Showing papers in "IEEE Transactions on Power Systems in 1992"

Voltage Stability Evaluation Using Modal Analysis

Abstract: The authors discuss the voltage stability analysis of large power systems by using a modal analysis technique. The method computes, using a steady-state system model, a specified number of the smallest eigenvalues and the associated eigenvectors of a reduced Jacobian matrix. The eigenvalues, each of which is associated with a mode of voltage/reactive power variation, provide a relative measure of proximity to voltage instability. The eigenvectors are used to describe the mode shape and to provide information about the network elements and generators which participate in each mode. A simultaneous iteration method, which is well suited to applications involving large power systems, is used for selective calculation of appropriate eigenvalues. Results obtained using a 3700 bus test system are presented illustrating the applicability of the approach. >

Understanding automatic generation control

Abstract: The authors describe what automatic generation control (AGC) might be expected to do, and what may not be possible or expedient for it to do. The purposes and objectives of AGC are limited by physical elements involved in the process and, hence, the relevant characteristics of these elements are described. For reasons given, it is desired that AGC act slowly and deliberately over tens of seconds or a few minutes. From a perspective of utility operations, there is no particular economic or control purpose served by speeding up the AGC action. >

Implementation of genetic algorithm for distribution systems loss minimum re-configuration

Abstract: The loss minimum reconfiguration problem in the open loop radial distribution system is basically one of complex combinatorial optimization, since the normal open sectionalizing switches must be determined appropriately. The genetic algorithm was successfully applied to the loss minimum reconfiguration problem. In the proposed algorithm, strings consist of sectionalizing switch status or radial configurations, and the fitness function consists of the total system losses and penalty value of voltage drop and current capacity violations. The loss minimum reconfiguration problem is formulated as a mixed integer programming problem. The essential components of the genetic algorithm are briefly described. A detailed solution methodology by the use of genetic algorithm is outlined. Numerical examples demonstrate the validity and effectiveness of the proposed methodology. >

Short-term load forecasting using an artificial neural network

Abstract: An artificial neural network (ANN) method is applied to forecast the short-term load for a large power system. The load has two distinct patterns: weekday and weekend-day patterns. The weekend-day pattern includes Saturday, Sunday, and Monday loads. A nonlinear load model is proposed and several structures of an ANN for short-term load forecasting were tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one or two hidden layers was tested with various combinations of neurons, and results are compared in terms of forecasting error. The neural network, when grouped into different load patterns, gives a good load forecast. >

A toolbox for power system dynamics and control engineering education and research

Abstract: The design concept and use of the power system toolbox (PST), a Matlab-based power system dynamics simulation and control design package, are discussed. The motivation for developing the package was to provide a flexible environment for teaching power system simulation techniques and control design concepts to advanced undergraduate and graduate students, and for graduate students to perform research and development on power systems. The authors discuss the capabilities of PST and the software development philosophy. Sample applications are given. Some potential educational usage is suggested. The future enhancement to the package is outlined. >

Fast calculation of a voltage stability index

Abstract: The minimum singular value of the power flow Jacobian matrix has been used as a static voltage stability index, indicating the distance between the studied operating point and the steady-state voltage stability limit. A fast method to calculate the minimum singular value and the corresponding (left and right) singular vectors is presented. The main advantages of the algorithm are the small amount of computation time needed, and that it only requires information available from an ordinary program for power flow calculations. The proposed method fully utilizes the sparsity of the power flow Jacobian matrix and the memory requirements for the computation are low. These advantages are preserved when applied to various submatrices of the Jacobian matrix. The algorithm was applied to small test systems and to a large system with over 1000 nodes, with satisfactory results. >

A zoom feature for a dynamic programming solution to economic dispatch including transmission losses

Abstract: An iterative dynamic programming method for solving the economic dispatch problems of a system of thermal generating units including transmission line losses is presented along with a clear explanation of modifying generator cost functions during each iteration A zoom feature is applied during the iterative process in order to converge to the economic dispatch solution with low computer time and storage requirements, Dynamic programming including a short-term load forecast is briefly discussed A three-generator example is used to illustrate the method Computer memory and time requirements are presented, along with results for a 15-unit system >

Advancement in the application of neural networks for short-term load forecasting

