Voltage Collapse Margin Research Articles

Multi-objective Adaptive Evolutionary Algorithm to Enhance Voltage Stability in Power Systems

Problems with two or more conflicting objectives have been handled as needing a multi-objective approach in recent years. The solution for these types of problems is normally to satisfy the conflicting objectives simultaneously in order to find tradeoffs between different criteria. Optimization in power systems is an important example of how to tackle multi-objective problems since these systems have conflicting performance indicators and normally operate close to their constraints due to the continuous increase in demand. In this paper, a multi-objective approach is applied to the voltage stability problem in power systems by using an adaptive evolutionary algorithm. The proposed method regards examining the following stability indicators of power systems: the voltage profile, the total reactive power loss, and the voltage collapse margin as requiring a multi-objective approach. Several experiments were conducted in IEEE 14, 57, and 118 busbar systems by using the proposed method and other probabilistic and heuristic optimization methods. The results showed that the proposed adaptive evolutionary algorithm enhanced the voltage stability and outperformed the other methods, especially when the size of the power system increases.

Solving Multi-Objective Voltage Stability Constrained Power Transfer Capability Problem using Evolutionary Computation

Competitive market forces and the ever-growing load demand are two of the key issues that cause power systems to operate closer to their system stability boundaries. Open access has since introduced competition and therefore promotes inter-regional electrical power trades. However, the economic flows of electrical energy between interconnected regions are usually constrained by system physical limits, e.g. transmission lines capacity and generation active/reactive power capability etc. As such, there is a limitation to the capacity of electrical power that regions can export or import. This maximum allowable electrical power transfer is normally referred to as Total Transfer Capability (TTC). It is critical to understand that TTC does not necessarily represent a safe and reliable amount of inter-regional power transfer as one or more operational limits are usually binding when quantifying TTC. Hence, it is of interest that power system stability issues are being considered during power transfer capability assessment in order to provide a more appropriate and secure power transfer level.The aim of this paper is to formulate an Optimal Power Flow (OPF) algorithm, which is capable of evaluating inter-area power transfer capability considering mathematically-complex voltage collapse margins. Through a multi-objective optimization setup, the proposed OPF-based approach can reveal the nonlinear relationships, i.e. the pareto-optimal front, between transfer capability and voltage stability margins. The feasibility of this approach has been intensively tested on a 3-machine 9-bus and the IEEE 118-bus systems.

VOLTAGE STABILITY ASSESSMENT: EXTENDED ANALYSIS ON THE ITALIAN POWER SYSTEM

The paper presents the results of extended analysis aimed to evaluatethe voltage collapse margin in the Italian electric power system,carried out through a dedicated application developed within a jointproject between TERNA [the Italian transmission system operators(TSO)], centro elettrotecnico sperimentale italiano (CESI) and MilanPolytechnic.

An Optimal Strategy to Maximise Voltage Stability Using Memetic Algorithms Based on Swarm Trajectory Movements

Many power systems in the world today are operating closer to their stability boundaries, and thus it is critical for independent system operators (ISOs) to ensure that systems have adequate stability margins during operation in case of unexpected losses of system components. Failure to do so may result in a catastrophic widespread blackout, ie. system voltage collapses. This paper presents a novel memetic algorithm (MA)-based strategy to effectively maximise system voltage stability margins, through the optimum control of automatic voltage regulator (AVR) of generators, on-load tap changer (OLTC) of transformers and the sizes of shunt capacitors (SCs) etc, given any system operating conditions. The proposed strategy can assist ISOs to perform corrective actions to increase stability margins when the system operates too close to the stability boundaries. A mix-integer non-linear programming (MINLP) problem is formulated here using a MA based on the trajectory movement rule of particle swarm optimisation (PSO). By using the MA-based approach, system voltage collapse margins can be improved and these enhancements can then be verified using a continuation power flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus and the IEEE 118-bus power systems.

