Stability Evaluation Of Power Systems Research Articles

Research on Fault Diagnosis and Transient Stability Evaluation of Power System Based on Machine Learning

Research on Fault Diagnosis and Transient Stability Evaluation of Power System Based on Machine Learning

Probabilistic Small Signal Stability Evaluation of Power Systems with High Penetration of Wind Farms

Wind farms are increasingly installed in power systems. Such power generation units increase stochastic power system variables, affecting power systems stability. Therefore, the impact of stochastic generation on power systems stability should be carefully taken into account, an aim which can be realized by probabilistic evaluation. This paper presents hybrid methods for the evaluation of probabilistic small-signal stability (PSSS) in power systems. These methods are based on clustering approaches and Monte Carlo simulation (MCS) which is employed in a probabilistic problem to achieve acceptable results. There are two steps for evaluation of PSSS in the hybrid methods. Clustering methods divide stochastic sets into small sets; the member of small sets is employed to calculate eigenvalues similar to MCS. Consequently, this method is faster than MCS. Two case studies are employed based on the IEEE 9-bus and 39-bus test systems for the evaluation of the proposed method. It is shown that the proposed methods yield accurate results and are faster than MCS.

Mobile Superconducting Magnetic Energy Storage for On-Site Estimations of Electric Power System Stability

Renewable energy and electric power liberalization have become important watchwords for present electric power systems. However, the stability of electric power systems is also a serious concern. Superconducting magnetic energy storage (SMES) systems are a promising candidate for the on-line evaluation of power system stability. From the viewpoint of the mobility of SMES systems, the authors carried out a design study on a 1 MJ-class mobile SMES system using MgB 2 Rutherford cables. The results showed that 1) the 1 MJ-class mobile SMES components can be installed in a 40 feet dry container; 2) because of the effect of the force-balanced coil design, the SMES coil can be excited up to 2.0 T or 3.0 T without reinforcements for the MgB 2 Rutherford cables; and 3) the 1 MJ-class mobile SMES coil can be cooled using 3 or 4 sets of conventional cryocoolers, including the cooling system for a 80 K thermal shield at a cooling temperature of 20 K or 10K. Therefore, the engineering feasibility of the 1 MJ-class mobile SMES system using MgB 2 Rutherford cables is confirmed.

Model predictive control scheme for coordinated voltage control of power systems at the presence of volatile wind power generation

This study proposes an optimal voltage control scheme to deal with long-term voltage stability of power systems. The control of voltage is accomplished by model predictive control (MPC) scheme. The control objective function considers the control efforts and the difference between the predicted and reference voltages. Also, this study considers the detailed non-linear dynamic model of the system including doubly-fed induction generator wind turbines, over excitation limiter and under-load tap changer, which are important elements, should be considered in voltage stability evaluation of power systems. The proposed approach composed of the following two major stages at each time instance: first, the power system non-linear dynamic equations are linearized and optimal control laws are obtained by MPC technique;in the second stage, the system dynamic behavior is investigated via time-domain simulations by applying the attained optimal control signals at the first step. The proposed MPC-based voltage control scheme is implemented on a well-known test system, under variable wind speed and fault conditions. Also, this method's performance is compared with state feedback control technique. The obtained numerical results validate the capability of the proposed control scheme to preserve voltage stability at the presence of stochastic wind speed variations and severe disturbances.

The Way of Modelling New Generation Sources and its Effect on Power System Stability Evaluation Parameters

The effect of the method and accuracy of modelling automatic control systems of generating units on parameters of the power system stability evaluation has been investigated. The evaluation parameters are the following: values of the critical short-circuit time t kr , and corresponding to them values of the stability margin k t ,, and the regulation times t 50% and t 15% . The obtained results make an attempt at establishing the effect of the way generating units are modelled on the conclusions following from the power system stability analyses.

Fuzzy neural network based voltage stability evaluation of power systems with SVC

Voltage stability has become of major concern for the power utilities. In this paper, multi input, single output fuzzy neural network is developed for voltage stability evaluation of the power systems with SVC by calculating the loadability margin. Uncertainties of real and reactive loads, real and reactive generations, bus voltages and SVC parameters are taken into account. All ac limits are considered. In the first stage, Kohonen self-organizing map is developed to cluster the real and reactive loads at all the buses to reduce the input features, thus limiting the size of the network and reducing computational burden. In the second stage, combination of different non-linear membership functions is proposed to transform the input variables into fuzzy domains. Then a three-layered feed forward neural network with fuzzy input variables is developed to evaluate the loadability margin. The proposed methodology is applied to IEEE-30 bus and IEEE-118 bus systems.