Abstract: An improved neural network approach to produce short-term electric load forecasts is proposed. A strategy suitable for selecting the training cases for the neural network is presented. This strategy has the advantage of circumventing the problem of holidays and drastic changes in weather patterns, which make the most recent observations unlikely candidates for training the network. In addition, an improved neural network algorithm is proposed. This algorithm includes a combination of linear and nonlinear terms which map past load and temperature inputs to the load forecast output. The search strategy and algorithm demonstrate improved accuracy over other methods when tested using two years of utility data. In addition to reporting the summary statistics of average and standard deviation of absolute percentage error, an alternate method using a cumulative distribution plot for presenting load forecasting results is demonstrated. >

An adaptive method for setting underfrequency load shedding relays

Abstract: An adaptive scheme is proposed for determining the amount of load to be shed by underfrequency relays based on the observed initial slope of the frequency deviation. following the system separation. This initial slope contains all the information required to estimate that size of the step change in load caused by the system separation and to forecast the depth and even the approximate time of the maximum frequency dip. From this observation, the first step of load shedding can be set up adaptively, with additional steps to follow at prescribed intervals and amounts. The first step is considered the most important and is selected arbitrarily at one-half the static load shed target, with additional increments of about 0.1 per unit to be shed at 0.3 Hz increments until the dynamic load shed amount has been reached. >

Short term load forecasting using a multilayer neural network with an adaptive learning algorithm

Abstract: A multilayer feedforward neural network is proposed for short-term load forecasting. To speed up the training process, a learning algorithm for the adaptive training of neural networks is presented. The effectiveness of the neural network with the proposed adaptive learning algorithm is demonstrated by short-term load forecasting of the Taiwan power system. It is found that, once trained by the proposed learning algorithm, the neural network can yield the desired hourly load forecast efficiently and accurately. The proposed adaptive learning algorithm converges much faster than the conventional backpropagation-momentum learning method. >

A solution method of unit commitment by artificial neural networks

Abstract: The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging. >

Voltage stability conditions considering load characteristics

Abstract: It is shown that when the characteristic of the composite load of a typical utility system is taken into account, large disturbance voltage stability is assured by the existence of the stable equilibrium state of the post-disturbance system, as determined from the standard power flow model. When the load contains static components, stability limits extend considerably. The exact limit for such loads can also be determined from a power flow model, properly modified to reflect the static component of the load. In specific situations, where the bulk of the load is composed of fast response loads, a correct assessment of voltage stability would require comprehensive analyses, employing detailed dynamic models of all system components. The use of the conventional power flow model may lead to considerable error. >

Weather sensitive short-term load forecasting using nonfully connected artificial neural network

Abstract: The authors present an artificial neural network (ANN) model for forecasting weather-sensitive loads The proposed model is capable of forecasting the hourly loads for an entire week The model is not fully connected; hence, it has a shorter training time than the fully connected ANN The proposed model can differentiate between the weekday loads and the weekend loads The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model This ANN model has been implemented on real load data The average percentage peak error for the test cases was 112% >

Point of collapse methods applied to AC/DC power systems

Abstract: The authors describe an extension of the point of collapse method developed for studies of AC systems to the determination of saddle-node bifurcations in power systems including high voltage direct current (HVDC) transmission. Bus voltage profiles are illustrated for an AC/DC test system. They significantly differ from the profiles of pure AC systems for typical system models. In particular, voltage dependent current order limits are shown to affect the voltage profiles and the loadability margin of the system. It is also shown that Hopf bifurcations, which are possible in purely AC lossless systems with second-order generator models, become plausible when the dynamics for the HVDC system are included. >

Transient Stability Test Systems for Direct Stability Methods

Abstract: The aim of this paper is to present a standard set of power system data with benchmark results against which direct stability techniques to assess transient stability could be compared and tested. The test systems have been selected to display a wide range of dynamic characteristics to provide a robust test of the efficacy and accuracy of the various analytical techniques to analyze transient stability. Transient stability test system data and benchmark results obtained from two commercially available time domain stability analysis packages are presented in this paper.