VOLTAGE COLLAPSE AND CONTROL ACTIONS: EFFECTS AND LIMITATIONS

ABSTRACT This paper focuses on control actions determination for voltage collapse prevention. Sensitivity matrices are proposed based on the knowledge of the critical bus. By critical bus one means that bus whose state variables vary at most at the voltage collapse point. A tangent vector is used to identify the critical bus, and its behavior as a function of load increase is also investigated and compared with other indices previously proposed. It is shown that generator control actions may not work to increase voltage collapse margin, whereas local control actions may produce better results. The tests are carried out in the IEEE-300 bus test system, with and without reactive power limits consideration.

Hybrid supervised and unsupervised neural network approach to voltage stability analysis

This paper presents a hybrid neural network approach to voltage stability analysis. Essentially, the proposed hybrid neural network consists of a Kohonen network and a multi-layer feed-forward network. By using voltage collapse margin method (VCM) and singular value decomposition method (SVD), the hybrid neural network is trained to identify voltage weak buses/areas and to evaluate the loadability of power systems in terms of voltage stability. Moreover, the Kohonen neural network works as a front end to cluster input patterns with similar features of operating conditions and hence the generalization capability of the multi-layer feed-forward network has been improved significantly. The effectiveness of the proposed network has been demonstrated on the IEEE 57-bus test system and the results are very encouraging.

A Novel Simplified Approach to Estimate a Closest Voltage Collapse Margin

Abstract This paper proposes a novel simplified approach to estimate a closest voltage collapse margin. The proposed approach has two stages of computations. The first stage is to compute an approximated closest voltage collapse point. This computation includes a pair of multiple load flow solutions. The left and right eigenvectors corresponding to the zero eigenvalue of load flow Jacobian are also approximated. Most of the computation time at this stage is taken to calculate the multiple load flow solution pair. Additional computation time is less than that of an ordinary load flow. The second stage is to refine the approximated solutions that are obtained in first stage, and this may be carried out only when the exact solution is needed. Equations to be solved at stage 2 is of dimension 2n+l, for n-dimensional load flow equations.

KOHONEN NEURAL NETWORK CLASSIFIER FOR VOLTAGE COLLAPSE MARGIN ESTIMATION

ABSTRACT Owing to frequent black-outs world wide, voltage collapse has received much attention in the electric utilities industry. This paper presents an artificial neural network based method for on-line voltage collapse margin estimation. Homotopy Continuation based Newton-Raphson method is used to drive system operating point to knee of nose curve. The distance of operating point from critical point, measured in terms of system loading may be regarded as margin to voltage collapse. This paper utilizes Kohonen classifier to estimate margin so that computational efforts are reduced compared to conventional methods. Also there is ample of saving in training time compared to error back propagation for parameter estimation. Kohonen neural network classifier transforms input patterns into neurons on the 2-dimensional grid. Power system conditions are assigned to neurons on the grid based on self-organized feature mapping. Finally these neurons are allocated voltage collapse margins corresponding to their system conditions representation. The effectiveness of the proposed technique is tested on sample systems.

New technique of network partitioning for voltage collapse margin calculations

Voltage collapse is associated with a stress condition of power systems. Control actions must provide the desired results, otherwise a system may operate in an unknown condition. It has been shown that this unknown condition is associated with two regions of operation and the boundary between them. The boundary between the two regions is related to a singular load-flow Jacobian. To identify the critical bus, a reduction of the load-flow Jacobian in relation to each load bus is developed. To reduce the computational burden associated with large power networks, a network partitioning is proposed which is based on voltage variation at each load bus in relation to load variation at the other load busses. Comparison between the proposed method and network partitioning using Sanchis' method is presented. The weak area of a power system is identified in the network partitioning; a weak area is defined as the area that contains the critical bus of the power network. To calculate the margins for each load bus of the weak area, the relation between load variation at each load bus and voltage magnitude and phase angle variations at the critical bus is normalised, one by one. The busses strongly connected to the critical bus have smaller load variation in relation to the busses weakly connected to the critical bus. The proposed method has been tested using the IEEE 24-bus and 300-bus systems.