Voltage stability evaluation of power system with FACTS devices using fuzzy neural network

Voltage stability has been a major concern for power system utilities because of several events of voltage collapses in the recent past. With the developments of flexible ac transmission system (FACTS) devices, power system performance has improved. This paper proposes an approach based on fuzzy neural network to calculate loadability margin of the power system with static synchronous compensator (STATCOM). A multi-input, single output fuzzy neural network is developed. Kohonen self-organizing map is employed to cluster the real and reactive loads at all the buses to reduce the input features, thus limiting the size of the network and reducing computational burden. Uncertainties of real and reactive loads, real and reactive generations, bus voltages and STATCOM parameters are taken into account by transforming them into fuzzy domains using combination of different nonlinear membership functions. A three-layered feed-forward neural network with fuzzy input variables is developed to evaluate the loadability margin. All ac limits are considered. The proposed methodology is applied to IEEE-30 bus and IEEE-118 bus systems. The proposed methodology is fast and accurate as compared to the conventional techniques. This method can also be used for online calculation of the voltage stability of the large power systems.

Evaluation of Power System Stability by use of Superconducting Magnetic Energy Storage (SMES)

Evaluation of Power System Stability by use of Superconducting Magnetic Energy Storage (SMES)

Generic properties, one-parameter deformations, and the BCU method [power system stability

This paper discusses the theoretical justification of the BCU method for the evaluation of power system stability. Recent literature has suggested that the method can be mathematically justified in terms of a one-parameter deformation between two systems. This paper shows that the assumptions of this method do not hold generically for power system models. In particular, the one-parameter transversality conditions inherent in the deformation argument do not apply for a large region in parameter space, thus, the validity of the BCU method cannot be theoretically justified by this procedure.

仮説推論の枠組みを用いた電力系統安定性診断に関する学習

仮説推論の枠組みを用いた電力系統安定性診断に関する学習

Transient stability evaluation by a pattern recognition approach using multivalent logics

The purpose of the paper is to define a transient stability index which can provide a measure for the level of security of a power system. The values of this index are associated with the values of a semantic truth degree showing the confidence that is placed on the accuracy of the index results. Its construction procedure is based on the principles of pattern recognition and fuzzy sets theory. The results that can be obtained from the application of this index in the transient stability evaluation of power systems are illustrated by the analysis of a sample transmission system.

Direct stability evaluation of power systems with detailed generator models using structure-preserving energy functions

Energy-based direct methods for transient stability analysis are potentially useful both as offline tools for planning purposes as well as for online security assessment. In this paper, a novel structure-preserving energy function (SPEF) is developed using the philosophy of structure-preserving model for the system and detailed generator model including flux decay, transient saliency, automatic voltage regulator (AVR), exciter and damper winding. A simpler and yet general expression for the SPEF is also derived which can simplify the computation of the energy function. The system equations and the energy function are derived using the centre-of-inertia (COI) formulation and the system loads are modelled as arbitrary functions of the respective bus voltages. Application of the proposed SPEF to transient stability evaluation of power systems is illustrated with numerical examples.

A Novel Structure Preserving Energy Function for Direct Stability Evaluation of Power Systems with Known Modes of Instability

ABSTRACT A new structure preserving energy function (SPEF) incorporating mode of instability is presented alongwith application for direct transient stability evaluation of power systems. It is assumed that a fault at the terminals of a generator splits the system into two groups, each group having one or more machines in it and that the machines within a group swing together. The kinetic energy term of the proposed energy function depends on the relative velocity of these two groups and accurately estimates the component of the energy responsible for system separation. Structure preserving model of the system is used and the loads are modelled as arbitrary functions of respective bus voltage. The application to direct stability analysis is provided with the help of a 7-machine and a 10-machine system examples.

Topological energy-function analysis of stability of power systems

In this paper the direct stability evaluation of power systems, with voltage-dependent loads, using topological energy function is given with applications. This is based on the philosophy of a structure-preserving model for power systems. The loads are modelled as arbitrary functions of respectively bus voltages. A classical model is assumed for generators and system equations are formed using centre of angle (COA) reference. A topological energy function is developed and applied for the stability evaluation using the potential-energy boundary surface (PEBS) method. Examples of a 4-machine and a 7-machine system are considered for illustration. The critical clearing time (T cr ) is obtained both by digital simulation and by the direct method for various load characteristics and for different fault locations.