Parallel processing in power systems computation

Abstract: The availability of parallel processing hardware and software presents an opportunity and a challenge to apply this new computation technology to solve power system problems. The allure of parallel processing is that this technology has the potential to be cost effectively used on computationally intense problems. The objective of this paper is to define the state of the art and identify what the authors see to be the most fertile grounds for future research in parallel processing as applied to power system computation. As always, such projections are risky in a fast changing field, but the authors hope that this paper will be useful to the researchers and practitioners in this growing area.

Control of voltage stability using sensitivity analysis

Abstract: The effects of static compensation on the voltage stability boundary are investigated. For a class of voltage instabilities which correspond to static bifurcations of load flow equations, minimum singular values of the Jacobian matrix and total generated reactive power are calculated as indicators of stability margin, and sensitivity methods are used for reactive support allocation. Improvement in stability margin under progressive loading was investigated on a 39-bus test system for different allocations and amounts of reactive support with reactive generation capabilities taken into account. >

Daily generation scheduling optimization with transmission constraints: a new class of algorithms

Abstract: The authors describe a novel class of algorithm dealing with the daily generation scheduling (DGS) problem These algorithms have been designed by adding artificial constraints to the original optimization problem; handling these artificial constraints by using a dual approach; using an augmented Lagrangian technique rather than a standard Lagrangian relaxation technique; and applying the auxiliary problem principle which can cope with the nonseparable terms introduced by the augmented Lagrangian To deal with the DGS optimization problem these algorithms are shown to be more effective than classical ones They are well suited to solve this DGS problem taking into account transmission constraints >

Transmission voltage recovery delayed by stalled air conditioner compressors

Abstract: Southern California Edison Company (SCE) has experienced delayed voltage recovery following fault clearing on the transmission system. The authors describe these occurrences and document the studies which successfully modeled the voltage recovery process. The studies showed how the stalling of single-phase air conditioner compressors following a power system fault was the root cause of delayed voltage recovery. Tests of air conditioner response to voltage dips are documented. The results of these tests are used to derive the mathematical load models used in voltage recovery studies. >

A fuzzy linear programming approach to the reactive power/voltage control problem

Abstract: The integration of traditional and heuristic techniques is considered for the reactive power/voltage control program. The steady-state reactive power problem is addressed. Fuzzy sets are used to modify the linear programming (LP) approach to voltage control and to incorporate some heuristic concepts of the expert system approach. Multiple objectives and soft constraints are modeled using fuzzy sets. Piecewise linear convex membership functions for the fuzzy sets are defined. Under this definition, the fuzzy optimization problem is reformulated as a standard linear programming problem. The objective function represents the compromise among the original competing objectives and the soft constraints. In addition, discrete constraints are considered. Numerical examples demonstrate the approach. >

A security based approach to composite power system reliability evaluation

Abstract: An electric power network containing generation and transmission facilities can be divided into several states in terms of the degree to which adequacy and security constraints are satisfied in a reliability evaluation of the composite system. The composite system is classified into different system states for which probabilistic indices are calculated. Both annualized and annual indices using a seven-step load model are presented for two test systems. Selection methods are used to detect problem-creating contingencies. A linear programming model for generation rescheduling and minimization of the amount of load shed is presented. A linear programming model for correcting the voltage problem is presented. >

Effect of superconducting magnetic energy storage on automatic generation control considering governor deadband and boiler dynamics

Abstract: A comprehensive digital computer model of a two-area interconnected power system including the governor deadband nonlinearity, steam reheat constraints, and the boiler dynamics is developed. The improvement in automatic generation control (AGC) with the addition of a small-capacity superconducting magnetic energy storage (SMES) unit is studied. Time-domain simulations were used to study the performance of the power system and control logic. Optimization of gain parameters and the stability studies were carried out by the second method of Lyapunov. Suitable methods for the control of SMES units are described. >

Estimation of parameter errors from measurement residuals in state estimation (power systems)

Abstract: Any error of network parameters affects the value of the measurement residuals calculated in state estimation. Explicit mathematical expressions relating the residuals to the parameter errors are derived. A two-step approach is proposed for parameter error estimation. The first step is to estimate a bias vector which combines the effects of parameter errors and the state of the system. A least-square approach using the measurement residuals calculated in each state estimation run is proposed for the first step. After several state estimation runs, a sequence of such bias vectors is obtained. The second step is to estimate the parameter errors from the sequence of bias vectors. A recursive least-square estimation method is proposed for this step. Theoretical and computational issues of the proposed method are addressed. Test results are presented. >

