Power System Voltage Stability Assessment Research Articles

Static Voltage Stability Assessment Using a Random UnderSampling Bagging BP Method

The increase in demand and generator reaching reactive power limits may operate the power system in stressed conditions leading to voltage instability. Thus, the voltage stability assessment is essential for estimating the loadability margin of the power system. The grid operators urgently need a voltage stability assessment (VSA) method with high accuracy, fast response speed, and good scalability. The static VSA problem is defined as a regression problem. Moreover, an artificial neural network is constructed for online assessment of the regression problem. Firstly, the training sample set is obtained through scene simulation, power flow calculation, and local voltage stability index calculation; then, the class imbalance problem of the training samples is solved by the random under-sampling bagging (RUSBagging) method. Then, the mapping relationship between each feature and voltage stability is obtained by an artificial neural network. Finally, taking the modified IEEE39 node system as an example, by setting up four groups of methods for comparison, it is verified that the proposed method has a relatively ideal modeling speed and high accuracy, and can meet the requirements of power system voltage stability assessment.

Recurrent network based power flow solution for voltage stability assessment and improvement with distributed energy sources

The increasing penetration of alternative energy sources into the integrated energy systems often influences the voltage stability (VS) of the entire system. However, developing a technique that comprehensively analyze the energy flow in this environment is a major challenge. This paper presents a novel heuristic-based recurrent type Hopfield Neural Network (h-HNN) planning tool for VS assessment of power system; towards reducing the computational cost of conventional power flow (PF) method. The proposed approach is a Jacobian-less, energy function-based approach, which was formulated using power residuals of the system. The dynamics of neural networks were governed by the differential equations of energy function, which would be minimized by the heuristic particle swarm optimization-gravitational search algorithm to deduce the unknown parameters of voltage magnitude and phase angle. The proposed technique was coded in MATLAB and its effectiveness was tested on IEEE 14-, 30-, and 57- buses, as well as a 1354-bus test system. The obtained results were compared with well-known PF techniques, and the robustness was demonstrated for ill-conditioned network. A composite severity index was proposed to rank the critical contingency of the energy network. Then, the VS assessment was performed in IEEE 14-bus system under severe contingency conditions and improvement of VS is observed under the penetration of distributed energy sources (DES). During the case of DES placement, (i) the voltage profile of the system is maintained within the acceptable range of 0.95 to 1.05 pu and (ii) the VS of the system evaluated using stability indices are enhanced by an amount of 16.54 % to 88.16 %. The application results indicate that the proposed method is useful for electric energy utilities to assess the state of the system under monitoring process.

A super-resolution perception-based incremental learning approach for power system voltage stability assessment with missing PMU measurements

This paper develops a fully data-driven, missing-data tolerant method for post-fault short-term voltage stability (STVS) assessment of power system against the incomplete PMU measurements. The super-resolution perception (SRP), based on deep residual learning convolutional neural network, is employed to cope with the missing PMU measurements. The incremental broad learning (BL) is used to rapidly update the model to maintain and enhance the online application performance. Different from state-of-the-art methods, the proposed method is fully data-driven and can fill up missing data under any PMU placement information loss and network topology change scenario. Simulation results demonstrate that the proposed method has the best performance in terms of STVS assessment accuracy and missing-data tolerance among the existing methods on the benchmark testing system.

Effect of ambient temperature variations on estimation of proximity of the voltage collapse point

Due to deforestation, greenhouse gases and increasing air conditioner systems, the temperatures are changing drastically and the effects are prominent in summer and winter seasons. These ambient temperature variations are affecting the transmission system parameters and also the voltages at various buses. Changes in the transmission line parameters have non-negligible impact on system performance and present Energy Management applications are not considering these temperature variations in the power system state estimation. In this paper, impact of temperature variations on the power system Voltage Stability assessment is considered. Voltage stability assessment methods make use of the load flow results. Load flow results without considering the temperature variations are inaccurate in the real-time and leading to the erroneous results in the voltage stability assessment. In view of this, studies have been carried out on IEEE 30-bus, 118-bus and 300-bus systems, incorporating the seasonal temperature variations on various types of conductors, and also demonstrated the impact with conventional and ZIP type load models. In this paper, four voltage stability indices NLVSI, L m n , FVSI and VCPI(power) are used for identifying the proximity of voltage collapse point, critical line, critical bus voltage, critical bus angle, total system active power loss, total system reactive power loss etc.

