the IEE of Japan (IEEJ) EAST 10 machine system.

Comparison between inductive and resistive SFCL in terms of current limitation and power system transient stability

The transient stability analysis incorporating wind power intermittency and volatility is studied in this study. The wind turbines with doubly fed induction generator (DFIG) and direct‐driven permanent magnet synchronous generator (PMSG) are considered, respectively, during analysis. In modelling of DFIG and PMSG, the default GE wind turbine model and the Western Electricity Coordinating Council generic wind turbine model are employed, respectively. Based on the Jensen model, which is applied to describe the wake effect in a wind farm located on flat terrain, an equivalent simplified model of a wind farm is established regarding wind farm layout. On the basis of the built wind farm model, the Monte Carlo simulation technique combined with two evaluation indices, namely angle‐based margin index and critical clearing time (CCT) is applied on the IEEE 10‐generator‐39‐bus test system and a real‐sized China‐Jiangxi power grid as a benchmark to exploit and explore the effects of wind power intermittency and volatility on power system transient stability. The frequency distributions of transient stability evaluation index, CCT, probability of system transient instability and the range of wind speeds causing system transient instability are simulated. In addition, the influence of wake effect on transient stability is discussed as well.

The transient stability constrained optimal power flow (TSC‐OPF) is a big challenge in the field of power systems because of its high complexity and extensive computation effort involved in its solution. This study presents a new approach to compute the transient stability constraint formulated by the critical clearing time (CCT) in TSC‐OPF. CCT has been determined by dual‐kriging, a space interpolation method which has primarily been used in natural resources evaluation. Given the huge dimensionality of the problem, Pareto analysis is firstly used to reduce the number of input variables in an initial database to those which are significant to compute CCT. With the reduced variables, a new database has been constructed using a design of experiment to obtain a reduced number of observed points. As a result of this approach, the sets of dynamic and transient stability constraints to be considered in the optimisation process are reduced to one single stability constraint with only a few variables. Ultimately, the size of the resulting optimisation problem is almost similar to that of a conventional optimal power flow. The effectiveness of the proposed method is tested on the New England 10‐machine 39‐bus system and the larger 50‐machine 145‐bus power system.

This study presents transient stability analysis to determine the value of critical clearing time (CCT) by considering the increased load and the addition of the transmission line from the 150 kV Malang interconnection system. These considerations are carried out because the Malang interconnection system is only centered on the Kebonagung substation. Therefore, the continuous increase in load will result in overcapacity at the substation. For this reason, the system must be tested by transient stability to determine the critical clearing time due to the 3-phase fault. Critical clearing time is the time the system determinant returns to its stable state or not. Determination of the CCT value can be obtained using the Runge-Kutta 4th Order method. In the calculation results, the smallest CCT value is 0.410 seconds when an error occurs on the Pakis-Infinite bus which is close to the Pakis bus with the value of critical clearing angle in the range 16.808° to 16.910° for the Wlingi generator and 46.970° to 47.183° for the Sutami generator.

Online transient stability assessment and preventive control are important measures to prevent power system blackouts. Based on the deep cross network (DCN), an online assessment and preventive control method for power system transient stability is proposed. The deep cross network is developed to estimate the critical clearing time (CCT) of the credible fault contingencies. Based on the deep cross network model, the sensitivity of the active power of the generator set to the fault critical clearing time is calculated and then the DCN model-embedded transient stability constrained optimal power flow (TSCOPF) model is proposed to compute the preventive generation re-dispatch strategy. This TSCOPF model is computed by successive linear programming assisted by the DCN. The simulation results of the IEEE 39-bus test system show that this method can give early warning to the operation mode with instability risk and generate preventive control strategies, which can effectively improve the safety level of power grid operation.

Short circuit in the power system can lead problems in term of stability system. Transient stability becomes the main focus because it can determine the critical clearing time (CCT) to find critical time before generator loss of synchronization. The Critical trajectory method can be used to determine trajectory from the disturbance occurs to just before the generator loses its synchronization. The generator to be analyzed is a critical generator or generator with the biggest energy value. This causes the number of variables to equal the number of equations and can accelerate the calculation process. The method tested on the system with single line and radial system configuration. In the test results, it is seen that this method produces the correct CCT value by using Time Domain Simulation method as a comparison.