Variable-structure facts controllers for power system transient stability

Abstract: Nonlinear, variable-structure control theory is employed for series capacitor control and braking resistor control to improve the transient stability of a single machine infinite-bus (SMIB) system. A related simulation showed that variable-structure control of the series capacitor (SC) and braking resistor is effective for enhancement of power system steady-state performance and transient stability. Good transient response and high transmission limits were obtained. For the idealized control, only two switching applications are required, which suggests that the controller will be easily realized and reliable. The research reported suggests that variable-structure control can be used effectively for transient stability control of power systems. >

A robust WLAV state estimator using transformations

Abstract: The authors present a robust weighted least absolute value (WLAV) power system state estimator which remains insensitive to bad measurements even when these are associated with leverage points. Leverage points are evenly distributed in the factor space of multiple regression via linear transformations. These transformations represent a change of coordinates in the state space. The transformed system of measurement equations is then used to obtain the WLAV estimator for the system states. The transformation-based WLAV estimator is shown to remain robust in the presence of leverage points. >

Extended equal area criterion revisited (EHV power systems)

Abstract: A study was conducted on the extra high voltage (EHV) French power system in order to explore the extended equal-area criterion and test its suitability as a fast transient stability indicator. The assumptions underlying the method are reexamined, causes liable to invalidate them are identified, and indices are devised to automatically circumvent them. The selection of candidate critical machines is also reconsidered, and an augmented criterion is proposed. The various improvements were developed and tested on about 1000 stability scenarios, covering the entire 400 kV system. The severity of the scenarios, resulting from the combination of weakened pre and post-fault configurations, subjected the method to particularly stringent conditions. The simulation results are summarized. >

A decomposition approach to nonlinear multi-area generation scheduling with tie-line constraints using expert systems

Abstract: The authors propose a rigorous method to treat multiarea generation scheduling with tie line limits. An expert system was used for obtaining the initial solution. As the generation scheduling problem involves unit commitment and economic dispatch, the method adopts an iterative procedure to deal with these two phases. The hourly load demand and the area power generation will cause the tie flows to change. To maintain the operation security in every area, the spinning reserve should comply with the area power generation rather than its load demand. After economic dispatch, it is necessary to adjust the unit commitment in each area for preserving the spinning reserve requirements. Heuristics were used to modify the generation unit combinations. The objective is to find an economic generation schedule for a multiarea system. The interchange transactions among areas represent the transportation problem, embedded within the nonlinear optimization process. The equivalent system concept is adopted, and the transmission losses are included in this study. A four-area system with each area consisting of 26 units was used to test the efficiency of the proposed algorithm. >

A hybrid artificial neural network-dynamic programming approach to unit commitment

Abstract: A hybrid dynamic programming-artificial neural network algorithm is studied. The proposed two-step process uses an artificial neural network to generate a preschedule according to the input load profile. A dynamic search is then performed at those stages where the commitment states of some of the units are not certain. The experimental results indicate that the proposed algorithm can significantly reduce the execution time of the traditional dynamic programming approach without degrading the quality of the generation schedule. >

A direct Newton-Raphson economic dispatch

Abstract: The authors present a method to solve the real-time economic dispatch problem using an alternative Jacobian matrix considering system constraints. The transmission loss is approximately expressed in terms of generating powers and the generalized generation shift distribution factor. Based on this expression, a set of simultaneous equations of the Jacobian matrix are formulated and solved by the Newton-Raphson method. The proposed method eliminates the penalty factor calculation and solves the economic dispatch directly. The method resulted in a very fast solution speed and maintained good accuracy in test examples. It is a good approach to solve the economic dispatch problem. >

Discrete shunt controls in a Newton optimal power flow

Abstract: A penalty-based discretization algorithm is proposed. The algorithm consistently provides a near-optimal discrete solution for shunt controls without combinatorial search. It has been implemented in a production grade Newton optimal power flow program and tested on two actual power networks. Tests showed that the algorithm provides near-optimal discrete solutions. >