Decision tree-based classification of multiple operating conditions for power system voltage stability assessment

This paper presents a method that performs classification of thousands of operating conditions w.r.t. power system voltage stability by using decision trees. The proposed method uses a new and flexible classification criterion that allows to identify operating conditions that are near or within the region for which the system is voltage unstable, and more importantly, that can consider operational requirements. The method creates both training and test data sets when building and validating the decision trees. To minimize computational burden, a sampling method is proposed, this method exploits the Saddle Node Bifurcation conditions to explore the operational space used to train the decision trees. Case studies were performed using the IEEE 9-bus system for several operating conditions and different network configurations. This paper also proposes the use of time domain simulations to assess the prediction accuracy of decision trees. Decision trees were created for network configurations involving outage of the line were tested on test sets and also using time domain simulations results from PSS/E. The ability to classify the degree of voltage stability of a multitude of operation conditions could be useful to aid operators in selecting and applying preventive measures to steer away the system from unstable conditions or conditions that are close to breaching operational requirements w.r.t. voltage stability.

Impact of local PMU‐based equivalent methods on real‐time voltage stability assessment

This study promotes a critical discussion about the phasor measurement unit (PMU)-based equivalent methods in order to investigate the impact of the Thevenin equivalent (TE) parameters on real-time power system voltage stability assessment. Although several articles indicate good performance at impedance magnitude matching theory, they neglect the influence of TE impedance angle and voltage source magnitude, introducing inaccuracies or mathematical inconsistencies in stability evaluation. The former three parameters are highlighted in this study due to being directly used to compute active power margins and indexes, according to the maximum power transfer theorem. The approach identifies the methods’ robustness and weaknesses by the use of generic expressions. The main contribution of this study is presenting equations for directly calculating the parameters that may lead to inconsistent interpretations, particularly when measurement noise is considered. Disregarding these inconsistencies may result in an incorrect determination of the system's critical limit, increasing the risk of voltage instability. The conception of the new formulation relies on network equivalents based on the least-squares method, Tellegen's theorem and adaptive method. Following the simulations carried out in Nordic Test System, and 9, 14 and 39 Bus Test Cases available in MATPOWER, analyses substantiated in the derived expressions are given.

Power System Voltage Stability Assessment Using a Hybrid Approach Combining Dragonfly Optimization Algorithm and Support Vector Regression

In this paper, an efficient approach based on the combination of dragonfly optimization (DFO) algorithm and support vector regression (SVR) has been proposed for online voltage stability assessment. As the performance of the SVR model extremely depends on careful selection of its parameters, the DFO algorithm involves SVR parameters setting, which significantly ameliorates their performance. In the proposed approach, the voltage magnitudes of the phasor measurement unit (PMU) buses are adopted as the input data for the hybrid DFO–SVR model, while the minimum values of voltage stability index (VSI) are taken as the output vector. Using the data provided by PMUs as the input variables makes the proposed model capable of assessing the voltage stability in a real-time manner, which helps the operators to adopt the required measures to avert large blackouts. The predictive ability of the proposed hybrid model was investigated and compared with the adaptive neuro-fuzzy inference system (ANFIS) through the IEEE 30-bus and the Algerian 59-bus systems. According to the obtained results, the proposed DFO–SVR model can successfully predict the VSI. Moreover, it provides a better performance than the ANFIS model.

Power System Voltage Stability Assessment Based on Branch Active Powers

We prove that the active power through a branch (referred to as branch active power) in a power system is a continuous function of the bifurcation parameter in the closed interval from an initial parameter value to the bifurcation-point value of a saddle-node bifurcation (SNB) or limit-induced bifurcation (LIB) of the power flow equation (PFE). Then we show that generally there is a sequence of branch active powers meeting maxima when the parameters are equal to intermediate values in the closed interval. These results can be used to qualitatively evaluate and classify the state of power system operation conditions in terms of voltage stability. Also, they well explain the limitation of the direct current (DC) power flow methods on stressed power systems. Numerical simulations of the IEEE 118-bus and 57-bus systems are used to illustrate the studies and their applications.

Analiza wpływu źródeł fotowoltaicznych na stabilność napięciową systemu elektroenergetycznego

The paper presents methods of modeling complex load model with photovoltaic sources for analysing the power system voltage stability. The survey was supplemented with examples of analysis of voltage stability using described models of complex load. In the study, the stability margin was determined using P-V curves determined by means of the Power Factory program. The obtained results indicate that the modeling of complex load with photovoltaic cells has a significant impact on the assessment of the power system voltage stability (Analysis of the impact of photovoltaic generation source on power system voltage stability).