The security-state categorization of complex power system networks based on "transient stability" is proposed in this research study as a "Convolutional Neural Network (CNN)" based pattern classification approach. The “pre- contingency operating states” of a “power system network” served as CNN’s input. The focus was on predicting the system's post-contingency stability condition, so the Critical Clearance Time (CCT) was used as the primary metric for categorizing the “pre-contingency operational states” into "secure" and "insecure" classes. The recommended method was successfully applied to the “IEEE 39-bus system”, and it was discovered that the CNN classifier can classify the power system's pre-contingency operational states based on an accurate forecast of the conditions that will lead to future post- contingency transient stability.

Transient stability has an essential role in the electric power system. When a disturbance occurs, the minimum protection relay will operate in about 0.22 seconds. However, this condition does not guarantee the system will return to steady state condition because of its Critical Clearing Time (CCT). If the fault is isolated before the value of CCT, the generator will return to stable. Otherwise, the generator becomes unstable if the fault is isolated after the value of CCT. This paper proposed the calculation of CCT on generators using an energy function method to determine the control of Unstable Equilibrium Point (CUEP) by considering unbalance fault. The result of this method has an allowable error in 0.0108% for the calculation in critical clearing time. Moreover, this method has faster calculation time than time domain simulation method with the difference in calculation time is 116.243 seconds.

In this paper, a novel approach for generation rescheduling as a preventive control for enhancing dynamic security using neural network is presented.. Critical clearing time (CCT) associated with each fault including the effect of system controllers and limitation, is adopted as dynamic security criteria. A Dynamic Security Analyzer Neural Network (DSANN) is trained to estimate CCTs associated with different system faults. For each given operating point, DSANN evaluate system CCTs by using steady state pre fault operating condition as input pattern. The most interesting feature of the proposed neural network application is evaluation of sensitivity of CCT with respect to generation pattern. These sensitivities are derived from the information stored in the weighting factors of trained DSANN. The sensitivity of CCT is used as a guideline for selecting the most effective pair of generators to reschedule their MW generation in the process of generation rescheduling aimed security enhancement. The proposed method has been demonstrated on the IEEE-39 bus system with promising results for enhancing dynamic security by generation rescheduling using sensitivity characteristic of neural network.

Nowadays, integration of large-scale wind farms (WFs) into power systems is experiencing rapid growth. As this rapid integration can affect transient stability significantly, employing doubly fed induction generator (DFIG)-based wind turbines, which have shown better behavior regarding system stability, has attracted much attention. This research contributes to the literature by investigating the transient stability of the power system with increasing penetration of DFIG-based WFs. In the proposed framework, the current-balance form is utilized for the network equations, and in this way, transient stability is performed using time-domain simulation. According to the simulation results, when the rate of wind power generation exceeds 0.7 per-unit, the increasing trend of the critical clearing time (CCT) is reversed and the CCT decreases greatly with the increased wind power penetration. In addition, the reactive power compensation by DFIG, the gearbox ratio, the power system strength, and DFIG parameters are comprehensively investigated as effective parameters on transient stability. Since the rated rotor speed of DFIG significantly impacts the electrical torque and machine currents, the reduction of the rated rotor speed due to the change of the gearbox ratio has been investigated as one of the effective factors to improve the transient stability. The simulation results demonstrate the effectiveness of the proposed approach in improving power system transient stability.

This paper presents the use of an intelligent multi-agent system (MAS) approach to investigate the effect of a sudden change in generator load and the impact of integrating renewable energy sources (RESs) on the enhancement of the transient stability of smart power grids. In this proposed approach, an algorithm is developed through which intelligent agents properly coordinate a system's protective relays using the critical clearing time (CCT) information to avoid loss of synchronism. An IEEE 14-bus test system is used to dynamically evaluate the on-line performance and effectiveness of the proposed approach. The simulation results show that the individual agents provide a powerful framework to successfully enhance the transient stability of the system for different fault locations under various generator load conditions and increasing penetration of wind power.