Fast transient stability assessment using corrective PEBS method

This paper introduces a stability control scheme based on a Lyapunov direct method, the potential energy boundary surface (PEBS) method, and analytical sensitivity of the transient energy margin. It classifies the stability control means into two categories, admittance-based control (ABC) and generator input-based control (GIBC), and uses a comprehensive method to analyze the contribution of each control. The scheme can get the optimal control from all the available control means by sensitivity analysis and then verify it in the transient stability program. Fast and accurate control goal is obtained from this stability control scheme.

In this paper, a transient energy function for AC/DC power systems is used to evaluate critical clearing times with consideration of the DC dynamics. The generator angular speeds, rotor angles and voltages at the HVDC link's AC buses are explicit variables in the transient energy function. A method for computing the transient energy function numerically is proposed. In the method, the angular speeds and rotor angles deemed as slow dynamics are integrated along sustained fault trajectories, and the integral path of the DC dynamics treated as fast dynamics are acquired by some modifications. The problems in applying potential energy boundary surface (PEBS) method to the computation of critical clearing time are also discussed. The critical clearing times for 4-generator and 10-generator AC/DC systems obtained using the transient energy function approximate well to those with the step-by-step time simulation.

Power system transient stability margin estimation using neural networks

The Transient Energy Function (TEF) method has been intensely investigated over the last decade as a reliable and accurate tool for transient stability assessment of multimachine power systems. In this paper we propose the Potential Energy Boundary Surface (PEBS) based method for direct analysis of transient stability. The critical value of transient energy and the Critical Clearing Time (TCCT) are evaluated without the need for successive calculation and memorization of the system kinetic and potential energy values along the sustained fault trajectory. The method is used for determination of output features in the training phase of the neural‐net based transient security assessment. The proposed approach is tested on the well‐known ten‐generator New England test system demonstrating the validity and accuracy of obtained results.

The virtual rotor angle stability of interconnected multi‐inverter power grids with virtual synchronous generator (VSG) model is studied under large disturbance. According to the VSG regulation scheme, its electromechanical dynamic model is derived. A Lyapunov like energy function candidate of the power system with VSG‐controlled inverters is constructed to adopt the transient energy function method. After system large disturbances, the critical clearing times are obtained through potential energy boundary surface method and also verified through time‐domain simulations. Furthermore, the impacts of virtual control parameters on system behaviours are also discussed and demonstrated through simulations.

Just as in static security constrained optimization of power systems, there is need to do preventive scheduling if the system state is found to be insecure through dynamic contingency analysis. Current work in this area based on energy functions and sensitivity considerations only computes another feasible and normal operating point either through the controlling u.e.p or the potential energy boundary surface method. In this paper we use the hybrid method which involves integration of the system equations only once till the controlling u.e.p is reached to compute the energy margin. Based on the sensitivity of the energy margin to generation powers, rescheduling is done through an OPF algorithm resulting in a secure but optimal solution. Results on a 3 machine, 9 bus system are very encouraging. This methodology could form a basis for integrating current OPF packages into the DSA package.

The nearest neighbor algorithm (NNA) has been applied for calculating the critical clearing time (CCT) of a 4-machine 11 bus system under variations in load, fault location and network structure. The CCT is one significant factor for the operator to set the protective relays and circuit breakers to maintain the transient stability of the power systems during the large disturbances, e.g. three-phase-to-ground faults. In this paper, Sobol sequences (Sob) were applied to the selection of the training patterns of the CCT. The results when compared with time domain simulation methods show less mean absolute errors than using a pseudo random choice of inputs. The nearest neighbor algorithm has been successfully applied for approximating the CCT of the system under those variations.

An analytic approximation for the critical clearing time (CCT) metric is derived from direct methods for power system stability. The formula has been designed to incorporate as many features of transient stability analysis as possible such as different fault locations and different post-fault network states. The purpose of this metric is to analyze trends in stability (in terms of CCT) of power systems under the variation of a system parameter. The performance of this metric to measure stability trends is demonstrated on an aggregated power network, the so-called two machine infinite bus network, by varying load parameters in the full bus admittance matrix using numerical continuation. The metric is compared to two other expressions for the CCT which incorporate additional nonlinearities present in the model.

This paper presents the use of the multi-agent technology in order to enhance the transient stability of power systems by the dynamic evaluation of the critical clearing time (CCT) including the effect of the distance of fault locations and the impact of the renewable energy source (RES) integration. A multi-agent framework is developed in this research, where the intelligent agents use an algorithm for coordinating the protection devices to isolate the faulted part from the rest of the system before the corresponding CCT, so as to increase the stability margin of the system for the transient stability assessment. Simulations are carried out on a multimachine test system for different fault locations and the stability of the system is observed from the simulation of the machine's relative rotor angles.

Real-time transient stability detection in the power system with high penetration of DFIG-based wind farms using transient energy function

In order to analyze the transient stability of grid-connected squirrel cage induction generators (SCIG) in wind power generating systems, various mathematical models, including the detailed and reduced generator models as well as the one-mass and two-mass shaft system models, are studied in this paper. Based on the different wind turbine system models, the dynamic behaviors are simulated and compared by using Matlab/Simulink, under the condition that the generator stator terminal is subjected to a three-phase short-circuit fault. In addition, the critical clearing time (CCT) is calculated and compared by using a direct method with critical clearing slip of induction generators and a method of trial and error by simulation, for different system models at the various operation conditions. The models and methods suitable to analyze the transient stability of the wind turbine systems based on SCIG are discussed in detail. Several comparative results have shown that the valid transient stability model of SCIG in wind turbine systems has to incorporate the two-mass shaft system model, the direct method of CCT estimation based on critical clearing slip is incompatible to determine the transient stability limit, owing to the large multi-oscillation of the generator rotor speed during the fault period.

Topological energy-function analysis of stability of power systems

As the electric power system grows in size and complexity with a large number of interconnections, the assessment of the transient stability of power systems became an extremely intricate and highly non‐linear problem. Its solution needs either numerical methods involving bulk computations or specific dedicated methods to analyse dynamic non‐linear systems. Either method mostly assesses, particularly in the post‐fault condition, the critical clearing time (CCT). This parameter constitutes very complex functional relationships between the pre‐fault condition, type, and location of fault beside the clearance sequence. The available methods for evaluating such parameter had been previously reviewed. New approaches using the locally‐tuned radial basis function (RBF) network, an artificial neural network (ANN) paradigm have been recently proposed. The goal of this paper is to develop methods that can combine both neural networks and genetic algorithms (GA) into a common framework, and apply them to prediction problems. In the paper, the application of genetic algorithms in selecting the input patterns for the RBF network is proposed. Description of this combined approach and the results of its application to two power systems, one for four‐machine six‐bus system and the other for an existing system of North Sumatra. Indonesia, are also given in the paper. The attainable results show that the performance of the RBF network can be maintained and improved in spite of less features in the input patterns.

Dynamic stability is one of the important aspects of maintaining the stability of an electrical power system as a whole. Dynamic stability study is the ability of the electric power system to return to the equilibrium point after a relatively small disturbance suddenly occurs for a long time. In this paper, we offer a method of rescheduling the generator to improve system stability by looking at the critical clearing time (CCT). Changes in the CCT value are due to changes in the load on each bus. The modelling of the IEEE 14 bus system is carried out with the help of the Simulink Power System Analysis Toolbox (PSAT) 2.1.10, which is integrated with the MATLAB R. 2016a program. The simulation results show that the CCT value decreases as the fault location gets closer to the main generator.

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.