Power System Voltage Stability Assessment through Artificial Neural Network

Voltage stability is concerned with the ability of a power system to maintain acceptable voltages at all buses in the system under normal conditions and after being subjected to a disturbance. This paper presents a new technique to determine the static voltage stability of load buses in a power system for a certain operating condition and hence identifies load buses which are close to voltage collapse. A voltage stability index with respect to a load bus is formulated from the voltage equation derived from a two bus network and it is computed using Thevenin equivalent circuit of the power system referred to a load bus.. Buses with values of voltage stability factors close to 1 .0 are identified as the critical buses. And after that, an Artificial Neural Network is developed for voltage stability monitoring. In ANN applications, selection of input variables is an important aspect

A probabilistic nodal loading model and worst case solutions for electric power system voltage stability assessment

In this paper, a new nodal loading model for use in voltage stability assessment of electric power systems is proposed, and the formulation of worst cases based on this model, as well as related numerical methods, are described. In this nodal loading model, called the "hyper-cone" model, a set of future operating points in a load parameter space is modeled. That is, the "vertex" of the hyper-cone is taken to be the current operating point, and the "thickness" of the hyper-cone represents the uncertainty of future loading. The worst loading case is the point, among the set of transfer limit points on or within the hyper-cone, at which the total load is smallest. In other words, in terms of the uncertainty of future loading, the worst case corresponds to the most conservative transfer limit. Efficient numerical methods to compute this worst case are shown, and these methods are demonstrated on some sample power systems including IEEE 118-node system.

Power System Voltage Stability Assessment Using Enhanced Power Flow Models

This paper presents an enhanced method for power system voltage stability assessment. Key features of this method are the inclusion of the characteristics of key dynamic devices and an efficient method to solve the associated algebraic and differential equation sets in steady-state conditions. It offers an accurate representation of equipment operating characteristics for voltage stability margin calculation. The importance of the proposed modeling approach is demonstrated with simulation results.

Modelling of stalling motors during voltage stability studies

This paper investigates the effect of induction motor stalling on power system voltage stability assessment. A new time-scale decomposition is suggested when a single motor stalls. This allows a better security margin assessment in cases where motor stalling has no serious consequences on system operation. Case studies on a two motor system and a 34-bus system demonstrate the applicability of the proposed approach.

Application of artificial neural networks in voltage stability assessment

Voltage stability problems have been one of the major concerns for electric utilities as a result of system heavy loading. This paper reports on an investigation on the application of ANNs in voltage stability assessment. A multilayer feedforward artificial neural network (ANN) with error backpropagation learning is proposed for calculation of voltage stability margins (VSM). Based on the energy method, a direct mapping relation between power system loading conditions and the VSMs is set up via the ANN. A systematic method for selecting the ANN's input variables was developed using sensitivity analysis. The effects of ANN's training pattern sensitivity problems were also studied by dividing system operating conditions into several loading levels based on sensitivity analysis. Extensive testing of the proposed ANN-based approach indicate its viability for power system voltage stability assessment. Simulation results on five test systems are reported in the paper.

95/00507 Power system voltage stability and security assessment

95/00507 Power system voltage stability and security assessment

Power system voltage stability and security assessment

The purpose of this investigation is to review the recent research progress in voltage stability and security assessment of power systems. This paper presents the fundamental concepts of voltage stability, gives a short recall of the classical voltage theory and different criteria currently available for predicting voltage collapse problems in power systems, which have been associated with multiple power flow solutions, bifurcation, dynamics of tap changers, stochastic loads, singular values, etc. The various methods for studying voltage collapse are compared. The application of expert system techniques to assist in the decision-making process of the reactive power/voltage control problem of power systems and future research directions towards voltage stability and security assessment will also be proposed in this paper.

Improved techniques for power system voltage stability assessment using energy methods

An improved method of assessing power system voltage stability using energy techniques is presented. The concept of an energy function providing a localized measure of voltage security in a particular portion of the system is developed. A crucial factor in the use of the energy function method is the ability to rapidly determine the appropriate low-voltage solution to use in the energy measure calculation. Also, an improved method of locating power alternative solutions with low associated energy measures is presented. Techniques are demonstrated on the IEEE 118 bus system and a 415-bus system. >