Pseudo-loadflow formulation as a starting process for the Newton Raphson

This paper introduces new models which approximate the AC loadflow problem, but are able to converge (using the Newton Raphson algorithm) from a wider range of starting points. The solution of the pseudo-loadflow models can provide a robust starting process for the Newton Raphson solution of the conventional loadflow problem. It is also shown that pseudo-loadflow solutions exist in many cases where the AC loadflow equations do not appear to have any solution, and in such cases the pseudo-loadflow solution can provide useful information to assist in locating the cause of infeasibility of the AC loadflow model. Test results are presented for illustrative small network examples and also for larger test networks. The computational requirements of the proposed methods are similar to those of the conventional Newton Raphson loadflow algorithm

Robust state estimation using mixed integer programming

This letter describes a robust state estimator based on the solution of a mixed integer program. A tolerance range is associated with each measurement and an estimate is chosen to maximize the number of estimated measurements that remain within tolerance (or equivalently minimize the number of measurements out of tolerance). Some small-scale examples are given which suggest that this approach is robust in the presence of gross errors, is not susceptible to leverage points, and can solve some pathological cases that have previously caused problems for robust estimation algorithms

Robust algorithm for generalized state estimation

This letter introduces a robust generalized state estimator which is able to detect and reject gross measurement errors, parameter errors, and topology errors simultaneously. The solution is based on finding a consistent estimate which minimizes the total number of hypothesized gross errors. The problem is formulated as a mixed integer nonlinear program. A small-scale ac estimation example is given which illustrates some of the properties of the method

NETMAT: A knowledge-based grid system analysis tool

The increasing expansion of electric power systems renders the power system operator's task increasingly complex. The integration into energy management systems of further analytical algorithms implies that more data has to be analysed by the control engineer. For these reasons and many others, more sophisticated tools are required by power engineers to ease the pressure under which they perform their task. The advent of knowledge-based systems has led to a new approach to the problem. The combination of expert systems and numerical algorithms can be advantageously exploited to assist the power system engineer in operating the system. This paper presents the development of a knowledge-based tool for grid system analysis. The tool, NETMAT (NETwork Modelling AssistanT) , is to be used to analyse the impact of grid system maintenance and modification procedures and of new generating plants on power utilities, and in particular on their ability to generate and sell electricity. NE TMAT consists of a number of numerical applications interfaced to an expert system shell through specific problem domain knowledge bases. Results are presented based on the use of the IEEE-30 busbar network as a test network

A comparative study of two methods for uncertainty analysis in power system state estimation

This letter presents a comparative study between twomethods for estimating the uncertainty interval in power system state estimation. Constrained nonlinear and linear formulations are proposed to estimate the tightest possible upper and lower bounds on the states. The study compares the performance of these methods in terms of estimating the bounds of the uncertainty interval. In addition,an assessment of time performance for both methods is carried out with varying measurement redundancy levels

Uncertainty modelling in power system state estimation

A method for uncertainty analysis in power system state estimation is proposed. The two-step method uses static weighted least-squares analysis to compute 'point' state estimates. Linear programming is then employed to obtain the upper and lower bounds of the uncertainty interval. It is shown that the method can provide useful additional information for both metered and nonmetered elements of the system. The effects of network parameter errors are also studied. For illustrative purposed, the proposed method is tested using the six-bus and IEEE 30-bus standard systems. Results show that the proposed method is an accurate and reliable tool for estimating the uncertainty bounds in power system state estimation

Transmission loss allocation through a modified Ybus

A methodology to allocate the active power transmission loss among agents of a power pool is proposed. The approach is based on the inclusion of the admittances equivalent to bus power injections in the bus admittance matrix. For a given power-flow solution, the relationship between the branch currents and the load/generator current injections is determined using a modified bus admittance matrix, which allows the power loss of each transmission line to be expressed in terms of bus current injections. The proposed technique is simple to implement and flexible enough to allow the assignment of loss parcels to a preselected set of buses. An example, with a six-bus system illustrates the main steps of the proposed allocation strategy, and numerical results obtained with the IEEE 57-bus system are used to assess the quality of the loss allocation

A new approach to secure economic power dispatch

This article presents a new nonlinear convex network flow programming model and algorithm for solving the on-line economic power dispatch with N and N−1 security. Based on the load flow equations, a new nonlinear convex network flow model for secure economic power dispatch is set up and then transformed into a quadratic programming model, in which the search direction in the space of the flow variables is to be solved. The concept of maximum basis in a network flow graph was introduced so that the constrained quadratic programming model was changed into an unconstrained quadratic programming model which was then solved by the reduced gradient method. The proposed model and its algorithm were examined numerically with an IEEE 30-bus test system on an ALPHA 400 Model 610 machine. Satisfactory results were obtaine

The SIMIAN architecture-an object-orientated framework for integrated power system modelling, analysis and control

This paper details the work conducted by the Brunel Institute of Power Systems, UK, into an object orientated framework for power systems modelling, analysis and control. Based around a central OODBMS (object orientated database management system), the architecture provides a framework for the construction of analysis and control applications and the sharing of calculated or real-time data between the applications. Although the paper details the architecture only in so far as its applicability to two applications, the framework is designed such that further applications, either client output (such as control applications) or input(such as SCADA systems) may easily be added to the basic structure. To illustrate the architecture, a load flow simulation application is presented, along with the strategy for incorporating other applications. The mechanism by which these `applications' interact with the OODBMS and core structure of the architecture is illustrate